The Experts below are selected from a list of 7794 Experts worldwide ranked by ideXlab platform
Bruce Fischl - One of the best experts on this subject based on the ideXlab platform.
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test retest reliability of FreeSurfer automated hippocampal subfield segmentation within and across scanners
NeuroImage, 2020Co-Authors: Emma M Brown, Bruce Fischl, Juan Eugenio Iglesias, Meghan E Pierce, Dustin C Clark, William P Milberg, Regina E Mcglinchey, David H SalatAbstract:Abstract The human hippocampus is vulnerable to a range of degenerative conditions and as such, accurate in vivo measurement of the hippocampus and hippocampal substructures via neuroimaging is of great interest for understanding mechanisms of disease as well as for use as a biomarker in clinical trials of novel therapeutics. Although total hippocampal volume can be measured relatively reliably, it is critical to understand how this reliability is affected by acquisition on different scanners, as multiple scanning platforms would likely be utilized in large-scale clinical trials. This is particularly true for hippocampal subregional measurements, which have only relatively recently been measurable through common image processing platforms such as FreeSurfer. Accurate segmentation of these subregions is challenging due to their small size, magnetic resonance imaging (MRI) signal loss in medial temporal regions of the brain, and lack of contrast for delineation from standard neuroimaging procedures. Here, we assess the test-retest reliability of the FreeSurfer automated hippocampal subfield segmentation procedure using two Siemens model scanners (a Siemens Trio and Prismafit Trio upgrade). T1-weighted images were acquired for 11 generally healthy younger participants (two scans on the Trio and one scan on the Prismafit). Each scan was processed through the standard cross-sectional stream and the recently released longitudinal pipeline in FreeSurfer v6.0 for hippocampal segmentation. Test-retest reliability of the volumetric measures was examined for individual subfields as well as percent volume difference and Dice overlap among scans and intra-class correlation coefficients (ICC). Reliability was high in the molecular layer, dentate gyrus, and whole hippocampus with the inclusion of three time points with mean volume differences among scans less than 3%, overlap greater than 80%, and ICC >0.95. The parasubiculum and hippocampal fissure showed the least improvement in reliability with mean volume difference greater than 5%, overlap less than 70%, and ICC scores ranging from 0.78 to 0.89. Other subregions, including the CA regions, were stable in their mean volume difference and overlap ( 75% respectively) and showed improvement in reliability with the inclusion of three scans (ICC > 0.9). Reliability was generally higher within scanner (Trio-Trio), however, Trio-Prismafit reliability was also high and did not exhibit an obvious bias. These results suggest that the FreeSurfer automated segmentation procedure is a reliable method to measure total as well as hippocampal subregional volumes and may be useful in clinical applications including as an endpoint for future clinical trials of conditions affecting the hippocampus.
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accurate nonlinear mapping between mni volumetric and FreeSurfer surface coordinate systems
Human Brain Mapping, 2018Co-Authors: Gia H Ngo, Douglas N Greve, Bruce Fischl, Simon B Eickhoff, B Thomas T YeoAbstract:The results of most neuroimaging studies are reported in volumetric (e.g., MNI152) or surface (e.g., fsaverage) coordinate systems. Accurate mappings between volumetric and surface coordinate systems can facilitate many applications, such as projecting fMRI group analyses from MNI152/Colin27 to fsaverage for visualization or projecting resting-state fMRI parcellations from fsaverage to MNI152/Colin27 for volumetric analysis of new data. However, there has been surprisingly little research on this topic. Here, we evaluated three approaches for mapping data between MNI152/Colin27 and fsaverage coordinate systems by simulating the above applications: projection of group-average data from MNI152/Colin27 to fsaverage and projection of fsaverage parcellations to MNI152/Colin27. Two of the approaches are currently widely used. A third approach (registration fusion) was previously proposed, but not widely adopted. Two implementations of the registration fusion (RF) approach were considered, with one implementation utilizing the Advanced Normalization Tools (ANTs). We found that RF-ANTs performed the best for mapping between fsaverage and MNI152/Colin27, even for new subjects registered to MNI152/Colin27 using a different software tool (FSL FNIRT). This suggests that RF-ANTs would be useful even for researchers not using ANTs. Finally, it is worth emphasizing that the most optimal approach for mapping data to a coordinate system (e.g., fsaverage) is to register individual subjects directly to the coordinate system, rather than via another coordinate system. Only in scenarios where the optimal approach is not possible (e.g., mapping previously published results from MNI152 to fsaverage), should the approaches evaluated in this manuscript be considered. In these scenarios, we recommend RF-ANTs (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/registration/Wu2017_RegistrationFusion).
