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Kyuragi Y, Oishi N, Hatakoshi M, Hirano J, Noda T, Yoshihara Y, Ito Y, Igarashi H, Miyata J, Takahashi K, Kamiya K, Matsumoto J, Okada T, Fushimi Y, Nakagome K, Mimura M, Murai T, Suwa T. Segmentation and Volume Estimation of the Habenula Using Deep Learning in Patients With Depression. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100314. [PMID: 38726037 PMCID: PMC11078767 DOI: 10.1016/j.bpsgos.2024.100314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/06/2024] [Accepted: 03/27/2024] [Indexed: 05/12/2024] Open
Abstract
Background The habenula is involved in the pathophysiology of depression. However, its small structure limits the accuracy of segmentation methods, and the findings regarding its volume have been inconsistent. This study aimed to create a highly accurate habenula segmentation model using deep learning, test its generalizability to clinical magnetic resonance imaging, and examine differences between healthy participants and patients with depression. Methods This multicenter study included 382 participants (patients with depression: N = 234, women 47.0%; healthy participants: N = 148, women 37.8%). A 3-dimensional residual U-Net was used to create a habenula segmentation model on 3T magnetic resonance images. The reproducibility and generalizability of the predictive model were tested on various validation cohorts. Thereafter, differences between the habenula volume of healthy participants and that of patients with depression were examined. Results A Dice coefficient of 86.6% was achieved in the derivation cohort. The test-retest dataset showed a mean absolute percentage error of 6.66, indicating sufficiently high reproducibility. A Dice coefficient of >80% was achieved for datasets with different imaging conditions, such as magnetic field strengths, spatial resolutions, and imaging sequences, by adjusting the threshold. A significant negative correlation with age was observed in the general population, and this correlation was more pronounced in patients with depression (p < 10-7, r = -0.59). Habenula volume decreased with depression severity in women even when the effects of age and scanner were excluded (p = .019, η2 = 0.099). Conclusions Habenula volume could be a pathophysiologically relevant factor and diagnostic and therapeutic marker for depression, particularly in women.
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Affiliation(s)
- Yusuke Kyuragi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Naoya Oishi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Momoko Hatakoshi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Jinichi Hirano
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Takamasa Noda
- Department of Psychiatry, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Yujiro Yoshihara
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yuri Ito
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Hiroyuki Igarashi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Jun Miyata
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Psychiatry, Aichi Medical University, Aichi, Japan
| | - Kento Takahashi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kei Kamiya
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Junya Matsumoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Tomohisa Okada
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | | | - Masaru Mimura
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Toshiya Murai
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Taro Suwa
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Srikrishna M, Seo W, Zettergren A, Kern S, Cantré D, Gessler F, Sotoudeh H, Seidlitz J, Bernstock JD, Wahlund LO, Westman E, Skoog I, Virhammar J, Fällmar D, Schöll M. Assessing CT-based Volumetric Analysis via Transfer Learning with MRI and Manual Labels for Idiopathic Normal Pressure Hydrocephalus. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.23.24309144. [PMID: 38978640 PMCID: PMC11230337 DOI: 10.1101/2024.06.23.24309144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Brain computed tomography (CT) is an accessible and commonly utilized technique for assessing brain structure. In cases of idiopathic normal pressure hydrocephalus (iNPH), the presence of ventriculomegaly is often neuroradiologically evaluated by visual rating and manually measuring each image. Previously, we have developed and tested a deep-learning-model that utilizes transfer learning from magnetic resonance imaging (MRI) for CT-based intracranial tissue segmentation. Accordingly, herein we aimed to enhance the segmentation of ventricular cerebrospinal fluid (VCSF) in brain CT scans and assess the performance of automated brain CT volumetrics in iNPH patient diagnostics. Methods The development of the model used a two-stage approach. Initially, a 2D U-Net model was trained to predict VCSF segmentations from CT scans, using paired MR-VCSF labels from healthy controls. This model was subsequently refined by incorporating manually segmented lateral CT-VCSF labels from iNPH patients, building on the features learned from the initial U-Net model. The training dataset included 734 CT datasets from healthy controls paired with T1-weighted MRI scans from the Gothenburg H70 Birth Cohort Studies and 62 CT scans from iNPH patients at Uppsala University Hospital. To validate the model's performance across diverse patient populations, external clinical images including scans of 11 iNPH patients from the Universitatsmedizin Rostock, Germany, and 30 iNPH patients from the University of Alabama at Birmingham, United States were used. Further, we obtained three CT-based volumetric measures (CTVMs) related to iNPH. Results Our analyses demonstrated strong volumetric correlations (ϱ=0.91, p<0.001) between automatically and manually derived CT-VCSF measurements in iNPH patients. The CTVMs exhibited high accuracy in differentiating iNPH patients from controls in external clinical datasets with an AUC of 0.97 and in the Uppsala University Hospital datasets with an AUC of 0.99. Discussion CTVMs derived through deep learning, show potential for assessing and quantifying morphological features in hydrocephalus. Critically, these measures performed comparably to gold-standard neuroradiology assessments in distinguishing iNPH from healthy controls, even in the presence of intraventricular shunt catheters. Accordingly, such an approach may serve to improve the radiological evaluation of iNPH diagnosis/monitoring (i.e., treatment responses). Since CT is much more widely available than MRI, our results have considerable clinical impact.
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Affiliation(s)
- Meera Srikrishna
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Physiology and Neuroscience, University of Gothenburg, Gothenburg, Sweden
| | - Woosung Seo
- Department of Surgical Sciences, Neuroradiology, Uppsala University, Uppsala, Sweden
| | - Anna Zettergren
- Neuropsychiatric Epidemiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), University of Gothenburg, Gothenburg, Sweden
| | - Silke Kern
- Neuropsychiatric Epidemiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), University of Gothenburg, Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Daniel Cantré
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center Rostock, Rostock, Germany
| | - Florian Gessler
- Department of Neurosurgery, University Medicine of Rostock, 18057 Rostock, Germany
| | - Houman Sotoudeh
- Department of Neuroradiology, University of Alabama, Birmingham, AL, United States
| | - Jakob Seidlitz
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, United States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, United States
| | - Joshua D. Bernstock
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Lars-Olof Wahlund
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Eric Westman
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Ingmar Skoog
- Neuropsychiatric Epidemiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), University of Gothenburg, Gothenburg, Sweden
| | - Johan Virhammar
- Department of Medical Sciences, Neurology, Uppsala University, Uppsala, Sweden
| | - David Fällmar
- Department of Surgical Sciences, Neuroradiology, Uppsala University, Uppsala, Sweden
| | - Michael Schöll
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Physiology and Neuroscience, University of Gothenburg, Gothenburg, Sweden
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK
- Department of Psychiatry, Cognition and Aging Psychiatry, Sahlgrenska University Hospital, Mölndal, Sweden
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Ragguett RM, Eagleson R, de Ribaupierre S. Evaluating normalized registration and preprocessing methodologies for the analysis of brain MRI in pediatric patients with shunt-treated hydrocephalus. Front Neurosci 2024; 18:1405363. [PMID: 38887369 PMCID: PMC11182356 DOI: 10.3389/fnins.2024.1405363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 05/06/2024] [Indexed: 06/20/2024] Open
Abstract
Introduction Registration to a standardized template (i.e. "normalization") is a critical step when performing neuroimaging studies. We present a comparative study involving the evaluation of general-purpose registration algorithms for pediatric patients with shunt treated hydrocephalus. Our sample dataset presents a number of intersecting challenges for registration, representing the potentially large deformations to both brain structures and overall brain shape, artifacts from shunts, and morphological differences corresponding to age. The current study assesses the normalization accuracy of shunt-treated hydrocephalus patients using freely available neuroimaging registration tools. Methods Anatomical neuroimages from eight pediatric patients with shunt-treated hydrocephalus were normalized. Four non-linear registration algorithms were assessed in addition to the preprocessing steps of skull-stripping and bias-correction. Registration accuracy was assessed using the Dice Coefficient (DC) and Hausdorff Distance (HD) in subcortical and cortical regions. Results A total of 592 registrations were performed. On average, normalizations performed using the brain extracted and bias-corrected images had a higher DC and lower HD compared to full head/ non-biased corrected images. The most accurate registration was achieved using SyN by ANTs with skull-stripped and bias corrected images. Without preprocessing, the DARTEL Toolbox was able to produce normalized images with comparable accuracy. The use of a pediatric template as an intermediate registration did not improve normalization. Discussion Using structural neuroimages from patients with shunt-treated pediatric hydrocephalus, it was demonstrated that there are tools which perform well after specified pre-processing steps were taken. Overall, these results provide insight to the performance of registration programs that can be used for normalization of brains with complex pathologies.
