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1,848 results on '"Collins, D. Louis"'

1. An Evaluation of Low-Cost Hardware on 3D Ultrasound Reconstruction Accuracy

Author

Léger, Étienne, Najafi, Niki, Gueziri, Houssem-Eddine, Collins, D. Louis, Kersten-Oertel, Marta, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gomez, Alberto, editor, Khanal, Bishesh, editor, King, Andrew, editor, and Namburete, Ana, editor

Published
2025
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2. Hessian-based Similarity Metric for Multimodal Medical Image Registration

Author

Eskandari, Mohammadreza, Gueziri, Houssem-Eddine, and Collins, D. Louis

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

One of the fundamental elements of both traditional and certain deep learning medical image registration algorithms is measuring the similarity/dissimilarity between two images. In this work, we propose an analytical solution for measuring similarity between two different medical image modalities based on the Hessian of their intensities. First, assuming a functional dependence between the intensities of two perfectly corresponding patches, we investigate how their Hessians relate to each other. Secondly, we suggest a closed-form expression to quantify the deviation from this relationship, given arbitrary pairs of image patches. We propose a geometrical interpretation of the new similarity metric and an efficient implementation for registration. We demonstrate the robustness of the metric to intensity nonuniformities using synthetic bias fields. By integrating the new metric in an affine registration framework, we evaluate its performance for MRI and ultrasound registration in the context of image-guided neurosurgery using target registration error and computation time.

Published
2023

3. Network structure and transcriptomic vulnerability shape atrophy in frontotemporal dementia

Author

Shafiei, Golia, Bazinet, Vincent, Dadar, Mahsa, Manera, Ana L, Collins, D Louis, Dagher, Alain, Borroni, Barbara, Sanchez-Valle, Raquel, Moreno, Fermin, Laforce, Robert, Graff, Caroline, Synofzik, Matthis, Galimberti, Daniela, Rowe, James B, Masellis, Mario, Tartaglia, Maria Carmela, Finger, Elizabeth, Vandenberghe, Rik, de Mendonça, Alexandre, Tagliavini, Fabrizio, Santana, Isabel, Butler, Chris, Gerhard, Alex, Danek, Adrian, Levin, Johannes, Otto, Markus, Sorbi, Sandro, Jiskoot, Lize C, Seelaar, Harro, van Swieten, John C, Rohrer, Jonathan D, Misic, Bratislav, Ducharme, Simon, Initiative, Frontotemporal Lobar Degeneration Neuroimaging, Rosen, Howard, Dickerson, Bradford C, Domoto-Reilly, Kimoko, Knopman, David, Boeve, Bradley F, Boxer, Adam L, Kornak, John, Miller, Bruce L, Seeley, William W, Gorno-Tempini, Maria-Luisa, McGinnis, Scott, Mandelli, Maria Luisa, Initiative, GENetic Frontotemporal dementia, Esteve, Aitana Sogorb, Nelson, Annabel, Bouzigues, Arabella, Heller, Carolin, Greaves, Caroline V, Cash, David, Thomas, David L, Todd, Emily, Benotmane, Hanya, Zetterberg, Henrik, Swift, Imogen J, Nicholas, Jennifer, Samra, Kiran, Russell, Lucy L, Bocchetta, Martina, Shafei, Rachelle, Convery, Rhian S, Timberlake, Carolyn, Cope, Thomas, Rittman, Timothy, Benussi, Alberto, Premi, Enrico, Gasparotti, Roberto, Archetti, Silvana, Gazzina, Stefano, Cantoni, Valentina, Arighi, Andrea, Fenoglio, Chiara, Scarpini, Elio, Fumagalli, Giorgio, Borracci, Vittoria, Rossi, Giacomina, Giaccone, Giorgio, Di Fede, Giuseppe, Caroppo, Paola, Tiraboschi, Pietro, Prioni, Sara, Redaelli, Veronica, Tang-Wai, David, Rogaeva, Ekaterina, Castelo-Branco, Miguel, Freedman, Morris, Keren, Ron, Black, Sandra, Mitchell, Sara, Shoesmith, Christen, Bartha, Robart, Rademakers, Rosa, van der Ende, Emma, Poos, Jackie, Papma, Janne M, Giannini, Lucia, and van Minkelen, Rick

Subjects
Biomedical and Clinical Sciences, Health Sciences, Psychology, Acquired Cognitive Impairment, Dementia, Aging, Neurodegenerative, Frontotemporal Dementia (FTD), Neurosciences, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Brain Disorders, Alzheimer's Disease, Aetiology, 2.1 Biological and endogenous factors, Neurological, Humans, Frontotemporal Dementia, Transcriptome, Brain, Pick Disease of the Brain, Atrophy, Connectome, Magnetic Resonance Imaging, Neuropsychological Tests, Frontotemporal Lobar Degeneration Neuroimaging Initiative, GENetic Frontotemporal dementia Initiative, connectome, disease epicentre, frontotemporal dementia, gene expression, network spreading, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract

