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Chen Y, Yue H, Kuang H, Wang J. RBS-Net: Hippocampus segmentation using multi-layer feature learning with the region, boundary and structure loss. Comput Biol Med 2023; 160:106953. [PMID: 37120987 DOI: 10.1016/j.compbiomed.2023.106953] [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: 10/19/2022] [Revised: 04/10/2023] [Accepted: 04/15/2023] [Indexed: 05/02/2023]
Abstract
Hippocampus has great influence over the Alzheimer's disease (AD) research because of its essential role as a biomarker in the human brain. Thus the performance of hippocampus segmentation influences the development of clinical research for brain disorders. Deep learning using U-net-like networks becomes prevalent in hippocampus segmentation on Magnetic Resonance Imaging (MRI) due to its efficiency and accuracy. However, current methods lose sufficient detailed information during pooling, which hinders the segmentation results. And weak supervision on the details like edges or positions results in fuzzy and coarse boundary segmentation, causing great differences between the segmentation and ground-truth. In view of these drawbacks, we propose a Region-Boundary and Structure Net (RBS-Net), which consists of a primary net and an auxiliary net. (1) Our primary net focuses on the region distribution of hippocampus and introduces a distance map for boundary supervision. Furthermore the primary net adds a multi-layer feature learning module to compensate the information loss during pooling and strengthen the differences between the foreground and background, improving the region and boundary segmentation. (2) The auxiliary net concentrates on the structure similarity and also utilizes the multi-layer feature learning module, and this parallel task can refine encoders by similarizing the structure of the segmentation and ground-truth. We train and test our network using 5-fold cross-validation on HarP, a public available hippocampus dataset. Experimental results demonstrate that our proposed RBS-Net achieves a Dice of 89.76% in average, outperforming several state-of-the-art hippocampus segmentation methods. Furthermore, in few shot circumstances, our proposed RBS-Net achieves better results in terms of a comprehensive evaluation compared to several state-of-the-art deep learning-based methods. Finally we can observe that visual segmentation results for the boundary and detailed regions are improved by our proposed RBS-Net.
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Affiliation(s)
- Yu Chen
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China
| | - Hailin Yue
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China
| | - Hulin Kuang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China.
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Manjón JV, Romero JE, Coupe P. A novel deep learning based hippocampus subfield segmentation method. Sci Rep 2022; 12:1333. [PMID: 35079061 PMCID: PMC8789929 DOI: 10.1038/s41598-022-05287-8] [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: 09/01/2021] [Accepted: 01/04/2022] [Indexed: 12/02/2022] Open
Abstract
The automatic assessment of hippocampus volume is an important tool in the study of several neurodegenerative diseases such as Alzheimer's disease. Specifically, the measurement of hippocampus subfields properties is of great interest since it can show earlier pathological changes in the brain. However, segmentation of these subfields is very difficult due to their complex structure and for the need of high-resolution magnetic resonance images manually labeled. In this work, we present a novel pipeline for automatic hippocampus subfield segmentation based on a deeply supervised convolutional neural network. Results of the proposed method are shown for two available hippocampus subfield delineation protocols. The method has been compared to other state-of-the-art methods showing improved results in terms of accuracy and execution time.
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Affiliation(s)
- José V Manjón
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain.
| | - José E Romero
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain
| | - Pierrick Coupe
- Univ. Bordeaux, LaBRI, UMR 5800, PICTURA, 33400, Talence, France.,CNRS, LaBRI, UMR 5800, PICTURA, 33400, Talence, France
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Quek YE, Fung YL, Cheung MWL, Vogrin SJ, Collins SJ, Bowden SC. Agreement Between Automated and Manual MRI Volumetry in Alzheimer's Disease: A Systematic Review and Meta-Analysis. J Magn Reson Imaging 2021; 56:490-507. [PMID: 34964531 DOI: 10.1002/jmri.28037] [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: 10/28/2021] [Revised: 12/09/2021] [Accepted: 12/09/2021] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Automated magnetic resonance imaging (MRI) volumetry is a promising tool to evaluate regional brain volumes in dementia and especially Alzheimer's disease (AD). PURPOSE To compare automated methods and the gold standard manual segmentation in measuring regional brain volumes on MRI across healthy controls, patients with mild cognitive impairment, and patients with dementia due to AD. STUDY TYPE Systematic review and meta-analysis. DATA SOURCES MEDLINE, Embase, and PsycINFO were searched through October 2021. FIELD STRENGTH 1.0 T, 1.5 T, or 3.0 T. ASSESSMENT Two review authors independently identified studies for inclusion and extracted data. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). STATISTICAL TESTS Standardized mean differences (SMD; Hedges' g) were pooled using random-effects meta-analysis with robust variance estimation. Subgroup analyses were undertaken to explore potential sources of heterogeneity. Sensitivity analyses were conducted to examine the impact of the within-study correlation between effect estimates on the meta-analysis results. RESULTS Seventeen studies provided sufficient data to evaluate the hippocampus, lateral ventricles, and parahippocampal gyrus. The pooled SMD for the hippocampus, lateral ventricles, and parahippocampal gyrus were 0.22 (95% CI -0.50 to 0.93), 0.12 (95% CI -0.13 to 0.37), and -0.48 (95% CI -1.37 to 0.41), respectively. For the hippocampal data, subgroup analyses suggested that the pooled SMD was invariant across clinical diagnosis and field strength. Subgroup analyses could not be conducted on the lateral ventricles data and the parahippocampal gyrus data due to insufficient data. The results were robust to the selected within-study correlation value. DATA CONCLUSION While automated methods are generally comparable to manual segmentation for measuring hippocampal, lateral ventricle, and parahippocampal gyrus volumes, wide 95% CIs and large heterogeneity suggest that there is substantial uncontrolled variance. Thus, automated methods may be used to measure these regions in patients with AD but should be used with caution. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Yi-En Quek
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Victoria, Australia
| | - Yi Leng Fung
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Victoria, Australia
| | - Mike W-L Cheung
- Department of Psychology, Faculty of Arts and Social Sciences, National University of Singapore, Singapore
| | - Simon J Vogrin
- Department of Clinical Neurosciences, St Vincent's Hospital Melbourne, Fitzroy, Victoria, Australia
| | - Steven J Collins
- Department of Clinical Neurosciences, St Vincent's Hospital Melbourne, Fitzroy, Victoria, Australia
| | - Stephen C Bowden
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Victoria, Australia.,Department of Clinical Neurosciences, St Vincent's Hospital Melbourne, Fitzroy, Victoria, Australia
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4
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Biffen SC, Warton CMR, Dodge NC, Molteno CD, Jacobson JL, Jacobson SW, Meintjes EM. Validity of automated FreeSurfer segmentation compared to manual tracing in detecting prenatal alcohol exposure-related subcortical and corpus callosal alterations in 9- to 11-year-old children. Neuroimage Clin 2020; 28:102368. [PMID: 32791491 PMCID: PMC7424233 DOI: 10.1016/j.nicl.2020.102368] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 07/07/2020] [Accepted: 07/29/2020] [Indexed: 12/12/2022]
Abstract
In recent years a number of semi-automated and automated segmentation tools and brain atlases have been developed to facilitate morphometric analyses of large MRI datasets. These tools are much faster than manual tracing and demonstrate excellent test-retest reliabilities. Reliabilities of automated segmentations relative to "gold standard" manual tracings have, however, been shown to vary by brain region and in different cohorts. It remains uncertain to what extent smaller brain volumes and potential changes in grey/white matter contrasts in paediatric brains impact on the performance of automated methods, and how pathology may influence performance. This study examined whether using data from automated FreeSurfer segmentation would alter our ability, compared to manual segmentation, to detect prenatal alcohol exposure (PAE)-related volume changes in subcortical regions and the corpus callosum (CC) in pre-adolescent children. High-resolution T1-weighted images were acquired, using a sequence optimized for morphometric neuroanatomical analysis, on a Siemens 3T Allegra MRI scanner in 71 right-handed, 9- to 11-year-old children (27 fetal alcohol syndrome (FAS) and partial FAS (PFAS), 25 non-syndromal heavily exposed (HE) and 19 non-exposed controls) from a high-risk community in Cape Town, South Africa. Data from timeline follow-back interviews administered to the mothers prospectively during pregnancy were used to quantify the amount of alcohol (in ounces absolute alcohol per day, AA/day) that the children had been exposed to prenatally. Volumes of corpus callosum (CC) and bilateral caudate nuclei, hippocampi and nucleus accumbens (NA) were obtained by manual tracing and automated segmentation using both FreeSurfer versions 5.1 and 6.0. Reliability across methods was assessed using intraclass correlation (ICC) estimates for consistency and absolute agreement, and Cronbach's α. Ability to detect regions showing PAE effects was assessed separately for each segmentation method using ANOVA and linear regression of regional volumes with AA/day. Our results support findings from other studies showing excellent reliability across methods for easy-to-segment structures, such as the CC and caudate nucleus. Volumes from FreeSurfer 6.0 were smaller than those from version 5.1 in all regions except the right caudate, for which they were similar, and right hippocampus and CC, for which they were larger. Despite poor absolute agreement between methods in the NA and hippocampus, all three segmentation methods detected dose-dependent volume reductions in regions for which reliabilities on ICC consistency across methods reached at least 0.70, namely the CC, and bilateral caudate nuclei and hippocampi. PAE-related changes in the NA for which ICC consistency did not reach this minimum were inconsistent across methods and should be interpreted with caution. This is the first study to demonstrate in a pre-adolescent cohort the ability of automated segmentation with FreeSurfer to detect regional volume changes associated with pathology similar to those found using manual tracing.
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Affiliation(s)
- Stevie C Biffen
- Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Christopher M R Warton
- Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Neil C Dodge
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, USA
| | - Christopher D Molteno
- Department of Psychiatry and Mental Health, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Joseph L Jacobson
- Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa; Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, USA; Department of Psychiatry and Mental Health, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Sandra W Jacobson
- Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa; Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, USA; Department of Psychiatry and Mental Health, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Ernesta M Meintjes
- Biomedical Engineering Research Centre, Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa; Neurosciences Institute, University of Cape Town, South Africa; Cape Universities Body Imaging Centre, University of Cape Town, South Africa.
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Yamanakkanavar N, Choi JY, Lee B. MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer's Disease: A Survey. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3243. [PMID: 32517304 PMCID: PMC7313699 DOI: 10.3390/s20113243] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 05/25/2020] [Accepted: 06/03/2020] [Indexed: 02/07/2023]
Abstract
Many neurological diseases and delineating pathological regions have been analyzed, and the anatomical structure of the brain researched with the aid of magnetic resonance imaging (MRI). It is important to identify patients with Alzheimer's disease (AD) early so that preventative measures can be taken. A detailed analysis of the tissue structures from segmented MRI leads to a more accurate classification of specific brain disorders. Several segmentation methods to diagnose AD have been proposed with varying complexity. Segmentation of the brain structure and classification of AD using deep learning approaches has gained attention as it can provide effective results over a large set of data. Hence, deep learning methods are now preferred over state-of-the-art machine learning methods. We aim to provide an outline of current deep learning-based segmentation approaches for the quantitative analysis of brain MRI for the diagnosis of AD. Here, we report how convolutional neural network architectures are used to analyze the anatomical brain structure and diagnose AD, discuss how brain MRI segmentation improves AD classification, describe the state-of-the-art approaches, and summarize their results using publicly available datasets. Finally, we provide insight into current issues and discuss possible future research directions in building a computer-aided diagnostic system for AD.
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Affiliation(s)
- Nagaraj Yamanakkanavar
- Department of Information and Communications Engineering, Chosun University, Gwangju 61452, Korea;
| | - Jae Young Choi
- Division of Computer & Electronic Systems Engineering, Hankuk University of Foreign Studies, Yongin 17035, Korea;
| | - Bumshik Lee
- Department of Information and Communications Engineering, Chosun University, Gwangju 61452, Korea;
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6
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Automated MRI volumetry as a diagnostic tool for Alzheimer's disease: Validation of icobrain dm. NEUROIMAGE-CLINICAL 2020; 26:102243. [PMID: 32193172 PMCID: PMC7082216 DOI: 10.1016/j.nicl.2020.102243] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2019] [Revised: 02/16/2020] [Accepted: 03/10/2020] [Indexed: 12/20/2022]
Abstract
icobrain dm is an automated brain MRI segmentation faster than Freesurfer. Significantly higher accuracy was obtained for several brain structures, including hippocampus. icobrain dm volumes had a test-retest error below normal annual atrophy rates. icobrain dm temporal lobe volume had highest sensitivity in discriminating Alzheimer's.
Brain volumes computed from magnetic resonance images have potential for assisting with the diagnosis of individual dementia patients, provided that they have low measurement error and high reliability. In this paper we describe and validate icobrain dm, an automatic tool that segments brain structures that are relevant for differential diagnosis of dementia, such as the hippocampi and cerebral lobes. Experiments were conducted in comparison to the widely used FreeSurfer software. The hippocampus segmentations were compared against manual segmentations, with significantly higher Dice coefficients obtained with icobrain dm (25–75th quantiles: 0.86–0.88) than with FreeSurfer (25–75th quantiles: 0.80–0.83). Other brain structures were also compared against manual delineations, with icobrain dm showing lower volumetric errors overall. Test-retest experiments show that the precision of all measurements is higher for icobrain dm than for FreeSurfer except for the parietal cortex volume. Finally, when comparing volumes obtained from Alzheimer's disease patients against age-matched healthy controls, all measures achieved high diagnostic performance levels when discriminating patients from cognitively healthy controls, with the temporal cortex volume measured by icobrain dm reaching the highest diagnostic performance level (area under the receiver operating characteristic curve = 0.99) in this dataset.
