1
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Thalamic volumetric abnormalities in type 1 diabetes mellitus and 'peripheral' neuropathy. Sci Rep 2022; 12:13053. [PMID: 35906253 PMCID: PMC9338092 DOI: 10.1038/s41598-022-16699-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 07/13/2022] [Indexed: 12/03/2022] Open
Abstract
We hypothesized that thalamic volumes of patients with type 1 diabetes mellitus (DM) and nonpainful diabetic peripheral neuropathy (DPN) would be reduced relative to thalamic volumes of patients with type 1 DM and painful DPN. We calculated the standardized thalamic volumetric difference between these groups in a pilot sample to obtain a statistical power of 80% at a 5% significance level. Hence, we measured thalamic volumes from 15 patients with nonpainful DPN (10 women, mean age = 49 years, standard deviation [SD] = 11.5) and from 13 patients with painful DPN (8 women, mean age = 43 years, SD = 12.5) by using a manual segmentation approach. A volumetric difference of approximately 15% was found between the nonpainful (mean = 5072 mm3, SD = 528.1) and painful (mean = 5976 mm3, SD = 643.1) DPN groups (P < 0.001). Curiously, a volumetric difference between the left (mean = 5198 mm3, SD = 495.0) and the right (mean = 4946 mm3, SD = 590.6) thalamus was also found in patients with nonpainful DPN (P < 0.01), but not in patients with painful DPN (P = 0.97). Patients with nonpainful DPN have lower thalamic volumes than those with painful DPN, especially in the right thalamus.
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2
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Zagorchev L, Brueck M, Flaschner N, Wenzel F, Hyde D, Ewald A, Peters J. Patient-Specific Sensor Registration for Electrical Source Imaging Using a Deformable Head Model. IEEE Trans Biomed Eng 2020; 68:267-275. [PMID: 32746029 DOI: 10.1109/tbme.2020.3003112] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Electrical source imaging of brain activity is most accurate when using individualized bioelectric head models. Constructing these models requires identifying electrode positions on the scalp surface. Current methods such as photogrammetry involve significant user interaction that limits integration in clinical workflows. This work introduces and validates a new, fully-automatic method for sensor registration. METHODS Average electrode coordinates are registered to the mean scalp mesh of a shape-constrained deformable head model used for tissue segmentation. Patient-specific electrode positions can be identified on the deformed scalp surface using point-based correspondence after model adaptation. RESULTS The performance of the proposed method for sensor registration is evaluated with simulated and real data. Electrode variability is quantified for a photogrammetry-based solution and compared against the proposed sensor registration. CONCLUSION A fully-automated model-based approach can identify electrode locations with similar accuracy as a current state-of-the-art photogrammetry system. SIGNIFICANCE The new method for sensor registration presented in this work is rapid and fully automatic. It eliminates any user dependent inaccuracy introduced in sensor registration and ensures reproducible results. More importantly, it can more easily be integrated in clinical workflows, enabling broader adoption of electrical source imaging technologies.
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3
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Haddow LJ, Godi C, Sokolska M, Cardoso MJ, Oliver R, Winston A, Stöhr W, Clarke A, Chen F, Williams IG, Johnson M, Paton N, Arenas-Pinto A, Golay X, Jäger HR. Brain Perfusion, Regional Volumes, and Cognitive Function in Human Immunodeficiency Virus-positive Patients Treated With Protease Inhibitor Monotherapy. Clin Infect Dis 2020; 68:1031-1040. [PMID: 30084882 DOI: 10.1093/cid/ciy617] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 07/30/2018] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Protease inhibitor monotherapy (PIM) for human immunodeficiency virus (HIV) may exert suboptimal viral control in the central nervous system. We determined whether cerebral blood flow (CBF) and regional brain volumes were associated with PIM, and whether specific cognitive domains were associated with imaging biomarkers. METHODS Cognitive assessments and brain magnetic resonance imaging were performed after the final visit of a randomized HIV-treatment strategy trial. Participants were virologically suppressed on triple therapy at trial entry and followed for 3-5 years. We studied 37 patients randomized to ongoing triple therapy and 39 randomized to PIM. Resting CBF and normalized volumes were calculated for brain regions of interest, and correlated with treatment strategy and neuropsychological performance. RESULTS Mean age was 48.1 years (standard deviation 8.6 years), 63 male (83%), and 64 white (84%). Participants had median 8.1 years (interquartile range 6.4, 10.8) of antiretroviral therapy experience and CD4+ counts of median 640 cells/mm3 (interquartile range 490, 780). We found no difference between treatment arms in CBF or regional volumes. Regardless of treatment arm, poorer fine motor performance correlated with lower CBF in the caudate nucleus (P = .01), thalamus (P = .04), frontal cortex (P = .01), occipital cortex (P = .004), and cingulate cortex (P = .02), and was associated with smaller supratentorial white matter volume (decrease of 0.16 in Z-score per -1% of intracranial volume, 95% confidence interval 0.02-0.29; P = .023). CONCLUSIONS PIM does not confer an additional risk of neurological injury compared with triple therapy. There were correlations between fine motor impairment, grey matter hypoperfusion, and white matter volume loss. CLINICAL TRIALS REGISTRATION ISRCTN-04857074.
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Affiliation(s)
- Lewis J Haddow
- Institute of Global Health, University College London, United Kingdom
| | - Claudia Godi
- Institute of Neurology, University College London, United Kingdom.,Department of Neuroradiology, Ospedale San Raffaele, Milan, Italy
| | - Magdalena Sokolska
- Institute of Neurology, University College London, United Kingdom.,Department of Medical Physics and Biomedical Engineering, University College London Hospitals National Health Service (NHS) Foundation Trust, United Kingdom
| | - M Jorge Cardoso
- Centre for Medical Image Computing, University College London, United Kingdom
| | - Ruth Oliver
- Institute of Neurology, University College London, United Kingdom.,Department of Engineering, Macquarie University, Sydney, New South Wales, Australia
| | - Alan Winston
- Department of Medicine, Imperial College London, United Kingdom
| | - Wolfgang Stöhr
- The Medical Research Council Clinical Trials Unit at University College London, United Kingdom
| | - Amanda Clarke
- Elton John Centre, Brighton and Sussex University Hospital, United Kingdom
| | - Fabian Chen
- The Florey Sexual Health Clinic, Royal Berkshire NHS Foundation Trust, Reading, United Kingdom
| | - Ian G Williams
- Institute of Global Health, University College London, United Kingdom
| | - Margaret Johnson
- Ian Charleson Day Centre, Royal Free London NHS Foundation Trust, United Kingdom
| | - Nick Paton
- The Medical Research Council Clinical Trials Unit at University College London, United Kingdom.,Department of Medicine, National University of Singapore
| | - Alejandro Arenas-Pinto
- Institute of Global Health, University College London, United Kingdom.,The Medical Research Council Clinical Trials Unit at University College London, United Kingdom
| | - Xavier Golay
- Institute of Neurology, University College London, United Kingdom
| | - Hans Rolf Jäger
- Institute of Neurology, University College London, United Kingdom.,Centre of Medical Imaging, University College London Hospitals NHS Foundation Trust, United Kingdom
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4
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Canas LS, Sudre CH, De Vita E, Nihat A, Mok TH, Slattery CF, Paterson RW, Foulkes AJM, Hyare H, Cardoso MJ, Thornton J, Schott JM, Barkhof F, Collinge J, Ourselin S, Mead S, Modat M. Prion disease diagnosis using subject-specific imaging biomarkers within a multi-kernel Gaussian process. Neuroimage Clin 2019; 24:102051. [PMID: 31734530 PMCID: PMC6978211 DOI: 10.1016/j.nicl.2019.102051] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 09/25/2019] [Accepted: 10/21/2019] [Indexed: 02/01/2023]
Abstract
Prion diseases are a group of rare neurodegenerative conditions characterised by a high rate of progression and highly heterogeneous phenotypes. Whilst the most common form of prion disease occurs sporadically (sporadic Creutzfeldt-Jakob disease, sCJD), other forms are caused by prion protein gene mutations, or exposure to prions in the diet or by medical procedures, such us surgeries. To date, there are no accurate quantitative imaging biomarkers that can be used to predict the future clinical diagnosis of a healthy subject, or to quantify the progression of symptoms over time. Besides, CJD is commonly mistaken for other forms of dementia. Due to the heterogeneity of phenotypes and the lack of a consistent geometrical pattern of disease progression, the approaches used to study other types of neurodegenerative diseases are not satisfactory to capture the progression of human form of prion disease. In this paper, using a tailored framework, we aim to classify and stratify patients with prion disease, according to the severity of their illness. The framework is initialised with the extraction of subject-specific imaging biomarkers. The extracted biomakers are then combined with genetic and demographic information within a Gaussian Process classifier, used to calculate the probability of a subject to be diagnosed with prion disease in the next year. We evaluate the effectiveness of the proposed method in a cohort of patients with inherited and sporadic forms of prion disease. The model has shown to be effective in the prediction of both inherited CJD (92% of accuracy) and sporadic CJD (95% of accuracy). However the model has shown to be less effective when used to stratify the different stages of the disease, in which the average accuracy is 85%, whilst the recall is 59%. Finally, our framework was extended as a differential diagnosis tool to identify both forms of CJD among another neurodegenerative disease. In summary we have developed a novel method for prion disease diagnosis and prediction of clinical onset using multiple sources of features, which may have use in other disorders with heterogeneous imaging features.
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Affiliation(s)
- Liane S Canas
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom.
| | - Carole H Sudre
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom; Dementia Research Centre, UCL Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Enrico De Vita
- Institute of Neurology, University College London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom
| | - Akin Nihat
- MRC Prion Unit at UCL, UCL Institute of Prion Diseases, London, United Kingdom; NHS National Prion Clinic, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Tze How Mok
- MRC Prion Unit at UCL, UCL Institute of Prion Diseases, London, United Kingdom; NHS National Prion Clinic, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Catherine F Slattery
- Dementia Research Centre, UCL Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Ross W Paterson
- Dementia Research Centre, UCL Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Alexander J M Foulkes
- Dementia Research Centre, UCL Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Harpreet Hyare
- NHS National Prion Clinic, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - M Jorge Cardoso
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom
| | - John Thornton
- Institute of Neurology, University College London, United Kingdom
| | - Jonathan M Schott
- Dementia Research Centre, UCL Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Frederik Barkhof
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - John Collinge
- MRC Prion Unit at UCL, UCL Institute of Prion Diseases, London, United Kingdom; NHS National Prion Clinic, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Sébastien Ourselin
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom
| | - Simon Mead
- MRC Prion Unit at UCL, UCL Institute of Prion Diseases, London, United Kingdom; NHS National Prion Clinic, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Marc Modat
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom
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5
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Quantitative assessment of myelination patterns in preterm neonates using T2-weighted MRI. Sci Rep 2019; 9:12938. [PMID: 31506514 PMCID: PMC6736873 DOI: 10.1038/s41598-019-49350-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Accepted: 08/14/2019] [Indexed: 11/08/2022] Open
Abstract
Myelination is considered to be an important developmental process during human brain maturation and closely correlated with gestational age. Quantitative assessment of the myelination status requires dedicated imaging, but the conventional T2-weighted scans routinely acquired during clinical imaging of neonates carry signatures that are thought to be associated with myelination. In this work, we develop a quatitative marker of progressing myelination for assessment preterm neonatal brain maturation based on novel automatic segmentation method for myelin-like signals on T2-weighted magnetic resonance images. Firstly we define a segmentation protocol for myelin-like signals. We then develop an expectation-maximization framework to obtain the automatic segmentations of myelin-like signals with explicit class for partial volume voxels whose locations are configured in relation to the composing pure tissues via second-order Markov random fields. The proposed segmentation achieves high Dice overlaps of 0.83 with manual annotations. The automatic segmentations are then used to track volumes of myelinated tissues in the regions of the central brain structures and brainstem. Finally, we construct a spatio-temporal growth models for myelin-like signals, which allows us to predict gestational age at scan in preterm infants with root mean squared error 1.41 weeks.