Connections among brain regions allow pathological perturbations to spread from a single source region to multiple regions. Patterns of neurodegeneration in multiple diseases, including behavioural variant of frontotemporal dementia (bvFTD), resemble the large-scale functional systems, but how bvFTD-related atrophy patterns relate to structural network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture. Regional atrophy patterns were estimated in both genetic bvFTD (75 patients, 247 controls) and sporadic bvFTD (70 patients, 123 controls). First, we identified distributed atrophy patterns in bvFTD, mainly targeting areas associated with the limbic intrinsic network and insular cytoarchitectonic class. Regional atrophy was significantly correlated with atrophy of structurally- and functionally-connected neighbours, demonstrating that network structure shapes atrophy patterns. The anterior insula was identified as the predominant group epicentre of brain atrophy using data-driven and simulation-based methods, with some secondary regions in frontal ventromedial and antero-medial temporal areas. We found that FTD-related genes, namely C9orf72 and TARDBP, confer local transcriptomic vulnerability to the disease, modulating the propagation of pathology through the connectome. Collectively, our results demonstrate that atrophy patterns in sporadic and genetic bvFTD are jointly shaped by global connectome architecture and local transcriptomic vulnerability, providing an explanation as to how heterogenous pathological entities can lead to the same clinical syndrome.

Published
2023

5. Ventricle features as reliable differentiators between the bvFTD and other dementias

Author

Manera, Ana L., Dadar, Mahsa, Collins, D. Louis, and Ducharme, Simon

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

INTRODUCTION: Lateral ventricles are reliable and sensitive indicators of brain atrophy and disease progression in behavioral variant frontotemporal dementia (bvFTD). We aimed to investigate whether an automated tool using ventricular features could improve diagnostic accuracy in bvFTD across neurodegenerative diseases. METHODS: Using 678 subjects (2750 timepoints), differences in ventricular features were assessed between bvFTD, normal controls and other dementia cohorts. RESULTS: Ventricular antero-posterior ratio (APR) was the only feature that was significantly different and increased faster in bvFTD compared to all other cohorts. We achieved a 10-fold cross-validation accuracy of 80% (77% sensitivity, 82% specificity) in differentiating bvFTD from all other cohorts with ventricular features, and 76% accuracy using only the single APR feature. DISCUSSION: APR could be a useful and easy-to-implement feature to aid bvFTD diagnosis. We have made our ventricle feature estimation and bvFTD diagnostic tool publicly available, allowing application of our model in other studies.

Published
2021

6. Automatic prediction of cognitive and functional decline can significantly decrease the number of subjects required for clinical trials in early Alzheimer's disease

Author

Shafiee, Neda, Dadar, Mahsa, Ducharme, Simon, and Collins, D. Louis

Subjects
Quantitative Biology - Quantitative Methods
Abstract

INTRODUCTION: Heterogeneity in the progression of Alzheimer's disease makes it challenging to predict the rate of cognitive and functional decline for individual patients. Tools for short-term prediction could help enrich clinical trial designs and focus prevention strategies on the most at-risk patients. METHOD: We built a prognostic model using baseline cognitive scores and MRI-based features to determine which subjects with mild cognitive impairment remained stable and which functionally declined (measured by a two-point increase in CDR-SB) over 2 and 3-year follow-up periods, periods typical of the length of clinical trials. RESULTS: Combining both sets of features yields 77% accuracy (81% sensitivity and 75% specificity) to predict cognitive decline at 2 years (74% accuracy at 3 years with 75% sensitivity and 73% specificity). Using this tool to select trial participants yields a 3.8-fold decrease in the required sample size for a 2-year study (2.8-fold decrease for a 3-year study) for a hypothesized 25% treatment effect to reduce cognitive decline. DISCUSSION: This cohort enrichment tool could accelerate treatment development by increasing power in clinical trials.

Published
2021

9. Towards a Computed Collateral Circulation Score in Ischemic Stroke

Author

Kersten-Oertel, Marta, Alamer, Ali, Fonov, Vladimir, Lo, Benjamin W. Y., Tampieri, Donatella, and Collins, D. Louis

Subjects
Physics - Medical Physics
Abstract

Stroke is the second leading cause of disability worldwide. In order to minimize disability, the goal of stroke treatment is to preserve tissue in the area where blood supply is decreased but sufficient to stave off cell death. Thrombectomy has been shown to offer fast and efficient reperfusion with high recanalization rates. However, due to the inherent risks of thrombectomy, indications including evidence of good collateral circulation should be present. Currently, methods for evaluating collateral circulation are limited. In this work, we present an automated technique to compute a collateral circulation score based on differences seen in mean intensities between left and right cerebral hemispheres in 4D angiography images. In this preliminary study, we analyzed scans of 28 subjects: 4 with normal collateral circulation, 7 with good, 10 with intermediate, and 7 with poor collateral circulation, (based on radiologist score) and found a good correlation between the computed score and radiologist score (r2 = 0.71) and good separation between good and intermediate/poor groups. This work shows promise as the first step in building an automated collateral circulation score., Comment: 8 pages, 7 7 figures, MICCAI Computing and Visualization for Intravascular Imaging (CVII) Workshop