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a FreeSurfer compliant consistent manual segmentation of infant brains spanning the 0 2 year age range
Frontiers in Human Neuroscience, 2015Co-Authors: Katyucia De Macedo Rodrigues, Bruce Fischl, Emma Benavi, Danielle D Sliva, Myongsun Choe, Marie Drottar, Ruopeng Wang, Patricia Ellen Grant, Lilla ZolleiAbstract:We present a detailed description of a set of FreeSurfer compatible segmentation guidelines tailored to infant MRI scans, and a unique data set of manually segmented acquisitions, with subjects nearly evenly distributed between 0 and 2 years of age. We believe that these segmentation guidelines and this dataset will have a wide range of potential uses in medicine and neuroscience.
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A Generative Model for Image Segmentation Based on Label Fusion
IEEE Transactions on Medical Imaging, 2010Co-Authors: Mert R. Sabuncu, Bruce Fischl, Koen Van Leemput, Polina GollandAbstract:We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final segmentation of the test subject. Such label fusion methods have been shown to yield accurate segmentation, since the use of multiple registrations captures greater inter-subject anatomical variability and improves robustness against occasional registration failures. To the best of our knowledge, this manuscript presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach. The proposed framework allows us to compare different label fusion algorithms theoretically and practically. In particular, recent label fusion or multiatlas segmentation algorithms are interpreted as special cases of our framework. We conduct two sets of experiments to validate the proposed methods. In the first set of experiments, we use 39 brain MRI scans - with manually segmented white matter, cerebral cortex, ventricles and subcortical structures - to compare different label fusion algorithms and the widely-used FreeSurfer whole-brain segmentation tool. Our results indicate that the proposed framework yields more accurate segmentation than FreeSurfer and previous label fusion algorithms. In a second experiment, we use brain MRI scans of 282 subjects to demonstrate that the proposed segmentation tool is sufficiently sensitive to robustly detect hippocampal volume changes in a study of aging and Alzheimer's Disease.
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human cerebral cortex a system for the integration of volume and surface based representations
NeuroImage, 2006Co-Authors: Nikos Makris, Bruce Fischl, Larry J Seidman, Jonathan Kaiser, Christian Haselgrove, Joseph Biederman, Denise Boriel, Eve M Valera, George Papadimitriou, Verne S CavinessAbstract:We describe an MRI-based system for topological analysis followed by measurements of topographic features for the human cerebral cortex that takes as its starting point volumetric segmentation data. This permits interoperation between volume-based and surface-based topographic analysis and extends the functionality of many existing segmentation schemes. We demonstrate the utility of these operations in individual as well as to group analysis. The methodology integrates analyses of cortical segmentation data generated by manual and semi-automated volumetric morphometry routines (such as the program cardviews) with the procedures of the FreeSurfer program to generate a cortical ribbon of the cerebrum and perform cortical topographic measurements (including thickness, surface area and curvature) in individual subjects as well as in subject populations. This system allows the computation of topographical cortical measurements for segmentation data generated from manual and semi-automated volumetric sources other than FreeSurfer. These measurements can be regionally specific and integrated with systems of cortical parcellation that subdivides the neocortex into gyral-based parcellation units (PUs). This system of topographical analysis of the cerebral cortex is consistent with current views of cortical development and neural systems organization of the human and non-human primate brain.
Hamid Soltanianzadeh - One of the best experts on this subject based on the ideXlab platform.
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hippocampal volumetry for lateralization of temporal lobe epilepsy automated versus manual methods
NeuroImage, 2011Co-Authors: Alireza Akhondiasl, Kourosh Jafarikhouzani, Kost Elisevich, Hamid SoltanianzadehAbstract:The hippocampus has been the primary region of interest in the preoperative imaging investigations of mesial temporal lobe epilepsy (mTLE). Hippocampal imaging and electroencephalographic features may be sufficient in several cases to declare the epileptogenic focus. In particular, hippocampal atrophy, as appreciated on T1-weighted (T1W) magnetic resonance (MR) images, may suggest a mesial temporal sclerosis. Qualitative visual assessment of hippocampal volume, however, is influenced by head position in the magnet and the amount of atrophy in different parts of the hippocampus. An entropy-based segmentation algorithm for subcortical brain structures (LocalInfo) was developed and supplemented by both a new multiple atlas strategy and a free-form deformation step to capture structural variability. Manually segmented T1-weighted magnetic resonance (MR) images of 10 non-epileptic subjects were used as atlases for the proposed automatic segmentation protocol which was applied to a cohort of 46 mTLE patients. The segmentation and lateralization accuracies of the proposed technique were compared with those of two other available programs, HAMMER and FreeSurfer, in addition to the manual method. The Dice coefficient for the proposed method was 11% (p < 10−5) and 14% (p < 10−4) higher in comparison with the HAMMER and FreeSurfer, respectively. Mean and Hausdorff distances in the proposed method were also 14% (p < 0.2) and 26% (p < 10−3) lower in comparison with HAMMER and 8% (p < 0.8) and 48% (p < 10−5) lower in comparison with FreeSurfer, respectively. LocalInfo proved to have higher concordance (87%) with the manual segmentation method than either HAMMER (85%) or FreeSurfer (83%). The accuracy of lateralization by volumetry in this study with LocalInfo was 74% compared to 78% with the manual segmentation method. LocalInfo yields a closer approximation to that of manual segmentation and may therefore prove to be more reliable than currently published automatic segmentation algorithms.