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6
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Shao M, Han S, Carass A, Li X, Blitz AM, Shin J, Prince JL, Ellingsen LM. Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly. Neuroimage Clin 2019; 23:101871. [PMID: 31174103 PMCID: PMC6551563 DOI: 10.1016/j.nicl.2019.101871] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 04/20/2019] [Accepted: 05/20/2019] [Indexed: 02/01/2023]
Abstract
Numerous brain disorders are associated with ventriculomegaly, including both neuro-degenerative diseases and cerebrospinal fluid disorders. Detailed evaluation of the ventricular system is important for these conditions to help understand the pathogenesis of ventricular enlargement and elucidate novel patterns of ventriculomegaly that can be associated with different diseases. One such disease is normal pressure hydrocephalus (NPH), a chronic form of hydrocephalus in older adults that causes dementia. Automatic parcellation of the ventricular system into its sub-compartments in patients with ventriculomegaly is quite challenging due to the large variation of the ventricle shape and size. Conventional brain labeling methods are time-consuming and often fail to identify the boundaries of the enlarged ventricles. We propose a modified 3D U-Net method to perform accurate ventricular parcellation, even with grossly enlarged ventricles, from magnetic resonance images (MRIs). We validated our method on a data set of healthy controls as well as a cohort of 95 patients with NPH with mild to severe ventriculomegaly and compared with several state-of-the-art segmentation methods. On the healthy data set, the proposed network achieved mean Dice similarity coefficient (DSC) of 0.895 ± 0.03 for the ventricular system. On the NPH data set, we achieved mean DSC of 0.973 ± 0.02, which is significantly (p < 0.005) higher than four state-of-the-art segmentation methods we compared with. Furthermore, the typical processing time on CPU-base implementation of the proposed method is 2 min, which is much lower than the several hours required by the other methods. Results indicate that our method provides: 1) highly robust parcellation of the ventricular system that is comparable in accuracy to state-of-the-art methods on healthy controls; 2) greater robustness and significantly more accurate results on cases of ventricular enlargement; and 3) a tool that enables computation of novel imaging biomarkers for dilated ventricular spaces that characterize the ventricular system.
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Affiliation(s)
- Muhan Shao
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
| | - Shuo Han
- Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 20892, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Xiang Li
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ari M Blitz
- Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Jaehoon Shin
- Department of Radiology, University of California San Francisco, San Francisco, CA 94117, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Lotta M Ellingsen
- Department of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland; Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
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7
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Kieselmann JP, Kamerling CP, Burgos N, Menten MJ, Fuller CD, Nill S, Cardoso MJ, Oelfke U. Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region. Phys Med Biol 2018; 63:145007. [PMID: 29882749 PMCID: PMC6296440 DOI: 10.1088/1361-6560/aacb65] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Revised: 06/01/2018] [Accepted: 06/08/2018] [Indexed: 11/19/2022]
Abstract
Owing to its excellent soft-tissue contrast, magnetic resonance (MR) imaging has found an increased application in radiation therapy (RT). By harnessing these properties for treatment planning, automated segmentation methods can alleviate the manual workload burden to the clinical workflow. We investigated atlas-based segmentation methods of organs at risk (OARs) in the head and neck (H&N) region using one approach that selected the most similar atlas from a library of segmented images and two multi-atlas approaches. The latter were based on weighted majority voting and an iterative atlas-fusion approach called STEPS. We built the atlas library from pre-treatment T1-weighted MR images of 12 patients with manual contours of the parotids, spinal cord and mandible, delineated by a clinician. Following a leave-one-out cross-validation strategy, we measured the geometric accuracy by calculating Dice similarity coefficients (DSC), standard and 95% Hausdorff distances (HD and HD95), and the mean surface distance (MSD), whereby the manual contours served as the gold standard. To benchmark the algorithm, we determined the inter-observer variability (IOV) between three observers. To investigate the dosimetric effect of segmentation inaccuracies, we implemented an auto-planning strategy within the treatment planning system Monaco (Elekta AB, Stockholm, Sweden). For each set of auto-segmented OARs, we generated a plan for a 9-beam step and shoot intensity modulated RT treatment, designed according to our institution's clinical H&N protocol. Superimposing the dose distributions on the gold standard OARs, we calculated dose differences to OARs caused by delineation differences between auto-segmented and gold standard OARs. We investigated the correlations between geometric and dosimetric differences. The mean DSC was larger than 0.8 and the mean MSD smaller than 2 mm for the multi-atlas approaches, resulting in a geometric accuracy comparable to previously published results and within the range of the IOV. While dosimetric differences could be as large as 23% of the clinical goal, treatment plans fulfilled all imposed clinical goals for the gold standard OARs. Correlations between geometric and dosimetric measures were low with R2 < 0.5. The geometric accuracy and the ability to achieve clinically acceptable treatment plans indicate the suitability of using atlas-based contours for RT treatment planning purposes. The low correlations between geometric and dosimetric measures suggest that geometric measures alone are not sufficient to predict the dosimetric impact of segmentation inaccuracies on treatment planning for the data utilised in this study.
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Affiliation(s)
- J P Kieselmann
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden
NHS Foundation Trust, London, United
Kingdom
| | - C P Kamerling
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden
NHS Foundation Trust, London, United
Kingdom
| | - N Burgos
- University
College London, Centre for Medical Image Computing, London,
United Kingdom
- Inria, Aramis project-team, Institut du Cerveau et de la Moelle
épinière, Sorbonne Université, Paris,
France
| | - M J Menten
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden
NHS Foundation Trust, London, United
Kingdom
| | - C D Fuller
- Department of Radiation Oncology,
MD Anderson Cancer Center,
Houston, TX, United States of America
| | - S Nill
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden
NHS Foundation Trust, London, United
Kingdom
| | - M J Cardoso
- University
College London, Centre for Medical Image Computing, London,
United Kingdom
- School of
Biomedical Engineering and Imaging Sciences, King’s College,
London, United Kingdom
| | - U Oelfke
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden
NHS Foundation Trust, London, United
Kingdom
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8
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Kieselmann JP, Kamerling CP, Burgos N, Menten MJ, Fuller CD, Nill S, Cardoso MJ, Oelfke U. Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region. Phys Med Biol 2018; 63:145007. [PMID: 29882749 PMCID: PMC6296440 DOI: 10.1088/1361-6560/aacb65;145007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Owing to its excellent soft-tissue contrast, magnetic resonance (MR) imaging has found an increased application in radiation therapy (RT). By harnessing these properties for treatment planning, automated segmentation methods can alleviate the manual workload burden to the clinical workflow. We investigated atlas-based segmentation methods of organs at risk (OARs) in the head and neck (H&N) region using one approach that selected the most similar atlas from a library of segmented images and two multi-atlas approaches. The latter were based on weighted majority voting and an iterative atlas-fusion approach called STEPS. We built the atlas library from pre-treatment T1-weighted MR images of 12 patients with manual contours of the parotids, spinal cord and mandible, delineated by a clinician. Following a leave-one-out cross-validation strategy, we measured the geometric accuracy by calculating Dice similarity coefficients (DSC), standard and 95% Hausdorff distances (HD and HD95), and the mean surface distance (MSD), whereby the manual contours served as the gold standard. To benchmark the algorithm, we determined the inter-observer variability (IOV) between three observers. To investigate the dosimetric effect of segmentation inaccuracies, we implemented an auto-planning strategy within the treatment planning system Monaco (Elekta AB, Stockholm, Sweden). For each set of auto-segmented OARs, we generated a plan for a 9-beam step and shoot intensity modulated RT treatment, designed according to our institution's clinical H&N protocol. Superimposing the dose distributions on the gold standard OARs, we calculated dose differences to OARs caused by delineation differences between auto-segmented and gold standard OARs. We investigated the correlations between geometric and dosimetric differences. The mean DSC was larger than 0.8 and the mean MSD smaller than 2 mm for the multi-atlas approaches, resulting in a geometric accuracy comparable to previously published results and within the range of the IOV. While dosimetric differences could be as large as 23% of the clinical goal, treatment plans fulfilled all imposed clinical goals for the gold standard OARs. Correlations between geometric and dosimetric measures were low with R2 < 0.5. The geometric accuracy and the ability to achieve clinically acceptable treatment plans indicate the suitability of using atlas-based contours for RT treatment planning purposes. The low correlations between geometric and dosimetric measures suggest that geometric measures alone are not sufficient to predict the dosimetric impact of segmentation inaccuracies on treatment planning for the data utilised in this study.
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Affiliation(s)
- J P Kieselmann
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden
NHS Foundation Trust, London, United
Kingdom,
| | - C P Kamerling
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden
NHS Foundation Trust, London, United
Kingdom
| | - N Burgos
- University
College London, Centre for Medical Image Computing, London,
United Kingdom,Inria, Aramis project-team, Institut du Cerveau et de la Moelle
épinière, Sorbonne Université, Paris,
France
| | - M J Menten
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden
NHS Foundation Trust, London, United
Kingdom
| | - C D Fuller
- Department of Radiation Oncology,
MD Anderson Cancer Center,
Houston, TX, United States of America
| | - S Nill
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden
NHS Foundation Trust, London, United
Kingdom
| | - M J Cardoso
- University
College London, Centre for Medical Image Computing, London,
United Kingdom,School of
Biomedical Engineering and Imaging Sciences, King’s College,
London, United Kingdom
| | - U Oelfke
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden
NHS Foundation Trust, London, United
Kingdom
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9
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Rapid fully automatic segmentation of subcortical brain structures by shape-constrained surface adaptation. Med Image Anal 2018; 46:146-161. [DOI: 10.1016/j.media.2018.03.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 02/23/2018] [Accepted: 03/08/2018] [Indexed: 11/18/2022]
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10
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Battaglini M, Jenkinson M, De Stefano N. SIENA-XL for improving the assessment of gray and white matter volume changes on brain MRI. Hum Brain Mapp 2018; 39:1063-1077. [PMID: 29222814 PMCID: PMC6866496 DOI: 10.1002/hbm.23828] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Revised: 07/10/2017] [Accepted: 09/15/2017] [Indexed: 01/18/2023] Open
Abstract
In this article, SIENA-XL, a new segmentation-based longitudinal pipeline is introduced, for: (i) increasing the precision of longitudinal volume change estimation for white (WM) and gray (GM) matter separately, compared with cross-sectional segmentation methods such as SIENAX; and (ii) avoiding potential biases in registration-based methods when Jacobians are used, with a smoothing extent larger than spatial scale between tissue-interfaces, which is where atrophy usually occurs. SIENA-XL implements a new brain extraction procedure and a multi-time-point intensity equalization step before performing the final segmentation that also includes separate segmentation of deep GM structures by using FMRIB's Integrated Registration and Segmentation Tool. The detection of GM and WM volume changes with SIENA-XL was evaluated using different healthy control (HC) and multiple sclerosis (MS) MRI datasets and compared with the traditional SIENAX and two Jacobian-based approaches, SPM12 and SIENAX-JI (a version of SIENAX including Jacobian integration - JI). In scan-rescan data from HCs, SIENA-XL showed: (i) a significant decrease in error, of 50-70% when compared with SIENAX; (ii) no significant differences in error when compared with SIENAX-JI and SPM12 in a scan-rescan HC dataset that included repositioning. When tested in a HC dataset with scan-rescan both at baseline and after 1 year of follow-up, SIENA-XL showed: (i) significantly higher precision (P < 0.01) than SIENAX; (ii) no significant differences to SIENAX-JI and SPM12. Finally, in a dataset of 79 MS patients with a 2 years follow-up, SIENA-XL showed a substantial reduction of sample size, by comparison with SIENAX, SIENAX-JI, and SPM12, for detecting treatment effects of 25, 30, and 50%. Hum Brain Mapp 39:1063-1077, 2018. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Marco Battaglini
- Department of Medicine, Surgery and NeuroscienceUniversity of SienaItaly
| | - Mark Jenkinson
- Department of Medicine, Surgery and NeuroscienceUniversity of SienaItaly
- Department of Clinical Neurology, University of OxfordOxford University Centre for Functional MRI of the Brain (FMRIB)United Kingdom
| | - Nicola De Stefano
- Department of Medicine, Surgery and NeuroscienceUniversity of SienaItaly
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Wilke M. A spline-based regression parameter set for creating customized DARTEL MRI brain templates from infancy to old age. Data Brief 2018; 16:959-966. [PMID: 29322076 PMCID: PMC5752094 DOI: 10.1016/j.dib.2017.12.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Revised: 11/20/2017] [Accepted: 12/05/2017] [Indexed: 12/02/2022] Open
Abstract
This dataset contains the regression parameters derived by analyzing segmented brain MRI images (gray matter and white matter) from a large population of healthy subjects, using a multivariate adaptive regression splines approach. A total of 1919 MRI datasets ranging in age from 1–75 years from four publicly available datasets (NIH, C-MIND, fCONN, and IXI) were segmented using the CAT12 segmentation framework, writing out gray matter and white matter images normalized using an affine-only spatial normalization approach. These images were then subjected to a six-step DARTEL procedure, employing an iterative non-linear registration approach and yielding increasingly crisp intermediate images. The resulting six datasets per tissue class were then analyzed using multivariate adaptive regression splines, using the CerebroMatic toolbox. This approach allows for flexibly modelling smoothly varying trajectories while taking into account demographic (age, gender) as well as technical (field strength, data quality) predictors. The resulting regression parameters described here can be used to generate matched DARTEL or SHOOT templates for a given population under study, from infancy to old age. The dataset and the algorithm used to generate it are publicly available at https://irc.cchmc.org/software/cerebromatic.php.