Published
2020

10. Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

Author

Kuijf, Hugo J., Biesbroek, J. Matthijs, de Bresser, Jeroen, Heinen, Rutger, Andermatt, Simon, Bento, Mariana, Berseth, Matt, Belyaev, Mikhail, Cardoso, M. Jorge, Casamitjana, Adrià, Collins, D. Louis, Dadar, Mahsa, Georgiou, Achilleas, Ghafoorian, Mohsen, Jin, Dakai, Khademi, April, Knight, Jesse, Li, Hongwei, Lladó, Xavier, Luna, Miguel, Mahmood, Qaiser, McKinley, Richard, Mehrtash, Alireza, Ourselin, Sébastien, Park, Bo-yong, Park, Hyunjin, Park, Sang Hyun, Pezold, Simon, Puybareau, Elodie, Rittner, Leticia, Sudre, Carole H., Valverde, Sergi, Vilaplana, Verónica, Wiest, Roland, Xu, Yongchao, Xu, Ziyue, Zeng, Guodong, Zhang, Jianguo, Zheng, Guoyan, Chen, Christopher, van der Flier, Wiesje, Barkhof, Frederik, Viergever, Max A., and Biessels, Geert Jan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. Automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their method on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge (https://wmh.isi.uu.nl/). Sixty T1+FLAIR images from three MR scanners were released with manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. Segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: (1) Dice similarity coefficient, (2) modified Hausdorff distance (95th percentile), (3) absolute log-transformed volume difference, (4) sensitivity for detecting individual lesions, and (5) F1-score for individual lesions. Additionally, methods were ranked on their inter-scanner robustness. Twenty participants submitted their method for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation., Comment: Accepted for publication in IEEE Transactions on Medical Imaging

Published
2019
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11. An Optimized PatchMatch for Multi-scale and Multi-feature Label Fusion

Author

Giraud, Rémi, Ta, Vinh-Thong, Papadakis, Nicolas, Manjón, José V., Collins, D. Louis, Coupé, Pierrick, and Initiative, Alzheimer's Disease Neuroimaging

Subjects
Computer Science - Computer Vision and Pattern Recognition
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., Comment: Neuroimage 2016

Published
2019

12. Accurate and robust segmentation of neuroanatomy in T1-weighted MRI by combining spatial priors with deep convolutional neural networks

Author

Novosad, Philip, Fonov, Vladimir, and Collins, D. Louis

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Neuroanatomical segmentation in magnetic resonance imaging (MRI) of the brain is a prerequisite for volume, thickness and shape measurements. This work introduces a new highly accurate and versatile method based on 3D convolutional neural networks for the automatic segmentation of neuroanatomy in T1-weighted MRI. In combination with a deep 3D fully convolutional architecture, efficient linear registration-derived spatial priors are used to incorporate additional spatial context into the network. An aggressive data augmentation scheme using random elastic deformations is also used to regularize the networks, allowing for excellent performance even in cases where only limited labelled training data are available. Applied to hippocampus segmentation in an elderly population (mean Dice coefficient = 92.1%) and sub-cortical segmentation in a healthy adult population (mean Dice coefficient = 89.5%), we demonstrate new state-of-the-art accuracies and a high robustness to outliers with the same architecture. Further validation on a multi-structure segmentation task in a scan-rescan dataset demonstrates accuracy (mean Dice coefficient = 86.6%) similar to the scan-rescan reliability of expert manual segmentations (mean Dice coefficient = 86.9%), and improved reliability compared to both expert manual segmentations and automated segmentations using FIRST. Furthermore, our method maintains a highly competitive runtime performance (e.g. requiring only 10 seconds for left/right hippocampal segmentation in 1x1x1 MNI stereotaxic space), orders of magnitude faster than conventional multi-atlas segmentation methods.

Published
2019

22. Longitudinal Trajectories of Brain Volume and Cortical Thickness in Treated and Untreated Primary Human Immunodeficiency Virus Infection

Author

Sanford, Ryan, Ances, Beau M, Meyerhoff, Dieter J, Price, Richard W, Fuchs, Dietmar, Zetterberg, Henrik, Spudich, Serena, and Collins, D Louis

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Immunology, HIV/AIDS, Pediatric, Sexually Transmitted Infections, Behavioral and Social Science, Biomedical Imaging, Neurosciences, Clinical Research, Mental Health, Brain Disorders, Infectious Diseases, 2.1 Biological and endogenous factors, Neurological, Infection, Adult, Anti-Retroviral Agents, Biomarkers, Brain, Cerebral Cortex, Cross-Sectional Studies, Drug Therapy, Combination, Female, HIV Infections, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Neuroimaging, primary HIV infection, brain morphometry, combination antiretroviral therapy, magnetic resonance imaging, HIV-associated neurocognitive disorders, Biological Sciences, Medical and Health Sciences, Microbiology, Clinical sciences
Abstract

