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Zöllei L, Iglesias JE, Ou Y, Grant PE, Fischl B. Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years. Neuroimage 2020; 218:116946. [PMID: 32442637 PMCID: PMC7415702 DOI: 10.1016/j.neuroimage.2020.116946] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 03/03/2020] [Accepted: 05/12/2020] [Indexed: 01/23/2023] Open
Abstract
The development of automated tools for brain morphometric analysis in infants has lagged significantly behind analogous tools for adults. This gap reflects the greater challenges in this domain due to: 1) a smaller-scaled region of interest, 2) increased motion corruption, 3) regional changes in geometry due to heterochronous growth, and 4) regional variations in contrast properties corresponding to ongoing myelination and other maturation processes. Nevertheless, there is a great need for automated image-processing tools to quantify differences between infant groups and other individuals, because aberrant cortical morphologic measurements (including volume, thickness, surface area, and curvature) have been associated with neuropsychiatric, neurologic, and developmental disorders in children. In this paper we present an automated segmentation and surface extraction pipeline designed to accommodate clinical MRI studies of infant brains in a population 0-2 year-olds. The algorithm relies on a single channel of T1-weighted MR images to achieve automated segmentation of cortical and subcortical brain areas, producing volumes of subcortical structures and surface models of the cerebral cortex. We evaluated the algorithm both qualitatively and quantitatively using manually labeled datasets, relevant comparator software solutions cited in the literature, and expert evaluations. The computational tools and atlases described in this paper will be distributed to the research community as part of the FreeSurfer image analysis package.
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Affiliation(s)
- Lilla Zöllei
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA.
| | - Juan Eugenio Iglesias
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA; Center for Medical Image Computing, University College London, United Kingdom; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
| | - Yangming Ou
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, USA
| | - Bruce Fischl
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
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Ferradal SL, Gagoski B, Jaimes C, Yi F, Carruthers C, Vu C, Litt JS, Larsen R, Sutton B, Grant PE, Zöllei L. System-Specific Patterns of Thalamocortical Connectivity in Early Brain Development as Revealed by Structural and Functional MRI. Cereb Cortex 2020; 29:1218-1229. [PMID: 29425270 DOI: 10.1093/cercor/bhy028] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Indexed: 01/31/2023] Open
Abstract
The normal development of thalamocortical connections plays a critical role in shaping brain connectivity in the prenatal and postnatal periods. Recent studies using advanced magnetic resonance imaging (MRI) techniques in neonates and infants have shown that abnormal thalamocortical connectivity is associated with adverse neurodevelopmental outcomes. However, all these studies have focused on a single neuroimaging modality, overlooking the dynamic relationship between structure and function at this early stage. Here, we study the relationship between structural and functional thalamocortical connectivity patterns derived from healthy full-term infants scanned with diffusion-weighted MRI and resting-state functional MRI within the first weeks of life (mean gestational age = 39.3 ± 1.2 weeks; age at scan = 24.2 ± 7.9 days). Our results show that while there is, in general, good spatial agreement between both MRI modalities, there are regional variations that are system-specific: regions involving primary-sensory cortices exhibit greater structural/functional overlap, whereas higher-order association areas such as temporal and posterior parietal cortices show divergence in spatial patterns of each modality. This variability illustrates the complementarity of both modalities and highlights the importance of multimodal approaches.