Nick C Fox - One of the best experts on this subject based on the ideXlab platform.
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accurate automatic estimation of total intracranial volume a nuisance variable with less nuisance
NeuroImage, 2015Co-Authors: Ian B Malone, Kelvin K Leung, Nick C Fox, Josephine Barnes, Shona Clegg, Jennifer L Whitwell, John Ashburner, Gerard R RidgwayAbstract:Total intracranial volume (TIV/ICV) is an important covariate for volumetric analyses of the brain and brain regions, especially in the study of neurodegenerative diseases, where it can provide a proxy of maximum pre-morbid brain volume. The gold-standard method is manual delineation of brain scans, but this requires careful work by trained operators. We evaluated Statistical Parametric Mapping 12 (SPM12) automated segmentation for TIV measurement in place of manual segmentation and also compared it with SPM8 and FreeSurfer 5.3.0. For T1-weighted MRI acquired from 288 participants in a multi-centre clinical trial in Alzheimer's disease we find a high correlation between SPM12 TIV and manual TIV (R2 = 0.940, 95% Confidence Interval (0.924, 0.953)), with a small mean difference (SPM12 40.4 ± 35.4 ml lower than manual, amounting to 2.8% of the overall mean TIV in the study). The correlation with manual measurements (the key aspect when using TIV as a covariate) for SPM12 was significantly higher (p < 0.001) than for either SPM8 (R2 = 0.577 CI (0.500, 0.644)) or FreeSurfer (R2 = 0.801 CI (0.744, 0.843)). These results suggest that SPM12 TIV estimates are an acceptable substitute for labour-intensive manual estimates even in the challenging context of multiple centres and the presence of neurodegenerative pathology. We also briefly discuss some aspects of the statistical modelling approaches to adjust for TIV.
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a comparison of voxel and surface based cortical thickness estimation methods
NeuroImage, 2011Co-Authors: Matthew J Clarkson, Manuel Jorge Cardoso, Gerard R Ridgway, Marc Modat, Kelvin K Leung, Jonathan D Rohrer, Nick C FoxAbstract:Cortical thickness estimation performed in-vivo via magnetic resonance imaging is an important technique for the diagnosis and understanding of the progression of neurodegenerative diseases. Currently, two different computational paradigms exist, with methods generally classified as either surface or voxel-based. This paper provides a much needed comparison of the surface-based method FreeSurfer and two voxel-based methods using clinical data. We test the effects of computing regional statistics using two different atlases and demonstrate that this makes a significant difference to the cortical thickness results. We assess reproducibility, and show that FreeSurfer has a regional standard deviation of thickness difference on same day scans that is significantly lower than either a Laplacian or Registration based method and discuss the trade off between reproducibility and segmentation accuracy caused by bending energy constraints. We demonstrate that voxel-based methods can detect similar patterns of group-wise differences as well as FreeSurfer in typical applications such as producing group-wise maps of statistically significant thickness change, but that regional statistics can vary between methods. We use a Support Vector Machine to classify patients against controls and did not find statistically significantly different results with voxel based methods compared to FreeSurfer. Finally we assessed longitudinal performance and concluded that currently FreeSurfer provides the most plausible measure of change over time, with further work required for voxel based methods.