BackgroundHuman immunodeficiency virus (HIV) penetrates the brain in early infection. We used neuroimaging to longitudinally examine the impact of HIV and combination antiretroviral therapy (cART) on the brain in treated and untreated HIV-infected participants, starting in primary HIV infection (PHI).MethodsSixty-five participants, enrolled during PHI, underwent longitudinal magnetic resonance imaging, 30 of whom commenced cART during follow-up. Cross-sectional data from 16 patients with chronic HIV infection (CHI) and 19 HIV-uninfected participants were included for comparison. Brain volume and cortical thickness were estimated using tensor-based morphometry and cortical modeling, respectively. Mixed-effects models longitudinally mapped structural brain changes before and after cART. The relationship between brain morphometry estimates and blood and cerebrospinal fluid (CSF) biomarkers were also tested. Region-of-interest analyses were performed to compare brain morphometry estimates between the groups.ResultsPrior to cART, longer duration of untreated infection in PHI correlated with volume loss in the thalamus, caudate, and cerebellum, and with cortical thinning in the frontal and temporal lobes and cingulate cortex. After cART, no further volume loss was observed. However, small increases of cortical thickness in the frontal and temporal lobe correlated with longer cART duration. No correlations were observed with blood or CSF measures. The PHI group did not have different brain morphometric measures compared to the HIV-uninfected group, but had larger volumes in the thalamus, caudate, putamen, and cortical gray matter compared with CHI participants.ConclusionsSubcortical atrophy and cortical thinning occur during untreated infection but may be arrested by cART. These findings emphasize the importance of early cART.

Published
2018

23. Validation of T1w‐based segmentations of white matter hyperintensity volumes in large‐scale datasets of aging

Author

Dadar, Mahsa, Maranzano, Josefina, Ducharme, Simon, Carmichael, Owen T, Decarli, Charles, Collins, D Louis, and Initiative, Alzheimer's Disease Neuroimaging

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Biomedical Imaging, Dementia, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurodegenerative, Brain Disorders, Acquired Cognitive Impairment, Alzheimer's Disease, Neurosciences, Aging, Aged, Alzheimer Disease, Brain, Datasets as Topic, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Organ Size, Pattern Recognition, Automated, White Matter, Azheimer's disease, cerebrovascular disease, cognitive impairment, small vessel disease, T1w hypointensities, white matter integrity, Alzheimer's Disease Neuroimaging Initiative, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

IntroductionFluid-attenuated Inversion Recovery (FLAIR) and dual T2w and proton density (PD) magnetic resonance images (MRIs) are considered to be the optimum sequences for detecting white matter hyperintensities (WMHs) in aging and Alzheimer's disease populations. However, many existing large multisite studies forgo their acquisition in favor of other MRI sequences due to economic and time constraints.MethodsIn this article, we have investigated whether FLAIR and T2w/PD sequences are necessary to detect WMHs in Alzheimer's and aging studies, compared to using only T1w images. Using a previously validated automated tool based on a Random Forests classifier, WMHs were segmented for the baseline visits of subjects from ADC, ADNI1, and ADNI2/GO studies with and without T2w/PD and FLAIR information. The obtained WMH loads (WMHLs) in different lobes were then correlated with manually segmented WMHLs, each other, age, cognitive, and clinical measures to assess the strength of the correlations with and without using T2w/PD and FLAIR information.ResultsThe WMHLs obtained from T1w-Only segmentations correlated with the manual WMHLs (ADNI1: r = .743, p

Published
2018

26. Fast Tractography Streamline Search

Author

St-Onge, Etienne, Garyfallidis, Eleftherios, Collins, D. Louis, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cetin-Karayumak, Suheyla, editor, Christiaens, Daan, editor, Figini, Matteo, editor, Guevara, Pamela, editor, Gyori, Noemi, editor, Nath, Vishwesh, editor, and Pieciak, Tomasz, editor

Published
2021
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27. Evaluation of Low-Cost Hardware Alternatives for 3D Freehand Ultrasound Reconstruction in Image-Guided Neurosurgery

Author

Léger, Étienne, Gueziri, Houssem Eddine, Collins, D. Louis, Popa, Tiberiu, Kersten-Oertel, Marta, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Noble, J. Alison, editor, Aylward, Stephen, editor, Grimwood, Alexander, editor, Min, Zhe, editor, Lee, Su-Lin, editor, and Hu, Yipeng, editor

Published
2021
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29. Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging