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Affiliation(s)
| | - Borjan Gagoski
- Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Camilo Jaimes
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Francesca Yi
- Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Catherine Vu
- Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Ryan Larsen
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Brad Sutton
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - P Ellen Grant
- Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lilla Zöllei
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Turesky T, Xie W, Kumar S, Sliva DD, Gagoski B, Vaughn J, Zöllei L, Haque R, Kakon SH, Islam N, Petri WA, Nelson CA, Gaab N. Relating anthropometric indicators to brain structure in 2-month-old Bangladeshi infants growing up in poverty: A pilot study. Neuroimage 2020; 210:116540. [PMID: 31945509 PMCID: PMC7068701 DOI: 10.1016/j.neuroimage.2020.116540] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 11/06/2019] [Accepted: 01/10/2020] [Indexed: 01/03/2023] Open
Abstract
Anthropometric indicators, including stunting, underweight, and wasting, have previously been associated with poor neurocognitive outcomes. This link may exist because malnutrition and infection, which are known to affect height and weight, also impact brain structure according to animal models. However, a relationship between anthropometric indicators and brain structural measures has not been tested yet, perhaps because stunting, underweight, and wasting are uncommon in higher-resource settings. Further, with diminished anthropometric growth prevalent in low-resource settings, where biological and psychosocial hazards are most severe, one might expect additional links between measures of poverty, anthropometry, and brain structure. To begin to examine these relationships, we conducted an MRI study in 2-3-month-old infants growing up in the extremely impoverished urban setting of Dhaka, Bangladesh. The sample size was relatively small because the challenges of investigating infant brain structure in a low-resource setting needed to be realized and resolved before introducing a larger cohort. Initially, fifty-four infants underwent T1 sequences using 3T MRI, and resulting structural images were segmented into gray and white matter maps, which were carefully evaluated for accurate tissue labeling by a pediatric neuroradiologist. Gray and white matter volumes from 29 infants (79 ± 10 days-of-age; F/M = 12/17), whose segmentations were of relatively high quality, were submitted to semi-partial correlation analyses with stunting, underweight, and wasting, which were measured using height-for-age (HAZ), weight-for-age (WAZ), and weight-for-height (WHZ) scores. Positive semi-partial correlations (after adjusting for chronological age and sex and correcting for multiple comparisons) were observed between white matter volume and HAZ and WAZ; however, WHZ was not correlated with any measure of brain volume. No associations were observed between income-to-needs or maternal education and brain volumetric measures, suggesting that measures of poverty were not associated with total brain tissue volume in this sample. Overall, these results provide the first link between diminished anthropometric growth and white matter volume in infancy. Challenges of conducting a developmental neuroimaging study in a low-resource country are also described.
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Affiliation(s)
- Ted Turesky
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
| | - Wanze Xie
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Swapna Kumar
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, United States
| | - Danielle D Sliva
- Department of Neuroscience, Brown University, Providence, RI, United States
| | - Borjan Gagoski
- Department of Radiology, Harvard Medical School, Boston, MA, United States; Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, United States
| | - Jennifer Vaughn
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Lilla Zöllei
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
| | - Rashidul Haque
- The International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | | | - Nazrul Islam
- National Institute of Neuroscience and Hospital, Dhaka, Bangladesh
| | - William A Petri
- Division of Infectious Diseases and International Health, Department of Medicine, School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Charles A Nelson
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Harvard Graduate School of Education, Cambridge, MA, United States
| | - Nadine Gaab
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
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Zöllei L, Jaimes C, Saliba E, Grant PE, Yendiki A. TRActs constrained by UnderLying INfant anatomy (TRACULInA): An automated probabilistic tractography tool with anatomical priors for use in the newborn brain. Neuroimage 2019; 199:1-17. [PMID: 31132451 PMCID: PMC6688923 DOI: 10.1016/j.neuroimage.2019.05.051] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 05/14/2019] [Accepted: 05/18/2019] [Indexed: 10/26/2022] Open
Abstract
The ongoing myelination of white-matter fiber bundles plays a significant role in brain development. However, reliable and consistent identification of these bundles from infant brain MRIs is often challenging due to inherently low diffusion anisotropy, as well as motion and other artifacts. In this paper we introduce a new tool for automated probabilistic tractography specifically designed for newborn infants. Our tool incorporates prior information about the anatomical neighborhood of white-matter pathways from a training data set. In our experiments, we evaluate this tool on data from both full-term and prematurely born infants and demonstrate that it can reconstruct known white-matter tracts in both groups robustly, even in the presence of differences between the training set and study subjects. Additionally, we evaluate it on a publicly available large data set of healthy term infants (UNC Early Brain Development Program). This paves the way for performing a host of sophisticated analyses in newborns that we have previously implemented for the adult brain, such as pointwise analysis along tracts and longitudinal analysis, in both health and disease.
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Affiliation(s)
- Lilla Zöllei
- Massachusetts General Hospital, Boston, United States.