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atrophy patterns in alzheimer s disease and semantic dementia a comparison of FreeSurfer and manual volumetric measurements
NeuroImage, 2010Co-Authors: Manja Lehmann, Marc Modat, Abdel Douiri, Lois G Kim, Dennis Chan, Sebastien Ourselin, Josephine Barnes, Nick C FoxAbstract:Alzheimer's disease (AD) and semantic dementia (SD) are characterized by different patterns of global and temporal lobe atrophy which can be studied using magnetic resonance imaging (MRI). Manual delineation of regions of interest is time-consuming FreeSurfer is a freely available automated technique which has a facility it) label cortical and subcortical brain regions automatically. As with all automated techniques comparison with existing methods is important. Eight temporal lobe structures in each hemisphere were delineated using FreeSurfer and compared with manual segmentations in 10 control, 10 AD, and 10 SD Subjects. The reproducibility errors for the manual segmentations ranged from 3% to 6% Differences in protocols between the two methods led to differences in absolute volumes with the greatest differences between methods found bilaterally in the hippocampus, entorhinal cortex and fusiform gyrus (p 0.9), followed by the bilateral amygdala and hippocampus. left superior temporal gyros, right medial-inferior temporal gyrus and left temporal lobe (r>0.8) Overlap ratios differed between methods bilaterally in the amygdala, superior temporal gyrus. temporal lobe, left fusiform gyrus and right parahippocampal gyrus (p:001) Despite differences in protocol and volumes. both methods showed similar atrophy patterns in the patient groups compared with controls, and similar right-left differences, suggesting that both methods accurately distinguish between the three groups.
Juan Eugenio Iglesias - One of the best experts on this subject based on the ideXlab platform.
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FreeSurfer based segmentation of hippocampal subfields a review of methods and applications with a novel quality control procedure for enigma studies and other collaborative efforts
Human Brain Mapping, 2020Co-Authors: Philipp G Samann, Juan Eugenio Iglesias, Boris A Gutman, Dominik Grotegerd, Ramona Leenings, Claas Flint, Udo Dannlowski, Emily K ClarkerubrightAbstract:Structural hippocampal abnormalities are common in many neurological and psychiatric disorders, and variation in hippocampal measures is related to cognitive performance and other complex phenotypes such as stress sensitivity. Hippocampal subregions are increasingly studied, as automated algorithms have become available for mapping and volume quantification. In the context of the Enhancing Neuro Imaging Genetics through Meta Analysis Consortium, several Disease Working Groups are using the FreeSurfer software to analyze hippocampal subregion (subfield) volumes in patients with neurological and psychiatric conditions along with data from matched controls. In this overview, we explain the algorithm's principles, summarize measurement reliability studies, and demonstrate two additional aspects (subfield autocorrelation and volume/reliability correlation) with illustrative data. We then explain the rationale for a standardized hippocampal subfield segmentation quality control (QC) procedure for improved pipeline harmonization. To guide researchers to make optimal use of the algorithm, we discuss how global size and age effects can be modeled, how QC steps can be incorporated and how subfields may be aggregated into composite volumes. This discussion is based on a synopsis of 162 published neuroimaging studies (01/2013-12/2019) that applied the FreeSurfer hippocampal subfield segmentation in a broad range of domains including cognition and healthy aging, brain development and neurodegeneration, affective disorders, psychosis, stress regulation, neurotoxicity, epilepsy, inflammatory disease, childhood adversity and posttraumatic stress disorder, and candidate and whole genome (epi-)genetics. Finally, we highlight points where FreeSurfer-based hippocampal subfield studies may be optimized.
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test retest reliability of FreeSurfer automated hippocampal subfield segmentation within and across scanners
NeuroImage, 2020Co-Authors: Emma M Brown, Bruce Fischl, Juan Eugenio Iglesias, Meghan E Pierce, Dustin C Clark, William P Milberg, Regina E Mcglinchey, David H SalatAbstract:Abstract The human hippocampus is vulnerable to a range of degenerative conditions and as such, accurate in vivo measurement of the hippocampus and hippocampal substructures via neuroimaging is of great interest for understanding mechanisms of disease as well as for use as a biomarker in clinical trials of novel therapeutics. Although total hippocampal volume can be measured relatively reliably, it is critical to understand how this reliability is affected by acquisition on different scanners, as multiple scanning platforms would likely be utilized in large-scale clinical trials. This is particularly true for hippocampal subregional measurements, which have only relatively recently been measurable through common image processing platforms such as FreeSurfer. Accurate segmentation of these subregions is challenging due to their small size, magnetic resonance imaging (MRI) signal loss in medial temporal regions of the brain, and lack of contrast for delineation from standard neuroimaging procedures. Here, we assess the test-retest reliability of the FreeSurfer automated hippocampal subfield segmentation procedure using two Siemens model scanners (a Siemens Trio and Prismafit Trio upgrade). T1-weighted images were acquired for 11 generally healthy younger participants (two scans on the Trio and one scan on the Prismafit). Each scan was processed through the standard cross-sectional stream and the recently released longitudinal pipeline in FreeSurfer v6.0 for hippocampal segmentation. Test-retest reliability of the volumetric measures was examined for individual subfields as well as percent volume difference and Dice overlap among scans and intra-class correlation coefficients (ICC). Reliability was high in the molecular layer, dentate gyrus, and whole hippocampus with the inclusion of three time points with mean volume differences among scans less than 3%, overlap greater than 80%, and ICC >0.95. The parasubiculum and hippocampal fissure showed the least improvement in reliability with mean volume difference greater than 5%, overlap less than 70%, and ICC scores ranging from 0.78 to 0.89. Other subregions, including the CA regions, were stable in their mean volume difference and overlap ( 75% respectively) and showed improvement in reliability with the inclusion of three scans (ICC > 0.9). Reliability was generally higher within scanner (Trio-Trio), however, Trio-Prismafit reliability was also high and did not exhibit an obvious bias. These results suggest that the FreeSurfer automated segmentation procedure is a reliable method to measure total as well as hippocampal subregional volumes and may be useful in clinical applications including as an endpoint for future clinical trials of conditions affecting the hippocampus.