Author

Dadar, Mahsa, Maranzano, Josefina, Misquitta, Karen, Anor, Cassandra J, Fonov, Vladimir S, Tartaglia, M Carmela, Carmichael, Owen T, Decarli, Charles, Collins, D Louis, and Initiative, Alzheimer's Disease Neuroimaging

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Brain Disorders, Aging, Biomedical Imaging, Alzheimer's Disease, Neurodegenerative, Dementia, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Acquired Cognitive Impairment, Neurosciences, Neurological, Aged, Aged, 80 and over, Alzheimer Disease, Datasets as Topic, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, White Matter, White matter hyperintensities, Segmentation, Classification, Alzheimer's disease, Alzheimer's Disease Neuroimaging Initiative, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract

IntroductionWhite matter hyperintensities (WMHs) are areas of abnormal signal on magnetic resonance images (MRIs) that characterize various types of histopathological lesions. The load and location of WMHs are important clinical measures that may indicate the presence of small vessel disease in aging and Alzheimer's disease (AD) patients. Manually segmenting WMHs is time consuming and prone to inter-rater and intra-rater variabilities. Automated tools that can accurately and robustly detect these lesions can be used to measure the vascular burden in individuals with AD or the elderly population in general. Many WMH segmentation techniques use a classifier in combination with a set of intensity and location features to segment WMHs, however, the optimal choice of classifier is unknown.MethodsWe compare 10 different linear and nonlinear classification techniques to identify WMHs from MRI data. Each classifier is trained and optimized based on a set of features obtained from co-registered MR images containing spatial location and intensity information. We further assess the performance of the classifiers using different combinations of MRI contrast information. The performances of the different classifiers were compared on three heterogeneous multi-site datasets, including images acquired with different scanners and different scan-parameters. These included data from the ADC study from University of California Davis, the NACC database and the ADNI study. The classifiers (naïve Bayes, logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors, bagging, and boosting) were evaluated using a variety of voxel-wise and volumetric similarity measures such as Dice Kappa similarity index (SI), Intra-Class Correlation (ICC), and sensitivity as well as computational burden and processing times. These investigations enable meaningful comparisons between the performances of different classifiers to determine the most suitable classifiers for segmentation of WMHs. In the spirit of open-source science, we also make available a fully automated tool for segmentation of WMHs with pre-trained classifiers for all these techniques.ResultsRandom Forests yielded the best performance among all classifiers with mean Dice Kappa (SI) of 0.66±0.17 and ICC=0.99 for the ADC dataset (using T1w, T2w, PD, and FLAIR scans), SI=0.72±0.10, ICC=0.93 for the NACC dataset (using T1w and FLAIR scans), SI=0.66±0.23, ICC=0.94 for ADNI1 dataset (using T1w, T2w, and PD scans) and SI=0.72±0.19, ICC=0.96 for ADNI2/GO dataset (using T1w and FLAIR scans). Not using the T2w/PD information did not change the performance of the Random Forest classifier (SI=0.66±0.17, ICC=0.99). However, not using FLAIR information in the ADC dataset significantly decreased the Dice Kappa, but the volumetric correlation did not drastically change (SI=0.47±0.21, ICC=0.95).ConclusionOur investigations showed that with appropriate features, most off-the-shelf classifiers are able to accurately detect WMHs in presence of FLAIR scan information, while Random Forests had the best performance across all datasets. However, we observed that the performances of most linear classifiers and some nonlinear classifiers drastically decline in absence of FLAIR information, with Random Forest still retaining the best performance.

Published
2017

30. Amyloid and Tau Pathology Associations With Personality Traits, Neuropsychiatric Symptoms, and Cognitive Lifestyle in the Preclinical Phases of Sporadic and Autosomal Dominant Alzheimer’s Disease