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Turesky TK, Jensen SK, Yu X, Kumar S, Wang Y, Sliva DD, Gagoski B, Sanfilippo J, Zöllei L, Boyd E, Haque R, Hafiz Kakon S, Islam N, Petri WA, Nelson CA, Gaab N. The relationship between biological and psychosocial risk factors and resting-state functional connectivity in 2-month-old Bangladeshi infants: A feasibility and pilot study. Dev Sci 2019; 22:e12841. [PMID: 31016808 PMCID: PMC6713583 DOI: 10.1111/desc.12841] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 04/16/2019] [Accepted: 04/17/2019] [Indexed: 01/25/2023]
Abstract
Childhood poverty has been associated with structural and functional alterations in the developing brain. However, poverty does not alter brain development directly, but acts through associated biological or psychosocial risk factors (e.g. malnutrition, family conflict). Yet few studies have investigated risk factors in the context of infant neurodevelopment, and none have done so in low-resource settings such as Bangladesh, where children are exposed to multiple, severe biological and psychosocial hazards. In this feasibility and pilot study, usable resting-state fMRI data were acquired in infants from extremely poor (n = 16) and (relatively) more affluent (n = 16) families in Dhaka, Bangladesh. Whole-brain intrinsic functional connectivity (iFC) was estimated using bilateral seeds in the amygdala, where iFC has shown susceptibility to early life stress, and in sensory areas, which have exhibited less susceptibility to early life hazards. Biological and psychosocial risk factors were examined for associations with iFC. Three resting-state networks were identified in within-group brain maps: medial temporal/striatal, visual, and auditory networks. Infants from extremely poor families compared with those from more affluent families exhibited greater (i.e. less negative) iFC in precuneus for amygdala seeds; however, no group differences in iFC were observed for sensory area seeds. Height-for-age, a proxy for malnutrition/infection, was not associated with amygdala/precuneus iFC, whereas prenatal family conflict was positively correlated. Findings suggest that it is feasible to conduct infant fMRI studies in low-resource settings. Challenges and practical steps for successful implementations are discussed.
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Affiliation(s)
- Ted K. Turesky
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of MedicineBoston Children’s HospitalBostonMassachusetts
- Harvard Medical SchoolBostonMassachusetts
| | - Sarah K.G. Jensen
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of MedicineBoston Children’s HospitalBostonMassachusetts
- Harvard Medical SchoolBostonMassachusetts
| | - Xi Yu
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of MedicineBoston Children’s HospitalBostonMassachusetts
- Harvard Medical SchoolBostonMassachusetts
| | - Swapna Kumar
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of MedicineBoston Children’s HospitalBostonMassachusetts
| | - Yingying Wang
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of MedicineBoston Children’s HospitalBostonMassachusetts
- Harvard Medical SchoolBostonMassachusetts
- College of Education and Human SciencesUniversity of Nebraska‐LincolnLincolnNebraska
| | - Danielle D. Sliva
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of MedicineBoston Children’s HospitalBostonMassachusetts
- Department of NeuroscienceBrown UniversityProvidenceRhode Island
| | - Borjan Gagoski
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of MedicineBoston Children’s HospitalBostonMassachusetts
- Harvard Medical SchoolBostonMassachusetts
| | - Joseph Sanfilippo
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of MedicineBoston Children’s HospitalBostonMassachusetts
| | - Lilla Zöllei
- A.A. Martinos Center for Biomedical ImagingMassachusetts General HospitalBostonMassachusetts
| | - Emma Boyd
- A.A. Martinos Center for Biomedical ImagingMassachusetts General HospitalBostonMassachusetts
| | - Rashidul Haque
- The International Centre for Diarrhoeal Disease ResearchDhakaBangladesh
| | | | - Nazrul Islam
- National Institute of Neurosciences & HospitalDhakaBangladesh
| | - William A. Petri
- Division of Infectious Diseases and International Health, Department of Medicine, School of MedicineUniversity of VirginiaCharlottesvilleVirginia
| | - Charles A. Nelson
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of MedicineBoston Children’s HospitalBostonMassachusetts
- Harvard Medical SchoolBostonMassachusetts
- Harvard Graduate School of EducationCambridgeMassachusetts
| | - Nadine Gaab
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of MedicineBoston Children’s HospitalBostonMassachusetts
- Harvard Medical SchoolBostonMassachusetts