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Affiliation(s)
- Marko Wilke
- Department of Pediatric Neurology and Developmental Medicine, Children's Hospital and Experimental Pediatric Neuroimaging group, Children's Hospital & Dept. of Neuroradiology, University of Tübingen, Germany
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A step-by-step review on patient-specific biomechanical finite element models for breast MRI to x-ray mammography registration. Med Phys 2017; 45:e6-e31. [DOI: 10.1002/mp.12673] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Revised: 09/27/2017] [Accepted: 11/03/2017] [Indexed: 01/08/2023] Open
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Bragman FJS, McClelland JR, Jacob J, Hurst JR, Hawkes DJ. Pulmonary Lobe Segmentation With Probabilistic Segmentation of the Fissures and a Groupwise Fissure Prior. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:1650-1663. [PMID: 28436850 PMCID: PMC5547024 DOI: 10.1109/tmi.2017.2688377] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
A fully automated, unsupervised lobe segmentation algorithm is presented based on a probabilistic segmentation of the fissures and the simultaneous construction of a populationmodel of the fissures. A two-class probabilistic segmentation segments the lung into candidate fissure voxels and the surrounding parenchyma. This was combined with anatomical information and a groupwise fissure prior to drive non-parametric surface fitting to obtain the final segmentation. The performance of our fissure segmentation was validated on 30 patients from the chronic obstructive pulmonary disease COPDGene cohort, achieving a high median F1 -score of 0.90 and showed general insensitivity to filter parameters. We evaluated our lobe segmentation algorithm on the Lobe and Lung Analysis 2011 dataset, which contains 55 cases at varying levels of pathology. We achieved the highest score of 0.884 of the automated algorithms. Our method was further tested quantitatively and qualitatively on 80 patients from the COPDgene study at varying levels of functional impairment. Accurate segmentation of the lobes is shown at various degrees of fissure incompleteness for 96% of all cases. We also show the utility of including a groupwise prior in segmenting the lobes in regions of grossly incomplete fissures.
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Magnetisation-prepared rapid gradient-echo versus inversion recovery turbo spin-echo T1-weighted images for segmentation of deep grey matter structures at 3 T. Clin Radiol 2016; 71:1304-1308. [DOI: 10.1016/j.crad.2016.09.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Revised: 08/19/2016] [Accepted: 09/08/2016] [Indexed: 01/27/2023]
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15
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Pardini M, Sudre CH, Prados F, Yaldizli Ö, Sethi V, Muhlert N, Samson RS, van de Pavert SH, Cardoso MJ, Ourselin S, Gandini Wheeler-Kingshott CAM, Miller DH, Chard DT. Relationship of grey and white matter abnormalities with distance from the surface of the brain in multiple sclerosis. J Neurol Neurosurg Psychiatry 2016; 87:1212-1217. [PMID: 27601434 DOI: 10.1136/jnnp-2016-313979] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2016] [Accepted: 08/14/2016] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To assess the association between proximity to the inner (ventricular and aqueductal) and outer (pial) surfaces of the brain and the distribution of normal appearing white matter (NAWM) and grey matter (GM) abnormalities, and white matter (WM) lesions, in multiple sclerosis (MS). METHODS 67 people with relapse-onset MS and 30 healthy controls were included in the study. Volumetric T1 images and high-resolution (1 mm3) magnetisation transfer ratio (MTR) images were acquired and segmented into 12 bands between the inner and outer surfaces of the brain. The first and last bands were discarded to limit partial volume effects with cerebrospinal fluid. MTR values were computed for all bands in supratentorial NAWM, cerebellar NAWM and brainstem NA tissue, and deep and cortical GM. Band WM lesion volumes were also measured. RESULTS Proximity to the ventricular surfaces was associated with progressively lower MTR values in the MS group but not in controls in supratentorial and cerebellar NAWM, brainstem NA and in deep and cortical GM. The density of WM lesions was associated with proximity to the ventricles only in the supratentorial compartment, and no link was found with distance from the pial surfaces. CONCLUSIONS In MS, MTR abnormalities in NAWM and GM are related to distance from the inner and outer surfaces of the brain, and this suggests that there is a common factor underlying their spatial distribution. A similar pattern was not found for WM lesions, raising the possibility that different factors promote their formation.
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Affiliation(s)
- Matteo Pardini
- Department of Neuroinflammation, NMR Research Unit, Queen Square MS Centre, UCL Institute of Neurology, London, UK Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Carole H Sudre
- Department of Medical Physics and Bioengineering, Translational Imaging Group, Centre for Medical Image Computing (CMIC), University College London, London, UK Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - Ferran Prados
- Department of Neuroinflammation, NMR Research Unit, Queen Square MS Centre, UCL Institute of Neurology, London, UK Department of Medical Physics and Bioengineering, Translational Imaging Group, Centre for Medical Image Computing (CMIC), University College London, London, UK
| | - Özgür Yaldizli
- Department of Neuroinflammation, NMR Research Unit, Queen Square MS Centre, UCL Institute of Neurology, London, UK Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Varun Sethi
- Department of Neuroinflammation, NMR Research Unit, Queen Square MS Centre, UCL Institute of Neurology, London, UK
| | - Nils Muhlert
- Department of Neuroinflammation, NMR Research Unit, Queen Square MS Centre, UCL Institute of Neurology, London, UK School of Psychology and Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK School of Psychological Sciences, University of Manchester, Manchester UK
| | - Rebecca S Samson
- Department of Neuroinflammation, NMR Research Unit, Queen Square MS Centre, UCL Institute of Neurology, London, UK
| | - Steven H van de Pavert
- Department of Neuroinflammation, NMR Research Unit, Queen Square MS Centre, UCL Institute of Neurology, London, UK
| | - M Jorge Cardoso
- Department of Medical Physics and Bioengineering, Translational Imaging Group, Centre for Medical Image Computing (CMIC), University College London, London, UK Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - Sebastien Ourselin
- Department of Medical Physics and Bioengineering, Translational Imaging Group, Centre for Medical Image Computing (CMIC), University College London, London, UK Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - Claudia A M Gandini Wheeler-Kingshott
- Department of Neuroinflammation, NMR Research Unit, Queen Square MS Centre, UCL Institute of Neurology, London, UK Brain MRI 3T Center, C. Mondino National Neurological Institute, Pavia, Italy Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - David H Miller
- Department of Neuroinflammation, NMR Research Unit, Queen Square MS Centre, UCL Institute of Neurology, London, UK National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre, London, UK
| | - Declan T Chard
- Department of Neuroinflammation, NMR Research Unit, Queen Square MS Centre, UCL Institute of Neurology, London, UK National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre, London, UK
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16
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Powell NM, Modat M, Cardoso MJ, Ma D, Holmes HE, Yu Y, O’Callaghan J, Cleary JO, Sinclair B, Wiseman FK, Tybulewicz VLJ, Fisher EMC, Lythgoe MF, Ourselin S. Fully-Automated μMRI Morphometric Phenotyping of the Tc1 Mouse Model of Down Syndrome. PLoS One 2016; 11:e0162974. [PMID: 27658297 PMCID: PMC5033246 DOI: 10.1371/journal.pone.0162974] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Accepted: 08/31/2016] [Indexed: 01/07/2023] Open
Abstract
We describe a fully automated pipeline for the morphometric phenotyping of mouse brains from μMRI data, and show its application to the Tc1 mouse model of Down syndrome, to identify new morphological phenotypes in the brain of this first transchromosomic animal carrying human chromosome 21. We incorporate an accessible approach for simultaneously scanning multiple ex vivo brains, requiring only a 3D-printed brain holder, and novel image processing steps for their separation and orientation. We employ clinically established multi-atlas techniques–superior to single-atlas methods–together with publicly-available atlas databases for automatic skull-stripping and tissue segmentation, providing high-quality, subject-specific tissue maps. We follow these steps with group-wise registration, structural parcellation and both Voxel- and Tensor-Based Morphometry–advantageous for their ability to highlight morphological differences without the laborious delineation of regions of interest. We show the application of freely available open-source software developed for clinical MRI analysis to mouse brain data: NiftySeg for segmentation and NiftyReg for registration, and discuss atlases and parameters suitable for the preclinical paradigm. We used this pipeline to compare 29 Tc1 brains with 26 wild-type littermate controls, imaged ex vivo at 9.4T. We show an unexpected increase in Tc1 total intracranial volume and, controlling for this, local volume and grey matter density reductions in the Tc1 brain compared to the wild-types, most prominently in the cerebellum, in agreement with human DS and previous histological findings.