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7
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Brinkmann BH, Guragain H, Kenney-Jung D, Mandrekar J, Watson RE, Welker KM, Britton JW, Witte RJ. Segmentation errors and intertest reliability in automated and manually traced hippocampal volumes. Ann Clin Transl Neurol 2019; 6:1807-1814. [PMID: 31489797 PMCID: PMC6764491 DOI: 10.1002/acn3.50885] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 07/26/2019] [Accepted: 07/30/2019] [Indexed: 12/15/2022] Open
Abstract
Objective To rigorously compare automated atlas‐based and manual tracing hippocampal segmentation for accuracy, repeatability, and clinical acceptability given a relevant range of imaging abnormalities in clinical epilepsy. Methods Forty‐nine patients with hippocampal asymmetry were identified from our institutional radiology database, including two patients with significant anatomic deformations. Manual hippocampal tracing was performed by experienced technologists on 3T MPRAGE images, measuring hippocampal volume up to the tectal plate, excluding the hippocampal tail. The same images were processed using NeuroQuant and FreeSurfer software. Ten subjects underwent repeated manual hippocampal tracings by two additional technologists blinded to previous results to evaluate consistency. Ten patients with two clinical MRI studies had volume measurements repeated using NeuroQuant and FreeSurfer. Results FreeSurfer raw volumes were significantly lower than NeuroQuant (P < 0.001, right and left), and hippocampal asymmetry estimates were lower for both automatic methods than manual tracing (P < 0.0001). Differences remained significant after scaling volumes to age, gender, and scanner matched normative percentiles. Volume reproducibility was fair (0.4–0.59) for manual tracing, and excellent (>0.75) for both automated methods. Asymmetry index reproducibility was excellent (>0.75) for manual tracing and FreeSurfer segmentation and fair (0.4–0.59) for NeuroQuant segmentation. Both automatic segmentation methods failed on the two cases with anatomic deformations. Segmentation errors were visually identified in 25 NeuroQuant and 27 FreeSurfer segmentations, and nine (18%) NeuroQuant and six (12%) FreeSurfer errors were judged clinically significant. Interpretation Automated hippocampal volumes are more reproducible than hand‐traced hippocampal volumes. However, these methods fail in some cases, and significant segmentation errors can occur.
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Affiliation(s)
- Benjamin H Brinkmann
- Department of Neurology, Mayo Clinic, Rochester, Minnesota.,Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota
| | - Hari Guragain
- Department of Neurology, Mayo Clinic, Rochester, Minnesota
| | - Daniel Kenney-Jung
- Department of Neurology, Division of Child Neurology, University of Minnesota, Minneapolis, Minnesota
| | - Jay Mandrekar
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | | | - Kirk M Welker
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | | | - Robert J Witte
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
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Iida K, Kagawa K, Katagiri M, Seyama G, Hashizume A, Abiko M, Katayama J, Suzuki H, Kurisu K, Otsubo H. Preservation of Memory Despite Unresected Contralateral Hippocampal Volume Loss After Resection of Hippocampal Sclerosis in Seizure-Free Patients. World Neurosurg 2019; 132:e759-e765. [PMID: 31415886 DOI: 10.1016/j.wneu.2019.08.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Revised: 08/03/2019] [Accepted: 08/03/2019] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To determine postoperative long-term changes of hippocampal volume (HV) correlating with cognitive functions in patients who underwent surgery for hippocampal sclerosis with postoperative freedom from seizures. METHODS We studied 1.5T magnetic resonance imaging before and after surgery in 24 patients (mean ± SD age, 36.9 ± 11.0 years) with hippocampal sclerosis. We performed serial magnetic resonance imaging at 6 months to 1 year, 1-2 years, 2-3 years, and 3-5 years postoperatively. We compared HVs of 24 patients with HVs of 14 age-matched control subjects. We analyzed correlations between consecutive HVs and seizure duration and age at surgery. We compared consecutive changes in HVs between dominant and nondominant hemispheres with concurrent cognitive functions. RESULTS Preoperative HVs of unresected contralateral hippocampus were significantly smaller than HVs of control subjects (P < 0.01). Unresected contralateral HV changes compared with preoperative HVs were -3.6% ± 6.9%, -2.3% ± 8.5%, -3.6% ± 10.2% (P < 0.05), and -5.0% ± 9.5% (P < 0.05) at consecutive postoperative periods. Largest change in HVs at 3-5 years was significantly correlated with older age at surgery (P < 0.05). Unresected contralateral dominant 14 HVs remained consistently smaller than nondominant 10 HVs up to 5 years with statistical significance (P < 0.05). Verbal memory was preserved in 14 patients with unresected contralateral smaller dominant hippocampus. CONCLUSIONS In seizure-free patients after hippocampal sclerosis resection , unresected contralateral HV significantly declined with older age at surgery. Visual memory was preserved regardless of side and volume loss. Despite significantly reduced HVs, verbal memory was preserved with the unresected contralateral dominant hippocampus. Earlier surgical intervention may have lower potential risk for memory decline secondary to postoperative HV loss.
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Affiliation(s)
- Koji Iida
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan; Epilepsy Center, Hiroshima University Hospital, Hiroshima, Japan.
| | - Kota Kagawa
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan; Epilepsy Center, Hiroshima University Hospital, Hiroshima, Japan
| | - Masaya Katagiri
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan; Epilepsy Center, Hiroshima University Hospital, Hiroshima, Japan
| | - Go Seyama
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan; Epilepsy Center, Hiroshima University Hospital, Hiroshima, Japan
| | - Akira Hashizume
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan; Epilepsy Center, Hiroshima University Hospital, Hiroshima, Japan
| | - Masaru Abiko
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Junko Katayama
- Division of Radiology, Hiroshima Chuo-Kenshin-Sho, Hiroshima, Japan
| | - Hiroharu Suzuki
- Division of Neurology, Department of Pediatrics, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Kaoru Kurisu
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Hiroshi Otsubo
- Division of Neurology, Department of Pediatrics, The Hospital for Sick Children, Toronto, Ontario, Canada
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Yoo JG, Jakabek D, Ljung H, Velakoulis D, van Westen D, Looi JCL, Källén K. MRI morphology of the hippocampus in drug-resistant temporal lobe epilepsy: Shape inflation of left hippocampus and correlation of right-sided hippocampal volume and shape with visuospatial function in patients with right-sided TLE. J Clin Neurosci 2019; 67:68-74. [PMID: 31221579 DOI: 10.1016/j.jocn.2019.06.019] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 06/10/2019] [Indexed: 11/27/2022]
Abstract
We sought to quantify the morphology in vivo of hippocampi in patients with drug resistant temporal lobe epilepsy (TLE) via magnetic resonance imaging (MRI), prior to temporal lobe resection, and the correlation of surface-based shape analysis of morphology and clinical cognitive function. Thirty patients with drug-resistant TLE and twenty healthy controls underwent clinical neuropsychological testing, and brain MRI at Lund University Hospital prior to hippocampal resection. A neuroradiologist categorised radiological findings into normal hippocampus, subtle changes or definite hippocampal sclerosis. We manually segmented MRI of the hippocampus of participants using ANALYZE 11.0 software; and analysed hippocampal shape using SPHARM-PDM software. For radiologist visual-ratings of definite left hippocampal sclerosis in those with left-sided TLE, hippocampal volumes were significantly smaller compared to normal controls. In right-sided TLE we found contralateral shape inflation of the left hippocampus, partially confirming previous shape analytic studies of the hippocampus in TLE. We found significant correlation of volume and surface deflation of the right hippocampus in right-sided TLE with reduced performance on the two right-lateralised visuospatial memory tests, the Rey Complex Figure Test (Immediate and Delayed recall) and the Recognition Memory Test for faces. Decreased hippocampal volume was correlated with poorer performance on these tasks. The morphology of the hippocampus can be quantified via neuroimaging shape analysis in TLE. Contralateral shape inflation of the left hippocampus in right-sided TLE is intriguing, and may result from functional compensation and/or abnormal tissue. In right-sided TLE, hippocampal structural integrity, quantified as hippocampal shape, is correlated with lateralised visuospatial function.
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Affiliation(s)
- Jae-Gon Yoo
- Academic Unit of Psychiatry and Addiction Medicine, Australian National University Medical School, Canberra Hospital, ACT, Australia
| | - David Jakabek
- Graduate School of Medicine, University of Wollongong, Wollongong, NSW, Australia
| | - Hanna Ljung
- Skåne University Hospital, Department of Neurology and Rehabilitation Medicine, Lund, Sweden
| | - Dennis Velakoulis
- Neuropsychiatry Unit, Royal Melbourne Hospital, Department of Psychiatry, University of Melbourne Medical School, Melbourne, Victoria, Australia
| | - Danielle van Westen
- Diagnostic Radiology, Department of Clinical Sciences, Lund University, Lund, Sweden; Image and Function, Skane University Hospital, Lund, Sweden
| | - Jeffrey C L Looi
- Academic Unit of Psychiatry and Addiction Medicine, Australian National University Medical School, Canberra Hospital, ACT, Australia; Neuropsychiatry Unit, Royal Melbourne Hospital, Department of Psychiatry, University of Melbourne Medical School, Melbourne, Victoria, Australia.
| | - Kristina Källén
- Division of Clinical Sciences, Helsingborg, Sweden & Department of Clinical Sciences, Lund, Sweden; Neurology, Lund, Sweden & Faculty of Medicine, Lund University, Lund, Sweden
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10
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Niemantsverdriet E, Ribbens A, Bastin C, Benoit F, Bergmans B, Bier JC, Bladt R, Claes L, De Deyn PP, Deryck O, Hanseeuw B, Ivanoiu A, Lemper JC, Mormont E, Picard G, Salmon E, Segers K, Sieben A, Smeets D, Struyfs H, Thiery E, Tournoy J, Triau E, Vanbinst AM, Versijpt J, Bjerke M, Engelborghs S. A Retrospective Belgian Multi-Center MRI Biomarker Study in Alzheimer's Disease (REMEMBER). J Alzheimers Dis 2019; 63:1509-1522. [PMID: 29782314 PMCID: PMC6004934 DOI: 10.3233/jad-171140] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background: Magnetic resonance imaging (MRI) acquisition/processing techniques assess brain volumes to explore neurodegeneration in Alzheimer’s disease (AD). Objective: We examined the clinical utility of MSmetrix and investigated if automated MRI volumes could discriminate between groups covering the AD continuum and could be used as a predictor for clinical progression. Methods: The Belgian Dementia Council initiated a retrospective, multi-center study and analyzed whole brain (WB), grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), cortical GM (CGM) volumes, and WM hyperintensities (WMH) using MSmetrix in the AD continuum. Baseline (n = 887) and follow-up (FU, n = 95) T1-weighted brain MRIs and time-linked neuropsychological data were available. Results: The cohort consisted of cognitively healthy controls (HC, n = 93), subjective cognitive decline (n = 102), mild cognitive impairment (MCI, n = 379), and AD dementia (n = 313). Baseline WB and GM volumes could accurately discriminate between clinical diagnostic groups and were significantly decreased with increasing cognitive impairment. MCI patients had a significantly larger change in WB, GM, and CGM volumes based on two MRIs (n = 95) compared to HC (FU>24months, p = 0.020). Linear regression models showed that baseline atrophy of WB, GM, CGM, and increased CSF volumes predicted cognitive impairment. Conclusion: WB and GM volumes extracted by MSmetrix could be used to define the clinical spectrum of AD accurately and along with CGM, they are able to predict cognitive impairment based on (decline in) MMSE scores. Therefore, MSmetrix can support clinicians in their diagnostic decisions, is able to detect clinical disease progression, and is of help to stratify populations for clinical trials.