Bennett A Landman - One of the best experts on this subject based on the ideXlab platform.
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establishing surface correspondence for post surgical cortical thickness changes in temporal lobe epilepsy
Medical Imaging 2021: Image Processing, 2021Co-Authors: Yue Liu, Bennett A Landman, Dario J Englot, Victoria L Morgan, Warren D Taylor, Ying Wei, Ipek Oguz, Ilwoo LyuAbstract:In pre- and post-surgical surface shape analysis, establishing shape correspondence is necessary to investigate the postoperative surface changes. However, structural absence after the operation accompanies focal non-rigid changes, which leads to challenges in existing surface registration methods. In this paper, we present a fully automatic particle-based method to establish surface correspondence that can handle partial structural abnormality in the temporal lobe resection. Our method optimizes the coordinates of points which are modeled as particles on surfaces in a hierarchical way to reduce a chance of being trapped in a local minimum during the optimization. In the experiments, we evaluate the effectiveness of our method in comparison with conventional spherical registration (FreeSurfer) on two scenarios: cortical thickness changes in healthy controls within a short scan-rescan time window and patients with temporal lobe resection. The postsurgical scan is acquired at least 1 year after the presurgical scan. In region of interest-wise (ROI-wise) analysis, no changes on cortical thickness are found in both methods on the healthy control group. In patients, since there is no ground truth available, we instead investigated the disagreement between our method and FreeSurfer. We see poorly matched ROIs and large cortical thickness changes using FreeSurfer. On the contrary, our method shows well-matched ROIs and subtle cortical thickness changes. This suggests that the proposed method can establish a stable shape correspondence, which is not fully captured in a conventional spherical registration.
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consistent cortical reconstruction and multi atlas brain segmentation
NeuroImage, 2016Co-Authors: Yuankai Huo, Andrew J Plassard, Aaron Carass, Susan M Resnick, Dzung L Pham, Jerry L Prince, Bennett A LandmanAbstract:Whole brain segmentation and cortical surface reconstruction are two essential techniques for investigating the human brain. Spatial inconsistences, which can hinder further integrated analyses of brain structure, can result due to these two tasks typically being conducted independently of each other. FreeSurfer obtains self-consistent whole brain segmentations and cortical surfaces. It starts with subcortical segmentation, then carries out cortical surface reconstruction, and ends with cortical segmentation and labeling. However, this "segmentation to surface to parcellation" strategy has shown limitations in various cohorts such as older populations with large ventricles. In this work, we propose a novel "multi-atlas segmentation to surface" method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. A modification called MaCRUISE(+) is designed to perform well when white matter lesions are present. Comparing to the benchmarks CRUISE and FreeSurfer, the surface accuracy of MaCRUISE and MaCRUISE(+) is validated using two independent datasets with expertly placed cortical landmarks. A third independent dataset with expertly delineated volumetric labels is employed to compare segmentation performance. Finally, 200MR volumetric images from an older adult sample are used to assess the robustness of MaCRUISE and FreeSurfer. The advantages of MaCRUISE are: (1) MaCRUISE constructs self-consistent voxelwise segmentations and cortical surfaces, while MaCRUISE(+) is robust to white matter pathology. (2) MaCRUISE achieves more accurate whole brain segmentations than independently conducting the multi-atlas segmentation. (3) MaCRUISE is comparable in accuracy to FreeSurfer (when FreeSurfer does not exhibit global failures) while achieving greater robustness across an older adult population. MaCRUISE has been made freely available in open source.