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Affiliation(s)
- Nick M. Powell
- Translational Imaging Group, Centre for Medical Image Computing, University College London, 3rd Floor, Wolfson House, 4 Stephenson Way, London NW1 2HE, United Kingdom
- Centre for Advanced Biomedical Imaging, Division of Medicine, University College London, Paul O’Gorman Building, 72 Huntley Street, London WC1E 6DD, United Kingdom
- * E-mail:
| | - Marc Modat
- Translational Imaging Group, Centre for Medical Image Computing, University College London, 3rd Floor, Wolfson House, 4 Stephenson Way, London NW1 2HE, United Kingdom
| | - M. Jorge Cardoso
- Translational Imaging Group, Centre for Medical Image Computing, University College London, 3rd Floor, Wolfson House, 4 Stephenson Way, London NW1 2HE, United Kingdom
| | - Da Ma
- Translational Imaging Group, Centre for Medical Image Computing, University College London, 3rd Floor, Wolfson House, 4 Stephenson Way, London NW1 2HE, United Kingdom
- Centre for Advanced Biomedical Imaging, Division of Medicine, University College London, Paul O’Gorman Building, 72 Huntley Street, London WC1E 6DD, United Kingdom
| | - Holly E. Holmes
- Centre for Advanced Biomedical Imaging, Division of Medicine, University College London, Paul O’Gorman Building, 72 Huntley Street, London WC1E 6DD, United Kingdom
| | - Yichao Yu
- Centre for Advanced Biomedical Imaging, Division of Medicine, University College London, Paul O’Gorman Building, 72 Huntley Street, London WC1E 6DD, United Kingdom
| | - James O’Callaghan
- Centre for Advanced Biomedical Imaging, Division of Medicine, University College London, Paul O’Gorman Building, 72 Huntley Street, London WC1E 6DD, United Kingdom
| | - Jon O. Cleary
- Centre for Advanced Biomedical Imaging, Division of Medicine, University College London, Paul O’Gorman Building, 72 Huntley Street, London WC1E 6DD, United Kingdom
- Melbourne Brain Centre Imaging Unit, Department of Anatomy and Neuroscience, University of Melbourne, Parkville, Victoria 3052, Australia
| | - Ben Sinclair
- Centre for Advanced Biomedical Imaging, Division of Medicine, University College London, Paul O’Gorman Building, 72 Huntley Street, London WC1E 6DD, United Kingdom
| | - Frances K. Wiseman
- Department of Neurodegenerative Disease, Institute of Neurology, University College, London WC1N 3BG, United Kingdom
| | - Victor L. J. Tybulewicz
- The Francis Crick Institute, Mill Hill Laboratory, London NW7 1AA, United Kingdom
- Imperial College, London W12 0NN, United Kingdom
| | - Elizabeth M. C. Fisher
- Department of Neurodegenerative Disease, Institute of Neurology, University College, London WC1N 3BG, United Kingdom
| | - Mark F. Lythgoe
- Centre for Advanced Biomedical Imaging, Division of Medicine, University College London, Paul O’Gorman Building, 72 Huntley Street, London WC1E 6DD, United Kingdom
| | - Sébastien Ourselin
- Translational Imaging Group, Centre for Medical Image Computing, University College London, 3rd Floor, Wolfson House, 4 Stephenson Way, London NW1 2HE, United Kingdom
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17
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Pereira S, Pinto A, Oliveira J, Mendrik AM, Correia JH, Silva CA. Automatic brain tissue segmentation in MR images using Random Forests and Conditional Random Fields. J Neurosci Methods 2016; 270:111-123. [DOI: 10.1016/j.jneumeth.2016.06.017] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2015] [Revised: 06/17/2016] [Accepted: 06/17/2016] [Indexed: 11/24/2022]
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18
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Andrews KA, Frost C, Modat M, Cardoso MJ, Rowe CC, Villemagne V, Fox NC, Ourselin S, Schott JM, Rowe CC, Villemagne V, Fox NC, Ourselin S, Schott JM. Acceleration of hippocampal atrophy rates in asymptomatic amyloidosis. Neurobiol Aging 2016; 39:99-107. [PMID: 26923406 DOI: 10.1016/j.neurobiolaging.2015.10.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2015] [Revised: 09/09/2015] [Accepted: 10/14/2015] [Indexed: 11/24/2022]
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19
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Ou Y, Gollub RL, Retzepi K, Reynolds N, Pienaar R, Pieper S, Murphy SN, Grant PE, Zöllei L. Brain extraction in pediatric ADC maps, toward characterizing neuro-development in multi-platform and multi-institution clinical images. Neuroimage 2015; 122:246-61. [PMID: 26260429 PMCID: PMC4966541 DOI: 10.1016/j.neuroimage.2015.08.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2014] [Revised: 07/29/2015] [Accepted: 08/03/2015] [Indexed: 01/18/2023] Open
Abstract
Apparent Diffusion Coefficient (ADC) maps can be used to characterize myelination and to detect abnormalities in the developing brain. However, given the normal variation in regional ADC with myelination, detection of abnormalities is difficult when based on visual assessment. Quantitative and automated analysis of pediatric ADC maps is thus desired but requires accurate brain extraction as the first step. Currently, most existing brain extraction methods are optimized for structural T1-weighted MR images of fully myelinated brains. Due to differences in age and image contrast, these approaches do not translate well to pediatric ADC maps. To address this problem, we present a multi-atlas brain extraction framework that has 1) specificity: designed and optimized specifically for pediatric ADC maps; 2) generality: applicable to multi-platform and multi-institution data, and to subjects at various neuro-developmental stages across the first 6 years of life; 3) accuracy: highly accurate compared to expert annotations; and 4) consistency: consistently accurate regardless of sources of data and ages of subjects. We show how we achieve these goals, via optimizing major components in a multi-atlas brain extraction framework, and via developing and evaluating new criteria for its atlas ranking component. Moreover, we demonstrate that these goals can be achieved with a fixed set of atlases and a fixed set of parameters, which opens doors for our optimized framework to be used in large-scale and multi-institution neuro-developmental and clinical studies. In a pilot study, we use this framework in a dataset containing scanner-generated ADC maps from 308 pediatric patients collected during the course of routine clinical care. Our framework leads to successful quantifications of the changes in whole-brain volumes and mean ADC values across the first 6 years of life.
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Affiliation(s)
- Yangming Ou
- Psychiatric Neuroimaging, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, 120 2nd Ave, Charlestown, MA 02129, USA; Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Charlestown, MA 02129, USA.
| | - Randy L Gollub
- Psychiatric Neuroimaging, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, 120 2nd Ave, Charlestown, MA 02129, USA; Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Charlestown, MA 02129, USA
| | - Kallirroi Retzepi
- Psychiatric Neuroimaging, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, 120 2nd Ave, Charlestown, MA 02129, USA; Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Charlestown, MA 02129, USA
| | - Nathaniel Reynolds
- Psychiatric Neuroimaging, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, 120 2nd Ave, Charlestown, MA 02129, USA; Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Charlestown, MA 02129, USA
| | - Rudolph Pienaar
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Children's Hospital Boston, Harvard Medical School, 1 Autumn St, Boston, MA 02115, USA
| | - Steve Pieper
- Isomics, Inc., 55 Kirkland St, Cambridge, MA 02138, USA
| | - Shawn N Murphy
- Research Computing, Partners HealthCare, 1 Constitution Center, Charlestown, MA 02129, USA; Laboratory of Computer Science, Massachusetts General Hospital, Harvard Medical School, 50 Staniford St, Boston, MA 02114, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Children's Hospital Boston, Harvard Medical School, 1 Autumn St, Boston, MA 02115, USA
| | - Lilla Zöllei
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Charlestown, MA 02129, USA
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20
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Sudre CH, Cardoso MJ, Bouvy WH, Biessels GJ, Barnes J, Ourselin S. Bayesian model selection for pathological neuroimaging data applied to white matter lesion segmentation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:2079-2102. [PMID: 25850086 DOI: 10.1109/tmi.2015.2419072] [Citation(s) in RCA: 98] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
In neuroimaging studies, pathologies can present themselves as abnormal intensity patterns. Thus, solutions for detecting abnormal intensities are currently under investigation. As each patient is unique, an unbiased and biologically plausible model of pathological data would have to be able to adapt to the subject's individual presentation. Such a model would provide the means for a better understanding of the underlying biological processes and improve one's ability to define pathologically meaningful imaging biomarkers. With this aim in mind, this work proposes a hierarchical fully unsupervised model selection framework for neuroimaging data which enables the distinction between different types of abnormal image patterns without pathological a priori knowledge. Its application on simulated and clinical data demonstrated the ability to detect abnormal intensity clusters, resulting in a competitive to improved behavior in white matter lesion segmentation when compared to three other freely-available automated methods.
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Cardoso MJ, Modat M, Wolz R, Melbourne A, Cash D, Rueckert D, Ourselin S. Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:1976-88. [PMID: 25879909 DOI: 10.1109/tmi.2015.2418298] [Citation(s) in RCA: 203] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Clinical annotations, such as voxel-wise binary or probabilistic tissue segmentations, structural parcellations, pathological regions-of-interest and anatomical landmarks are key to many clinical studies. However, due to the time consuming nature of manually generating these annotations, they tend to be scarce and limited to small subsets of data. This work explores a novel framework to propagate voxel-wise annotations between morphologically dissimilar images by diffusing and mapping the available examples through intermediate steps. A spatially-variant graph structure connecting morphologically similar subjects is introduced over a database of images, enabling the gradual diffusion of information to all the subjects, even in the presence of large-scale morphological variability. We illustrate the utility of the proposed framework on two example applications: brain parcellation using categorical labels and tissue segmentation using probabilistic features. The application of the proposed method to categorical label fusion showed highly statistically significant improvements when compared to state-of-the-art methodologies. Significant improvements were also observed when applying the proposed framework to probabilistic tissue segmentation of both synthetic and real data, mainly in the presence of large morphological variability.
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Roy S, He Q, Sweeney E, Carass A, Reich DS, Prince JL, Pham DL. Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation. IEEE J Biomed Health Inform 2015; 19:1598-609. [PMID: 26340685 PMCID: PMC4562064 DOI: 10.1109/jbhi.2015.2439242] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting where different tissues are expected to be located, and a hard segmentation. Unlike most atlas-based classification methods that require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject-specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole-brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available state-of-the art approaches.
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23
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Liu E, Schmidt ME, Margolin R, Sperling R, Koeppe R, Mason NS, Klunk WE, Mathis CA, Salloway S, Fox NC, Hill DL, Les AS, Collins P, Gregg KM, Di J, Lu Y, Tudor IC, Wyman BT, Booth K, Broome S, Yuen E, Grundman M, Brashear HR. Amyloid-β 11C-PiB-PET imaging results from 2 randomized bapineuzumab phase 3 AD trials. Neurology 2015. [PMID: 26208959 DOI: 10.1212/wnl.0000000000001877] [Citation(s) in RCA: 114] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To evaluate the effects of bapineuzumab on brain β-amyloid (Aβ) burden using (11)C-Pittsburgh compound B ((11)C-PiB)-PET. METHODS Two phase 3 clinical trials, 1 each in apolipoprotein APOE ε4 carriers and noncarriers, were conducted in patients with mild to moderate Alzheimer disease dementia. Bapineuzumab, an anti-Aβ monoclonal antibody, or placebo, was administered by IV infusion every 13 weeks for 78 weeks. PET substudies assessed change in brain fibrillar Aβ over 71 weeks using an (11)C-PiB-PET standardized uptake value ratio (SUVr) global cortical average (GCA) comprising the average SUVr from 5 cortical regions of interest with cerebellar gray matter as the reference region. RESULTS A total of 115 carriers and 39 noncarriers were analyzed. The difference (δ) in mean baseline to 71 week change in (11)C-PiB-PET GCA between bapineuzumab and placebo was significant in carriers (0.5 mg/kg vs placebo δ = -0.101; p = 0.004) and in pooled analyses of both carriers and noncarriers (0.5 mg/kg vs placebo δ = -0.068; p = 0.027; 1.0 mg/kg vs placebo δ = -0.133; p = 0.028) but not in the noncarrier trial separately. Analyses by individual region of interest and in mild disease yielded findings similar to the main trial results. CONCLUSIONS The (11)C-PiB-PET imaging results demonstrated reduction of fibrillar Aβ accumulation in patients with Alzheimer disease treated with bapineuzumab; however, as no clinical benefit was observed, the findings are consistent with the hypotheses that bapineuzumab may not have been initiated early enough in the disease course, the doses were insufficient, or the most critical Aβ species were inadequately targeted.
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Affiliation(s)
- Enchi Liu
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego.
| | - Mark E Schmidt
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - Richard Margolin
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - Reisa Sperling
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - Robert Koeppe
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - Neale S Mason
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - William E Klunk
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - Chester A Mathis
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - Stephen Salloway
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - Nick C Fox
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - Derek L Hill
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - Andrea S Les
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - Peter Collins
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - Keith M Gregg
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - Jianing Di
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - Yuan Lu
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - I Cristina Tudor
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - Bradley T Wyman
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - Kevin Booth
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - Stephanie Broome
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - Eric Yuen
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - Michael Grundman
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
| | - H Robert Brashear
- From Janssen Alzheimer Immunotherapy Research & Development, LLC (E.L., R.M., P.C., K.M.G., J.D., Y.L., I.C.T., S.B., E.Y., H.R.B.), South San Francisco, CA; Janssen Pharmaceutical (M.E.S.), Beerse, NV; Brigham & Women's Hospital (R.S.), Boston, MA; University of Michigan (R.K.), Ann Arbor; University of Pittsburgh (N.S.M., W.E.K., C.A.M.), PA; Butler Hospital (S.S.), Providence, RI; UCL Institute of Neurology (N.C.F.), London, UK; IXICO plc (D.L.H., A.S.L.), London, UK; Pfizer Inc. (B.T.W.), Groton, CT; Pfizer Inc. (K.B.), Collegeville, PA; Global R&D Partners, LLC (M.G.), San Diego, CA; and University of California (M.G.), San Diego
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Cedarbaum J, Green RC, Harvey D, Jack CR, Jagust W, Luthman J, Morris JC, Petersen RC, Saykin AJ, Shaw L, Shen L, Schwarz A, Toga AW, Trojanowski JQ. 2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception. Alzheimers Dement 2015; 11:e1-120. [PMID: 26073027 PMCID: PMC5469297 DOI: 10.1016/j.jalz.2014.11.001] [Citation(s) in RCA: 203] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 04/18/2013] [Indexed: 01/18/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jesse Cedarbaum
- Neurology Early Clinical Development, Biogen Idec, Cambridge, MA, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - Johan Luthman
- Neuroscience Clinical Development, Neuroscience & General Medicine Product Creation Unit, Eisai Inc., Philadelphia, PA, USA
| | - John C Morris
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Shen
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Adam Schwarz
- Tailored Therapeutics, Eli Lilly and Company, Indianapolis, IN, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Wang G, Zhang X, Su Q, Shi J, Caselli RJ, Wang Y. A novel cortical thickness estimation method based on volumetric Laplace-Beltrami operator and heat kernel. Med Image Anal 2015; 22:1-20. [PMID: 25700360 PMCID: PMC4405465 DOI: 10.1016/j.media.2015.01.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2013] [Revised: 01/22/2015] [Accepted: 01/23/2015] [Indexed: 12/31/2022]
Abstract
Cortical thickness estimation in magnetic resonance imaging (MRI) is an important technique for research on brain development and neurodegenerative diseases. This paper presents a heat kernel based cortical thickness estimation algorithm, which is driven by the graph spectrum and the heat kernel theory, to capture the gray matter geometry information from the in vivo brain magnetic resonance (MR) images. First, we construct a tetrahedral mesh that matches the MR images and reflects the inherent geometric characteristics. Second, the harmonic field is computed by the volumetric Laplace-Beltrami operator and the direction of the steamline is obtained by tracing the maximum heat transfer probability based on the heat kernel diffusion. Thereby we can calculate the cortical thickness information between the point on the pial and white matter surfaces. The new method relies on intrinsic brain geometry structure and the computation is robust and accurate. To validate our algorithm, we apply it to study the thickness differences associated with Alzheimer's disease (AD) and mild cognitive impairment (MCI) on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our preliminary experimental results on 151 subjects (51 AD, 45 MCI, 55 controls) show that the new algorithm may successfully detect statistically significant difference among patients of AD, MCI and healthy control subjects. Our computational framework is efficient and very general. It has the potential to be used for thickness estimation on any biological structures with clearly defined inner and outer surfaces.