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Affiliation(s)
- Ellis Niemantsverdriet
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | | | - Christine Bastin
- GIGA Cyclotron Research Centre in vivo Imaging, University of Liège, Liège, Belgium
| | - Florence Benoit
- Department of Geriatrics, Centre Hospitalier Universitaire (CHU) Brugmann, Brussels, Belgium
| | - Bruno Bergmans
- Department of Neurology and Center for Cognitive Disorders, AZ Sint-Jan Brugge-Oostende AV, Brugge, Belgium
| | | | - Roxanne Bladt
- Department of Radiology, Vrije Universiteit Brussel (VUB), UZ Brussel, Brussels, Belgium
| | | | - Peter Paul De Deyn
- Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
| | - Olivier Deryck
- Department of Neurology and Center for Cognitive Disorders, AZ Sint-Jan Brugge-Oostende AV, Brugge, Belgium
| | - Bernard Hanseeuw
- Department of Neurology, Cliniques Universitaires St Luc and Institute of Neuroscience, Université catholique de Louvain, Woluwe-Saint-Lambert (Brussels), Belgium
| | - Adrian Ivanoiu
- Department of Neurology, Cliniques Universitaires St Luc and Institute of Neuroscience, Université catholique de Louvain, Woluwe-Saint-Lambert (Brussels), Belgium
| | - Jean-Claude Lemper
- Department of Geriatrics, UZ Brussel, Brussels, Belgium.,Silva medical Scheutbos, Molenbeek-Saint-Jean (Brussels), Belgium
| | - Eric Mormont
- Department of Neurology, Centre Hospitalier Universitaire (CHU) Namur, Université catholique de Louvain, Yvoir, Belgium.,Université catholique de Louvain, Institute of Neuroscience (IoNS), Louvain-la-Neuve (Brussels), Belgium
| | - Gaëtane Picard
- Department of Neurology, Clinique Saint-Pierre, Ottignies, Belgium
| | - Eric Salmon
- GIGA Cyclotron Research Centre in vivo Imaging, University of Liège, Liège, Belgium.,Department of Neurology, Memory Clinic, Centre Hospitalier Universitaire (CHU) Liège, Liège, Belgium
| | - Kurt Segers
- Department of Neurology, Centre Hospitalier Universitaire (CHU) Brugmann, Brussels, Belgium
| | - Anne Sieben
- Department of Neurology, University Hospital Ghent, Ghent University, Ghent, Belgium
| | | | - Hanne Struyfs
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Evert Thiery
- Department of Neurology, University Hospital Ghent, Ghent University, Ghent, Belgium
| | - Jos Tournoy
- Gerontology and Geriatrics, Department of Clinical and Experimental Medicine, KU Leuven, Leuven, Belgium.,Geriatric Medicine and Memory Clinic, University Hospital Leuven, Leuven, Belgium
| | | | - Anne-Marie Vanbinst
- Department of Radiology, Vrije Universiteit Brussel (VUB), UZ Brussel, Brussels, Belgium
| | - Jan Versijpt
- Department of Neurology, Vrije Universiteit Brussel (VUB), UZ Brussel, Brussels, Belgium
| | - Maria Bjerke
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.,Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
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11
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Mettenburg JM, Branstetter BF, Wiley CA, Lee P, Richardson RM. Improved Detection of Subtle Mesial Temporal Sclerosis: Validation of a Commercially Available Software for Automated Segmentation of Hippocampal Volume. AJNR Am J Neuroradiol 2019; 40:440-445. [PMID: 30733255 DOI: 10.3174/ajnr.a5966] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 12/23/2018] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Identification of mesial temporal sclerosis is critical in the evaluation of individuals with temporal lobe epilepsy. Our aim was to assess the performance of FDA-approved software measures of hippocampal volume to identify mesial temporal sclerosis in patients with medically refractory temporal lobe epilepsy compared with the initial clinical interpretation of a neuroradiologist. MATERIALS AND METHODS Preoperative MRIs of 75 consecutive patients who underwent a temporal resection for temporal lobe epilepsy from 2011 to 2016 were retrospectively reviewed, and 71 were analyzed using Neuroreader, a commercially available automated segmentation and volumetric analysis package. Volume measures, including hippocampal volume as a percentage of total intracranial volume and the Neuroreader Index, were calculated. Radiologic interpretations of the MR imaging and pathology from subsequent resections were classified as either mesial temporal sclerosis or other, including normal findings. These measures of hippocampal volume were evaluated by receiver operating characteristic curves on the basis of pathologic confirmation of mesial temporal sclerosis in the resected temporal lobe. Sensitivity and specificity were calculated for each method and compared by means of the McNemar test using the optimal threshold as determined by the Youden J point. RESULTS Optimized thresholds of hippocampal percentage of a structural volume relative to total intracranial volume (<0.19%) and the Neuroreader Index (≤-3.8) were selected to optimize sensitivity and specificity (89%/71% and 89%/78%, respectively) for the identification of mesial temporal sclerosis in temporal lobe epilepsy compared with the initial clinical interpretation of the neuroradiologist (50% and 87%). Automated measures of hippocampal volume predicted mesial temporal sclerosis more accurately than radiologic interpretation (McNemar test, P < .0001). CONCLUSIONS Commercially available automated segmentation and volume analysis of the hippocampus accurately identifies mesial temporal sclerosis and performs significantly better than the interpretation of the radiologist.
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Affiliation(s)
| | - B F Branstetter
- From the Departments of Radiology (J.M.M., B.F.B.,)
- Biomedical Informatics (B.F.B.)
| | | | - P Lee
- Neurosurgery (P.L., R.M.R.), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - R M Richardson
- Neurosurgery (P.L., R.M.R.), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
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12
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Bartel F, Vrenken H, van Herk M, de Ruiter M, Belderbos J, Hulshof J, de Munck JC. FAst Segmentation Through SURface Fairing (FASTSURF): A novel semi-automatic hippocampus segmentation method. PLoS One 2019; 14:e0210641. [PMID: 30657776 PMCID: PMC6338359 DOI: 10.1371/journal.pone.0210641] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 12/26/2018] [Indexed: 11/18/2022] Open
Abstract
Objective The objective is to present a proof-of-concept of a semi-automatic method to reduce hippocampus segmentation time on magnetic resonance images (MRI). Materials and methods FAst Segmentation Through SURface Fairing (FASTSURF) is based on a surface fairing technique which reconstructs the hippocampus from sparse delineations. To validate FASTSURF, simulations were performed in which sparse delineations extracted from full manual segmentations served as input. On three different datasets with different diagnostic groups, FASTSURF hippocampi were compared to the original segmentations using Jaccard overlap indices and percentage volume differences (PVD). In one data set for which back-to-back scans were available, unbiased estimates of overlap and PVD were obtained. Using longitudinal scans, we compared hippocampal atrophy rates measured by manual, FASTSURF and two automatic segmentations (FreeSurfer and FSL-FIRST). Results With only seven input contours, FASTSURF yielded mean Jaccard indices ranging from 72(±4.3)% to 83(±2.6)% and PVDs ranging from 0.02(±2.40)% to 3.2(±3.40)% across the three datasets. Slightly poorer results were obtained for the unbiased analysis, but the performance was still considerably better than both tested automatic methods with only five contours. Conclusions FASTSURF segmentations have high accuracy and require only a fraction of the delineation effort of fully manual segmentation. Atrophy rate quantification based on completely manual segmentation is well reproduced by FASTSURF. Therefore, FASTSURF is a promising tool to be implemented in clinical workflow, provided a future prospective validation confirms our findings.
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Affiliation(s)
- Fabian Bartel
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
- * E-mail:
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Marcel van Herk
- Manchester Cancer Research Centre, Division of Cancer Science, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Sciences Centre, Manchester, United Kingdom
| | - Michiel de Ruiter
- Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jose Belderbos
- Department of Radiotherapy, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Joost Hulshof
- Department of Mathematics, VU University Amsterdam, Amsterdam, The Netherlands
| | - Jan C. de Munck
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
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13
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Novak P, Schmidt R, Kontsekova E, Kovacech B, Smolek T, Katina S, Fialova L, Prcina M, Parrak V, Dal-Bianco P, Brunner M, Staffen W, Rainer M, Ondrus M, Ropele S, Smisek M, Sivak R, Zilka N, Winblad B, Novak M. FUNDAMANT: an interventional 72-week phase 1 follow-up study of AADvac1, an active immunotherapy against tau protein pathology in Alzheimer's disease. ALZHEIMERS RESEARCH & THERAPY 2018; 10:108. [PMID: 30355322 PMCID: PMC6201586 DOI: 10.1186/s13195-018-0436-1] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Accepted: 09/26/2018] [Indexed: 11/30/2022]
Abstract
Background Neurofibrillary pathology composed of tau protein is closely correlated with severity and phenotype of cognitive impairment in patients with Alzheimer’s disease and non-Alzheimer’s tauopathies. Targeting pathological tau proteins via immunotherapy is a promising strategy for disease-modifying treatment of Alzheimer’s disease. Previously, we reported a 24-week phase 1 trial on the active vaccine AADvac1 against pathological tau protein; here, we present the results of a further 72 weeks of follow-up on those patients. Methods We did a phase 1, 72-week, open-label study of AADvac1 in patients with mild to moderate Alzheimer’s disease who had completed the preceding phase 1 study. Patients who were previously treated with six doses of AADvac1 at monthly intervals received two booster doses at 24-week intervals. Patients who were previously treated with only three doses received another three doses at monthly intervals, and subsequently two boosters at 24-week intervals. The primary objective was the assessment of long-term safety of AADvac1 treatment. Secondary objectives included assessment of antibody titres, antibody isotype profile, capacity of the antibodies to bind to AD tau and AADvac1, development of titres of AADvac1-induced antibodies over time, and effect of booster doses; cognitive assessment via 11-item Alzheimer’s Disease Assessment Scale cognitive assessment (ADAS-Cog), Category Fluency Test and Controlled Oral Word Association Test; assessment of brain atrophy via magnetic resonance imaging (MRI) volumetry; and assessment of lymphocyte populations via flow cytometry. Results The study was conducted between 18 March 2014 and 10 August 2016. Twenty-six patients who completed the previous study were enrolled. Five patients withdrew because of adverse events. One patient was withdrawn owing to noncompliance. The most common adverse events were injection site reactions (reported in 13 [50%] of vaccinated patients). No cases of meningoencephalitis or vasogenic oedema were observed. New micro-haemorrhages were observed only in one ApoE4 homozygote. All responders retained an immunoglobulin G (IgG) antibody response against the tau peptide component of AADvac1 over 6 months without administration, with titres regressing to a median 15.8% of titres attained after the initial six-dose vaccination regimen. Booster doses restored previous IgG levels. Hippocampal atrophy rate was lower in patients with high IgG levels; a similar relationship was observed in cognitive assessment. Conclusions AADvac1 displayed a benign safety profile. The evolution of IgG titres over vaccination-free periods warrants a more frequent booster dose regimen. The tendency towards slower atrophy in MRI evaluation and less of a decline in cognitive assessment in patients with high titres is encouraging. Further trials are required to expand the safety database and to establish proof of clinical efficacy of AADvac1. Trial registration The studies are registered with the EU Clinical Trials Register and ClinicalTrials.gov: the preceding first-in-human study under EudraCT 2012-003916-29 and NCT01850238 (registered on 9 May 2013) and the follow-up study under EudraCT 2013-004499-36 and NCT02031198 (registered 9 Jan 2014), respectively. Electronic supplementary material The online version of this article (10.1186/s13195-018-0436-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Petr Novak
- Axon Neuroscience CRM Services SE, Dvorakovo nabrezie 11, 811 02, Bratislava, Slovakia.
| | - Reinhold Schmidt
- Clinical Division of Neurogeriatrics and Division of General Neurology, Department of Neurology, Medical University Graz, Auenbruggerplatz 2, 8036, Graz, Austria
| | - Eva Kontsekova
- Axon Neuroscience R&D Services SE, Dvorakovo nabrezie 10, 811 02, Bratislava, Slovakia
| | - Branislav Kovacech
- Axon Neuroscience R&D Services SE, Dvorakovo nabrezie 10, 811 02, Bratislava, Slovakia
| | - Tomas Smolek
- Axon Neuroscience R&D Services SE, Dvorakovo nabrezie 10, 811 02, Bratislava, Slovakia
| | - Stanislav Katina
- Axon Neuroscience CRM Services SE, Dvorakovo nabrezie 11, 811 02, Bratislava, Slovakia
| | - Lubica Fialova
- Axon Neuroscience R&D Services SE, Dvorakovo nabrezie 10, 811 02, Bratislava, Slovakia
| | - Michal Prcina
- Axon Neuroscience R&D Services SE, Dvorakovo nabrezie 10, 811 02, Bratislava, Slovakia
| | - Vojtech Parrak
- Axon Neuroscience R&D Services SE, Dvorakovo nabrezie 10, 811 02, Bratislava, Slovakia
| | - Peter Dal-Bianco
- University Clinic of Neurology, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Martin Brunner
- University Clinic of Clinical Pharmacology, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Wolfgang Staffen
- University Clinic of Neurology, Christian-Doppler-Clinic, Ignaz-Harrer-Straße 79, 5020, Salzburg, Austria
| | - Michael Rainer
- Social and Medical Centre East, Danube Hospital, Karl Landsteiner Institute for Memory and Alzheimer Research, Langobardenstraße 122, 1220, Vienna, Austria
| | - Matej Ondrus
- Axon Neuroscience CRM Services SE, Dvorakovo nabrezie 11, 811 02, Bratislava, Slovakia
| | - Stefan Ropele
- Clinical Division of Neurogeriatrics and Division of General Neurology, Department of Neurology, Medical University Graz, Auenbruggerplatz 2, 8036, Graz, Austria
| | - Miroslav Smisek
- Axon Neuroscience CRM Services SE, Dvorakovo nabrezie 11, 811 02, Bratislava, Slovakia
| | - Roman Sivak
- Axon Neuroscience CRM Services SE, Dvorakovo nabrezie 11, 811 02, Bratislava, Slovakia
| | - Norbert Zilka
- Axon Neuroscience R&D Services SE, Dvorakovo nabrezie 10, 811 02, Bratislava, Slovakia
| | - Bengt Winblad
- Division of Neurogeriatrics, Department NVS Clinical Trial Unit, Karolinska Institute Alzheimer Disease Research Centre, Geriatric Clinic, Karolinska University Hospital, Hälsovägen 7, S-14157, Huddinge, Sweden
| | - Michal Novak
- Axon Neuroscience SE, 4, Arch. Makariou & Kalogreon, Nicolaides Sea View City, 5th floor, office 506, 6016, Larnaca, Cyprus
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14
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Jiji GW. Identifying stage of Alzheimer disease using multiclass particle swarm optimisation technique. J EXP THEOR ARTIF IN 2018. [DOI: 10.1080/0952813x.2018.1509380] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- G. Wiselin Jiji
- Dr Sivanthi Aditanar College of Engineering, Tiruchendur, Tamil Nadu, India
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15
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Ledig C, Schuh A, Guerrero R, Heckemann RA, Rueckert D. Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database. Sci Rep 2018; 8:11258. [PMID: 30050078 PMCID: PMC6062561 DOI: 10.1038/s41598-018-29295-9] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 07/06/2018] [Indexed: 12/22/2022] Open
Abstract
Magnetic resonance (MR) imaging is a powerful technique for non-invasive in-vivo imaging of the human brain. We employed a recently validated method for robust cross-sectional and longitudinal segmentation of MR brain images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Specifically, we segmented 5074 MR brain images into 138 anatomical regions and extracted time-point specific structural volumes and volume change during follow-up intervals of 12 or 24 months. We assessed the extracted biomarkers by determining their power to predict diagnostic classification and by comparing atrophy rates to published meta-studies. The approach enables comprehensive analysis of structural changes within the whole brain. The discriminative power of individual biomarkers (volumes/atrophy rates) is on par with results published by other groups. We publish all quality-checked brain masks, structural segmentations, and extracted biomarkers along with this article. We further share the methodology for brain extraction (pincram) and segmentation (MALPEM, MALPEM4D) as open source projects with the community. The identified biomarkers hold great potential for deeper analysis, and the validated methodology can readily be applied to other imaging cohorts.