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Affiliation(s)
| | - Roy Eagleson
- School of Biomedical Engineering, Western University, London, ON, Canada
- Department of Electrical and Computer Engineering, Western University, London, ON, Canada
- Centre for Brain and Mind, Western University, London, ON, Canada
| | - Sandrine de Ribaupierre
- School of Biomedical Engineering, Western University, London, ON, Canada
- Centre for Brain and Mind, Western University, London, ON, Canada
- Department of Clinical Neurological Sciences, Schulich School of Medicine, Western University, London, ON, Canada
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Nigro S, Filardi M, Tafuri B, Nicolardi M, De Blasi R, Giugno A, Gnoni V, Milella G, Urso D, Zoccolella S, Logroscino G. Deep Learning-based Approach for Brainstem and Ventricular MR Planimetry: Application in Patients with Progressive Supranuclear Palsy. Radiol Artif Intell 2024; 6:e230151. [PMID: 38506619 PMCID: PMC11140505 DOI: 10.1148/ryai.230151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 02/01/2024] [Accepted: 03/06/2024] [Indexed: 03/21/2024]
Abstract
Purpose To develop a fast and fully automated deep learning (DL)-based method for the MRI planimetric segmentation and measurement of the brainstem and ventricular structures most affected in patients with progressive supranuclear palsy (PSP). Materials and Methods In this retrospective study, T1-weighted MR images in healthy controls (n = 84) were used to train DL models for segmenting the midbrain, pons, middle cerebellar peduncle (MCP), superior cerebellar peduncle (SCP), third ventricle, and frontal horns (FHs). Internal, external, and clinical test datasets (n = 305) were used to assess segmentation model reliability. DL masks from test datasets were used to automatically extract midbrain and pons areas and the width of MCP, SCP, third ventricle, and FHs. Automated measurements were compared with those manually performed by an expert radiologist. Finally, these measures were combined to calculate the midbrain to pons area ratio, MR parkinsonism index (MRPI), and MRPI 2.0, which were used to differentiate patients with PSP (n = 71) from those with Parkinson disease (PD) (n = 129). Results Dice coefficients above 0.85 were found for all brain regions when comparing manual and DL-based segmentations. A strong correlation was observed between automated and manual measurements (Spearman ρ > 0.80, P < .001). DL-based measurements showed excellent performance in differentiating patients with PSP from those with PD, with an area under the receiver operating characteristic curve above 0.92. Conclusion The automated approach successfully segmented and measured the brainstem and ventricular structures. DL-based models may represent a useful approach to support the diagnosis of PSP and potentially other conditions associated with brainstem and ventricular alterations. Keywords: MR Imaging, Brain/Brain Stem, Segmentation, Quantification, Diagnosis, Convolutional Neural Network Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Mohajer in this issue.
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Affiliation(s)
- Salvatore Nigro
- From the Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy (S.N., M.F., B.T., A.G., V.G., D.U., G.L.); Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy (M.F., B.T., G.M., G.L.); Department of Radiology, Pia Fondazione Cardinale G. Panico, Tricase, Italy (M.N., R.D.B.); Department of Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, England (D.U.); and Operative Unit of Neurology, San Paolo Hospital, ASL Bari, Bari, Italy (S.Z.)
| | - Marco Filardi
- From the Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy (S.N., M.F., B.T., A.G., V.G., D.U., G.L.); Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy (M.F., B.T., G.M., G.L.); Department of Radiology, Pia Fondazione Cardinale G. Panico, Tricase, Italy (M.N., R.D.B.); Department of Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, England (D.U.); and Operative Unit of Neurology, San Paolo Hospital, ASL Bari, Bari, Italy (S.Z.)
| | - Benedetta Tafuri
- From the Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy (S.N., M.F., B.T., A.G., V.G., D.U., G.L.); Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy (M.F., B.T., G.M., G.L.); Department of Radiology, Pia Fondazione Cardinale G. Panico, Tricase, Italy (M.N., R.D.B.); Department of Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, England (D.U.); and Operative Unit of Neurology, San Paolo Hospital, ASL Bari, Bari, Italy (S.Z.)
| | - Martina Nicolardi
- From the Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy (S.N., M.F., B.T., A.G., V.G., D.U., G.L.); Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy (M.F., B.T., G.M., G.L.); Department of Radiology, Pia Fondazione Cardinale G. Panico, Tricase, Italy (M.N., R.D.B.); Department of Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, England (D.U.); and Operative Unit of Neurology, San Paolo Hospital, ASL Bari, Bari, Italy (S.Z.)
| | - Roberto De Blasi
- From the Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy (S.N., M.F., B.T., A.G., V.G., D.U., G.L.); Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy (M.F., B.T., G.M., G.L.); Department of Radiology, Pia Fondazione Cardinale G. Panico, Tricase, Italy (M.N., R.D.B.); Department of Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, England (D.U.); and Operative Unit of Neurology, San Paolo Hospital, ASL Bari, Bari, Italy (S.Z.)
| | - Alessia Giugno
- From the Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy (S.N., M.F., B.T., A.G., V.G., D.U., G.L.); Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy (M.F., B.T., G.M., G.L.); Department of Radiology, Pia Fondazione Cardinale G. Panico, Tricase, Italy (M.N., R.D.B.); Department of Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, England (D.U.); and Operative Unit of Neurology, San Paolo Hospital, ASL Bari, Bari, Italy (S.Z.)
| | - Valentina Gnoni
- From the Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy (S.N., M.F., B.T., A.G., V.G., D.U., G.L.); Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy (M.F., B.T., G.M., G.L.); Department of Radiology, Pia Fondazione Cardinale G. Panico, Tricase, Italy (M.N., R.D.B.); Department of Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, England (D.U.); and Operative Unit of Neurology, San Paolo Hospital, ASL Bari, Bari, Italy (S.Z.)
| | - Giammarco Milella
- From the Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy (S.N., M.F., B.T., A.G., V.G., D.U., G.L.); Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy (M.F., B.T., G.M., G.L.); Department of Radiology, Pia Fondazione Cardinale G. Panico, Tricase, Italy (M.N., R.D.B.); Department of Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, England (D.U.); and Operative Unit of Neurology, San Paolo Hospital, ASL Bari, Bari, Italy (S.Z.)
| | - Daniele Urso
- From the Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy (S.N., M.F., B.T., A.G., V.G., D.U., G.L.); Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy (M.F., B.T., G.M., G.L.); Department of Radiology, Pia Fondazione Cardinale G. Panico, Tricase, Italy (M.N., R.D.B.); Department of Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, England (D.U.); and Operative Unit of Neurology, San Paolo Hospital, ASL Bari, Bari, Italy (S.Z.)
| | - Stefano Zoccolella
- From the Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy (S.N., M.F., B.T., A.G., V.G., D.U., G.L.); Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy (M.F., B.T., G.M., G.L.); Department of Radiology, Pia Fondazione Cardinale G. Panico, Tricase, Italy (M.N., R.D.B.); Department of Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, England (D.U.); and Operative Unit of Neurology, San Paolo Hospital, ASL Bari, Bari, Italy (S.Z.)
| | - Giancarlo Logroscino
- From the Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy (S.N., M.F., B.T., A.G., V.G., D.U., G.L.); Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy (M.F., B.T., G.M., G.L.); Department of Radiology, Pia Fondazione Cardinale G. Panico, Tricase, Italy (M.N., R.D.B.); Department of Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, England (D.U.); and Operative Unit of Neurology, San Paolo Hospital, ASL Bari, Bari, Italy (S.Z.)