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Affiliation(s)
- Gang Wang
- School of Information and Electrical Engineering, Ludong University, Yantai, China; School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Xiaofeng Zhang
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Qingtang Su
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Richard J Caselli
- Department of Neurology, Mayo Clinic Arizona, Scottsdale, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA.
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26
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Robust whole-brain segmentation: application to traumatic brain injury. Med Image Anal 2014; 21:40-58. [PMID: 25596765 DOI: 10.1016/j.media.2014.12.003] [Citation(s) in RCA: 100] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Revised: 12/14/2014] [Accepted: 12/15/2014] [Indexed: 11/23/2022]
Abstract
We propose a framework for the robust and fully-automatic segmentation of magnetic resonance (MR) brain images called "Multi-Atlas Label Propagation with Expectation-Maximisation based refinement" (MALP-EM). The presented approach is based on a robust registration approach (MAPER), highly performant label fusion (joint label fusion) and intensity-based label refinement using EM. We further adapt this framework to be applicable for the segmentation of brain images with gross changes in anatomy. We propose to account for consistent registration errors by relaxing anatomical priors obtained by multi-atlas propagation and a weighting scheme to locally combine anatomical atlas priors and intensity-refined posterior probabilities. The method is evaluated on a benchmark dataset used in a recent MICCAI segmentation challenge. In this context we show that MALP-EM is competitive for the segmentation of MR brain scans of healthy adults when compared to state-of-the-art automatic labelling techniques. To demonstrate the versatility of the proposed approach, we employed MALP-EM to segment 125 MR brain images into 134 regions from subjects who had sustained traumatic brain injury (TBI). We employ a protocol to assess segmentation quality if no manual reference labels are available. Based on this protocol, three independent, blinded raters confirmed on 13 MR brain scans with pathology that MALP-EM is superior to established label fusion techniques. We visually confirm the robustness of our segmentation approach on the full cohort and investigate the potential of derived symmetry-based imaging biomarkers that correlate with and predict clinically relevant variables in TBI such as the Marshall Classification (MC) or Glasgow Outcome Score (GOS). Specifically, we show that we are able to stratify TBI patients with favourable outcomes from non-favourable outcomes with 64.7% accuracy using acute-phase MR images and 66.8% accuracy using follow-up MR images. Furthermore, we are able to differentiate subjects with the presence of a mass lesion or midline shift from those with diffuse brain injury with 76.0% accuracy. The thalamus, putamen, pallidum and hippocampus are particularly affected. Their involvement predicts TBI disease progression.
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Burgos N, Cardoso MJ, Thielemans K, Modat M, Pedemonte S, Dickson J, Barnes A, Ahmed R, Mahoney CJ, Schott JM, Duncan JS, Atkinson D, Arridge SR, Hutton BF, Ourselin S. Attenuation correction synthesis for hybrid PET-MR scanners: application to brain studies. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:2332-2341. [PMID: 25055381 DOI: 10.1109/tmi.2014.2340135] [Citation(s) in RCA: 211] [Impact Index Per Article: 21.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Attenuation correction is an essential requirement for quantification of positron emission tomography (PET) data. In PET/CT acquisition systems, attenuation maps are derived from computed tomography (CT) images. However, in hybrid PET/MR scanners, magnetic resonance imaging (MRI) images do not directly provide a patient-specific attenuation map. The aim of the proposed work is to improve attenuation correction for PET/MR scanners by generating synthetic CTs and attenuation maps. The synthetic images are generated through a multi-atlas information propagation scheme, locally matching the MRI-derived patient's morphology to a database of MRI/CT pairs, using a local image similarity measure. Results show significant improvements in CT synthesis and PET reconstruction accuracy when compared to a segmentation method using an ultrashort-echo-time MRI sequence and to a simplified atlas-based method.
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Clarkson MJ, Zombori G, Thompson S, Totz J, Song Y, Espak M, Johnsen S, Hawkes D, Ourselin S. The NifTK software platform for image-guided interventions: platform overview and NiftyLink messaging. Int J Comput Assist Radiol Surg 2014; 10:301-16. [PMID: 25408304 PMCID: PMC4338364 DOI: 10.1007/s11548-014-1124-7] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2014] [Accepted: 10/17/2014] [Indexed: 11/24/2022]
Abstract
PURPOSE To perform research in image-guided interventions, researchers need a wide variety of software components, and assembling these components into a flexible and reliable system can be a challenging task. In this paper, the NifTK software platform is presented. A key focus has been high-performance streaming of stereo laparoscopic video data, ultrasound data and tracking data simultaneously. METHODS A new messaging library called NiftyLink is introduced that uses the OpenIGTLink protocol and provides the user with easy-to-use asynchronous two-way messaging, high reliability and comprehensive error reporting. A small suite of applications called NiftyGuide has been developed, containing lightweight applications for grabbing data, currently from position trackers and ultrasound scanners. These applications use NiftyLink to stream data into NiftyIGI, which is a workstation-based application, built on top of MITK, for visualisation and user interaction. Design decisions, performance characteristics and initial applications are described in detail. NiftyLink was tested for latency when transmitting images, tracking data, and interleaved imaging and tracking data. RESULTS NiftyLink can transmit tracking data at 1,024 frames per second (fps) with latency of 0.31 milliseconds, and 512 KB images with latency of 6.06 milliseconds at 32 fps. NiftyIGI was tested, receiving stereo high-definition laparoscopic video at 30 fps, tracking data from 4 rigid bodies at 20-30 fps and ultrasound data at 20 fps with rendering refresh rates between 2 and 20 Hz with no loss of user interaction. CONCLUSION These packages form part of the NifTK platform and have proven to be successful in a variety of image-guided surgery projects. Code and documentation for the NifTK platform are available from http://www.niftk.org . NiftyLink is provided open-source under a BSD license and available from http://github.com/NifTK/NiftyLink . The code for this paper is tagged IJCARS-2014.
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Affiliation(s)
- Matthew J Clarkson
- Centre For Medical Image Computing, University College London, Engineering Front Building, Malet Place, London, UK,
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Bryan FW, Xu Z, Asman AJ, Allen WM, Reich DS, Landman BA. Self-assessed performance improves statistical fusion of image labels. Med Phys 2014; 41:031903. [PMID: 24593721 DOI: 10.1118/1.4864236] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Expert manual labeling is the gold standard for image segmentation, but this process is difficult, time-consuming, and prone to inter-individual differences. While fully automated methods have successfully targeted many anatomies, automated methods have not yet been developed for numerous essential structures (e.g., the internal structure of the spinal cord as seen on magnetic resonance imaging). Collaborative labeling is a new paradigm that offers a robust alternative that may realize both the throughput of automation and the guidance of experts. Yet, distributing manual labeling expertise across individuals and sites introduces potential human factors concerns (e.g., training, software usability) and statistical considerations (e.g., fusion of information, assessment of confidence, bias) that must be further explored. During the labeling process, it is simple to ask raters to self-assess the confidence of their labels, but this is rarely done and has not been previously quantitatively studied. Herein, the authors explore the utility of self-assessment in relation to automated assessment of rater performance in the context of statistical fusion. METHODS The authors conducted a study of 66 volumes manually labeled by 75 minimally trained human raters recruited from the university undergraduate population. Raters were given 15 min of training during which they were shown examples of correct segmentation, and the online segmentation tool was demonstrated. The volumes were labeled 2D slice-wise, and the slices were unordered. A self-assessed quality metric was produced by raters for each slice by marking a confidence bar superimposed on the slice. Volumes produced by both voting and statistical fusion algorithms were compared against a set of expert segmentations of the same volumes. RESULTS Labels for 8825 distinct slices were obtained. Simple majority voting resulted in statistically poorer performance than voting weighted by self-assessed performance. Statistical fusion resulted in statistically indistinguishable performance from self-assessed weighted voting. The authors developed a new theoretical basis for using self-assessed performance in the framework of statistical fusion and demonstrated that the combined sources of information (both statistical assessment and self-assessment) yielded statistically significant improvement over the methods considered separately. CONCLUSIONS The authors present the first systematic characterization of self-assessed performance in manual labeling. The authors demonstrate that self-assessment and statistical fusion yield similar, but complementary, benefits for label fusion. Finally, the authors present a new theoretical basis for combining self-assessments with statistical label fusion.
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Affiliation(s)
- Frederick W Bryan
- Electrical Engineering, Vanderbilt University, Nashville, Tennessee 37235
| | - Zhoubing Xu
- Electrical Engineering, Vanderbilt University, Nashville, Tennessee 37235
| | - Andrew J Asman
- Electrical Engineering, Vanderbilt University, Nashville, Tennessee 37235
| | - Wade M Allen
- Electrical Engineering, Vanderbilt University, Nashville, Tennessee 37235
| | - Daniel S Reich
- Translational Neuroradiology Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland 20892
| | - Bennett A Landman
- Electrical Engineering, Vanderbilt University, Nashville, Tennessee 37235; Biomedical Engineering, Vanderbilt University, Nashville, Tennessee 37235; and Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee 37235
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Parker CS, Deligianni F, Cardoso MJ, Daga P, Modat M, Dayan M, Clark CA, Ourselin S, Clayden JD. Consensus between pipelines in structural brain networks. PLoS One 2014; 9:e111262. [PMID: 25356977 PMCID: PMC4214749 DOI: 10.1371/journal.pone.0111262] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2014] [Accepted: 09/23/2014] [Indexed: 02/07/2023] Open
Abstract
Structural brain networks may be reconstructed from diffusion MRI tractography data and have great potential to further our understanding of the topological organisation of brain structure in health and disease. Network reconstruction is complex and involves a series of processesing methods including anatomical parcellation, registration, fiber orientation estimation and whole-brain fiber tractography. Methodological choices at each stage can affect the anatomical accuracy and graph theoretical properties of the reconstructed networks, meaning applying different combinations in a network reconstruction pipeline may produce substantially different networks. Furthermore, the choice of which connections are considered important is unclear. In this study, we assessed the similarity between structural networks obtained using two independent state-of-the-art reconstruction pipelines. We aimed to quantify network similarity and identify the core connections emerging most robustly in both pipelines. Similarity of network connections was compared between pipelines employing different atlases by merging parcels to a common and equivalent node scale. We found a high agreement between the networks across a range of fiber density thresholds. In addition, we identified a robust core of highly connected regions coinciding with a peak in similarity across network density thresholds, and replicated these results with atlases at different node scales. The binary network properties of these core connections were similar between pipelines but showed some differences in atlases across node scales. This study demonstrates the utility of applying multiple structural network reconstrution pipelines to diffusion data in order to identify the most important connections for further study.