Author

Allegri, Ricardo, Amtashar, Fatima, Bateman, Randy, Benzinger, Tammie, Berman, Sarah, Bodge, Courtney, Brandon, Susan, Brooks, William (Bill), Buck, Jill, Buckles, Virginia, Chea, Sochenda, Chhatwal, Jasmeer, Chrem, Patricio, Chui, Helena, Cinco, Jake, Clifford, Jack, Cruchaga, Carlos, D‘Mello, Mirelle, Donahue, Tamara, Douglas, Jane, Edigo, Noelia, Erekin-Taner, Nilufer, Fagan, Anne, Farlow, Marty, Farrar, Angela, Feldman, Howard, Flynn, Gigi, Fox, Nick, Franklin, Erin, Fujii, Hisako, Gant, Cortaiga, Gardener, Samantha, Ghetti, Bernardino, Goate, Alison, Goldman, Jill, Gordon, Brian, Graff-Radford, Neill, Gray, Julia, Gurney, Jenny, Hassenstab, Jason, Hirohara, Mie, Holtzman, David, Hornbeck, Russ, DiBari, Siri Houeland, Ikeuchi, Takeshi, Ikonomovic, Snezana, Jerome, Gina, Jucker, Mathias, Karch, Celeste, Kasuga, Kensaku, Kawarabayashi, Takeshi, Klunk, William (Bill), Koeppe, Robert, Kuder-Buletta, Elke, Laske, Christoph, Lee, Jae-Hong, Levin, Johannes, Marcus, Daniel, Martins, Ralph, Mason, Neal Scott, Masters, Colin, Maue-Dreyfus, Denise, McDade, Eric, Montoya, Lucy, Mori, Hiroshi, Morris, John, Nagamatsu, Akem, Neimeyer, Katie, Noble, James, Norton, Joanne, Perrin, Richard, Raichle, Marc, Ringman, John, Roh, Jee Hoon, Salloway, Stephen, Schofield, Peter, Shimada, Hiroyuki, Shiroto, Tomoyo, Shoji, Mikio, Sigurdson, Wendy, Sohrabi, Hamid, Sparks, Paige, Suzuki, Kazushi, Swisher, Laura, Taddei, Kevin, Wang, Jen, Wang, Peter, Weiner, Mike, Wolfsberger, Mary, Xiong, Chengjie, Xu, Xiong, Tam, Angela, Labonté, Anne, Pichet Binette, Alexa, Faubert, Anne-Marie, Mathieu, Axel, Madjar, Cécile, Carrier, Charles Edouard, Dansereau, Christian, Kazazian, Christina, Lepage, Claude, Picard, Cynthia, Maillet, David, Michaud, Diane, Couture, Doris, Dea, Doris, Cuello, Claudio, Barkun, Alan, Evans, Alan, Courcot, Blandine, Tardif, Christine, Debacker, Clément, Jack, Clifford R., Fontaine, David, Knopman, David S., Maultaup, Gerhard, Near, Jamie, Leoutsakos, Jeannie-Marie, Maltais, Jean-Robert, Brandt, Jason, Pruessner, Jens, Morris, John C., Breitner, John C.S., Poirier, Judes, Cheewakriengkrai, Laksanun, Münter, Lisa-Marie, Collins, Louis, Chakravarty, Mallar, Sager, Mark A., Dauar-Tedeschi, Marina, Eisenberg, Mark, Rajah, Natasha, Aisen, Paul, Toussaint, Joanne, Rosa-Neto, Pedro, Bellec, Pierre, Kostopoulos, Penelope, Etienne, Pierre, Tariot, Pierre N., Orban, Pierre, Sperling, Reisa A., Hoge, Rick, Thomas, Ronald G., Gauthier, Serge, Craft, Suzanne, Villeneuve, Sylvia, Montine, Thomas J., Nair, Vasavan, Bohbot, Véronique, Venugopalan, Vinod, Fonov, Vladimir, Ituria-Medina, Yasser, Khachaturian, Zaven S., Teigner, Eduard, Anthal, Elena, Yu, Elsa, Ferdinand, Fabiola, Pogossova, Galina, Mayrand, Ginette, Duclair, Guerda, Gagné, Guylaine, Newbold-Fox, Holly, Leppert, Illana, Vallée, Isabelle, Vogel, Jacob W., Tremblay-Mercier, Jennifer, Frenette, Joanne, Frappier, Josée, Kat, Justin, Miron, Justin, Wan, Karen, Mahar, Laura, Carmo, Leopoldina, Théroux, Louise, Dadar, Mahsa, Dufour, Marianne, Lafaille-Magnan, Marie-Elyse, Appleby, Melissa, Savard, Mélissa, Tuwaig, Miranda, Petkova, Mirela, Rioux, Pierre, Meyer, Pierre-François, El-Khoury, Rana, Gordon, Renee, Giles, Renuka, Das, Samir, Wang, Seqian, Tabrizi, Shirin, Mathotaarachchi, Sulantha, Dubuc, Sylvie, Lee, Tanya, Beaudry, Thomas, Gervais, Valérie, Pagé, Véronique, Gonneaud, Julie, Ayranci, Gülebru, Pascoal, Tharick A., Desautels, René, Benbouhoud, Fatiha, Saint-Fort, Eunice Farah, Verfaillie, Sander C.J., Farzin, Sarah, Salaciak, Alyssa, Tullo, Stephanie, Vachon-Presseau, Etienne, Daoust, Leslie-Ann, Köbe, Theresa, Spreng, Nathan, McSweeney, Melissa, Nilsson, Nathalie, Pishnamazi, Morteza, Bedetti, Christophe, Vachon-Presseau, Étienne, Bateman, Randall, and Collins, D. Louis

Published
2021
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41. Fast Registration of CT with Intra-operative Ultrasound Images for Spine Surgery

Author

Gueziri, Houssem-Eddine, Collins, D. Louis, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Zheng, Guoyan, editor, Belavy, Daniel, editor, Cai, Yunliang, editor, and Li, Shuo, editor

Published
2019
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42. Striatal shape abnormalities as novel neurodevelopmental endophenotypes in schizophrenia: A longitudinal study