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Park B, Kim Y, Park J, Choi H, Kim SE, Ryu H, Seo K. Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study. J Med Internet Res 2024; 26:e54538. [PMID: 38631021 PMCID: PMC11063880 DOI: 10.2196/54538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 12/29/2023] [Accepted: 03/09/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach. OBJECTIVE We aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers. METHODS The study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T1-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls. RESULTS The support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and F1-score (93.3%). CONCLUSIONS The results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers.
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Affiliation(s)
- Bogyeom Park
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
| | - Yuwon Kim
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
| | - Jinseok Park
- Department of Neurology, College of Medicine, Hanyang University, Seoul, Republic of Korea
| | - Hojin Choi
- Department of Neurology, College of Medicine, Hanyang University, Seoul, Republic of Korea
| | - Seong-Eun Kim
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
| | - Hokyoung Ryu
- Graduate School of Technology and Innovation Management, Hanyang University, Seoul, Republic of Korea
| | - Kyoungwon Seo
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
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Kadaba Sridhar S, Dysterheft Robb J, Gupta R, Cheong S, Kuang R, Samadani U. Structural neuroimaging markers of normal pressure hydrocephalus versus Alzheimer's dementia and Parkinson's disease, and hydrocephalus versus atrophy in chronic TBI-a narrative review. Front Neurol 2024; 15:1347200. [PMID: 38576534 PMCID: PMC10991762 DOI: 10.3389/fneur.2024.1347200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 02/07/2024] [Indexed: 04/06/2024] Open
Abstract
Introduction Normal Pressure Hydrocephalus (NPH) is a prominent type of reversible dementia that may be treated with shunt surgery, and it is crucial to differentiate it from irreversible degeneration caused by its symptomatic mimics like Alzheimer's Dementia (AD) and Parkinson's Disease (PD). Similarly, it is important to distinguish between (normal pressure) hydrocephalus and irreversible atrophy/degeneration which are among the chronic effects of Traumatic Brain Injury (cTBI), as the former may be reversed through shunt placement. The purpose of this review is to elucidate the structural imaging markers which may be foundational to the development of accurate, noninvasive, and accessible solutions to this problem. Methods By searching the PubMed database for keywords related to NPH, AD, PD, and cTBI, we reviewed studies that examined the (1) distinct neuroanatomical markers of degeneration in NPH versus AD and PD, and atrophy versus hydrocephalus in cTBI and (2) computational methods for their (semi-) automatic assessment on Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scans. Results Structural markers of NPH and those that can distinguish it from AD have been well studied, but only a few studies have explored its structural distinction between PD. The structural implications of cTBI over time have been studied. But neuroanatomical markers that can predict shunt response in patients with either symptomatic idiopathic NPH or post-traumatic hydrocephalus have not been reliably established. MRI-based markers dominate this field of investigation as compared to CT, which is also reflected in the disproportionate number of MRI-based computational methods for their automatic assessment. Conclusion Along with an up-to-date literature review on the structural neurodegeneration due to NPH versus AD/PD, and hydrocephalus versus atrophy in cTBI, this article sheds light on the potential of structural imaging markers as (differential) diagnostic aids for the timely recognition of patients with reversible (normal pressure) hydrocephalus, and opportunities to develop computational tools for their objective assessment.
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Affiliation(s)
- Sharada Kadaba Sridhar
- Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, United States
- Neurotrauma Research Lab, Center for Veterans Research and Education, Minneapolis, MN, United States
| | - Jen Dysterheft Robb
- Neurotrauma Research Lab, Center for Veterans Research and Education, Minneapolis, MN, United States
| | - Rishabh Gupta
- Neurotrauma Research Lab, Center for Veterans Research and Education, Minneapolis, MN, United States
- University of Minnesota Twin Cities Medical School, Minneapolis, MN, United States
| | - Scarlett Cheong
- Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, United States
- Neurotrauma Research Lab, Center for Veterans Research and Education, Minneapolis, MN, United States
| | - Rui Kuang
- Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, United States
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Uzma Samadani
- Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, United States
- Neurotrauma Research Lab, Center for Veterans Research and Education, Minneapolis, MN, United States
- University of Minnesota Twin Cities Medical School, Minneapolis, MN, United States
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, United States
- Division of Neurosurgery, Department of Surgery, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN, United States
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7
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Wang X, Liu S, Yang N, Chen F, Ma L, Ning G, Zhang H, Qiu X, Liao H. A Segmentation Framework With Unsupervised Learning-Based Label Mapper for the Ventricular Target of Intracranial Germ Cell Tumor. IEEE J Biomed Health Inform 2023; 27:5381-5392. [PMID: 37651479 DOI: 10.1109/jbhi.2023.3310492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Intracranial germ cell tumors are rare tumors that mainly affect children and adolescents. Radiotherapy is the cornerstone of interdisciplinary treatment methods. Radiation of the whole ventricle system and the local tumor can reduce the complications in the late stage of radiotherapy while ensuring the curative effect. However, manually delineating the ventricular system is labor-intensive and time-consuming for physicians. The diverse ventricle shape and the hydrocephalus-induced ventricle dilation increase the difficulty of automatic segmentation algorithms. Therefore, this study proposed a fully automatic segmentation framework. Firstly, we designed a novel unsupervised learning-based label mapper, which is used to handle the ventricle shape variations and obtain the preliminary segmentation result. Then, to boost the segmentation performance of the framework, we improved the region growth algorithm and combined the fully connected conditional random field to optimize the preliminary results from both regional and voxel scales. In the case of only one set of annotated data is required, the average time cost is 153.01 s, and the average target segmentation accuracy can reach 84.69%. Furthermore, we verified the algorithm in practical clinical applications. The results demonstrate that our proposed method is beneficial for physicians to delineate radiotherapy targets, which is feasible and clinically practical, and may fill the gap of automatic delineation methods for the ventricular target of intracranial germ celltumors.
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8
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Wang Y, Feng A, Xue Y, Zuo L, Liu Y, Blitz AM, Luciano MG, Carass A, Prince JL. AUTOMATED VENTRICLE PARCELLATION AND EVAN'S RATIO COMPUTATION IN PRE- AND POST-SURGICAL VENTRICULOMEGALY. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2023; 2023:10.1109/isbi53787.2023.10230729. [PMID: 38013948 PMCID: PMC10679954 DOI: 10.1109/isbi53787.2023.10230729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Normal pressure hydrocephalus (NPH) is a brain disorder associated with enlarged ventricles and multiple cognitive and motor symptoms. The degree of ventricular enlargement can be measured using magnetic resonance images (MRIs) and characterized quantitatively using the Evan's ratio (ER). Automatic computation of ER is desired to avoid the extra time and variations associated with manual measurements on MRI. Because shunt surgery is often used to treat NPH, it is necessary that this process be robust to image artifacts caused by the shunt and related implants. In this paper, we propose a 3D regions-of-interest aware (ROI-aware) network for segmenting the ventricles. The method achieves state-of-the-art performance on both pre-surgery MRIs and post-surgery MRIs with artifacts. Based on our segmentation results, we also describe an automated approach to compute ER from these results. Experimental results on multiple datasets demonstrate the potential of the proposed method to assist clinicians in the diagnosis and management of NPH.