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Chen LW, Sun D, Davis SL, Haswell CC, Dennis EL, Swanson CA, Whelan CD, Gutman B, Jahanshad N, Iglesias JE, Thompson P, Wagner HR, Saemann P, LaBar KS, Morey RA. Smaller hippocampal CA1 subfield volume in posttraumatic stress disorder. Depress Anxiety 2018; 35:1018-1029. [PMID: 30256497 PMCID: PMC6261348 DOI: 10.1002/da.22833] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Revised: 05/26/2018] [Accepted: 05/29/2018] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Smaller hippocampal volume in patients with posttraumatic stress disorder (PTSD) represents the most consistently reported structural alteration in the brain. Subfields of the hippocampus play distinct roles in encoding and processing of memories, which are disrupted in PTSD. We examined PTSD-associated alterations in 12 hippocampal subfields in relation to global hippocampal shape, and clinical features. METHODS Case-control cross-sectional studies of U.S. military veterans (n = 282) from the Iraq and Afghanistan era were grouped into PTSD (n = 142) and trauma-exposed controls (n = 140). Participants underwent clinical evaluation for PTSD and associated clinical parameters followed by MRI at 3 T. Segmentation with FreeSurfer v6.0 produced hippocampal subfield volumes for the left and right CA1, CA3, CA4, DG, fimbria, fissure, hippocampus-amygdala transition area, molecular layer, parasubiculum, presubiculum, subiculum, and tail, as well as hippocampal meshes. Covariates included age, gender, trauma exposure, alcohol use, depressive symptoms, antidepressant medication use, total hippocampal volume, and MRI scanner model. RESULTS Significantly lower subfield volumes were associated with PTSD in left CA1 (P = 0.01; d = 0.21; uncorrected), CA3 (P = 0.04; d = 0.08; uncorrected), and right CA3 (P = 0.02; d = 0.07; uncorrected) only if ipsilateral whole hippocampal volume was included as a covariate. A trend level association of L-CA1 with PTSD (F4, 221 = 3.32, P = 0.07) is present and the other subfield findings are nonsignificant if ipsilateral whole hippocampal volume is not included as a covariate. PTSD-associated differences in global hippocampal shape were nonsignificant. CONCLUSIONS The present finding of smaller hippocampal CA1 in PTSD is consistent with model systems in rodents that exhibit increased anxiety-like behavior from repeated exposure to acute stress. Behavioral correlations with hippocampal subfield volume differences in PTSD will elucidate their relevance to PTSD, particularly behaviors of associative fear learning, extinction training, and formation of false memories.
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Affiliation(s)
- Lyon W. Chen
- Mid-Atlantic Mental Illness Research and Clinical Center, Durham VA Medical Center
- Duke-UNC Brain Imaging and Analysis Center, Durham NC
| | - Delin Sun
- Mid-Atlantic Mental Illness Research and Clinical Center, Durham VA Medical Center
- Duke-UNC Brain Imaging and Analysis Center, Durham NC
| | - Sarah L. Davis
- Mid-Atlantic Mental Illness Research and Clinical Center, Durham VA Medical Center
- Duke-UNC Brain Imaging and Analysis Center, Durham NC
| | - Courtney C. Haswell
- Mid-Atlantic Mental Illness Research and Clinical Center, Durham VA Medical Center
- Duke-UNC Brain Imaging and Analysis Center, Durham NC
| | - Emily L. Dennis
- Imaging Genetics Center, Keck School of Medicine of USC, Los Angeles CA
| | - Chelsea A. Swanson
- Mid-Atlantic Mental Illness Research and Clinical Center, Durham VA Medical Center
- Duke-UNC Brain Imaging and Analysis Center, Durham NC
| | | | - Boris Gutman
- Imaging Genetics Center, Keck School of Medicine of USC, Los Angeles CA
| | - Neda Jahanshad
- Imaging Genetics Center, Keck School of Medicine of USC, Los Angeles CA
| | | | - Paul Thompson
- Imaging Genetics Center, Keck School of Medicine of USC, Los Angeles CA
| | | | - H. Ryan Wagner
- Mid-Atlantic Mental Illness Research and Clinical Center, Durham VA Medical Center
| | | | - Kevin S. LaBar
- Mid-Atlantic Mental Illness Research and Clinical Center, Durham VA Medical Center
- Duke-UNC Brain Imaging and Analysis Center, Durham NC
| | - Rajendra A. Morey
- Mid-Atlantic Mental Illness Research and Clinical Center, Durham VA Medical Center
- Duke-UNC Brain Imaging and Analysis Center, Durham NC
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Blaiotta C, Freund P, Cardoso MJ, Ashburner J. Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction. Neuroimage 2017; 166:117-134. [PMID: 29100938 PMCID: PMC5770340 DOI: 10.1016/j.neuroimage.2017.10.060] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Revised: 10/23/2017] [Accepted: 10/26/2017] [Indexed: 11/05/2022] Open
Abstract
In this paper we present a hierarchical generative model of medical image data, which can capture simultaneously the variability of both signal intensity and anatomical shapes across large populations. Such a model has a direct application for learning average-shaped probabilistic tissue templates in a fully automated manner. While in principle the generality of the proposed Bayesian approach makes it suitable to address a wide range of medical image computing problems, our work focuses primarily on neuroimaging applications. In particular we validate the proposed method on both real and synthetic brain MR scans including the cervical cord and demonstrate that it yields accurate alignment of brain and spinal cord structures, as compared to state-of-the-art tools for medical image registration. At the same time we illustrate how the resulting tissue probability maps can readily be used to segment, bias correct and spatially normalise unseen data, which are all crucial pre-processing steps for MR imaging studies. We present a generative modelling framework to process large MRI data sets. The proposed framework can serve to learn average-shaped tissue probability maps and empirical intensity priors. We explore semi-supervised learning and variational inference schemes. The method is validated against state-of-the-art tools using publicly available data.