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Affiliation(s)
- Christian Ledig
- Imperial College London, Department of Computing, London, SW7 2AZ, UK.
| | - Andreas Schuh
- Imperial College London, Department of Computing, London, SW7 2AZ, UK
| | - Ricardo Guerrero
- Imperial College London, Department of Computing, London, SW7 2AZ, UK
| | - Rolf A Heckemann
- MedTech West, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden.,Department of Radiation Therapy, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Division of Brain Sciences, Imperial College London, London, UK
| | - Daniel Rueckert
- Imperial College London, Department of Computing, London, SW7 2AZ, UK
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16
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Fraser MA, Shaw ME, Anstey KJ, Cherbuin N. Longitudinal Assessment of Hippocampal Atrophy in Midlife and Early Old Age: Contrasting Manual Tracing and Semi-automated Segmentation (FreeSurfer). Brain Topogr 2018; 31:949-962. [PMID: 29974288 DOI: 10.1007/s10548-018-0659-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 06/29/2018] [Indexed: 01/26/2023]
Abstract
It is important to have accurate estimates of normal age-related brain structure changes and to understand how the choice of measurement technique may bias those estimates. We compared longitudinal change in hippocampal volume, laterality and atrophy measured by manual tracing and FreeSurfer (version 5.3) in middle age (n = 244, 47.2[1.4] years) and older age (n = 199, 67.0[1.4] years) individuals over 8 years. The proportion of overlap (Dice coefficient) between the segmented hippocampi was calculated and we hypothesised that the proportion of overlap would be higher for older individuals as a consequence of higher atrophy. Hippocampal volumes produced by FreeSurfer were larger than manually traced volumes. Both methods produced a left less than right volume laterality difference. Over time this laterality difference increased for manual tracing and decreased for FreeSurfer leading to laterality differences in left and right estimated atrophy rates. The overlap proportion between methods was not significantly different for older individuals, but was greater for the right hippocampus. Estimated middle age annualised atrophy rates were - 0.39(1.0) left, 0.07(1.01) right, - 0.17(0.88) total for manual tracing and - 0.15(0.69) left, - 0.20(0.63) right, - 0.18(0.57) total for FreeSurfer. Older age atrophy rates were - 0.43(1.32) left, - 0.15(1.41) right, - 0.30 (1.23) total for manual tracing and - 0.34(0.79) left, - 0.68(0.78) right, - 0.51(0.65) total for FreeSurfer. FreeSurfer reliably segments the hippocampus producing atrophy rates that are comparable to manual tracing with some biases that need to be considered in study design. FreeSurfer is suited for use in large longitudinal studies where it is not cost effective to use manual tracing.
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Affiliation(s)
- Mark A Fraser
- Centre for Research on Ageing, Health and Wellbeing, Australian National University, Florey, Building 54, Mills Road, Canberra, ACT, 2601, Australia.
| | - Marnie E Shaw
- College of Engineering & Computer Science, Australian National University, Brian Anderson Building 115, 115 North Road, Canberra, ACT, 2601, Australia
| | - Kaarin J Anstey
- Centre for Research on Ageing, Health and Wellbeing, Australian National University, Florey, Building 54, Mills Road, Canberra, ACT, 2601, Australia
| | - Nicolas Cherbuin
- Centre for Research on Ageing, Health and Wellbeing, Australian National University, Florey, Building 54, Mills Road, Canberra, ACT, 2601, Australia
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17
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Bartel F, van Herk M, Vrenken H, Vandaele F, Sunaert S, de Jaeger K, Dollekamp NJ, Carbaat C, Lamers E, Dieleman EMT, Lievens Y, de Ruysscher D, Schagen SB, de Ruiter MB, de Munck JC, Belderbos J. Inter-observer variation of hippocampus delineation in hippocampal avoidance prophylactic cranial irradiation. Clin Transl Oncol 2018; 21:178-186. [PMID: 29876759 DOI: 10.1007/s12094-018-1903-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Accepted: 05/24/2018] [Indexed: 01/22/2023]
Abstract
BACKGROUND Hippocampal avoidance prophylactic cranial irradiation (HA-PCI) techniques have been developed to reduce radiation damage to the hippocampus. An inter-observer hippocampus delineation analysis was performed and the influence of the delineation variability on dose to the hippocampus was studied. MATERIALS AND METHODS For five patients, seven observers delineated both hippocampi on brain MRI. The intra-class correlation (ICC) with absolute agreement and the generalized conformity index (CIgen) were computed. Median surfaces over all observers' delineations were created for each patient and regional outlining differences were analysed. HA-PCI dose plans were made from the median surfaces and we investigated whether dose constraints in the hippocampus could be met for all delineations. RESULTS The ICC for the left and right hippocampus was 0.56 and 0.69, respectively, while the CIgen ranged from 0.55 to 0.70. The posterior and anterior-medial hippocampal regions had most variation with SDs ranging from approximately 1 to 2.5 mm. The mean dose (Dmean) constraint was met for all delineations, but for the dose received by 1% of the hippocampal volume (D1%) violations were observed. CONCLUSION The relatively low ICC and CIgen indicate that delineation variability among observers for both left and right hippocampus was large. The posterior and anterior-medial border have the largest delineation inaccuracy. The hippocampus Dmean constraint was not violated.
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Affiliation(s)
- F Bartel
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - M van Herk
- Department of Cancer Sciences, University of Manchester, Manchester, UK
| | - H Vrenken
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - F Vandaele
- Department of Radiotherapy, Iridium Cancer Network, Antwerp, Belgium
| | - S Sunaert
- Department of Radiology, University Hospitals Leuven, Louvain, Belgium
| | - K de Jaeger
- Department of Radiotherapy, Catharina Hospital, Eindhoven, The Netherlands
| | - N J Dollekamp
- Department of Radiotherapy, The University Medical Center Groningen, Groningen, The Netherlands
| | - C Carbaat
- Department of Radiotherapy, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - E Lamers
- Department of Radiotherapy, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - E M T Dieleman
- Department of Radiotherapy, Academic Medical Center, Amsterdam, The Netherlands
| | - Y Lievens
- Department of Radiation Oncology, Ghent University Hospital, Ghent, Belgium
| | - D de Ruysscher
- Department of Radiotherapy, Maastricht University Medical Center, Maastricht, The Netherlands
| | - S B Schagen
- Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - M B de Ruiter
- Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - J C de Munck
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - J Belderbos
- Department of Radiotherapy, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
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18
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Dill V, Klein PC, Franco AR, Pinho MS. Atlas selection for hippocampus segmentation: Relevance evaluation of three meta-information parameters. Comput Biol Med 2018; 95:90-98. [DOI: 10.1016/j.compbiomed.2018.02.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 02/07/2018] [Accepted: 02/08/2018] [Indexed: 10/18/2022]
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19
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HIPS: A new hippocampus subfield segmentation method. Neuroimage 2017; 163:286-295. [DOI: 10.1016/j.neuroimage.2017.09.049] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Revised: 09/22/2017] [Accepted: 09/22/2017] [Indexed: 11/19/2022] Open
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20
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Cavedo E, Suppa P, Lange C, Opfer R, Lista S, Galluzzi S, Schwarz AJ, Spies L, Buchert R, Hampel H. Fully Automatic MRI-Based Hippocampus Volumetry Using FSL-FIRST: Intra-Scanner Test-Retest Stability, Inter-Field Strength Variability, and Performance as Enrichment Biomarker for Clinical Trials Using Prodromal Target Populations at Risk for Alzheimer’s Disease. J Alzheimers Dis 2017; 60:151-164. [DOI: 10.3233/jad-161108] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Enrica Cavedo
- AXA Research Fund and UPMC Chair, Sorbonne Universités, Université Pierre et Marie Curie (UPMC) Paris 06, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- IRCCS Centro San Giovanni di Dio, Brescia, Italy
| | - Per Suppa
- Department of Nuclear Medicine, Charité– Universitätsmedizin Berlin, Berlin, Germany
- Jung diagnostics GmbH, Hamburg, Germany
| | - Catharina Lange
- Department of Nuclear Medicine, Charité– Universitätsmedizin Berlin, Berlin, Germany
| | | | - Simone Lista
- AXA Research Fund and UPMC Chair, Sorbonne Universités, Université Pierre et Marie Curie (UPMC) Paris 06, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
| | | | | | | | - Ralph Buchert
- Department of Nuclear Medicine, Charité– Universitätsmedizin Berlin, Berlin, Germany
- Department of Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Harald Hampel
- AXA Research Fund and UPMC Chair, Sorbonne Universités, Université Pierre et Marie Curie (UPMC) Paris 06, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
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21
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Cole JH, Poudel RPK, Tsagkrasoulis D, Caan MWA, Steves C, Spector TD, Montana G. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage 2017; 163:115-124. [PMID: 28765056 DOI: 10.1016/j.neuroimage.2017.07.059] [Citation(s) in RCA: 399] [Impact Index Per Article: 57.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2017] [Revised: 07/20/2017] [Accepted: 07/28/2017] [Indexed: 01/02/2023] Open
Abstract
Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of 'brain-predicted age' as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain-predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data. CNN accurately predicted chronological age using GM (correlation between brain-predicted age and chronological age r = 0.96, mean absolute error [MAE] = 4.16 years) and raw (r = 0.94, MAE = 4.65 years) data. This was comparable to GPR brain-predicted age using GM data (r = 0.95, MAE = 4.66 years). Brain-predicted age was a heritable phenotype for all models and input data (h2 ≥ 0.5). Brain-predicted age showed high test-retest reliability (intraclass correlation coefficient [ICC] = 0.90-0.99). Multi-centre reliability was more variable within high ICCs for GM (0.83-0.96) and poor-moderate levels for WM and raw data (0.51-0.77). Brain-predicted age represents an accurate, highly reliable and genetically-influenced phenotype, that has potential to be used as a biomarker of brain ageing. Moreover, age predictions can be accurately generated on raw T1-MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real-time information on brain health in clinical settings.
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Affiliation(s)
- James H Cole
- Computational, Cognitive & Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London, UK
| | - Rudra P K Poudel
- Department of Biomedical Engineering, King's College London, London, UK
| | | | - Matthan W A Caan
- Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands
| | - Claire Steves
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Tim D Spector
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Giovanni Montana
- Department of Biomedical Engineering, King's College London, London, UK; Department of Mathematics, Imperial College London, London, UK.
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22
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Zandifar A, Fonov V, Coupé P, Pruessner J, Collins DL. A comparison of accurate automatic hippocampal segmentation methods. Neuroimage 2017; 155:383-393. [PMID: 28404458 DOI: 10.1016/j.neuroimage.2017.04.018] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Revised: 04/06/2017] [Accepted: 04/07/2017] [Indexed: 01/26/2023] Open
Abstract
The hippocampus is one of the first brain structures affected by Alzheimer's disease (AD). While many automatic methods for hippocampal segmentation exist, few studies have compared them on the same data. In this study, we compare four fully automated hippocampal segmentation methods in terms of their conformity with manual segmentation and their ability to be used as an AD biomarker in clinical settings. We also apply error correction to the four automatic segmentation methods, and complete a comprehensive validation to investigate differences between the methods. The effect size and classification performance is measured for AD versus normal control (NC) groups and for stable mild cognitive impairment (sMCI) versus progressive mild cognitive impairment (pMCI) groups. Our study shows that the nonlinear patch-based segmentation method with error correction is the most accurate automatic segmentation method and yields the most conformity with manual segmentation (κ=0.894). The largest effect size between AD versus NC and sMCI versus pMCI is produced by FreeSurfer with error correction. We further show that, using only hippocampal volume, age, and sex as features, the area under the receiver operating characteristic curve reaches up to 0.8813 for AD versus NC and 0.6451 for sMCI versus pMCI. However, the automatic segmentation methods are not significantly different in their performance.