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Affiliation(s)
- Christopher S. Parker
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Imaging and Biophysics Unit, UCL Institute of Child Health, London, United Kingdom
- * E-mail:
| | - Fani Deligianni
- Imaging and Biophysics Unit, UCL Institute of Child Health, London, United Kingdom
| | - M. Jorge Cardoso
- Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Pankaj Daga
- Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Marc Modat
- Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Michael Dayan
- Imaging and Biophysics Unit, UCL Institute of Child Health, London, United Kingdom
- Department of Radiology, Weill Cornell Medical College, New York, New York, United States of America
| | - Chris A. Clark
- Imaging and Biophysics Unit, UCL Institute of Child Health, London, United Kingdom
| | - Sebastien Ourselin
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Dementia Research Centre, University College London, London, United Kingdom
| | - Jonathan D. Clayden
- Imaging and Biophysics Unit, UCL Institute of Child Health, London, United Kingdom
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Panda S, Asman AJ, Khare SP, Thompson L, Mawn LA, Smith SA, Landman BA. Evaluation of Multi-Atlas Label Fusion for In Vivo MRI Orbital Segmentation. J Med Imaging (Bellingham) 2014; 1:024002. [PMID: 25558466 PMCID: PMC4280790 DOI: 10.1117/1.jmi.1.2.024002] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2013] [Revised: 05/15/2014] [Accepted: 06/24/2014] [Indexed: 11/14/2022] Open
Abstract
Multi-atlas methods have been successful for brain segmentation, but their application to smaller anatomies remains relatively unexplored. We evaluate 7 statistical and voting-based label fusion algorithms (and 6 additional variants) to segment the optic nerves, eye globes and chiasm. For non-local STAPLE, we evaluate different intensity similarity measures (including mean square difference, locally normalized cross correlation, and a hybrid approach). Each algorithm is evaluated in terms of the Dice overlap and symmetric surface distance metrics. Finally, we evaluate refinement of label fusion results using a learning based correction method for consistent bias correction and Markov random field regularization. The multi-atlas labeling pipelines were evaluated on a cohort of 35 subjects including both healthy controls and patients. Across all three structures, NLSS with a mixed weighting type provided the most consistent results; for the optic nerve NLSS resulted in a median Dice similarity coefficient of 0.81, mean surface distance of 0.41 mm and Hausdorff distance 2.18 mm for the optic nerves. Joint label fusion resulted in slightly superior median performance for the optic nerves (0.82, 0.39 mm and 2.15 mm), but slightly worse on the globes. The fully automated multi-atlas labeling approach provides robust segmentations of orbital structures on MRI even in patients for whom significant atrophy (optic nerve head drusen) or inflammation (multiple sclerosis) is present.
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Affiliation(s)
- Swetasudha Panda
- Vanderbilt University, Department of Electrical Engineering, Nashville, Tennessee 37235, United States
| | - Andrew J. Asman
- Vanderbilt University, Department of Electrical Engineering, Nashville, Tennessee 37235, United States
| | - Shweta P. Khare
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee 37235, United States
| | - Lindsey Thompson
- Vanderbilt University, Institute of Imaging Science, Nashville, Tennessee 37235, United States
| | - Louise A. Mawn
- Vanderbilt University, Department of Ophthalmology and Neurological Surgery, Nashville, Tennessee 37232, United States
| | - Seth A. Smith
- Vanderbilt University, Institute of Imaging Science, Nashville, Tennessee 37235, United States
- Vanderbilt University, Department of Radiology and Radiological Sciences, Nashville, Tennessee 37235, United States
| | - Bennett A. Landman
- Vanderbilt University, Department of Electrical Engineering, Nashville, Tennessee 37235, United States
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee 37235, United States
- Vanderbilt University, Institute of Imaging Science, Nashville, Tennessee 37235, United States
- Vanderbilt University, Department of Radiology and Radiological Sciences, Nashville, Tennessee 37235, United States
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Oguz I, Sonka M. LOGISMOS-B: layered optimal graph image segmentation of multiple objects and surfaces for the brain. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:1220-35. [PMID: 24760901 PMCID: PMC4324764 DOI: 10.1109/tmi.2014.2304499] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Automated reconstruction of the cortical surface is one of the most challenging problems in the analysis of human brain magnetic resonance imaging (MRI). A desirable segmentation must be both spatially and topologically accurate, as well as robust and computationally efficient. We propose a novel algorithm, LOGISMOS-B, based on probabilistic tissue classification, generalized gradient vector flows and the LOGISMOS graph segmentation framework. Quantitative results on MRI datasets from both healthy subjects and multiple sclerosis patients using a total of 16,800 manually placed landmarks illustrate the excellent performance of our algorithm with respect to spatial accuracy. Remarkably, the average signed error was only 0.084 mm for the white matter and 0.008 mm for the gray matter, even in the presence of multiple sclerosis lesions. Statistical comparison shows that LOGISMOS-B produces a significantly more accurate cortical reconstruction than FreeSurfer, the current state-of-the-art approach (p << 0.001). Furthermore, LOGISMOS-B enjoys a run time that is less than a third of that of FreeSurfer, which is both substantial, considering the latter takes 10 h/subject on average, and a statistically significant speedup.
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Affiliation(s)
- Ipek Oguz
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242 USA
| | - Milan Sonka
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242 USA
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Yan Z, Zhang S, Liu X, Metaxas DN, Montillo A. Accurate Whole-Brain Segmentation for Alzheimer's Disease Combining an Adaptive Statistical Atlas and Multi-atlas. MEDICAL COMPUTER VISION : LARGE DATA IN MEDICAL IMAGING : THIRD INTERNATIONAL MICCAI WORKSHOP, MCV 2013, NAGOYA, JAPAN, SEPTEMBER 26, 2013 : REVISED SELECTED PAPERS. MCV (WORKSHOP) (3RD : 2013 : NAGOYA-SHI, JAPAN) 2014; 8331:65-73. [PMID: 31723945 PMCID: PMC6853627 DOI: 10.1007/978-3-319-05530-5_7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Accurate segmentation of whole brain MR images including the cortex, white matter and subcortical structures is challenging due to inter-subject variability and the complex geometry of brain anatomy. However a precise solution would enable accurate, objective measurement of structure volumes for disease quantification. Our contribution is three-fold. First we construct an adaptive statistical atlas that combines structure specific relaxation and spatially varying adaptivity. Second we integrate an isotropic pairwise class-specific MRF model of label connectivity. Together these permit precise control over adaptivity, allowing many structures to be segmented simultaneously with superior accuracy. Third, we develop a framework combining the improved adaptive statistical atlas with a multi-atlas method which achieves simultaneous accurate segmentation of the cortex, ventricles, and sub-cortical structures in severely diseased brains, a feat not attained in [18]. We test the proposed method on 46 brains including 28 diseased brain with Alzheimer's and 18 healthy brains. Our proposed method yields higher accuracy than state-of-the-art approaches on both healthy and diseased brains.
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Affiliation(s)
- Zhennan Yan
- CBIM, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Shaoting Zhang
- CBIM, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
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Panda S, Asman AJ, Delisi MP, Mawn LA, Galloway RL, Landman BA. Robust Optic Nerve Segmentation on Clinically Acquired CT. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2014; 9034:90341G. [PMID: 24817810 DOI: 10.1117/12.2043715] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
The optic nerve is a sensitive central nervous system structure, which plays a critical role in many devastating pathological conditions. Several methods have been proposed in recent years to segment the optic nerve automatically, but progress toward full automation has been limited. Multi-atlas methods have been successful for brain segmentation, but their application to smaller anatomies remains relatively unexplored. Herein we evaluate a framework for robust and fully automated segmentation of the optic nerves, eye globes and muscles. We employ a robust registration procedure for accurate registrations, variable voxel resolution and image field-of-view. We demonstrate the efficacy of an optimal combination of SyN registration and a recently proposed label fusion algorithm (Non-local Spatial STAPLE) that accounts for small-scale errors in registration correspondence. On a dataset containing 30 highly varying computed tomography (CT) images of the human brain, the optimal registration and label fusion pipeline resulted in a median Dice similarity coefficient of 0.77, symmetric mean surface distance error of 0.55 mm, symmetric Hausdorff distance error of 3.33 mm for the optic nerves. Simultaneously, we demonstrate the robustness of the optimal algorithm by segmenting the optic nerve structure in 316 CT scans obtained from 182 subjects from a thyroid eye disease (TED) patient population.
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Affiliation(s)
- Swetasudha Panda
- Electrical Engineering, Vanderbilt University, Nashville, TN, USA 37235
| | - Andrew J Asman
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA 37235
| | - Michael P Delisi
- Ophthalmology and Neurological Surgery, Vanderbilt University, Nashville, TN, USA 37235
| | - Louise A Mawn
- Ophthalmology and Neurological Surgery, Vanderbilt University, Nashville, TN, USA 37235
| | - Robert L Galloway
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA 37235
| | - Bennett A Landman
- Electrical Engineering, Vanderbilt University, Nashville, TN, USA 37235 ; Biomedical Engineering, Vanderbilt University, Nashville, TN, USA 37235
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Samson RS, Cardoso MJ, Muhlert N, Sethi V, Wheeler-Kingshott CA, Ron M, Ourselin S, Miller DH, Chard DT. Investigation of outer cortical magnetisation transfer ratio abnormalities in multiple sclerosis clinical subgroups. Mult Scler 2014; 20:1322-30. [PMID: 24552746 DOI: 10.1177/1352458514522537] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
BACKGROUND Pathological abnormalities including demyelination and neuronal loss are reported in the outer cortex in multiple sclerosis (MS). OBJECTIVE We investigated for in vivo evidence of outer cortical abnormalities by measuring the magnetisation transfer ratio (MTR) in MS patients of different subgroups. METHODS Forty-four relapsing-remitting (RR) (mean age 41.9 years, median Expanded Disability Status Scale (EDSS) 2.0), 25 secondary progressive (SP) (54.1 years, EDSS 6.5) and 19 primary progressive (PP) (53.1 years, EDSS 6.0) MS patients and 35 healthy control subjects (mean age 39.2 years) were studied. Three-dimensional (3D) 1×1×1mm(3) T1-weighted images and MTR data were acquired. The cortex was segmented, then subdivided into outer and inner bands, and MTR values were calculated for each band. RESULTS In a pairwise analysis, mean outer cortical MTR was lower than mean inner cortical MTR in all MS groups and controls (p<0.001). Compared with controls, outer cortical MTR was decreased in SPMS (p<0.001) and RRMS (p<0.01), but not PPMS. Outer cortical MTR was lower in SPMS than PPMS (p<0.01) and RRMS (p<0.01). CONCLUSIONS Lower outer than inner cortical MTR in healthy controls may reflect differences in myelin content. The lowest outer cortical MTR was seen in SPMS and is consistent with more extensive outer cortical (including subpial) pathology, such as demyelination and neuronal loss, as observed in post-mortem studies of SPMS patients.
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Affiliation(s)
| | - Manuel J Cardoso
- Centre for Medical Image Computing, Department of Computer Sciences, University College London, UK Dementia Research Centre, Department of Neurodegenerative Diseases, Institute of Neurology, University College London, UK
| | - Nils Muhlert
- NMR Research Unit, UCL Institute of Neurology, London, UK
| | - Varun Sethi
- NMR Research Unit, UCL Institute of Neurology, London, UK
| | | | - Maria Ron
- NMR Research Unit, UCL Institute of Neurology, London, UK
| | - Sebastian Ourselin
- Centre for Medical Image Computing, Department of Computer Sciences, University College London, UK Dementia Research Centre, Department of Neurodegenerative Diseases, Institute of Neurology, University College London, UK
| | - David H Miller
- NMR Research Unit, UCL Institute of Neurology, London, UK
| | - Declan T Chard
- NMR Research Unit, UCL Institute of Neurology, London, UK National Institute for Health Research University College London Hospitals Biomedical Research Centre, UK
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Ribbens A, Hermans J, Maes F, Vandermeulen D, Suetens P. Unsupervised segmentation, clustering, and groupwise registration of heterogeneous populations of brain MR images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:201-224. [PMID: 23797244 DOI: 10.1109/tmi.2013.2270114] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Population analysis of brain morphology from magnetic resonance images contributes to the study and understanding of neurological diseases. Such analysis typically involves segmentation of a large set of images and comparisons of these segmentations between relevant subgroups of images (e.g., "normal" versus "diseased"). The images of each subgroup are usually selected in advance in a supervised way based on clinical knowledge. Their segmentations are typically guided by one or more available atlases, assumed to be suitable for the images at hand. We present a data-driven probabilistic framework that simultaneously performs atlas-guided segmentation of a heterogeneous set of brain MR images and clusters the images in homogeneous subgroups, while constructing separate probabilistic atlases for each cluster to guide the segmentation. The main benefits of integrating segmentation, clustering and atlas construction in a single framework are that: 1) our method can handle images of a heterogeneous group of subjects and automatically identifies homogeneous subgroups in an unsupervised way with minimal prior knowledge, 2) the subgroups are formed by automatical detection of the relevant morphological features based on the segmentation, 3) the atlases used by our method are constructed from the images themselves and optimally adapted for guiding the segmentation of each subgroup, and 4) the probabilistic atlases represent the morphological pattern that is specific for each subgroup and expose the groupwise differences between different subgroups. We demonstrate the feasibility of the proposed framework and evaluate its performance with respect to image segmentation, clustering and atlas construction on simulated and real data sets including the publicly available BrainWeb and ADNI data. It is shown that combined segmentation and atlas construction leads to improved segmentation accuracy. Furthermore, it is demonstrated that the clusters generated by our unsupervised framework largely coincide with the clinically determined subgroups in case of disease-specific differences in brain morphology and that the differences between the cluster-specific atlases are in agreement with the expected disease-specific patterns, indicating that our method is capable of detecting the different modes in a population. Our method can thus be seen as a comprehensive image-driven population analysis framework that can contribute to the detection of novel subgroups and distinctive image features, potentially leading to new insights in the brain development and disease.