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Affiliation(s)
- Yuli Wang
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, USA
| | - Anqi Feng
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, USA
| | - Yuan Xue
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
| | - Lianrui Zuo
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, USA
| | - Yihao Liu
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
| | - Ari M Blitz
- Department of Radiology, Case Western Reserve University School of Medicine, USA
| | - Mark G Luciano
- Department of Neurosurgery, Johns Hopkins School of Medicine, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
| | - Jerry L Prince
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, USA
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
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9
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Wang Y, Feng A, Xue Y, Shao M, Blitz AM, Luciano MG, Carass A, Prince JL. Investigation of probability maps in deep-learning-based brain ventricle parcellation. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2023; 12464:124642G. [PMID: 38013746 PMCID: PMC10679955 DOI: 10.1117/12.2653999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Normal Pressure Hydrocephalus (NPH) is a brain disorder associated with ventriculomegaly. Accurate segmentation of the ventricle system into its sub-compartments from magnetic resonance images (MRIs) could help evaluate NPH patients for surgical intervention. In this paper, we modify a 3D U-net utilizing probability maps to perform accurate ventricle parcellation, even with grossly enlarged ventricles and post-surgery shunt artifacts, from MRIs. Our method achieves a mean dice similarity coefficient (DSC) on whole ventricles for healthy controls of 0.864 ± 0.047 and 0.961 ± 0.024 for NPH patients. Furthermore, with the benefit of probability maps, the proposed method provides superior performance on MRI with grossly enlarged ventricles (mean DSC value of 0.965 ± 0.027) or post-surgery shunt artifacts (mean DSC value of 0.964 ± 0.031). Results indicate that our method provides a high robust parcellation tool on the ventricular systems which is comparable to other state-of-the-art methods.
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Affiliation(s)
- Yuli Wang
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Anqi Feng
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Yuan Xue
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Muhan Shao
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ari M. Blitz
- Department of Radiology, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Mark G. Luciano
- Department of Neurosurgery, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jerry L. Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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10
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Jha TR, Quigley MF, Mozaffari K, Lathia O, Hofmann K, Myseros JS, Oluigbo C, Keating RF. Prediction of shunt failure facilitated by rapid and accurate volumetric analysis: a single institution's preliminary experience. Childs Nerv Syst 2022; 38:1907-1912. [PMID: 35595938 DOI: 10.1007/s00381-022-05552-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 05/01/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Shunt malfunction is a common complication and often presents with hydrocephalus. While the diagnosis is often supported by radiographic studies, subtle changes in CSF volume may not be detectable on routine evaluation. The purpose of this study was to develop a novel automated volumetric software for evaluation of shunt failure in pediatric patients, especially in patients who may not manifest a significant change in their ventricular size. METHODS A single-institution retrospective review of shunted patients was conducted. Ventricular volume measurements were performed using manual and automated methods by three independent analysts. Manual measurements were produced using OsiriX software, whereas automated measurements were produced using the proprietary software. A p value < 0.05 was considered statistically significant. RESULTS Twenty-two patients met the inclusion criteria (13 males, 9 females). Mean age of the cohort was 4.9 years (range 0.1-18 years). Average measured CSF volume was similar between the manual and automated methods (169.8 mL vs 172.5 mL, p = 0.56). However, the average time to generate results was significantly shorter with the automated algorithm compared to the manual method (2244 s vs 38.3 s, p < 0.01). In 3/5 symptomatic patients whose neuroimaging was interpreted as stable, the novel algorithm detected the otherwise radiographically undetectable CSF volume changes. CONCLUSION The automated software accurately measures the ventricular volumes in pediatric patients with hydrocephalus. The application of this technology is valuable in patients who present clinically without obvious radiographic changes. Future studies with larger cohorts are needed to validate our preliminary findings and further assess the utility of this technology.
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Affiliation(s)
- Tushar R Jha
- Division of Neurosurgery, Children's National Hospital, Washington, DC, USA
| | - Mark F Quigley
- Division of Neurosurgery, Children's National Hospital, Washington, DC, USA
| | - Khashayar Mozaffari
- Division of Neurosurgery, Children's National Hospital, Washington, DC, USA.
| | - Orgest Lathia
- Division of Neurosurgery, Children's National Hospital, Washington, DC, USA
| | - Katherine Hofmann
- Division of Neurosurgery, Children's National Hospital, Washington, DC, USA
| | - John S Myseros
- Division of Neurosurgery, Children's National Hospital, Washington, DC, USA
| | - Chima Oluigbo
- Division of Neurosurgery, Children's National Hospital, Washington, DC, USA
| | - Robert F Keating
- Division of Neurosurgery, Children's National Hospital, Washington, DC, USA
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11
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A joint ventricle and WMH segmentation from MRI for evaluation of healthy and pathological changes in the aging brain. PLoS One 2022; 17:e0274212. [PMID: 36067136 PMCID: PMC9447923 DOI: 10.1371/journal.pone.0274212] [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: 02/01/2022] [Accepted: 08/23/2022] [Indexed: 11/20/2022] Open
Abstract
Age-related changes in brain structure include atrophy of the brain parenchyma and white matter changes of presumed vascular origin. Enlargement of the ventricles may occur due to atrophy or impaired cerebrospinal fluid (CSF) circulation. The co-occurrence of these changes in neurodegenerative diseases and in aging brains often requires investigators to take both into account when studying the brain, however, automated segmentation of enlarged ventricles and white matter hyperintensities (WMHs) can be a challenging task. Here, we present a hybrid multi-atlas segmentation and convolutional autoencoder approach for joint ventricle parcellation and WMH segmentation from magnetic resonance images (MRIs). Our fully automated approach uses a convolutional autoencoder to generate a standardized image of grey matter, white matter, CSF, and WMHs, which, in conjunction with labels generated by a multi-atlas segmentation approach, is then fed into a convolutional neural network to parcellate the ventricular system. Hence, our approach does not depend on manually delineated training data for new data sets. The segmentation pipeline was validated on both healthy elderly subjects and subjects with normal pressure hydrocephalus using ground truth manual labels and compared with state-of-the-art segmentation methods. We then applied the method to a cohort of 2401 elderly brains to investigate associations of ventricle volume and WMH load with various demographics and clinical biomarkers, using a multiple regression model. Our results indicate that the ventricle volume and WMH load are both highly variable in a cohort of elderly subjects and there is an independent association between the two, which highlights the importance of taking both the possibility of enlarged ventricles and WMHs into account when studying the aging brain.