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Affiliation(s)
- Azar Zandifar
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada; Department of Biomedical Engineering, McGill University, Montreal, Canada
| | - Vladimir Fonov
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Pierrick Coupé
- Univ. Bordeaux, LaBRI, UMR 5800, PICTURA, F-33400, Talence, France
| | - Jens Pruessner
- McGill University Research Centre for Studies in Aging, Canada
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada; Department of Biomedical Engineering, McGill University, Montreal, Canada.
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23
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Sankar T, Park MTM, Jawa T, Patel R, Bhagwat N, Voineskos AN, Lozano AM, Chakravarty MM. Your algorithm might think the hippocampus grows in Alzheimer's disease: Caveats of longitudinal automated hippocampal volumetry. Hum Brain Mapp 2017; 38:2875-2896. [PMID: 28295799 DOI: 10.1002/hbm.23559] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Revised: 01/31/2017] [Accepted: 02/27/2017] [Indexed: 11/10/2022] Open
Abstract
Hippocampal atrophy rate-measured using automated techniques applied to structural MRI scans-is considered a sensitive marker of disease progression in Alzheimer's disease, frequently used as an outcome measure in clinical trials. Using publicly accessible data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we examined 1-year hippocampal atrophy rates generated by each of five automated or semiautomated hippocampal segmentation algorithms in patients with Alzheimer's disease, subjects with mild cognitive impairment, or elderly controls. We analyzed MRI data from 398 and 62 subjects available at baseline and at 1 year at MRI field strengths of 1.5 T and 3 T, respectively. We observed a high rate of hippocampal segmentation failures across all algorithms and diagnostic categories, with only 50.8% of subjects at 1.5 T and 58.1% of subjects at 3 T passing stringent segmentation quality control. We also found that all algorithms identified several subjects (between 2.94% and 48.68%) across all diagnostic categories showing increases in hippocampal volume over 1 year. For any given algorithm, hippocampal "growth" could not entirely be explained by excluding patients with flawed hippocampal segmentations, scan-rescan variability, or MRI field strength. Furthermore, different algorithms did not uniformly identify the same subjects as hippocampal "growers," and showed very poor concordance in estimates of magnitude of hippocampal volume change over time (intraclass correlation coefficient 0.319 at 1.5 T and 0.149 at 3 T). This precluded a meaningful analysis of whether hippocampal "growth" represents a true biological phenomenon. Taken together, our findings suggest that longitudinal hippocampal volume change should be interpreted with considerable caution as a biomarker. Hum Brain Mapp 38:2875-2896, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Tejas Sankar
- Division of Neurosurgery, Department of Surgery, University of Alberta, Alberta, Canada
| | - Min Tae M Park
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada.,Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Tasha Jawa
- Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada
| | - Raihaan Patel
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada.,Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Nikhil Bhagwat
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada.,Kimel Family Translational Imaging Genetics Research Laboratory, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada.,Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Aristotle N Voineskos
- Kimel Family Translational Imaging Genetics Research Laboratory, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Andres M Lozano
- Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada.,Department of Psychiatry, McGill University, Montreal, Quebec, Canada.,Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada
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24
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Bartel F, Vrenken H, Bijma F, Barkhof F, van Herk M, de Munck JC. Regional analysis of volumes and reproducibilities of automatic and manual hippocampal segmentations. PLoS One 2017; 12:e0166785. [PMID: 28182655 PMCID: PMC5300281 DOI: 10.1371/journal.pone.0166785] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Accepted: 11/03/2016] [Indexed: 01/08/2023] Open
Abstract
PURPOSE Precise and reproducible hippocampus outlining is important to quantify hippocampal atrophy caused by neurodegenerative diseases and to spare the hippocampus in whole brain radiation therapy when performing prophylactic cranial irradiation or treating brain metastases. This study aimed to quantify systematic differences between methods by comparing regional volume and outline reproducibility of manual, FSL-FIRST and FreeSurfer hippocampus segmentations. MATERIALS AND METHODS This study used a dataset from ADNI (Alzheimer's Disease Neuroimaging Initiative), including 20 healthy controls, 40 patients with mild cognitive impairment (MCI), and 20 patients with Alzheimer's disease (AD). For each subject back-to-back (BTB) T1-weighted 3D MPRAGE images were acquired at time-point baseline (BL) and 12 months later (M12). Hippocampi segmentations of all methods were converted into triangulated meshes, regional volumes were extracted and regional Jaccard indices were computed between the hippocampi meshes of paired BTB scans to evaluate reproducibility. Regional volumes and Jaccard indices were modelled as a function of group (G), method (M), hemisphere (H), time-point (T), region (R) and interactions. RESULTS For the volume data the model selection procedure yielded the following significant main effects G, M, H, T and R and interaction effects G-R and M-R. The same model was found for the BTB scans. For all methods volumes reduces with the severity of disease. Significant fixed effects for the regional Jaccard index data were M, R and the interaction M-R. For all methods the middle region was most reproducible, independent of diagnostic group. FSL-FIRST was most and FreeSurfer least reproducible. DISCUSSION/CONCLUSION A novel method to perform detailed analysis of subtle differences in hippocampus segmentation is proposed. The method showed that hippocampal segmentation reproducibility was best for FSL-FIRST and worst for Freesurfer. We also found systematic regional differences in hippocampal segmentation between different methods reinforcing the need of adopting harmonized protocols.
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Affiliation(s)
- Fabian Bartel
- Department of Physics and Medical Technology, VU University Medical Center, Amsterdam, The Netherlands
| | - Hugo Vrenken
- Department of Physics and Medical Technology, VU University Medical Center, Amsterdam, The Netherlands
- Department of Radiology, VU University Medical Center, Amsterdam, The Netherlands
| | - Fetsje Bijma
- Department of Mathematics, VU University Amsterdam, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology, VU University Medical Center, Amsterdam, The Netherlands
- Image Analysis Center, VU University Medical Center, Amsterdam, The Netherlands
| | - Marcel van Herk
- Department of Radiotherapy Physics, University of Manchester, Manchester, United Kingdom
| | - Jan C. de Munck
- Department of Physics and Medical Technology, VU University Medical Center, Amsterdam, The Netherlands
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25
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Lyden H, Gimbel SI, Del Piero L, Tsai AB, Sachs ME, Kaplan JT, Margolin G, Saxbe D. Associations between Family Adversity and Brain Volume in Adolescence: Manual vs. Automated Brain Segmentation Yields Different Results. Front Neurosci 2016; 10:398. [PMID: 27656121 PMCID: PMC5011142 DOI: 10.3389/fnins.2016.00398] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Accepted: 08/12/2016] [Indexed: 12/03/2022] Open
Abstract
Associations between brain structure and early adversity have been inconsistent in the literature. These inconsistencies may be partially due to methodological differences. Different methods of brain segmentation may produce different results, obscuring the relationship between early adversity and brain volume. Moreover, adolescence is a time of significant brain growth and certain brain areas have distinct rates of development, which may compromise the accuracy of automated segmentation approaches. In the current study, 23 adolescents participated in two waves of a longitudinal study. Family aggression was measured when the youths were 12 years old, and structural scans were acquired an average of 4 years later. Bilateral amygdalae and hippocampi were segmented using three different methods (manual tracing, FSL, and NeuroQuant). The segmentation estimates were compared, and linear regressions were run to assess the relationship between early family aggression exposure and all three volume segmentation estimates. Manual tracing results showed a positive relationship between family aggression and right amygdala volume, whereas FSL segmentation showed negative relationships between family aggression and both the left and right hippocampi. However, results indicate poor overlap between methods, and different associations were found between early family aggression exposure and brain volume depending on the segmentation method used.
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Affiliation(s)
- Hannah Lyden
- Department of Psychology, University of Southern California Los Angeles, CA, USA
| | - Sarah I Gimbel
- Department of Psychology, Brain and Creativity Institute, University of Southern California Los Angeles, CA, USA
| | - Larissa Del Piero
- Department of Psychology, University of Southern California Los Angeles, CA, USA
| | - A Bryna Tsai
- Department of Psychology, University of Southern California Los Angeles, CA, USA
| | - Matthew E Sachs
- Department of Psychology, Brain and Creativity Institute, University of Southern California Los Angeles, CA, USA
| | - Jonas T Kaplan
- Department of Psychology, University of Southern CaliforniaLos Angeles, CA, USA; Department of Psychology, Brain and Creativity Institute, University of Southern CaliforniaLos Angeles, CA, USA
| | - Gayla Margolin
- Department of Psychology, University of Southern California Los Angeles, CA, USA
| | - Darby Saxbe
- Department of Psychology, University of Southern California Los Angeles, CA, USA
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26
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Pini L, Pievani M, Bocchetta M, Altomare D, Bosco P, Cavedo E, Galluzzi S, Marizzoni M, Frisoni GB. Brain atrophy in Alzheimer's Disease and aging. Ageing Res Rev 2016; 30:25-48. [PMID: 26827786 DOI: 10.1016/j.arr.2016.01.002] [Citation(s) in RCA: 438] [Impact Index Per Article: 54.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Revised: 01/15/2016] [Accepted: 01/20/2016] [Indexed: 01/22/2023]
Abstract
Thanks to its safety and accessibility, magnetic resonance imaging (MRI) is extensively used in clinical routine and research field, largely contributing to our understanding of the pathophysiology of neurodegenerative disorders such as Alzheimer's disease (AD). This review aims to provide a comprehensive overview of the main findings in AD and normal aging over the past twenty years, focusing on the patterns of gray and white matter changes assessed in vivo using MRI. Major progresses in the field concern the segmentation of the hippocampus with novel manual and automatic segmentation approaches, which might soon enable to assess also hippocampal subfields. Advancements in quantification of hippocampal volumetry might pave the way to its broader use as outcome marker in AD clinical trials. Patterns of cortical atrophy have been shown to accurately track disease progression and seem promising in distinguishing among AD subtypes. Disease progression has also been associated with changes in white matter tracts. Recent studies have investigated two areas often overlooked in AD, such as the striatum and basal forebrain, reporting significant atrophy, although the impact of these changes on cognition is still unclear. Future integration of different MRI modalities may further advance the field by providing more powerful biomarkers of disease onset and progression.
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Affiliation(s)
- Lorenzo Pini
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Michela Pievani
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Martina Bocchetta
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, London, UK
| | - Daniele Altomare
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Paolo Bosco
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Enrica Cavedo
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Sorbonne Universités, Université Pierre et Marie Curie, Paris 06, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A) Hôpital de la Pitié-Salpétrière & Institut du Cerveau et de la Moelle épinière (ICM), UMR S 1127, Hôpital de la Pitié-Salpétrière Paris & CATI Multicenter Neuroimaging Platform, France
| | - Samantha Galluzzi
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Moira Marizzoni
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Giovanni B Frisoni
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Memory Clinic and LANVIE-Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland.
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27
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Manjón JV, Coupé P. volBrain: An Online MRI Brain Volumetry System. Front Neuroinform 2016; 10:30. [PMID: 27512372 PMCID: PMC4961698 DOI: 10.3389/fninf.2016.00030] [Citation(s) in RCA: 310] [Impact Index Per Article: 38.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2016] [Accepted: 07/11/2016] [Indexed: 01/18/2023] Open
Abstract
The amount of medical image data produced in clinical and research settings is rapidly growing resulting in vast amount of data to analyze. Automatic and reliable quantitative analysis tools, including segmentation, allow to analyze brain development and to understand specific patterns of many neurological diseases. This field has recently experienced many advances with successful techniques based on non-linear warping and label fusion. In this work we present a novel and fully automatic pipeline for volumetric brain analysis based on multi-atlas label fusion technology that is able to provide accurate volumetric information at different levels of detail in a short time. This method is available through the volBrain online web interface (http://volbrain.upv.es), which is publically and freely accessible to the scientific community. Our new framework has been compared with current state-of-the-art methods showing very competitive results.
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Affiliation(s)
- José V Manjón
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València Valencia, Spain
| | - Pierrick Coupé
- Pictura Research Group, Unité Mixte de Recherche Centre National de la Recherche Scientifique (UMR 5800), Laboratoire Bordelais de Recherche en Informatique, Centre National de la Recherche ScientifiqueTalence, France; Pictura Research Group, Unité Mixte de Recherche Centre National de la Recherche Scientifique (UMR 5800), Laboratoire Bordelais de Recherche en Informatique, University BordeauxTalence, France
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28
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Niedworok CJ, Brown APY, Jorge Cardoso M, Osten P, Ourselin S, Modat M, Margrie TW. aMAP is a validated pipeline for registration and segmentation of high-resolution mouse brain data. Nat Commun 2016; 7:11879. [PMID: 27384127 PMCID: PMC4941048 DOI: 10.1038/ncomms11879] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Accepted: 05/09/2016] [Indexed: 01/16/2023] Open
Abstract
The validation of automated image registration and segmentation is crucial for accurate and reliable mapping of brain connectivity and function in three-dimensional (3D) data sets. While validation standards are necessarily high and routinely met in the clinical arena, they have to date been lacking for high-resolution microscopy data sets obtained from the rodent brain. Here we present a tool for optimized automated mouse atlas propagation (aMAP) based on clinical registration software (NiftyReg) for anatomical segmentation of high-resolution 3D fluorescence images of the adult mouse brain. We empirically evaluate aMAP as a method for registration and subsequent segmentation by validating it against the performance of expert human raters. This study therefore establishes a benchmark standard for mapping the molecular function and cellular connectivity of the rodent brain.