Author

Chakravarty, M Mallar, Rapoport, Judith L, Giedd, Jay N, Raznahan, Armin, Shaw, Philip, Collins, D Louis, Lerch, Jason P, and Gogtay, Nitin

Subjects
Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Brain Disorders, Neurosciences, Clinical Research, Pediatric, Schizophrenia, Mental Health, Aetiology, 2.1 Biological and endogenous factors, Mental health, Adolescent, Age of Onset, Algorithms, Corpus Striatum, Endophenotypes, Female, Humans, Image Processing, Computer-Assisted, Longitudinal Studies, Magnetic Resonance Imaging, Male, Organ Size, Pattern Recognition, Automated, Socioeconomic Factors, Young Adult, childhood onset schizophrenia, striatum, striatal morphology, shape analysis, unaffected siblings, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

There are varying, often conflicting, reports with respect to altered striatal volume and morphometry in the major psychoses due to the influences of antipsychotic medications on striatal volume. Thus, disassociating disease effects from those of medication become exceedingly difficult. For the first time, using a longitudinally studied sample of structural magnetic resonance images from patients with childhood onset schizophrenia (COS; neurobiologically contiguous with the adult onset form of schizophrenia), their nonpsychotic siblings (COSSIBs), and novel shape mapping algorithms that are volume independent, we report the familial contribution of striatal morphology in schizophrenia. The results of our volumetric analyses demonstrate age-related increases in overall striatal volumes specific only to COS. However, both COS and COSSIBs showed overlapping shape differences in the striatal head, which normalized in COSSIBs by late adolescence. These results mirror previous studies from our group, demonstrating cortical thickness deficits in COS and COSSIBs as these deficits normalize in COSSIBs in the same age range as our striatal findings. Finally, there is a single region of nonoverlapping outward displacement in the dorsal aspect of the caudate body, potentially indicative of a response to medication. Striatal shape may be considered complimentary to volume as an endophenotype, and, in some cases may provide information that is not detectable using standard volumetric techniques. Our striatal shape findings demonstrate the striking localization of abnormalities in striatal the head. The neuroanatomical localization of these findings suggest the presence of abnormalities in the striatal-prefrontal circuits in schizophrenia and resilience mechanisms in COSSIBs with age dependent normalization.

Published
2015

43. The EADC‐ADNI Harmonized Protocol for manual hippocampal segmentation on magnetic resonance: Evidence of validity

Author

Frisoni, Giovanni B, Jack, Clifford R, Bocchetta, Martina, Bauer, Corinna, Frederiksen, Kristian S, Liu, Yawu, Preboske, Gregory, Swihart, Tim, Blair, Melanie, Cavedo, Enrica, Grothe, Michel J, Lanfredi, Mariangela, Martinez, Oliver, Nishikawa, Masami, Portegies, Marileen, Stoub, Travis, Ward, Chadwich, Apostolova, Liana G, Ganzola, Rossana, Wolf, Dominik, Barkhof, Frederik, Bartzokis, George, DeCarli, Charles, Csernansky, John G, deToledo‐Morrell, Leyla, Geerlings, Mirjam I, Kaye, Jeffrey, Killiany, Ronald J, Lehéricy, Stephane, Matsuda, Hiroshi, O'Brien, John, Silbert, Lisa C, Scheltens, Philip, Soininen, Hilkka, Teipel, Stefan, Waldemar, Gunhild, Fellgiebel, Andreas, Barnes, Josephine, Firbank, Michael, Gerritsen, Lotte, Henneman, Wouter, Malykhin, Nikolai, Pruessner, Jens C, Wang, Lei, Watson, Craig, Wolf, Henrike, deLeon, Mony, Pantel, Johannes, Ferrari, Clarissa, Bosco, Paolo, Pasqualetti, Patrizio, Duchesne, Simon, Duvernoy, Henri, Boccardi, Marina, Initiative, EADC‐ADNI Working Group on The Harmonized Protocol for Manual Hippocampal Volumetry and for the Alzheimer's Disease Neuroimaging, Albert, Marilyn S, Bennet, David, Camicioli, Richard, Collins, D Louis, Dubois, Bruno, Hampel, Harald, denHeijer, Tom, Hock, Christofer, Jagust, William, Launer, Leonore, Maller, Jerome J, Mueller, Susan, Sachdev, Perminder, Simmons, Andy, Thompson, Paul M, Visser, Peter‐Jelle, Wahlund, Lars‐Olof, Weiner, Michael W, and Winblad, Bengt

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Biomedical Imaging, Neurodegenerative, Clinical Research, Brain Disorders, Acquired Cognitive Impairment, Bioengineering, Aged, Alzheimer Disease, Atrophy, Female, Functional Laterality, Hippocampus, Humans, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, Internet, Magnetic Resonance Imaging, Male, Middle Aged, Neuroimaging, Organ Size, Reproducibility of Results, Hippocampal volumetry, Magnetic resonance, Alzheimer's disease, Biomarkers, Diagnostic criteria, Enrichment, Clinical trials, Validation, Harmonized protocol, Standard operating procedures, Manual segmentation, EADC-ADNI Working Group on The Harmonized Protocol for Manual Hippocampal Volumetry and for the Alzheimer's Disease Neuroimaging Initiative, Geriatrics, Clinical sciences, Biological psychology
Abstract