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GONÇALVES NICOLAU, NIKKILÄ JANNE, VIGÁRIO RICARDO. SELF-SUPERVISED MRI TISSUE SEGMENTATION BY DISCRIMINATIVE CLUSTERING. Int J Neural Syst 2013; 24:1450004. [DOI: 10.1142/s012906571450004x] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The study of brain lesions can benefit from a clear identification of transitions between healthy and pathological tissues, through the analysis of brain imaging data. Current signal processing methods, able to address these issues, often rely on strong prior information. In this article, a new method for tissue segmentation is proposed. It is based on a discriminative strategy, in a self-supervised machine learning approach. This method avoids the use of prior information, which makes it very versatile, and able to cope with different tissue types. It also returns tissue probabilities for each voxel, crucial for a good characterization of the evolution of brain lesions. Simulated as well as real benchmark data were used to validate the accuracy of the method and compare it against other segmentation algorithms.
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Affiliation(s)
- NICOLAU GONÇALVES
- Department of Information and Computer Science, Aalto University School of Science, P. O. Box 15400, FI-00076 Aalto, Espoo, Finland
| | | | - RICARDO VIGÁRIO
- Department of Information and Computer Science, Aalto University School of Science, P.O. Box 15400, FI-00076 Aalto, Espoo, Finland
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Sanjuán A, Price CJ, Mancini L, Josse G, Grogan A, Yamamoto AK, Geva S, Leff AP, Yousry TA, Seghier ML. Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors. Front Neurosci 2013; 7:241. [PMID: 24381535 PMCID: PMC3865426 DOI: 10.3389/fnins.2013.00241] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2013] [Accepted: 11/27/2013] [Indexed: 11/20/2022] Open
Abstract
Brain tumors can have different shapes or locations, making their identification very challenging. In functional MRI, it is not unusual that patients have only one anatomical image due to time and financial constraints. Here, we provide a modified automatic lesion identification (ALI) procedure which enables brain tumor identification from single MR images. Our method rests on (A) a modified segmentation-normalization procedure with an explicit “extra prior” for the tumor and (B) an outlier detection procedure for abnormal voxel (i.e., tumor) classification. To minimize tissue misclassification, the segmentation-normalization procedure requires prior information of the tumor location and extent. We therefore propose that ALI is run iteratively so that the output of Step B is used as a patient-specific prior in Step A. We test this procedure on real T1-weighted images from 18 patients, and the results were validated in comparison to two independent observers' manual tracings. The automated procedure identified the tumors successfully with an excellent agreement with the manual segmentation (area under the ROC curve = 0.97 ± 0.03). The proposed procedure increases the flexibility and robustness of the ALI tool and will be particularly useful for lesion-behavior mapping studies, or when lesion identification and/or spatial normalization are problematic.
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Affiliation(s)
- Ana Sanjuán
- Wellcome Trust Centre for Neuroimaging, University College of London London, UK ; Departamento de Psicología Básica, Clínica y Psicobiología, Universitat Jaume I Castellón, Spain
| | - Cathy J Price
- Wellcome Trust Centre for Neuroimaging, University College of London London, UK
| | - Laura Mancini
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery London, UK
| | - Goulven Josse
- Hôpital de la Pitié-Salpêtrière, Institut du Cerveau et de la Moëlle épinière Paris, France
| | - Alice Grogan
- Wellcome Trust Centre for Neuroimaging, University College of London London, UK
| | - Adam K Yamamoto
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery London, UK
| | - Sharon Geva
- Developmental Cognitive Neuroscience Unit, Institute of Child Health, University College of London London, UK
| | - Alex P Leff
- Wellcome Trust Centre for Neuroimaging, University College of London London, UK ; Institute of Cognitive Neuroscience, University College of London London, UK
| | - Tarek A Yousry
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery London, UK
| | - Mohamed L Seghier
- Wellcome Trust Centre for Neuroimaging, University College of London London, UK
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Liu E, Morris JC, Petersen RC, Saykin AJ, Schmidt ME, Shaw L, Shen L, Siuciak JA, Soares H, Toga AW, Trojanowski JQ. The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimers Dement 2013; 9:e111-94. [PMID: 23932184 DOI: 10.1016/j.jalz.2013.05.1769] [Citation(s) in RCA: 308] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 04/18/2013] [Indexed: 01/19/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The study aimed to enroll 400 subjects with early mild cognitive impairment (MCI), 200 subjects with early AD, and 200 normal control subjects; $67 million funding was provided by both the public and private sectors, including the National Institute on Aging, 13 pharmaceutical companies, and 2 foundations that provided support through the Foundation for the National Institutes of Health. This article reviews all papers published since the inception of the initiative and summarizes the results as of February 2011. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimers Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, CSF biomarkers, and clinical tests; (4) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects, and are leading candidates for the detection of AD in its preclinical stages; (5) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Baseline cognitive and/or MRI measures generally predicted future decline better than other modalities, whereas MRI measures of change were shown to be the most efficient outcome measures; (6) the confirmation of the AD risk loci CLU, CR1, and PICALM and the identification of novel candidate risk loci; (7) worldwide impact through the establishment of ADNI-like programs in Europe, Asia, and Australia; (8) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker data with clinical data from ADNI to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (9) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world. The ADNI study was extended by a 2-year Grand Opportunities grant in 2009 and a renewal of ADNI (ADNI-2) in October 2010 through to 2016, with enrollment of an additional 550 participants.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA.
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Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment. NEUROIMAGE-CLINICAL 2013; 2:735-45. [PMID: 24179825 PMCID: PMC3777690 DOI: 10.1016/j.nicl.2013.05.004] [Citation(s) in RCA: 165] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2013] [Revised: 05/07/2013] [Accepted: 05/08/2013] [Indexed: 01/23/2023]
Abstract
Accurately identifying the patients that have mild cognitive impairment (MCI) who will go on to develop Alzheimer's disease (AD) will become essential as new treatments will require identification of AD patients at earlier stages in the disease process. Most previous work in this area has centred around the same automated techniques used to diagnose AD patients from healthy controls, by coupling high dimensional brain image data or other relevant biomarker data to modern machine learning techniques. Such studies can now distinguish between AD patients and controls as accurately as an experienced clinician. Models trained on patients with AD and control subjects can also distinguish between MCI patients that will convert to AD within a given timeframe (MCI-c) and those that remain stable (MCI-s), although differences between these groups are smaller and thus, the corresponding accuracy is lower. The most common type of classifier used in these studies is the support vector machine, which gives categorical class decisions. In this paper, we introduce Gaussian process (GP) classification to the problem. This fully Bayesian method produces naturally probabilistic predictions, which we show correlate well with the actual chances of converting to AD within 3 years in a population of 96 MCI-s and 47 MCI-c subjects. Furthermore, we show that GPs can integrate multimodal data (in this study volumetric MRI, FDG-PET, cerebrospinal fluid, and APOE genotype with the classification process through the use of a mixed kernel). The GP approach aids combination of different data sources by learning parameters automatically from training data via type-II maximum likelihood, which we compare to a more conventional method based on cross validation and an SVM classifier. When the resulting probabilities from the GP are dichotomised to produce a binary classification, the results for predicting MCI conversion based on the combination of all three types of data show a balanced accuracy of 74%. This is a substantially higher accuracy than could be obtained using any individual modality or using a multikernel SVM, and is competitive with the highest accuracy yet achieved for predicting conversion within three years on the widely used ADNI dataset. Prediction of MCI to AD conversion using ADNI data and Gaussian processes. 74% accuracy, 0.795 area under ROC curve for predicting conversion within 3 years. Gaussian processes allow automatic parameter tuning including multimodal weights. Statistically significant improvement for multimodal vs best unimodal prediction. Probabilistic interpretation of results to better reflect continuum of disease.
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An unbiased longitudinal analysis framework for tracking white matter changes using diffusion tensor imaging with application to Alzheimer's disease. Neuroimage 2013; 72:153-63. [DOI: 10.1016/j.neuroimage.2013.01.044] [Citation(s) in RCA: 90] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2012] [Revised: 01/09/2013] [Accepted: 01/13/2013] [Indexed: 01/09/2023] Open
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Yan Z, Zhang S, Liu X, Metaxas DN, Montillo A. Accurate segmentation of brain images into 34 structures combining a non-stationary adaptive statistical atlas and a multi-atlas with applications to Alzheimer's disease. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2013; 2013:1202-1205. [PMID: 31788155 PMCID: PMC6884356 DOI: 10.1109/isbi.2013.6556696] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Accurate segmentation of the 30+ subcortical structures in MR images of whole diseased brains is challenging due to inter-subject variability and complex geometry of brain anatomy. However a clinically viable solution yielding precise segmentation of the structures would enable: 1) accurate, objective measurement of structure volumes many of which are associated with diseases such as Alzheimer's, 2) therapy monitoring and 3) drug development. Our contributions are two-fold. First we construct an extended adaptive statistical atlas method (EASA) to use a non-stationary relaxation factor rather than a global one. This permits finer control over adaptivity allowing 34 structures to be simultaneously segmented rather than just 4 as in [13]. Second we use the output of a weighted majority voting (WMV) label fusion multi-atlas method as the input to EASA in a hybrid WMV-EASA approach. We assess our proposed approaches on 18 healthy subjects in the public IBSR database and on 9 subjects with Alzheimer's disease in the AIBL database. EASA is shown to produce state-of-the-art accuracy on healthy brains in a fraction of the time of comparable methods, while our hybrid WMV-EASA visibly improves segmentation accuracy for structures throughout the diseased brains.
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Affiliation(s)
- Zhennan Yan
- Department of Computer Science, Rutgers University, Piscataway, NJ, USA
| | - Shaoting Zhang
- Department of Computer Science, Rutgers University, Piscataway, NJ, USA
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Andrews KA, Modat M, Macdonald KE, Yeatman T, Cardoso MJ, Leung KK, Barnes J, Villemagne VL, Rowe CC, Fox NC, Ourselin S, Schott JM. Atrophy rates in asymptomatic amyloidosis: implications for Alzheimer prevention trials. PLoS One 2013; 8:e58816. [PMID: 23554933 PMCID: PMC3599038 DOI: 10.1371/journal.pone.0058816] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2012] [Accepted: 02/07/2013] [Indexed: 01/18/2023] Open
Abstract
There is considerable interest in designing therapeutic studies of individuals at risk of Alzheimer disease (AD) to prevent the onset of symptoms. Cortical β-amyloid plaques, the first stage of AD pathology, can be detected in vivo using positron emission tomography (PET), and several studies have shown that ~1/3 of healthy elderly have significant β-amyloid deposition. Here we assessed whether asymptomatic amyloid-PET-positive controls have increased rates of brain atrophy, which could be harnessed as an outcome measure for AD prevention trials. We assessed 66 control subjects (age = 73.5±7.3 yrs; MMSE = 29±1.3) from the Australian Imaging Biomarkers & Lifestyle study who had a baseline Pittsburgh Compound B (PiB) PET scan and two 3T MRI scans ~18-months apart. We calculated PET standard uptake value ratios (SUVR), and classified individuals as amyloid-positive/negative. Baseline and 18-month MRI scans were registered, and brain, hippocampal, and ventricular volumes and annualized volume changes calculated. Increasing baseline PiB-PET measures of β-amyloid load correlated with hippocampal atrophy rate independent of age (p = 0.014). Twenty-two (1/3) were PiB-positive (SUVR>1.40), the remaining 44 PiB-negative (SUVR≤1.31). Compared to PiB-negatives, PiB-positive individuals were older (76.8±7.5 vs. 71.7±7.5, p<0.05) and more were APOE4 positive (63.6% vs. 19.2%, p<0.01) but there were no differences in baseline brain, ventricle or hippocampal volumes, either with or without correction for total intracranial volume, once age and gender were accounted for. The PiB-positive group had greater total hippocampal loss (0.06±0.08 vs. 0.02±0.05 ml/yr, p = 0.02), independent of age and gender, with non-significantly higher rates of whole brain (7.1±9.4 vs. 4.7±5.5 ml/yr) and ventricular (2.0±3.0 vs. 1.1±1.0 ml/yr) change. Based on the observed effect size, recruiting 384 (95%CI 195-1080) amyloid-positive subjects/arm will provide 80% power to detect 25% absolute slowing of hippocampal atrophy rate in an 18-month treatment trial. We conclude that hippocampal atrophy may be a feasible outcome measure for secondary prevention studies in asymptomatic amyloidosis.