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Affiliation(s)
- Christian J. Niedworok
- The Division of Neurophysiology, MRC National Institute for Medical Research, London NW7 1AA, UK
- The Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London W1T 4JG, UK
| | - Alexander P. Y. Brown
- The Division of Neurophysiology, MRC National Institute for Medical Research, London NW7 1AA, UK
- The Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London W1T 4JG, UK
| | - M. Jorge Cardoso
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London WC1E 6BT, UK
| | - Pavel Osten
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Sebastien Ourselin
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London WC1E 6BT, UK
| | - Marc Modat
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London WC1E 6BT, UK
| | - Troy W. Margrie
- The Division of Neurophysiology, MRC National Institute for Medical Research, London NW7 1AA, UK
- The Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London W1T 4JG, UK
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29
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Cover KS, van Schijndel RA, Versteeg A, Leung KK, Mulder ER, Jong RA, Visser PJ, Redolfi A, Revillard J, Grenier B, Manset D, Damangir S, Bosco P, Vrenken H, van Dijk BW, Frisoni GB, Barkhof F. Reproducibility of hippocampal atrophy rates measured with manual, FreeSurfer, AdaBoost, FSL/FIRST and the MAPS-HBSI methods in Alzheimer's disease. Psychiatry Res Neuroimaging 2016; 252:26-35. [PMID: 27179313 DOI: 10.1016/j.pscychresns.2016.04.006] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Revised: 02/16/2016] [Accepted: 04/08/2016] [Indexed: 11/23/2022]
Abstract
The purpose of this study is to assess the reproducibility of hippocampal atrophy rate measurements of commonly used fully-automated algorithms in Alzheimer disease (AD). The reproducibility of hippocampal atrophy rate for FSL/FIRST, AdaBoost, FreeSurfer, MAPS independently and MAPS combined with the boundary shift integral (MAPS-HBSI) were calculated. Back-to-back (BTB) 3D T1-weighted MPRAGE MRI from the Alzheimer's Disease Neuroimaging Initiative (ADNI1) study at baseline and year one were used. Analysis on 3 groups of subjects was performed - 562 subjects at 1.5T, a 75 subject group that also had manual segmentation and 111 subjects at 3T. A simple and novel statistical test based on the binomial distribution was used that handled outlying data points robustly. Median hippocampal atrophy rates were -1.1%/year for healthy controls, -3.0%/year for mildly cognitively impaired and -5.1%/year for AD subjects. The best reproducibility was observed for MAPS-HBSI (1.3%), while the other methods tested had reproducibilities at least 50% higher at 1.5T and 3T which was statistically significant. For a clinical trial, MAPS-HBSI should require less than half the subjects of the other methods tested. All methods had good accuracy versus manual segmentation. The MAPS-HBSI method has substantially better reproducibility than the other methods considered.
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Affiliation(s)
- Keith S Cover
- VU University Medical Center, Amsterdam, Netherlands.
| | | | | | | | - Emma R Mulder
- VU University Medical Center, Amsterdam, Netherlands
| | - Remko A Jong
- VU University Medical Center, Amsterdam, Netherlands
| | | | | | | | | | | | | | - Paolo Bosco
- IRCCS San Giovanni di Dio Fatebenefratelli, Italy
| | - Hugo Vrenken
- VU University Medical Center, Amsterdam, Netherlands
| | | | - Giovanni B Frisoni
- IRCCS San Giovanni di Dio Fatebenefratelli, Italy; University Hospitals and University of Geneva, Switzerland
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30
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Tuszynski T, Rullmann M, Luthardt J, Butzke D, Tiepolt S, Gertz HJ, Hesse S, Seese A, Lobsien D, Sabri O, Barthel H. Evaluation of software tools for automated identification of neuroanatomical structures in quantitative β-amyloid PET imaging to diagnose Alzheimer’s disease. Eur J Nucl Med Mol Imaging 2016; 43:1077-87. [DOI: 10.1007/s00259-015-3300-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2015] [Accepted: 12/22/2015] [Indexed: 11/25/2022]
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31
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Giraud R, Ta VT, Papadakis N, Manjón JV, Collins DL, Coupé P. An Optimized PatchMatch for multi-scale and multi-feature label fusion. Neuroimage 2015; 124:770-782. [PMID: 26244277 DOI: 10.1016/j.neuroimage.2015.07.076] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Revised: 07/27/2015] [Accepted: 07/28/2015] [Indexed: 01/18/2023] Open
Abstract
Automatic segmentation methods are important tools for quantitative analysis of Magnetic Resonance Images (MRI). Recently, patch-based label fusion approaches have demonstrated state-of-the-art segmentation accuracy. In this paper, we introduce a new patch-based label fusion framework to perform segmentation of anatomical structures. The proposed approach uses an Optimized PAtchMatch Label fusion (OPAL) strategy that drastically reduces the computation time required for the search of similar patches. The reduced computation time of OPAL opens the way for new strategies and facilitates processing on large databases. In this paper, we investigate new perspectives offered by OPAL, by introducing a new multi-scale and multi-feature framework. During our validation on hippocampus segmentation we use two datasets: young adults in the ICBM cohort and elderly adults in the EADC-ADNI dataset. For both, OPAL is compared to state-of-the-art methods. Results show that OPAL obtained the highest median Dice coefficient (89.9% for ICBM and 90.1% for EADC-ADNI). Moreover, in both cases, OPAL produced a segmentation accuracy similar to inter-expert variability. On the EADC-ADNI dataset, we compare the hippocampal volumes obtained by manual and automatic segmentation. The volumes appear to be highly correlated that enables to perform more accurate separation of pathological populations.
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Affiliation(s)
- Rémi Giraud
- Univ. Bordeaux, LaBRI, UMR 5800, PICTURA, F-33400 Talence, France; CNRS, LaBRI, UMR 5800, PICTURA, F-33400 Talence, France; Univ. Bordeaux, IMB, UMR 5251, F-33400 Talence, France; CNRS, IMB, UMR 5251, F-33400 Talence, France; Bordeaux INP, LaBRI, UMR 5800, PICTURA, F-33600 Pessac, France.
| | - Vinh-Thong Ta
- Univ. Bordeaux, LaBRI, UMR 5800, PICTURA, F-33400 Talence, France; CNRS, LaBRI, UMR 5800, PICTURA, F-33400 Talence, France; Bordeaux INP, LaBRI, UMR 5800, PICTURA, F-33600 Pessac, France
| | - Nicolas Papadakis
- Univ. Bordeaux, IMB, UMR 5251, F-33400 Talence, France; CNRS, IMB, UMR 5251, F-33400 Talence, France
| | - José V Manjón
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Pierrick Coupé
- Univ. Bordeaux, LaBRI, UMR 5800, PICTURA, F-33400 Talence, France; CNRS, LaBRI, UMR 5800, PICTURA, F-33400 Talence, France
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A label fusion method using conditional random fields with higher-order potentials: Application to hippocampal segmentation. Artif Intell Med 2015; 64:117-29. [DOI: 10.1016/j.artmed.2015.04.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2014] [Revised: 01/21/2015] [Accepted: 04/26/2015] [Indexed: 11/19/2022]
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Suppa P, Hampel H, Spies L, Fiebach JB, Dubois B, Buchert R. Fully Automated Atlas-Based Hippocampus Volumetry for Clinical Routine: Validation in Subjects with Mild Cognitive Impairment from the ADNI Cohort. J Alzheimers Dis 2015; 46:199-209. [DOI: 10.3233/jad-142280] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Per Suppa
- Department of Nuclear Medicine, Charité, Berlin, Germany
- jung diagnostics GmbH, Hamburg, Germany
| | - Harald Hampel
- Université Pierre et Marie Curie, Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital de la Salpêtrière, Paris, France
| | | | | | - Bruno Dubois
- Université Pierre et Marie Curie, Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital de la Salpêtrière, Paris, France
| | - Ralph Buchert
- Department of Nuclear Medicine, Charité, Berlin, Germany
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Reproducibilidad de la valoración cualitativa de la atrofia del lóbulo temporal por RM. RADIOLOGIA 2015; 57:225-8. [DOI: 10.1016/j.rx.2014.04.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2013] [Revised: 04/16/2014] [Accepted: 04/23/2014] [Indexed: 11/19/2022]
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Sarria-Estrada S, Acevedo C, Mitjana R, Frascheri L, Siurana S, Auger C, Rovira A. Reproducibility of qualitative assessments of temporal lobe atrophy in MRI studies. RADIOLOGIA 2015. [DOI: 10.1016/j.rxeng.2014.04.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Hill DLG, Schwarz AJ, Isaac M, Pani L, Vamvakas S, Hemmings R, Carrillo MC, Yu P, Sun J, Beckett L, Boccardi M, Brewer J, Brumfield M, Cantillon M, Cole PE, Fox N, Frisoni GB, Jack C, Kelleher T, Luo F, Novak G, Maguire P, Meibach R, Patterson P, Bain L, Sampaio C, Raunig D, Soares H, Suhy J, Wang H, Wolz R, Stephenson D. Coalition Against Major Diseases/European Medicines Agency biomarker qualification of hippocampal volume for enrichment of clinical trials in predementia stages of Alzheimer's disease. Alzheimers Dement 2015; 10:421-429.e3. [PMID: 24985687 DOI: 10.1016/j.jalz.2013.07.003] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2013] [Revised: 06/26/2013] [Accepted: 07/23/2013] [Indexed: 01/24/2023]
Abstract
BACKGROUND Regulatory qualification of a biomarker for a defined context of use provides scientifically robust assurances to sponsors and regulators that accelerate appropriate adoption of biomarkers into drug development. METHODS The Coalition Against Major Diseases submitted a dossier to the Scientific Advice Working Party of the European Medicines Agency requesting a qualification opinion on the use of hippocampal volume as a biomarker for enriching clinical trials in subjects with mild cognitive impairment, incorporating a scientific rationale, a literature review and a de novo analysis of Alzheimer's Disease Neuroimaging Initiative data. RESULTS The literature review and de novo analysis were consistent with the proposed context of use, and the Committee for Medicinal Products for Human Use released an opinion in November 2011. CONCLUSIONS We summarize the scientific rationale and the data that supported the first qualification of an imaging biomarker by the European Medicines Agency.
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Affiliation(s)
| | | | | | - Luca Pani
- European Medicines Agency, London, UK
| | | | | | | | - Peng Yu
- Eli Lilly and Company, Indianapolis, IN, USA
| | - Jia Sun
- Eli Lilly and Company, Indianapolis, IN, USA; The University of Texas School of Public Health, Houston, TX, USA
| | | | | | | | - Martha Brumfield
- Coalition Against Major Diseases, Critical Path Institute, Tucson, AZ, USA
| | | | | | - Nick Fox
- UCL Institute of Neurology, London, UK
| | | | | | | | - Feng Luo
- Bristol Myers Squibb, Wallingford, CT, USA
| | - Gerald Novak
- Janssen Pharmaceutical Research and Development, Titusville, NJ, USA
| | | | | | | | - Lisa Bain
- Independent science writer, Elverson, PA, USA
| | | | | | | | | | | | - Robin Wolz
- IXICO Ltd., London, UK; Department of Computing, Imperial College London, London, UK
| | - Diane Stephenson
- Coalition Against Major Diseases, Critical Path Institute, Tucson, AZ, USA.