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Affiliation(s)
- Claudia Blaiotta
- Wellcome Trust Centre for Neuroimaging, University College London, London, UK.
| | - Patrick Freund
- Wellcome Trust Centre for Neuroimaging, University College London, London, UK; Spinal Cord Injury Center Balgrist, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - M Jorge Cardoso
- Translational Imaging Group, CMIC, University College London, London, UK
| | - John Ashburner
- Wellcome Trust Centre for Neuroimaging, University College London, London, UK
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8
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Jaimes C, Cheng HH, Soul J, Ferradal S, Rathi Y, Gagoski B, Newburger JW, Grant PE, Zöllei L. Probabilistic tractography-based thalamic parcellation in healthy newborns and newborns with congenital heart disease. J Magn Reson Imaging 2017; 47:1626-1637. [PMID: 29080379 DOI: 10.1002/jmri.25875] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2017] [Accepted: 10/03/2017] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Given the central role of the thalamus in motor, sensory, and cognitive development, methods to study emerging thalamocortical connectivity in early infancy are of great interest. PURPOSE To determine the feasibility of performing probabilistic tractography-based thalamic parcellation (PTbTP) in typically developing (TD) neonates and to compare the results with a pilot sample of neonates with congenital heart disease (CHD). STUDY TYPE Institutional Review Board (IRB)-approved cross-sectional study. MODEL We prospectively recruited 20 TD neonates and five CHD neonates (imaged preoperatively). FIELD STRENGTH/SEQUENCE MRI was performed at 3.0T including diffusion-weighted imaging (DWI) and 3D magnetization prepared rapid gradient-echo (MPRAGE). ASSESSMENT A radiologist and trained research assistants segmented the thalamus and seven cortical targets for each hemisphere. Using the thalami as seeds and the cortical labels as targets, FSL library tools were used to generate probabilistic tracts. A Hierarchical Dirichlet Process algorithm was then used for clustering analysis. A radiologist qualitatively assessed the results of clustering. Quantitative analyses were also performed. STATISTICAL TESTS We summarized the demographic data and results of clustering with descriptive statistics. Linear regressions covarying for gestational age were used to compare groups. RESULTS In 17 of 20 TD neonates, we identified five connectivity-determined clusters, which correlate with known thalamic nuclei and subnuclei. In four neonates with CHD we observed a spectrum of abnormalities including fewer and disorganized clusters or small supernumerary clusters (up to seven per thalamus). After covarying for differences in corrected gestational age (cGA), the fractional anisotropy (FA), volume, and normalized thalamic volume were significantly lower in CHD neonates (P < 0.01). DATA CONCLUSIONS Using PTbTP clusters, correlating well with the location and connectivity of known thalamic nuclei, were identified in TD neonates. Differences in thalamic clustering outputs were identified in four neonates with CHD, raising concern for disordered thalamic connectivity. PTbTP is feasible in TD and CHD neonates. Preliminary findings suggest the prenatal origins of altered connectivity in CHD. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2018;47:1626-1637.