BackgroundAn international Delphi panel has defined a harmonized protocol (HarP) for the manual segmentation of the hippocampus on MR. The aim of this study is to study the concurrent validity of the HarP toward local protocols, and its major sources of variance.MethodsFourteen tracers segmented 10 Alzheimer's Disease Neuroimaging Initiative (ADNI) cases scanned at 1.5 T and 3T following local protocols, qualified for segmentation based on the HarP through a standard web-platform and resegmented following the HarP. The five most accurate tracers followed the HarP to segment 15 ADNI cases acquired at three time points on both 1.5 T and 3T.ResultsThe agreement among tracers was relatively low with the local protocols (absolute left/right ICC 0.44/0.43) and much higher with the HarP (absolute left/right ICC 0.88/0.89). On the larger set of 15 cases, the HarP agreement within (left/right ICC range: 0.94/0.95 to 0.99/0.99) and among tracers (left/right ICC range: 0.89/0.90) was very high. The volume variance due to different tracers was 0.9% of the total, comparing favorably to variance due to scanner manufacturer (1.2), atrophy rates (3.5), hemispheric asymmetry (3.7), field strength (4.4), and significantly smaller than the variance due to atrophy (33.5%, P < .001), and physiological variability (49.2%, P < .001).ConclusionsThe HarP has high measurement stability compared with local segmentation protocols, and good reproducibility within and among human tracers. Hippocampi segmented with the HarP can be used as a reference for the qualification of human tracers and automated segmentation algorithms.

Published
2015

45. Developmental trajectories of neuroanatomical alterations associated with the 16p11.2 Copy Number Variations

Author

Addor, Marie-Claude, Andrieux, Joris, Arveiler, Benoît, Baujatm, Geneviève, Sloan-Bénan, Frédérique, Belfiore, Marco, Bonneau, Dominique, Bouquillon, Sonia, Boute, Odile, Brusco, Alfredo, Busa, Tiffany, Caberg, Jean-Hubert, Campion, Dominique, Colombert, Vanessa, Cordier, Marie-Pierre, David, Albert, Debray, François-Guillaume, Delrue, Marie-Ange, Doco-Fenzy, Martine, Dunkhase-Heinl, Ulrike, Edery, Patrick, Fagerberg, Christina, Faivre, Laurence, Forzano, Francesca, Genevieve, David, Gérard, Marion, Giachino, Daniela, Guichet, Agnès, Guillin, Olivier, Héron, Delphine, Isidor, Bertrand, Jacquette, Aurélia, Jaillard, Sylvie, Journel, Hubert, Keren, Boris, Lacombe, Didier, Lebon, Sébastien, Le Caignec, Cédric, Lemaître, Marie-Pierre, Lespinasse, James, Mathieu-Dramart, Michèle, Mercier, Sandra, Mignot, Cyril, Missirian, Chantal, Petit, Florence, Pilekær Sørensen, Kristina, Pinson, Lucile, Plessis, Ghislaine, Prieur, Fabienne, Rooryck-Thambo, Caroline, Rossi, Massimiliano, Sanlaville, Damien, Schlott Kristiansen, Britta, Schluth-Bolard, Caroline, Till, Marianne, Van Haelst, Mieke, Van Maldergem, Lionel, Cárdenas-de-la-Parra, Alonso, Martin-Brevet, Sandra, Moreau, Clara, Rodriguez-Herreros, Borja, Fonov, Vladimir S., Maillard, Anne M., Zürcher, Nicole R., Hadjikhani, Nouchine, Beckmann, Jacques S., Reymond, Alexandre, Draganski, Bogdan, Jacquemont, Sébastien, and Collins, D. Louis

Published
2019
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50. The FinnBrain Multimodal Neonatal Template and Atlas Collection: T1, T2, and DTI brain templates, and accompanying cortical and subcortical atlases

Author

Tuulari, Jetro J., primary, Rosberg, Aylin, additional, Pulli, Elmo P., additional, Hashempour, Niloofar, additional, Ukharova, Elena, additional, Lidauer, Kristian, additional, Jolly, Ashmeet, additional, Luotonen, Silja, additional, Audah, Hilyatushalihah K., additional, Vartiainen, Elena, additional, Bano, Wajiha, additional, Suuronen, Ilkka, additional, Mariani Wigley, Isabella L.C., additional, Fonov, Vladimir S., additional, Collins, D. Louis, additional, Merisaari, Harri, additional, Karlsson, Linnea, additional, Karlsson, Hasse, additional, and Lewis, John D., additional

Published
2024
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