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12
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Amorosino G, Peruzzo D, Redaelli D, Olivetti E, Arrigoni F, Avesani P. DBB - A Distorted Brain Benchmark for Automatic Tissue Segmentation in Paediatric Patients. Neuroimage 2022; 260:119486. [PMID: 35843515 DOI: 10.1016/j.neuroimage.2022.119486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 06/30/2022] [Accepted: 07/13/2022] [Indexed: 10/17/2022] Open
Abstract
T1-weighted magnetic resonance images provide a comprehensive view of the morphology of the human brain at the macro scale. These images are usually the input of a segmentation process that aims detecting the anatomical structures labeling them according to a predefined set of target tissues. Automated methods for brain tissue segmentation rely on anatomical priors of the human brain structures. This is the reason why their performance is quite accurate on healthy individuals. Nevertheless model-based tools become less accurate in clinical practice, specifically in the cases of severe lesions or highly distorted cerebral anatomy. More recently there are empirical evidences that a data-driven approach can be more robust in presence of alterations of brain structures, even though the learning model is trained on healthy brains. Our contribution is a benchmark to support an open investigation on how the tissue segmentation of distorted brains can be improved by adopting a supervised learning approach. We formulate a precise definition of the task and propose an evaluation metric for a fair and quantitative comparison. The training sample is composed of almost one thousand healthy individuals. Data include both T1-weighted MR images and their labeling of brain tissues. The test sample is a collection of several tens of individuals with severe brain distortions. Data and code are openly published on BrainLife, an open science platform for reproducible neuroscience data analysis.
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Affiliation(s)
- Gabriele Amorosino
- NeuroInformatics Laboratory (NILab), Bruno Kessler Foundation (FBK), Trento, Italy; Center for Mind and Brain Sciences (CIMeC), University of Trento, Italy.
| | - Denis Peruzzo
- Neuroimaging Lab, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | | | - Emanuele Olivetti
- NeuroInformatics Laboratory (NILab), Bruno Kessler Foundation (FBK), Trento, Italy; Center for Mind and Brain Sciences (CIMeC), University of Trento, Italy
| | - Filippo Arrigoni
- Paediatric Radiology and Neuroradiology Department, V. Buzzi Children's Hospital, Milan, Italy
| | - Paolo Avesani
- NeuroInformatics Laboratory (NILab), Bruno Kessler Foundation (FBK), Trento, Italy; Center for Mind and Brain Sciences (CIMeC), University of Trento, Italy
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13
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Zhang L, Jiang B, Chen Q, Wang L, Zhao K, Zhang Y, Vliegenthart R, Xie X. Motion artifact removal in coronary CT angiography based on generative adversarial networks. Eur Radiol 2022; 33:43-53. [PMID: 35829786 DOI: 10.1007/s00330-022-08971-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 04/23/2022] [Accepted: 06/20/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES Coronary motion artifacts affect the diagnostic accuracy of coronary CT angiography (CCTA), especially in the mid right coronary artery (mRCA). The purpose is to correct CCTA motion artifacts of the mRCA using a GAN (generative adversarial network). METHODS We included 313 patients with CCTA scans, who had paired motion-affected and motion-free reference images at different R-R interval phases in the same cardiac cycle and included another 53 CCTA cases with invasive coronary angiography (ICA) comparison. Pix2pix, an image-to-image conversion GAN, was trained by the motion-affected and motion-free reference pairs to generate motion-free images from the motion-affected images. Peak signal-to-noise ratio (PSNR), structural similarity (SSIM), Dice similarity coefficient (DSC), and Hausdorff distance (HD) were calculated to evaluate the image quality of GAN-generated images. RESULTS At the image level, the median of PSNR, SSIM, DSC, and HD of GAN-generated images were 26.1 (interquartile: 24.4-27.5), 0.860 (0.830-0.882), 0.783 (0.714-0.825), and 4.47 (3.00-4.47), respectively, significantly better than the motion-affected images (p < 0.001). At the patient level, the image quality results were similar. GAN-generated images improved the motion artifact alleviation score (4 vs. 1, p < 0.001) and overall image quality score (4 vs. 1, p < 0.001) than those of the motion-affected images. In patients with ICA comparison, GAN-generated images achieved accuracy of 81%, 85%, and 70% in identifying no, < 50%, and ≥ 50% stenosis, respectively, higher than 66%, 72%, and 68% for the motion-affected images. CONCLUSION Generative adversarial network-generated CCTA images greatly improved the image quality and diagnostic accuracy compared to motion-affected images. KEY POINTS • A generative adversarial network greatly reduced motion artifacts in coronary CT angiography and improved image quality. • GAN-generated images improved diagnosis accuracy of identifying no, < 50%, and ≥ 50% stenosis.
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Affiliation(s)
- Lu Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China
| | - Beibei Jiang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China
| | - Qiang Chen
- Shukun (Beijing) Technology Co, Ltd., Jinhui Bd, Qiyang Rd, Beijing, 100102, China
| | - Lingyun Wang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China
| | - Keke Zhao
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China
| | - Yaping Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China
| | - Rozemarijn Vliegenthart
- Department of Radiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700RB, Groningen, The Netherlands
| | - Xueqian Xie
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China.
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Bjornsson PA, Baker A, Fleps I, Pauchard Y, Palsson H, Ferguson SJ, Sigurdsson S, Gudnason V, Helgason B, Ellingsen LM. Fast and robust femur segmentation from computed tomography images for patient-specific hip fracture risk screening. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2022. [DOI: 10.1080/21681163.2022.2068160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Pall Asgeir Bjornsson
- The Department of Electrical and Computer Engineering, The University of Iceland, Reykjavik, Iceland
| | - Alexander Baker
- The Institute for Biomechanics, ETH Zurich, Zurich, Switzerland
| | - Ingmar Fleps
- The Institute for Biomechanics, ETH Zurich, Zurich, Switzerland
| | - Yves Pauchard
- McCaig Institute for Bone and Joint Health, The University of Calgary, Calgary, AB Canada
| | - Halldor Palsson
- The Department of Industrial Engineering, Mechanical Engineering, and Computer Science, The University of Iceland, Reykjavik, Iceland
| | | | | | - Vilmundur Gudnason
- The Icelandic Heart Association, Kopavogur, Iceland
- The Department of Medicine, The University of Iceland, Reykjavik, Iceland
| | | | - Lotta Maria Ellingsen
- The Department of Electrical and Computer Engineering, The University of Iceland, Reykjavik, Iceland
- The Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, USA
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15
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Zhou X, Ye Q, Yang X, Chen J, Ma H, Xia J, Del Ser J, Yang G. AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus. Neural Comput Appl 2022; 35:1-10. [PMID: 35228779 PMCID: PMC8866920 DOI: 10.1007/s00521-022-07048-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 01/31/2022] [Indexed: 11/16/2022]
Abstract
Based on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multimodal and high-performance automatic ventricle segmentation method to achieve an efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 143 definite NPH patients. Second, we manually label the ventricular volume (VV) and intracranial volume (ICV). Then, we use the machine learning method to extract features and establish automatic ventricle segmentation model. Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV. In CT images, the Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), Pearson correlation, and Bland-Altman analysis of the automatic and manual segmentation result of the VV were 0.95, 0.99, 0.99, and 4.2 ± 2.6, respectively. The results of ICV were 0.96, 0.99, 0.99, and 6.0 ± 3.8, respectively. The whole process takes 3.4 ± 0.3 s. In MRI images, the DSC, ICC, Pearson correlation, and Bland-Altman analysis of the automatic and manual segmentation result of the VV were 0.94, 0.99, 0.99, and 2.0 ± 0.6, respectively. The results of ICV were 0.93, 0.99, 0.99, and 7.9 ± 3.8, respectively. The whole process took 1.9 ± 0.1 s. We have established a multimodal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume of NPH patients. This can help clinicians quickly and accurately understand the situation of NPH patient's ventricles.