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Duchesne S, Valdivia F, Robitaille N, Mouiha A, Valdivia FA, Bocchetta M, Apostolova LG, Ganzola R, Preboske G, Wolf D, Boccardi M, Jack CR, Frisoni GB. Manual segmentation qualification platform for the EADC‐ADNI harmonized protocol for hippocampal segmentation project. Alzheimers Dement 2015; 11:161-74. [DOI: 10.1016/j.jalz.2015.01.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2013] [Revised: 12/22/2014] [Accepted: 01/06/2015] [Indexed: 11/16/2022]
Affiliation(s)
- Simon Duchesne
- Department of RadiologyUniversité Laval and Centre de Recherche de l'Institut universitaire en santé mentale de QuébecQuebec CityCanada
| | - Fernando Valdivia
- Department of RadiologyUniversité Laval and Centre de Recherche de l'Institut universitaire en santé mentale de QuébecQuebec CityCanada
| | - Nicolas Robitaille
- Department of RadiologyUniversité Laval and Centre de Recherche de l'Institut universitaire en santé mentale de QuébecQuebec CityCanada
| | - Abderazzak Mouiha
- Department of RadiologyUniversité Laval and Centre de Recherche de l'Institut universitaire en santé mentale de QuébecQuebec CityCanada
| | - F. Abiel Valdivia
- Department of RadiologyUniversité Laval and Centre de Recherche de l'Institut universitaire en santé mentale de QuébecQuebec CityCanada
| | - Martina Bocchetta
- LENITEM (Laboratory of EpidemiologyNeuroimaging and Telemedicine) IRCCS – S. Giovanni di Dio – FatebenefratelliBresciaItaly
- Department of Molecular and Translational MedicineUniversity of BresciaBresciaItaly
| | - Liana G. Apostolova
- Mary S. Easton Center for Alzheimer's Disease Research and Laboratory of NeuroImaging, David Geffen School of Medicine, University of CaliforniaLos AngelesUSA
| | - Rossana Ganzola
- Department of RadiologyUniversité Laval and Centre de Recherche de l'Institut universitaire en santé mentale de QuébecQuebec CityCanada
| | - Greg Preboske
- Department of Diagnostic RadiologyMayo Clinic and FoundationRochesterMNUSA
| | - Dominik Wolf
- Klinik für Psychiatrie und PsychotherapieJohannes Gutenberg‐UniversitätMainzGermany
| | - Marina Boccardi
- LENITEM (Laboratory of EpidemiologyNeuroimaging and Telemedicine) IRCCS – S. Giovanni di Dio – FatebenefratelliBresciaItaly
| | - Clifford R. Jack
- Department of Diagnostic RadiologyMayo Clinic and FoundationRochesterMNUSA
| | - Giovanni B. Frisoni
- LENITEM (Laboratory of EpidemiologyNeuroimaging and Telemedicine) IRCCS – S. Giovanni di Dio – FatebenefratelliBresciaItaly
- University Hospitals and University of GenevaGenevaSwitzerland
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Crivello F, Tzourio-Mazoyer N, Tzourio C, Mazoyer B. Longitudinal assessment of global and regional rate of grey matter atrophy in 1,172 healthy older adults: modulation by sex and age. PLoS One 2014; 9:e114478. [PMID: 25469789 PMCID: PMC4255026 DOI: 10.1371/journal.pone.0114478] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2014] [Accepted: 11/07/2014] [Indexed: 11/23/2022] Open
Abstract
To characterize the neuroanatomical changes in healthy older adults is important to differentiate pathological from normal brain structural aging. The present study investigated the annualized rate of GM atrophy in a large sample of older participants, focusing on the hippocampus, and searching for modulation by age and sex. In this 4-year longitudinal community cohort study, we used a VBM analysis to estimate the annualized rate of GM loss, at both the global and regional levels, in 1,172 healthy older adults (65–82 years) scanned at 1.5T. The global annualized rate of GM was −4.0 cm3/year (−0.83%/year). The highest rates of regional GM loss were found in the frontal and parietal cortices, middle occipital gyri, temporal cortex and hippocampus. The rate of GM atrophy was higher in women (−4.7 cm3/year, −0.91%/year) than men (−3.3 cm3/year, −0.65%/year). The global annualized rate of GM atrophy remained constant throughout the age range of the cohort, in both sexes. This pattern was replicated at the regional level, with the exception of the hippocampi, which showed a rate of GM atrophy that accelerated with age (2.8%/year per year of age) similarly for men and women. The present study reports a global and regional description of the annualized rate of grey matter loss and its evolution after the age of 65. Our results suggest greater anatomical vulnerability of women in late life and highlight a specific vulnerability of the hippocampus to the aging processes after 65 years of age.
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Affiliation(s)
- Fabrice Crivello
- Université de Bordeaux, GIN, UMR 5296, Bordeaux, France
- CNRS, GIN, UMR 5296, Bordeaux, France
- CEA, GIN, UMR 5296, Bordeaux, France
- * E-mail:
| | - Nathalie Tzourio-Mazoyer
- Université de Bordeaux, GIN, UMR 5296, Bordeaux, France
- CNRS, GIN, UMR 5296, Bordeaux, France
- CEA, GIN, UMR 5296, Bordeaux, France
| | | | - Bernard Mazoyer
- Université de Bordeaux, GIN, UMR 5296, Bordeaux, France
- CNRS, GIN, UMR 5296, Bordeaux, France
- CEA, GIN, UMR 5296, Bordeaux, France
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Pipitone J, Park MTM, Winterburn J, Lett TA, Lerch JP, Pruessner JC, Lepage M, Voineskos AN, Chakravarty MM. Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates. Neuroimage 2014; 101:494-512. [DOI: 10.1016/j.neuroimage.2014.04.054] [Citation(s) in RCA: 268] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2013] [Revised: 04/15/2014] [Accepted: 04/19/2014] [Indexed: 11/16/2022] Open
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Dill V, Franco AR, Pinho MS. Automated Methods for Hippocampus Segmentation: the Evolution and a Review of the State of the Art. Neuroinformatics 2014; 13:133-50. [DOI: 10.1007/s12021-014-9243-4] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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High-Dimensional Medial Lobe Morphometry: An Automated MRI Biomarker for the New AD Diagnostic Criteria. Int J Alzheimers Dis 2014; 2014:278096. [PMID: 25254139 PMCID: PMC4164123 DOI: 10.1155/2014/278096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2014] [Accepted: 07/25/2014] [Indexed: 11/21/2022] Open
Abstract
Introduction. Medial temporal lobe atrophy assessment via magnetic resonance imaging (MRI) has been proposed in recent criteria as an in vivo diagnostic biomarker of Alzheimer's disease (AD). However, practical application of these criteria in a clinical setting will require automated MRI analysis techniques. To this end, we wished to validate our automated, high-dimensional morphometry technique to the hypothetical prediction of future clinical status from baseline data in a cohort of subjects in a large, multicentric setting, compared to currently known clinical status for these subjects. Materials and Methods. The study group consisted of 214 controls, 371 mild cognitive impairment (147 having progressed to probable AD and 224 stable), and 181 probable AD from the Alzheimer's Disease Neuroimaging Initiative, with data acquired on 58 different 1.5 T scanners. We measured the sensitivity and specificity of our technique in a hierarchical fashion, first testing the effect of intensity standardization, then between different volumes of interest, and finally its generalizability for a large, multicentric cohort. Results. We obtained 73.2% prediction accuracy with 79.5% sensitivity for the prediction of MCI progression to clinically probable AD. The positive predictive value was 81.6% for MCI progressing on average within 1.5 (0.3 s.d.) year. Conclusion. With high accuracy, the technique's ability to identify discriminant medial temporal lobe atrophy has been demonstrated in a large, multicentric environment. It is suitable as an aid for clinical diagnostic of AD.
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Hogan RE, Moseley ED, Maccotta L. Hippocampal surface deformation accuracy in T1-weighted volumetric MRI sequences in subjects with epilepsy. J Neuroimaging 2014; 25:452-9. [PMID: 24942549 DOI: 10.1111/jon.12135] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2014] [Revised: 03/07/2014] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND AND PURPOSE To demonstrate the accuracy across different acquisition and analysis methods, we evaluated the variability in hippocampal volumetric and surface displacement measurements resulting from two different MRI (magnetic resonance imaging) acquisition protocols. METHODS Nine epilepsy patients underwent two independent T1-weighted magnetization prepared spoiled gradient sequences during a single 3T MRI session. Using high-dimension mapping-large deformation (HDM-LD) segmentation, we calculated volumetric estimates and generated a vector-based 3-dimensional surface model of each subject's hippocampi, and evaluated volume and surface changes, the latter using a cluster-based noise estimation model. RESULTS Mean hippocampal volumes and standard deviations for the left hippocampi were 2,750 (826) mm3 and 2,782 (859) mm3 (P = .13), and for the right hippocampi were 2,558 (750) mm3 and 2,547 (692) mm3 (P = .76), respectively for the MPR1 and MPR2 sequences. Average Dice coefficient comparing overlap for segmentations was 86%. There was no significant effect of MRI sequence on volume estimates and no significant hippocampal surface change between sequences. CONCLUSION Statistical comparison of hippocampal volumes and statistically thresholded HDM-LD surfaces in TLE patients showed no differences between the segmentations obtained in the two MRI acquisition sequences. This validates the robustness across MRI sequences of the HDM-LD technique for estimating volume and surface changes in subjects with epilepsy.
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Affiliation(s)
- R Edward Hogan
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri
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Achterberg HC, van der Lijn F, den Heijer T, Vernooij MW, Ikram MA, Niessen WJ, de Bruijne M. Hippocampal shape is predictive for the development of dementia in a normal, elderly population. Hum Brain Mapp 2014; 35:2359-71. [PMID: 24039001 PMCID: PMC6869385 DOI: 10.1002/hbm.22333] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2012] [Revised: 03/11/2013] [Accepted: 05/06/2013] [Indexed: 11/11/2022] Open
Abstract
Previous studies have shown that hippocampal volume is an early marker for dementia. We investigated whether hippocampal shape characteristics extracted from MRI scans are predictive for the development of dementia during follow up in subjects who were nondemented at baseline. Furthermore, we assessed whether hippocampal shape provides additional predictive value independent of hippocampal volume. Five hundred eleven brain MRI scans from elderly nondemented participants of a prospective population-based imaging study were used. During the 10-year follow-up period, 52 of these subjects developed dementia. For training and evaluation independent of age and gender, a subset of 50 cases and 150 matched controls was selected. The hippocampus was segmented using an automated method. From the segmentation, the volume was determined and a statistical shape model was constructed. We trained a classifier to distinguish between subjects who developed dementia and subjects who stayed cognitively healthy. For all subjects the a posteriori probability to develop dementia was estimated using the classifier in a cross-validation experiment. The area under the ROC curve for volume, shape, and the combination of both were, respectively, 0.724, 0.743, and 0.766. A logistic regression model showed that adding shape to a model using volume corrected for age and gender increased the global model-fit significantly (P = 0.0063). We conclude that hippocampal shape derived from MRI scans is predictive for dementia before clinical symptoms arise, independent of age and gender. Furthermore, the results suggest that hippocampal shape provides additional predictive value over hippocampal volume and that combining shape and volume leads to better prediction.
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Affiliation(s)
- Hakim C Achterberg
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
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Weier K, Fonov V, Lavoie K, Doyon J, Collins DL. Rapid automatic segmentation of the human cerebellum and its lobules (RASCAL)--implementation and application of the patch-based label-fusion technique with a template library to segment the human cerebellum. Hum Brain Mapp 2014; 35:5026-39. [PMID: 24777876 DOI: 10.1002/hbm.22529] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2013] [Revised: 03/27/2014] [Accepted: 04/02/2014] [Indexed: 12/20/2022] Open
Abstract
Reliable and fast segmentation of the human cerebellum with its complex architecture of lobes and lobules has been a challenge for the past decades. Emerging knowledge of the functional integration of the cerebellum in various sensori-motor and cognitive-behavioral circuits demands new automatic segmentation techniques, with accuracies similar to manual segmentations, but applicable to large subject numbers in a reasonable time frame. This article presents the development and application of a novel pipeline for rapid automatic segmentation of the human cerebellum and its lobules (RASCAL) combining patch-based label-fusion and a template library of manually labeled cerebella of 16 healthy controls from the International Consortium for Brain Mapping (ICBM) database. Leave-one-out experiments revealed a good agreement between manual and automatic segmentations (Dice kappa = 0.82). Intraclass correlation coefficients (ICC) were calculated to test reliability of segmented volumes and were highest (ICC > 0.9) for global measures (total and hemispherical grey and white matter) followed by larger lobules of the posterior lobe (ICC > 0.8). Further we applied the pipeline to all 152 young healthy controls of the ICBM database to look for hemispheric and gender differences. The results demonstrated larger native space volumes in men then women (mean (± SD) total cerebellar volume in women = 217 cm(3) (± 26), men = 259 cm(3) (± 29); P < 0.001). Significant gender-by-hemisphere interaction was only found in stereotaxic space volumes for white matter core (men > women) and anterior lobe volume (women > men). This new method shows great potential for the precise and efficient analysis of the cerebellum in large patient cohorts.
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Affiliation(s)
- Katrin Weier
- McConnell Brain Imaging Center, Montreal Neurological Hospital and Institute, McGill University, Montreal, Canada; Department Biomedical Engineering, McGill University, Montreal, Canada
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Wenger E, Mårtensson J, Noack H, Bodammer NC, Kühn S, Schaefer S, Heinze HJ, Düzel E, Bäckman L, Lindenberger U, Lövdén M. Comparing manual and automatic segmentation of hippocampal volumes: reliability and validity issues in younger and older brains. Hum Brain Mapp 2014; 35:4236-48. [PMID: 24532539 DOI: 10.1002/hbm.22473] [Citation(s) in RCA: 125] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2013] [Accepted: 01/14/2014] [Indexed: 11/08/2022] Open
Abstract
We compared hippocampal volume measures obtained by manual tracing to automatic segmentation with FreeSurfer in 44 younger (20-30 years) and 47 older (60-70 years) adults, each measured with magnetic resonance imaging (MRI) over three successive time points, separated by four months. Retest correlations over time were very high for both manual and FreeSurfer segmentations. With FreeSurfer, correlations over time were significantly lower in the older than in the younger age group, which was not the case with manual segmentation. Pearson correlations between manual and FreeSurfer estimates were sufficiently high, numerically even higher in the younger group, whereas intra-class correlation coefficient (ICC) estimates were lower in the younger than in the older group. FreeSurfer yielded higher volume estimates than manual segmentation, particularly in the younger age group. Importantly, FreeSurfer consistently overestimated hippocampal volumes independently of manually assessed volume in the younger age group, but overestimated larger volumes in the older age group to a less extent, introducing a systematic age bias in the data. Age differences in hippocampal volumes were significant with FreeSurfer, but not with manual tracing. Manual tracing resulted in a significant difference between left and right hippocampus (right > left), whereas this asymmetry effect was considerably smaller with FreeSurfer estimates. We conclude that FreeSurfer constitutes a feasible method to assess differences in hippocampal volume in young adults. FreeSurfer estimates in older age groups should, however, be interpreted with care until the automatic segmentation pipeline has been further optimized to increase validity and reliability in this age group.