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Affiliation(s)
- Camilo Jaimes
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Henry H Cheng
- Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Janet Soul
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Silvina Ferradal
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston, Massachusetts, USA.,Department of Medicine, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Yogesh Rathi
- Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Borjan Gagoski
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston, Massachusetts, USA
| | - Jane W Newburger
- Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston, Massachusetts, USA.,Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA.,Department of Medicine, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Lilla Zöllei
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA; all: Harvard Medical School, Boston, Massachusetts, USA
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9
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Puonti O, Iglesias JE, Van Leemput K. Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling. Neuroimage 2016; 143:235-249. [PMID: 27612647 DOI: 10.1016/j.neuroimage.2016.09.011] [Citation(s) in RCA: 107] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Revised: 09/02/2016] [Accepted: 09/05/2016] [Indexed: 12/18/2022] Open
Abstract
Quantitative analysis of magnetic resonance imaging (MRI) scans of the brain requires accurate automated segmentation of anatomical structures. A desirable feature for such segmentation methods is to be robust against changes in acquisition platform and imaging protocol. In this paper we validate the performance of a segmentation algorithm designed to meet these requirements, building upon generative parametric models previously used in tissue classification. The method is tested on four different datasets acquired with different scanners, field strengths and pulse sequences, demonstrating comparable accuracy to state-of-the-art methods on T1-weighted scans while being one to two orders of magnitude faster. The proposed algorithm is also shown to be robust against small training datasets, and readily handles images with different MRI contrast as well as multi-contrast data.
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Affiliation(s)
- Oula Puonti
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby, Denmark.
| | - Juan Eugenio Iglesias
- Basque Center on Cognition, Brain and Language (BCBL), Paseo Mikeletegi, 20009 San Sebastian - Donostia, Gipuzkoa, Spain; Department of Medical Physics and Biomedical Engineering, University College London, Gower St, London WC1E 6BT, United Kingdom
| | - Koen Van Leemput
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby, Denmark; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Charlestown, MA 02129, USA
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10
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Narayanan PL, Warton C, Rosella Boonzaier N, Molteno CD, Joseph J, Jacobson JL, Jacobson SW, Zöllei L, Meintjes EM. Improved segmentation of cerebellar structures in children. J Neurosci Methods 2015; 262:1-13. [PMID: 26743973 DOI: 10.1016/j.jneumeth.2015.12.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2015] [Revised: 12/09/2015] [Accepted: 12/17/2015] [Indexed: 11/27/2022]
Abstract
BACKGROUND Consistent localization of cerebellar cortex in a standard coordinate system is important for functional studies and detection of anatomical alterations in studies of morphometry. To date, no pediatric cerebellar atlas is available. NEW METHOD The probabilistic Cape Town Pediatric Cerebellar Atlas (CAPCA18) was constructed in the age-appropriate National Institute of Health Pediatric Database asymmetric template space using manual tracings of 16 cerebellar compartments in 18 healthy children (9-13 years) from Cape Town, South Africa. The individual atlases of the training subjects were also used to implement multi atlas label fusion using multi atlas majority voting (MAMV) and multi atlas generative model (MAGM) approaches. Segmentation accuracy in 14 test subjects was compared for each method to 'gold standard' manual tracings. RESULTS Spatial overlap between manual tracings and CAPCA18 automated segmentation was 73% or higher for all lobules in both hemispheres, except VIIb and X. Automated segmentation using MAGM yielded the best segmentation accuracy over all lobules (mean Dice Similarity Coefficient 0.76; range 0.55-0.91; mean Hausdorff distance 0.9 mm; range 0.8-2.7 mm). COMPARISON WITH EXISTING METHODS In all lobules, spatial overlap of CAPCA18 segmentations with manual tracings was similar or higher than those obtained with SUIT (spatially unbiased infra-tentorial template), providing additional evidence of the benefits of an age appropriate atlas. MAGM segmentation accuracy was comparable to values reported recently by Park et al. (Neuroimage 2014;95(1):217) in adults (across all lobules mean DSC=0.73, range 0.40-0.89). CONCLUSIONS CAPCA18 and the associated multi-subject atlases of the training subjects yield improved segmentation of cerebellar structures in children.