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Affiliation(s)
- Xi Zhou
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People’s Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China
| | - Qinghao Ye
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA USA
| | - Xiaolin Yang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People’s Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China
| | - Jiakun Chen
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People’s Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China
| | - Haiqin Ma
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People’s Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China
| | - Jun Xia
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People’s Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China
| | - Javier Del Ser
- University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
- TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
| | - Guang Yang
- Royal Brompton Hospital, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
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De Feo R, Hämäläinen E, Manninen E, Immonen R, Valverde JM, Ndode-Ekane XE, Gröhn O, Pitkänen A, Tohka J. Convolutional Neural Networks Enable Robust Automatic Segmentation of the Rat Hippocampus in MRI After Traumatic Brain Injury. Front Neurol 2022; 13:820267. [PMID: 35250823 PMCID: PMC8891699 DOI: 10.3389/fneur.2022.820267] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 01/24/2022] [Indexed: 11/13/2022] Open
Abstract
Registration-based methods are commonly used in the automatic segmentation of magnetic resonance (MR) brain images. However, these methods are not robust to the presence of gross pathologies that can alter the brain anatomy and affect the alignment of the atlas image with the target image. In this work, we develop a robust algorithm, MU-Net-R, for automatic segmentation of the normal and injured rat hippocampus based on an ensemble of U-net-like Convolutional Neural Networks (CNNs). MU-Net-R was trained on manually segmented MR images of sham-operated rats and rats with traumatic brain injury (TBI) by lateral fluid percussion. The performance of MU-Net-R was quantitatively compared with methods based on single and multi-atlas registration using MR images from two large preclinical cohorts. Automatic segmentations using MU-Net-R and multi-atlas registration were of excellent quality, achieving cross-validated Dice scores above 0.90 despite the presence of brain lesions, atrophy, and ventricular enlargement. In contrast, the performance of single-atlas segmentation was unsatisfactory (cross-validated Dice scores below 0.85). Interestingly, the registration-based methods were better at segmenting the contralateral than the ipsilateral hippocampus, whereas MU-Net-R segmented the contralateral and ipsilateral hippocampus equally well. We assessed the progression of hippocampal damage after TBI by using our automatic segmentation tool. Our data show that the presence of TBI, time after TBI, and whether the hippocampus was ipsilateral or contralateral to the injury were the parameters that explained hippocampal volume.
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Affiliation(s)
- Riccardo De Feo
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
- SAIMLAL Department (Human Anatomy, Histology, Forensic Medicine and Orthopedics), Sapienza Università di Roma, Rome, Italy
- *Correspondence: Riccardo De Feo
| | - Elina Hämäläinen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Eppu Manninen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Riikka Immonen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Juan Miguel Valverde
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | | | - Olli Gröhn
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Asla Pitkänen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Jussi Tohka
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
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17
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Shao M, Zuo L, Carass A, Zhuo J, Gullapalli RP, Prince JL. Evaluating the impact of MR image harmonization on thalamus deep network segmentation. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12032:120320H. [PMID: 35514535 PMCID: PMC9070007 DOI: 10.1117/12.2613159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Medical image segmentation is one of the core tasks of medical image analysis. Automatic segmentation of brain magnetic resonance images (MRIs) can be used to visualize and track changes of the brain's anatomical structures that may occur due to normal aging or disease. Machine learning techniques are widely used in automatic structure segmentation. However, the contrast variation between the training and testing data makes it difficult for segmentation algorithms to generate consistent results. To address this problem, an image-to-image translation technique called MR image harmonization can be used to match the contrast between different data sets. It is important for the harmonization to transform image intensity while maintaining the underlying anatomy. In this paper, we present a 3D U-Net algorithm to segment the thalamus from multiple MR image modalities and investigate the impact of harmonization on the segmentation algorithm. Manual delineations of thalamic nuclei on two data sets are available. However, we aim to analyze the thalamus in another large data set where ground truth labels are lacking. We trained two segmentation networks, one with unharmonized images and the other with harmonized images, on one data set with manual labels, and compared their performances on the other data set with manual labels. These two data groups were diagnosed with two brain disorders and were acquired with similar imaging protocols. The harmonization target is the large data set without manual labels, which also has a different imaging protocol. The networks trained on unharmonized and harmonized data showed no significant difference when evaluating on the other data set; demonstrating that image harmonization can maintain the anatomy and does not affect the segmentation task. The two networks were evaluated on the harmonization target data set and the network trained on harmonized data showed significant improvement over the network trained on unharmonized data. Therefore, the network trained on harmonized data provides the potential to process large amounts of data from other sites, even in the absence of site-specific training data.
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Affiliation(s)
- Muhan Shao
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Lianrui Zuo
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institute of Health, Baltimore, MD 21224, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jiachen Zhuo
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Rao P. Gullapalli
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jerry L. Prince
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
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18
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Mládek A, Gerla V, Skalický P, Vlasák A, Zazay A, Lhotská L, Beneš V, Beneš V, Bradáč O. Prediction of Shunt Responsiveness in Suspected Patients With Normal Pressure Hydrocephalus Using the Lumbar Infusion Test: A Machine Learning Approach. Neurosurgery 2022; 90:407-418. [PMID: 35080523 DOI: 10.1227/neu.0000000000001838] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 10/27/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Machine learning (ML) approaches can significantly improve the classical Rout-based evaluation of the lumbar infusion test (LIT) and the clinical management of the normal pressure hydrocephalus. OBJECTIVE To develop a ML model that accurately identifies patients as candidates for permanent cerebral spinal fluid shunt implantation using only intracranial pressure and electrocardiogram signals recorded throughout LIT. METHODS This was a single-center cohort study of prospectively collected data of 96 patients who underwent LIT and 5-day external lumbar cerebral spinal fluid drainage (external lumbar drainage) as a reference diagnostic method. A set of selected 48 intracranial pressure/electrocardiogram complex signal waveform features describing nonlinear behavior, wavelet transform spectral signatures, or recurrent map patterns were calculated for each patient. After applying a leave-one-out cross-validation training-testing split of the data set, we trained and evaluated the performance of various state-of-the-art ML algorithms. RESULTS The highest performing ML algorithm was the eXtreme Gradient Boosting. This model showed a good calibration and discrimination on the testing data, with an area under the receiver operating characteristic curve of 0.891 (accuracy: 82.3%, sensitivity: 86.1%, and specificity: 73.9%) obtained for 8 selected features. Our ML model clearly outperforms the classical Rout-based manual classification commonly used in clinical practice with an accuracy of 62.5%. CONCLUSION This study successfully used the ML approach to predict the outcome of a 5-day external lumbar drainage and hence which patients are likely to benefit from permanent shunt implantation. Our automated ML model thus enhances the diagnostic utility of LIT in management.