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Affiliation(s)
- K Abigail Andrews
- Dementia Research Centre, University College London, London, United Kingdom.
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Jorge Cardoso M, Leung K, Modat M, Keihaninejad S, Cash D, Barnes J, Fox NC, Ourselin S. STEPS: Similarity and Truth Estimation for Propagated Segmentations and its application to hippocampal segmentation and brain parcelation. Med Image Anal 2013; 17:671-84. [PMID: 23510558 DOI: 10.1016/j.media.2013.02.006] [Citation(s) in RCA: 201] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2012] [Revised: 02/06/2013] [Accepted: 02/18/2013] [Indexed: 11/17/2022]
Abstract
Anatomical segmentation of structures of interest is critical to quantitative analysis in medical imaging. Several automated multi-atlas based segmentation propagation methods that utilise manual delineations from multiple templates appear promising. However, high levels of accuracy and reliability are needed for use in diagnosis or in clinical trials. We propose a new local ranking strategy for template selection based on the locally normalised cross correlation (LNCC) and an extension to the classical STAPLE algorithm by Warfield et al. (2004), which we refer to as STEPS for Similarity and Truth Estimation for Propagated Segmentations. It addresses the well-known problems of local vs. global image matching and the bias introduced in the performance estimation due to structure size. We assessed the method on hippocampal segmentation using a leave-one-out cross validation with optimised model parameters; STEPS achieved a mean Dice score of 0.925 when compared with manual segmentation. This was significantly better in terms of segmentation accuracy when compared to other state-of-the-art fusion techniques. Furthermore, due to the finer anatomical scale, STEPS also obtains more accurate segmentations even when using only a third of the templates, reducing the dependence on large template databases. Using a subset of Alzheimer's Disease Neuroimaging Initiative (ADNI) scans from different MRI imaging systems and protocols, STEPS yielded similarly accurate segmentations (Dice=0.903). A cross-sectional and longitudinal hippocampal volumetric study was performed on the ADNI database. Mean±SD hippocampal volume (mm(3)) was 5195 ± 656 for controls; 4786 ± 781 for MCI; and 4427 ± 903 for Alzheimer's disease patients and hippocampal atrophy rates (%/year) of 1.09 ± 3.0, 2.74 ± 3.5 and 4.04 ± 3.6 respectively. Statistically significant (p<10(-3)) differences were found between disease groups for both hippocampal volume and volume change rates. Finally, STEPS was also applied in a multi-label segmentation propagation scenario using a leave-one-out cross validation, in order to parcellate 83 separate structures of the brain. Comparisons of STEPS with state-of-the-art multi-label fusion algorithms showed statistically significant segmentation accuracy improvements (p<10(-4)) in several key structures.
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Affiliation(s)
- M Jorge Cardoso
- Centre for Medical Image Computing (CMIC), University College London, UK.
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Xing W, Nan C, ZhenTao Z, Rong X, Luo J, Zhuo Y, DingGang S, KunCheng L. Probabilistic MRI brain anatomical atlases based on 1,000 Chinese subjects. PLoS One 2013; 8:e50939. [PMID: 23341878 PMCID: PMC3540754 DOI: 10.1371/journal.pone.0050939] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2012] [Accepted: 10/26/2012] [Indexed: 11/19/2022] Open
Abstract
Brain atlases are designed to provide a standard reference coordinate system of the
brain for neuroscience research. Existing human brain atlases are widely used to
provide anatomical references and information regarding structural characteristics of
the brain. The majority of them, however, are derived from one paticipant or small
samples of the Western population. This poses a limitation for scientific studies on
Eastern subjects. In this study, 10 new Chinese brain atlases for different ages and
genders were constructed using MR anatomical images based on HAMMER (Hierarchical
Attribute Matching Mechanism for Elastic Registration). A total of 1,000 Chinese
volunteers ranging from 18 to 70 years old participated in this study. These
population-specific brain atlases represent the basic structural characteristics of
the Chinese population. They may be utilized for basic neuroscience studies and
clinical diagnosis, including evaluation of neurological and neuropsychiatric
disorders, in Chinese patients and those from other Eastern countries.
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Affiliation(s)
- Wang Xing
- Department of Radiology, Xuanwu Hospital, Capital
Medical University, Beijing, China
| | - Chen Nan
- Department of Radiology, Xuanwu Hospital, Capital
Medical University, Beijing, China
| | - Zuo ZhenTao
- State Key Laboratory of Brain and Cognitive
Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese
Academy of Sciences, Beijing, China
| | - Xue Rong
- State Key Laboratory of Brain and Cognitive
Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese
Academy of Sciences, Beijing, China
- * E-mail: (LKC); (XR)
| | - Jing Luo
- State Key Laboratory of Brain and Cognitive
Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese
Academy of Sciences, Beijing, China
| | - Yan Zhuo
- State Key Laboratory of Brain and Cognitive
Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese
Academy of Sciences, Beijing, China
| | - Shen DingGang
- Department of Radiology and Biomedical Research
Imaging Center, The University of North Carolina at Chapel Hill (UNC-CH), Chapel
Hill, North Carolina
| | - Li KunCheng
- Department of Radiology, Xuanwu Hospital, Capital
Medical University, Beijing, China
- * E-mail: (LKC); (XR)
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Quantitative Airway Analysis in Longitudinal Studies Using Groupwise Registration and 4D Optimal Surfaces. ACTA ACUST UNITED AC 2013; 16:287-94. [PMID: 24579152 DOI: 10.1007/978-3-642-40763-5_36] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
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47
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Cosa A, Canals S, Valles-Lluch A, Moratal D. Unsupervised segmentation of brain regions with similar microstructural properties: application to alcoholism. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:1053-1056. [PMID: 24109872 DOI: 10.1109/embc.2013.6609685] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
In this work, a novel brain MRI segmentation approach evaluates microstructural differences between groups. Going further from the traditional segmentation of brain tissues (white matter -WM-, gray matter -GM- and cerebrospinal fluid -CSF- or a mixture of them), a new way to classify brain areas is proposed using their microstructural MR properties. Eight rats were studied using the proposed methodology identifying regions which present microstructural differences as a consequence on one month of hard alcohol consumption. Differences in relaxation times of the tissues have been found in different brain regions (p<0.05). Furthermore, these changes allowed the automatic classification of the animals based on their drinking history (hit rate of 93.75 % of the cases).
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Dahnke R, Yotter RA, Gaser C. Cortical thickness and central surface estimation. Neuroimage 2013; 65:336-48. [DOI: 10.1016/j.neuroimage.2012.09.050] [Citation(s) in RCA: 262] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2011] [Revised: 09/17/2012] [Accepted: 09/20/2012] [Indexed: 10/27/2022] Open
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Hobbs NZ, Cole JH, Farmer RE, Rees EM, Crawford HE, Malone IB, Roos RAC, Sprengelmeyer R, Durr A, Landwehrmeyer B, Scahill RI, Tabrizi SJ, Frost C. Evaluation of multi-modal, multi-site neuroimaging measures in Huntington's disease: Baseline results from the PADDINGTON study. NEUROIMAGE-CLINICAL 2012; 2:204-11. [PMID: 24179770 PMCID: PMC3777685 DOI: 10.1016/j.nicl.2012.12.001] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2012] [Revised: 11/30/2012] [Accepted: 12/01/2012] [Indexed: 12/02/2022]
Abstract
Background Macro- and micro-structural neuroimaging measures provide valuable information on the pathophysiology of Huntington's disease (HD) and are proposed as biomarkers. Despite theoretical advantages of microstructural measures in terms of sensitivity to pathology, there is little evidence directly comparing the two. Methods 40 controls and 61 early HD subjects underwent 3 T MRI (T1- and diffusion-weighted), as part of the PADDINGTON study. Macrostructural volumetrics were obtained for the whole brain, caudate, putamen, corpus callosum (CC) and ventricles. Microstructural diffusion metrics of fractional anisotropy (FA), mean-, radial- and axial-diffusivity (MD, RD, AD) were computed for white matter (WM), CC, caudate and putamen. Group differences were examined adjusting for age, gender and site. A formal comparison of effect sizes determined which modality and metrics provided a statistically significant advantage over others. Results Macrostructural measures showed decreased regional and global volume in HD (p < 0.001); except the ventricles which were enlarged (p < 0.01). In HD, FA was increased in the deep grey-matter structures (p < 0.001), and decreased in the WM (CC, p = 0.035; WM, p = 0.053); diffusivity metrics (MD, RD, AD) were increased for all brain regions (p < 0.001). The largest effect sizes were for putamen volume, caudate volume and putamen diffusivity (AD, RD and MD); each was significantly larger than those for all other metrics (p < 0.05). Conclusion The highest performing macro- and micro-structural metrics had similar sensitivity to HD pathology quantified via effect sizes. Region-of-interest may be more important than imaging modality, with deep grey-matter regions outperforming the CC and global measures, for both volume and diffusivity. FA appears to be relatively insensitive to disease effects.
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Affiliation(s)
- Nicola Z Hobbs
- UCL Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK
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50
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Cardoso MJ, Melbourne A, Kendall GS, Modat M, Robertson NJ, Marlow N, Ourselin S. AdaPT: An adaptive preterm segmentation algorithm for neonatal brain MRI. Neuroimage 2012; 65:97-108. [PMID: 22906793 DOI: 10.1016/j.neuroimage.2012.08.009] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2012] [Revised: 07/13/2012] [Accepted: 08/05/2012] [Indexed: 11/25/2022] Open
Abstract
Advances in neonatal care have improved the survival of infants born prematurely although these infants remain at increased risk of adverse neurodevelopmental outcome. The measurement of white matter structure and features of the cortical surface can help define biomarkers that predict this risk. The measurement of these structures relies upon accurate automated segmentation routines, but these are often confounded by neonatal-specific imaging difficulties including poor contrast, low resolution, partial volume effects and the presence of significant natural and pathological anatomical variability. In this work we develop and evaluate an adaptive preterm multi-modal maximum a posteriori expectation-maximisation segmentation algorithm (AdaPT) incorporating an iterative relaxation strategy that adapts the tissue proportion priors toward the subject data. Also incorporated are intensity non-uniformity correction, a spatial homogeneity term in the form of a Markov random field and furthermore, the proposed method explicitly models the partial volume effect specifically mitigating the neonatal specific grey and white matter contrast inversion. Spatial priors are iteratively relaxed, enabling the segmentation of images with high anatomical disparity from a normal population. Experiments performed on a clinical cohort of 92 infants are validated against manual segmentation of normal and pathological cortical grey matter, cerebellum and ventricular volumes. Dice overlap scores increase significantly when compared to a widely-used maximum likelihood expectation maximisation algorithm for pathological cortical grey matter, cerebellum and ventricular volumes. Adaptive maximum a posteriori expectation maximisation is shown to be a useful tool for accurate and robust neonatal brain segmentation.
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Affiliation(s)
- M Jorge Cardoso
- Centre for Medical Image Computing, University College London, UK
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