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Affiliation(s)
- Elisabeth Wenger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Germany
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Mulder ER, de Jong RA, Knol DL, van Schijndel RA, Cover KS, Visser PJ, Barkhof F, Vrenken H. Hippocampal volume change measurement: quantitative assessment of the reproducibility of expert manual outlining and the automated methods FreeSurfer and FIRST. Neuroimage 2014; 92:169-81. [PMID: 24521851 DOI: 10.1016/j.neuroimage.2014.01.058] [Citation(s) in RCA: 101] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2013] [Revised: 01/23/2014] [Accepted: 01/31/2014] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND To measure hippocampal volume change in Alzheimer's disease (AD) or mild cognitive impairment (MCI), expert manual delineation is often used because of its supposed accuracy. It has been suggested that expert outlining yields poorer reproducibility as compared to automated methods, but this has not been investigated. AIM To determine the reproducibilities of expert manual outlining and two common automated methods for measuring hippocampal atrophy rates in healthy aging, MCI and AD. METHODS From the Alzheimer's Disease Neuroimaging Initiative (ADNI), 80 subjects were selected: 20 patients with AD, 40 patients with mild cognitive impairment (MCI) and 20 healthy controls (HCs). Left and right hippocampal volume change between baseline and month-12 visit was assessed by using expert manual delineation, and by the automated software packages FreeSurfer (longitudinal processing stream) and FIRST. To assess reproducibility of the measured hippocampal volume change, both back-to-back (BTB) MPRAGE scans available for each visit were analyzed. Hippocampal volume change was expressed in μL, and as a percentage of baseline volume. Reproducibility of the 1-year hippocampal volume change was estimated from the BTB measurements by using linear mixed model to calculate the limits of agreement (LoA) of each method, reflecting its measurement uncertainty. Using the delta method, approximate p-values were calculated for the pairwise comparisons between methods. Statistical analyses were performed both with inclusion and exclusion of visibly incorrect segmentations. RESULTS Visibly incorrect automated segmentation in either one or both scans of a longitudinal scan pair occurred in 7.5% of the hippocampi for FreeSurfer and in 6.9% of the hippocampi for FIRST. After excluding these failed cases, reproducibility analysis for 1-year percentage volume change yielded LoA of ±7.2% for FreeSurfer, ±9.7% for expert manual delineation, and ±10.0% for FIRST. Methods ranked the same for reproducibility of 1-year μL volume change, with LoA of ±218 μL for FreeSurfer, ±319 μL for expert manual delineation, and ±333 μL for FIRST. Approximate p-values indicated that reproducibility was better for FreeSurfer than for manual or FIRST, and that manual and FIRST did not differ. Inclusion of failed automated segmentations led to worsening of reproducibility of both automated methods for 1-year raw and percentage volume change. CONCLUSION Quantitative reproducibility values of 1-year microliter and percentage hippocampal volume change were roughly similar between expert manual outlining, FIRST and FreeSurfer, but FreeSurfer reproducibility was statistically significantly superior to both manual outlining and FIRST after exclusion of failed segmentations.
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Affiliation(s)
- Emma R Mulder
- Image Analysis Center, VU University Medical Center, Amsterdam, The Netherlands; Department of Radiology, VU University Medical Center, Amsterdam, The Netherlands
| | - Remko A de Jong
- Image Analysis Center, VU University Medical Center, Amsterdam, The Netherlands; Department of Radiology, VU University Medical Center, Amsterdam, The Netherlands
| | - Dirk L Knol
- Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands
| | - Ronald A van Schijndel
- Image Analysis Center, VU University Medical Center, Amsterdam, The Netherlands; Department of Information and Communication Technology, VU University Medical Center, Amsterdam, The Netherlands
| | - Keith S Cover
- Department of Physics and Medical Technology, VU University Medical Center, Amsterdam, The Netherlands
| | - Pieter J Visser
- Department of Neurology, VU University Medical Center, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Image Analysis Center, VU University Medical Center, Amsterdam, The Netherlands; Department of Radiology, VU University Medical Center, Amsterdam, The Netherlands
| | - Hugo Vrenken
- Department of Radiology, VU University Medical Center, Amsterdam, The Netherlands; Department of Physics and Medical Technology, VU University Medical Center, Amsterdam, The Netherlands.
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Tokola AM, Salli EK, Åberg LE, Autti TH. Hippocampal volumes in juvenile neuronal ceroid lipofuscinosis: a longitudinal magnetic resonance imaging study. Pediatr Neurol 2014; 50:158-63. [PMID: 24411222 DOI: 10.1016/j.pediatrneurol.2013.10.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2013] [Revised: 10/14/2013] [Accepted: 10/28/2013] [Indexed: 11/27/2022]
Abstract
BACKGROUND Juvenile neuronal ceroid lipofuscinosis is an inherited, autosomal recessive, progressive, neurodegenerative disorder of childhood. It belongs to the lysosomal storage diseases, which manifest with loss of vision, seizures, and loss of cognitive and motor functions, and lead to premature death. Imaging studies have shown cerebral and cerebellar atrophy, yet no previous studies evaluating particularly hippocampal atrophy have been published. This study evaluates the hippocampal volumes in adolescent juvenile neuronal ceroid lipofuscinosis patients in a controlled 5-year follow-up magnetic resonance imaging study. METHODS Hippocampal volumes of eight patients (three female, five male) and 10 healthy age- and sex-matched control subjects were measured from two repeated magnetic resonance imaging examinations. Three male patients did not have controls and were excluded from the statistics. In the patient group, the first examination was performed at the mean age of 12.2 years and the second examination at the mean age of 17.3 years. In the control group, the mean ages at the time of examinations were 12.5 years and 19.3 years. RESULTS Progressive hippocampal atrophy was found in the patient group. The mean total hippocampal volume decreased by 0.85 cm³ during the 5-year follow-up in the patient group, which corresponds to a 3.3% annual rate of volume loss. The whole brain volume decreased by 2.9% per year. The observed annual rate of hippocampal atrophy also exceeded the previously reported 2.4% annual loss of total gray matter volume in juvenile neuronal ceroid lipofuscinosis patients. CONCLUSIONS These data suggest that progressive hippocampal atrophy is one of the characteristic features of brain atrophy in juvenile neuronal ceroid lipofuscinosis in adolescence.
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Affiliation(s)
- Anna M Tokola
- Department of Radiology, HUS Medical Imaging Center, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland.
| | - Eero K Salli
- Department of Radiology, HUS Medical Imaging Center, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
| | - Laura E Åberg
- Clinic for the Intellectually Disabled, Department of Social Services and Health Care, City of Helsinki, Helsinki, Finland
| | - Taina H Autti
- Department of Radiology, HUS Medical Imaging Center, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
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Abstract
BACKGROUND Hippocampal volumetry on magnetic resonance imaging is recognized as an Alzheimer's disease (AD) biomarker, and manual segmentation is the gold standard for measurement. However, a standard procedure is lacking. We operationalize and quantitate landmark differences to help a Delphi panel converge on a set of landmarks. METHODS One hundred percent of anatomic landmark variability across 12 different protocols for manual segmentation was reduced into four segmentation units (the minimum hippocampus, the alveus/fimbria, the tail, and the subiculum), which were segmented on magnetic resonance images by expert raters to estimate reliability and AD-related atrophy. RESULTS Intra- and interrater reliability were more than 0.96 and 0.92, respectively, except for the alveus/fimbria, which were 0.86 and 0.77, respectively. Of all AD-related atrophy, the minimum hippocampus contributed to 67%; tail, 24%; alveus/fimbria, 4%; and the subiculum, 5%. CONCLUSIONS Anatomic landmark variability in available protocols can be reduced to four discrete and measurable segmentation units. Their quantitative assessment will help a Delphi panel to define a set of landmarks for a harmonized protocol.
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Teipel SJ, Grothe M, Lista S, Toschi N, Garaci FG, Hampel H. Relevance of magnetic resonance imaging for early detection and diagnosis of Alzheimer disease. Med Clin North Am 2013; 97:399-424. [PMID: 23642578 DOI: 10.1016/j.mcna.2012.12.013] [Citation(s) in RCA: 118] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Hippocampus volumetry currently is the best-established imaging biomarker for AD. However, the effect of multicenter acquisition on measurements of hippocampus volume needs to be explicitly considered when it is applied in large clinical trials, for example by using mixed-effects models to take the clustering of data within centers into account. The marker needs further validation in respect of the underlying neurobiological substrate and potential confounds such as vascular disease, inflammation, hydrocephalus, and alcoholism, and with regard to clinical outcomes such as cognition but also to demographic and socioeconomic outcomes such as mortality and institutionalization. The use of hippocampus volumetry for risk stratification of predementia study samples will further increase with the availability of automated measurement approaches. An important step in this respect will be the development of a standard hippocampus tracing protocol that harmonizes the large range of presently available manual protocols. In the near future, regionally differentiated automated methods will become available together with an appropriate statistical model, such as multivariate analysis of deformation fields, or techniques such as cortical-thickness measurements that yield a meaningful metrics for the detection of treatment effects. More advanced imaging protocols, including DTI, DSI, and functional MRI, are presently being used in monocenter and first multicenter studies. In the future these techniques will be relevant for the risk stratification in phase IIa type studies (small proof-of-concept trials). By contrast, the application of the broader established structural imaging biomarkers, such as hippocampus volume, for risk stratification and as surrogate end point is already today part of many clinical trial protocols. However, clinical care will also be affected by these new technologies. Radiologic expert centers already offer “dementia screening” for well-off middle-aged people who undergo an MRI scan with subsequent automated, typically VBM-based analysis, and determination of z-score deviation from a matched control cohort. Next-generation scanner software will likely include radiologic expert systems for automated segmentation, deformation-based morphometry, and multivariate analysis of anatomic MRI scans for the detection of a typical AD pattern. As these developments will start to change medical practice, first for selected subject groups that can afford this type of screening but later eventually also for other cohorts, clinicians must become aware of the potentials and limitations of these technologies. It is decidedly unclear to date how a middle-aged cognitively intact subject with a seemingly AD-positive MRI scan should be clinically advised. There is no evidence for individual risk prediction and even less for specific treatments. Thus, the development of preclinical diagnostic imaging poses not only technical but also ethical problems that must be critically discussed on the basis of profound knowledge. From a neurobiological point of view, the main determinants of cognitive impairment in AD are the density of synapses and neurons in distributed cortical and subcortical networks. MRI-based measures of regional gray matter volume and associated multivariate analysis techniques of regional interactions of gray matter densities provide insight into the onset and temporal dynamics of cortical atrophy as a close proxy for regional neuronal loss and a basis of functional impairment in specific neuronal networks. From the clinical point of view, clinicians must bear in mind that patients do not suffer from hippocampus atrophy or disconnection but from memory impairment, and that dementia screening in asymptomatic subjects should not be used outside of clinical studies.
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Malone IB, Cash D, Ridgway GR, MacManus DG, Ourselin S, Fox NC, Schott JM. MIRIAD--Public release of a multiple time point Alzheimer's MR imaging dataset. Neuroimage 2013; 70:33-6. [PMID: 23274184 PMCID: PMC3809512 DOI: 10.1016/j.neuroimage.2012.12.044] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2012] [Revised: 12/14/2012] [Accepted: 12/18/2012] [Indexed: 11/18/2022] Open
Abstract
The Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) dataset is a series of longitudinal volumetric T1 MRI scans of 46 mild-moderate Alzheimer's subjects and 23 controls. It consists of 708 scans conducted by the same radiographer with the same scanner and sequences at intervals of 2, 6, 14, 26, 38 and 52 weeks, 18 and 24 months from baseline, with accompanying information on gender, age and Mini Mental State Examination (MMSE) scores. Details of the cohort and imaging results have been described in peer-reviewed publications, and the data are here made publicly available as a common resource for researchers to develop, validate and compare techniques, particularly for measurement of longitudinal volume change in serially acquired MR.
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Affiliation(s)
- Ian B. Malone
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
| | - David Cash
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
- Centre for Medical Image Computing, UCL, Gower Street, London, WC1E 6BT, UK
| | - Gerard R. Ridgway
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - David G. MacManus
- NMR Research Unit, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
| | - Sebastien Ourselin
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
- Centre for Medical Image Computing, UCL, Gower Street, London, WC1E 6BT, UK
| | - Nick C. Fox
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
| | - Jonathan M. Schott
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
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