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Affiliation(s)
- Arnošt Mládek
- Department of Neurosurgery and Neurooncology, Military University Hospital, 1st Faculty of Medicine, Charles University in Prague, Prague, Czech Republic.,Department of Neurosurgery, Motol University Hospital, 2nd Faculty of Medicine, Charles University in Prague, Prague, Czech Republic.,Czech Technical University, Prague, Czech Republic
| | - Václav Gerla
- Department of Cognitive Systems and Neurosciences, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University, Prague, Czech Republic
| | - Petr Skalický
- Department of Neurosurgery and Neurooncology, Military University Hospital, 1st Faculty of Medicine, Charles University in Prague, Prague, Czech Republic.,Department of Neurosurgery, Motol University Hospital, 2nd Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
| | - Aleš Vlasák
- Department of Neurosurgery and Neurooncology, Military University Hospital, 1st Faculty of Medicine, Charles University in Prague, Prague, Czech Republic.,Department of Neurosurgery, Motol University Hospital, 2nd Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
| | - Awista Zazay
- Institute of Pathological Physiology, 1st Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
| | - Lenka Lhotská
- Department of Cognitive Systems and Neurosciences, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University, Prague, Czech Republic.,Department of Natural Sciences, Faculty of Biomedical Engineering, Czech Technical University, Prague, Czech Republic
| | - Vladimír Beneš
- Department of Neurosurgery and Neurooncology, Military University Hospital, 1st Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
| | - Vladimír Beneš
- Department of Neurosurgery, Motol University Hospital, 2nd Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
| | - Ondřej Bradáč
- Department of Neurosurgery and Neurooncology, Military University Hospital, 1st Faculty of Medicine, Charles University in Prague, Prague, Czech Republic.,Department of Neurosurgery, Motol University Hospital, 2nd Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
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19
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Rudhra B, Malu G, Sherly E, Mathew R. A Novel deep learning approach for the automated diagnosis of normal pressure hydrocephalus. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-189852] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Normal Pressure Hydrocephalus (NPH), an Atypical Parkinsonian syndrome, is a neurological syndrome that mainly affects elderly people. This syndrome shows the symptoms of Parkinson’s disease (PD), such as walking impairment, dementia, impaired bladder control, and mental impairment. The Magnetic Resonance Imaging (MRI) is the aptest modality for the detection of the abnormal build-up of cerebrospinal fluid in the brain’s cavities or ventricles, which is the major cause of NPH. This work aims to develop an automated biomarker for NPH segmentation and classification (NPH-SC) that efficiently detect hydrocephalus using a deep learning-based approach. Removal of non-cerebral tissues (skull, scalp, and dura) and noise from brain images by skull stripping, unsharp-mask based edge sharpening, segmentation by marker-based watershed algorithm, and labelling are performed to improve the accuracy of the CNN based classification system. The brain ventricles are extracted using the external and internal markers and then fed into the convolutional neural networks (CNN) for classification. This automated NPH-SC model achieved a sensitivity of 96%, a specificity of 100%, and a validation accuracy of 97%. The prediction system, with the help of a CNN classifier, is used for the calculation of test accuracy of the system and obtained promising 98% accuracy.
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Affiliation(s)
- B Rudhra
- Indian Institute of Information Technology and Management, Trivandrum, India
| | - G Malu
- Indian Institute of Information Technology and Management, Trivandrum, India
| | - Elizabeth Sherly
- Indian Institute of Information Technology and Management, Trivandrum, India
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20
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Fully Automatic Adaptive Meshing Based Segmentation of the Ventricular System for Augmented Reality Visualization and Navigation. World Neurosurg 2021; 156:e9-e24. [PMID: 34333157 DOI: 10.1016/j.wneu.2021.07.099] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/19/2021] [Accepted: 07/21/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVE Effective image segmentation of cerebral structures is fundamental to 3-dimensional techniques such as augmented reality. To be clinically viable, segmentation algorithms should be fully automatic and easily integrated in existing digital infrastructure. We created a fully automatic adaptive-meshing-based segmentation system for T1-weighted magnetic resonance images (MRI) to automatically segment the complete ventricular system, running in a cloud-based environment that can be accessed on an augmented reality device. This study aims to assess the accuracy and segmentation time of the system by comparing it to a manually segmented ground truth dataset. METHODS A ground truth (GT) dataset of 46 contrast-enhanced and non-contrast-enhanced T1-weighted MRI scans was manually segmented. These scans also were uploaded to our system to create a machine-segmented (MS) dataset. The GT data were compared with the MS data using the Sørensen-Dice similarity coefficient and 95% Hausdorff distance to determine segmentation accuracy. Furthermore, segmentation times for all GT and MS segmentations were measured. RESULTS Automatic segmentation was successful for 45 (98%) of 46 cases. Mean Sørensen-Dice similarity coefficient score was 0.83 (standard deviation [SD] = 0.08) and mean 95% Hausdorff distance was 19.06 mm (SD = 11.20). Segmentation time was significantly longer for the GT group (mean = 14405 seconds, SD = 7089) when compared with the MS group (mean = 1275 seconds, SD = 714) with a mean difference of 13,130 seconds (95% confidence interval 10,130-16,130). CONCLUSIONS The described adaptive meshing-based segmentation algorithm provides accurate and time-efficient automatic segmentation of the ventricular system from T1 MRI scans and direct visualization of the rendered surface models in augmented reality.
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21
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Zhou X, Ye Q, Jiang Y, Wang M, Niu Z, Menpes-Smith W, Fang EF, Liu Z, Xia J, Yang G. Systematic and Comprehensive Automated Ventricle Segmentation on Ventricle Images of the Elderly Patients: A Retrospective Study. Front Aging Neurosci 2020; 12:618538. [PMID: 33390930 PMCID: PMC7772233 DOI: 10.3389/fnagi.2020.618538] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 11/23/2020] [Indexed: 11/13/2022] Open
Abstract
Background and Objective: Ventricle volume is closely related to hydrocephalus, brain atrophy, Alzheimer's, Parkinson's syndrome, and other diseases. To accurately measure the volume of the ventricles for elderly patients, we use deep learning to establish a systematic and comprehensive automated ventricle segmentation framework. Methods: The study participation included 20 normal elderly people, 20 patients with cerebral atrophy, 64 patients with normal pressure hydrocephalus, and 51 patients with acquired hydrocephalus. Second, get their imaging data through the picture archiving and communication systems (PACS) system. Then use ITK software to manually label participants' ventricular structures. Finally, extract imaging features through machine learning. Results: This automated ventricle segmentation method can be applied not only to CT and MRI images but also to images with different scan slice thicknesses. More importantly, it produces excellent segmentation results (Dice > 0.9). Conclusion: This automated ventricle segmentation method has wide applicability and clinical practicability. It can help clinicians find early disease, diagnose disease, understand the patient's disease progression, and evaluate the patient's treatment effect.
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Affiliation(s)
- Xi Zhou
- Department of Radiology, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Qinghao Ye
- Hangzhou Ocean's Smart Boya Co., Ltd., Hangzhou, China.,Mind Rank Ltd., Hongkong, China
| | - Yinghui Jiang
- Hangzhou Ocean's Smart Boya Co., Ltd., Hangzhou, China.,Mind Rank Ltd., Hongkong, China
| | - Minhao Wang
- Hangzhou Ocean's Smart Boya Co., Ltd., Hangzhou, China.,Mind Rank Ltd., Hongkong, China
| | - Zhangming Niu
- Aladdin Healthcare Technologies Ltd., London, United Kingdom
| | | | - Evandro Fei Fang
- Department of Clinical Molecular Biology, University of Oslo, Oslo, Norway
| | - Zhi Liu
- School of Information Science and Engineering, Shandong University, Qingdao, China
| | - Jun Xia
- Department of Radiology, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Guang Yang
- Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom.,National Heart and Lung Institute, Imperial College London, London, United Kingdom
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22
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Chaves H, Dorr F, Costa ME, Serra MM, Slezak DF, Farez MF, Sevlever G, Yañez P, Cejas C. Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL. J Neuroradiol 2020; 48:147-156. [PMID: 33137334 DOI: 10.1016/j.neurad.2020.10.001] [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: 05/29/2020] [Revised: 09/13/2020] [Accepted: 10/19/2020] [Indexed: 01/22/2023]
Abstract
BACKGROUND AND PURPOSE There are instances in which an estimate of the brain volume should be obtained from MRI in clinical practice. Our objective is to calculate cross-sectional robustness of a convolutional neural network (CNN) based software (Entelai Pic) for brain volume estimation and compare it to traditional software such as FreeSurfer, CAT12 and FSL in healthy controls (HC). MATERIALS AND METHODS Sixteen HC were scanned four times, two different days on two different MRI scanners (1.5 T and 3 T). Volumetric T1-weighted images were acquired and post-processed with FreeSurfer v6.0.0, Entelai Pic v2, CAT12 v12.5 and FSL v5.0.9. Whole-brain, grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) volumes were calculated. Correlation and agreement between methods was assessed using intraclass correlation coefficient (ICC) and Bland Altman plots. Robustness was assessed using the coefficient of variation (CV). RESULTS Whole-brain volume estimation had better correlation between FreeSurfer and Entelai Pic (ICC (95% CI) 0.96 (0.94-0.97)) than FreeSurfer and CAT12 (0.92 (0.88-0.96)) and FSL (0.87 (0.79-0.91)). WM, GM and CSF showed a similar trend. Compared to FreeSurfer, Entelai Pic provided similarly robust segmentations of brain volumes both on same-scanner (mean CV 1.07, range 0.20-3.13% vs. mean CV 1.05, range 0.21-3.20%, p = 0.86) and on different-scanner variables (mean CV 3.84, range 2.49-5.91% vs. mean CV 3.84, range 2.62-5.13%, p = 0.96). Mean post-processing times were 480, 5, 40 and 5 min for FreeSurfer, Entelai Pic, CAT12 and FSL respectively. CONCLUSION Based on robustness and processing times, our CNN-based model is suitable for cross-sectional volumetry on clinical practice.
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Affiliation(s)
- Hernán Chaves
- Diagnostic Imaging Department, Fleni, Buenos Aires, Argentina; Entelai, Buenos Aires, Argentina.
| | | | | | - María Mercedes Serra
- Diagnostic Imaging Department, Fleni, Buenos Aires, Argentina; Entelai, Buenos Aires, Argentina
| | - Diego Fernández Slezak
- Entelai, Buenos Aires, Argentina; Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina; Instituto en Ciencias de la Computación (ICC), CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Mauricio F Farez
- Entelai, Buenos Aires, Argentina; Neurology Department, Fleni, Buenos Aires, Argentina; Center for Research on Neuroimmunological Diseases (CIEN), Fleni, Buenos Aires, Argentina; Center for Biostatistics, Epidemiology and Public Health (CEBES), Fleni, Buenos Aires, Argentina
| | | | - Paulina Yañez
- Diagnostic Imaging Department, Fleni, Buenos Aires, Argentina
| | - Claudia Cejas
- Diagnostic Imaging Department, Fleni, Buenos Aires, Argentina
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23
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Dubost F, Bruijne MD, Nardin M, Dalca AV, Donahue KL, Giese AK, Etherton MR, Wu O, Groot MD, Niessen W, Vernooij M, Rost NS, Schirmer MD. Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation. Med Image Anal 2020; 63:101698. [PMID: 32339896 PMCID: PMC7275913 DOI: 10.1016/j.media.2020.101698] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 12/03/2019] [Accepted: 04/06/2020] [Indexed: 02/08/2023]
Abstract
Registration is a core component of many imaging pipelines. In case of clinical scans, with lower resolution and sometimes substantial motion artifacts, registration can produce poor results. Visual assessment of registration quality in large clinical datasets is inefficient. In this work, we propose to automatically assess the quality of registration to an atlas in clinical FLAIR MRI scans of the brain. The method consists of automatically segmenting the ventricles of a given scan using a neural network, and comparing the segmentation to the atlas ventricles propagated to image space. We used the proposed method to improve clinical image registration to a general atlas by computing multiple registrations - one directly to the general atlas and others via different age-specific atlases - and then selecting the registration that yielded the highest ventricle overlap. Finally, as an example application of the complete pipeline, a voxelwise map of white matter hyperintensity burden was computed using only the scans with registration quality above a predefined threshold. Methods were evaluated in a single-site dataset of more than 1000 scans, as well as a multi-center dataset comprising 142 clinical scans from 12 sites. The automated ventricle segmentation reached a Dice coefficient with manual annotations of 0.89 in the single-site dataset, and 0.83 in the multi-center dataset. Registration via age-specific atlases could improve ventricle overlap compared to a direct registration to the general atlas (Dice similarity coefficient increase up to 0.15). Experiments also showed that selecting scans with the registration quality assessment method could improve the quality of average maps of white matter hyperintensity burden, instead of using all scans for the computation of the white matter hyperintensity map. In this work, we demonstrated the utility of an automated tool for assessing image registration quality in clinical scans. This image quality assessment step could ultimately assist in the translation of automated neuroimaging pipelines to the clinic.
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Affiliation(s)
- Florian Dubost
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA; Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands.
| | - Marleen de Bruijne
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Marco Nardin
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Adrian V Dalca
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA
| | - Kathleen L Donahue
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Anne-Katrin Giese
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Mark R Etherton
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Marius de Groot
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC - University Medical Center Rotterdam, the Netherlands
| | - Wiro Niessen
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands; Department of Imaging Physics, Faculty of Applied Science, TU Delft, Delft, The Netherlands
| | - Meike Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, the Netherlands; Department of Epidemiology, Erasmus MC - University Medical Center Rotterdam, the Netherlands
| | - Natalia S Rost
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Markus D Schirmer
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA; Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA; Department of Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Germany.
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24
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Carass A, Roy S, Gherman A, Reinhold JC, Jesson A, Arbel T, Maier O, Handels H, Ghafoorian M, Platel B, Birenbaum A, Greenspan H, Pham DL, Crainiceanu CM, Calabresi PA, Prince JL, Roncal WRG, Shinohara RT, Oguz I. Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis. Sci Rep 2020; 10:8242. [PMID: 32427874 PMCID: PMC7237671 DOI: 10.1038/s41598-020-64803-w] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 04/20/2020] [Indexed: 11/09/2022] Open
Abstract
The Sørensen-Dice index (SDI) is a widely used measure for evaluating medical image segmentation algorithms. It offers a standardized measure of segmentation accuracy which has proven useful. However, it offers diminishing insight when the number of objects is unknown, such as in white matter lesion segmentation of multiple sclerosis (MS) patients. We present a refinement for finer grained parsing of SDI results in situations where the number of objects is unknown. We explore these ideas with two case studies showing what can be learned from our two presented studies. Our first study explores an inter-rater comparison, showing that smaller lesions cannot be reliably identified. In our second case study, we demonstrate fusing multiple MS lesion segmentation algorithms based on the insights into the algorithms provided by our analysis to generate a segmentation that exhibits improved performance. This work demonstrates the wealth of information that can be learned from refined analysis of medical image segmentations.
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Affiliation(s)
- 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.
| | - Snehashis Roy
- CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, 20817, USA
| | - Adrian Gherman
- Department of Biostatistics, The Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Jacob C Reinhold
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Andrew Jesson
- Centre For Intelligent Machines, McGill University, Montréal, QC, H3A 0E9, Canada
| | - Tal Arbel
- Centre For Intelligent Machines, McGill University, Montréal, QC, H3A 0E9, Canada
| | - Oskar Maier
- Institute of Medical Informatics, University of Lübeck, 23538, Lübeck, Germany
| | - Heinz Handels
- Institute of Medical Informatics, University of Lübeck, 23538, Lübeck, Germany
| | - Mohsen Ghafoorian
- Institute for Computing and Information Sciences, Radboud University, 6525, HP, Nijmegen, Netherlands
| | - Bram Platel
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6525, GA, Nijmegen, Netherlands
| | - Ariel Birenbaum
- Department of Electrical Engineering, Tel-Aviv University, Tel-Aviv, 69978, Israel
| | - Hayit Greenspan
- Department of Biomedical Engineering, Tel-Aviv University, Tel-Aviv, 69978, Israel
| | - Dzung L Pham
- CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, 20817, USA
| | - Ciprian M Crainiceanu
- Department of Biostatistics, The Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Peter A Calabresi
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, 21287, 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
| | - William R Gray Roncal
- Department of Computer Science, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics & Epidemiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ipek Oguz
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, 37203, USA
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