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Affiliation(s)
- Priya Lakshmi Narayanan
- Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa; MRC/UCT Medical Imaging Research Unit, Division of Biomedical Engineering, University of Cape Town, Cape Town, South Africa.
| | - Christopher Warton
- Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Natalie Rosella Boonzaier
- Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Christopher D Molteno
- Department of Psychiatry and Mental Health, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Jesuchristopher Joseph
- Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa; MRC/UCT Medical Imaging Research Unit, Division of Biomedical Engineering, 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 Mental Health, 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, United States
| | - Sandra W Jacobson
- Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa; Department of Psychiatry and Mental Health, 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, United States
| | - Lilla Zöllei
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
| | - Ernesta M Meintjes
- Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa; MRC/UCT Medical Imaging Research Unit, Division of Biomedical Engineering, University of Cape Town, Cape Town, South Africa
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11
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Iglesias JE, Sabuncu MR. Multi-atlas segmentation of biomedical images: A survey. Med Image Anal 2015; 24:205-219. [PMID: 26201875 PMCID: PMC4532640 DOI: 10.1016/j.media.2015.06.012] [Citation(s) in RCA: 358] [Impact Index Per Article: 39.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2014] [Revised: 06/12/2015] [Accepted: 06/15/2015] [Indexed: 10/23/2022]
Abstract
Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, et al. (2004), Klein, et al. (2005), and Heckemann, et al. (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of "atlases" (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003-2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation.
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Affiliation(s)
| | - Mert R Sabuncu
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA.
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12
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Iglesias JE, Sabuncu MR, Aganj I, Bhatt P, Casillas C, Salat D, Boxer A, Fischl B, Van Leemput K. An algorithm for optimal fusion of atlases with different labeling protocols. Neuroimage 2015; 106:451-63. [PMID: 25463466 PMCID: PMC4286284 DOI: 10.1016/j.neuroimage.2014.11.031] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2014] [Revised: 11/13/2014] [Accepted: 11/14/2014] [Indexed: 10/24/2022] Open
Abstract
In this paper we present a novel label fusion algorithm suited for scenarios in which different manual delineation protocols with potentially disparate structures have been used to annotate the training scans (hereafter referred to as "atlases"). Such scenarios arise when atlases have missing structures, when they have been labeled with different levels of detail, or when they have been taken from different heterogeneous databases. The proposed algorithm can be used to automatically label a novel scan with any of the protocols from the training data. Further, it enables us to generate new labels that are not present in any delineation protocol by defining intersections on the underling labels. We first use probabilistic models of label fusion to generalize three popular label fusion techniques to the multi-protocol setting: majority voting, semi-locally weighted voting and STAPLE. Then, we identify some shortcomings of the generalized methods, namely the inability to produce meaningful posterior probabilities for the different labels (majority voting, semi-locally weighted voting) and to exploit the similarities between the atlases (all three methods). Finally, we propose a novel generative label fusion model that can overcome these drawbacks. We use the proposed method to combine four brain MRI datasets labeled with different protocols (with a total of 102 unique labeled structures) to produce segmentations of 148 brain regions. Using cross-validation, we show that the proposed algorithm outperforms the generalizations of majority voting, semi-locally weighted voting and STAPLE (mean Dice score 83%, vs. 77%, 80% and 79%, respectively). We also evaluated the proposed algorithm in an aging study, successfully reproducing some well-known results in cortical and subcortical structures.
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Affiliation(s)
| | - Mert Rory Sabuncu
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, MA, USA; MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), USA
| | - Iman Aganj
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, MA, USA
| | - Priyanka Bhatt
- Memory and Aging Center, University of California, San Francisco, USA
| | - Christen Casillas
- Memory and Aging Center, University of California, San Francisco, USA
| | - David Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, MA, USA
| | - Adam Boxer
- Memory and Aging Center, University of California, San Francisco, USA
| | - Bruce Fischl
- MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), USA; Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, MA, USA
| | - Koen Van Leemput
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, MA, USA; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark; Department of Information and Computer Science, Aalto University, Finland; Department of Biomedical Engineering and Computational Science, Aalto University, Finland
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13
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Eugenio Iglesias J, Rory Sabuncu M, Van Leemput K. A unified framework for cross-modality multi-atlas segmentation of brain MRI. Med Image Anal 2013; 17:1181-91. [PMID: 24001931 DOI: 10.1016/j.media.2013.08.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2013] [Revised: 08/01/2013] [Accepted: 08/05/2013] [Indexed: 10/26/2022]
Abstract
Multi-atlas label fusion is a powerful image segmentation strategy that is becoming increasingly popular in medical imaging. A standard label fusion algorithm relies on independently computed pairwise registrations between individual atlases and the (target) image to be segmented. These registrations are then used to propagate the atlas labels to the target space and fuse them into a single final segmentation. Such label fusion schemes commonly rely on the similarity between intensity values of the atlases and target scan, which is often problematic in medical imaging - in particular, when the atlases and target images are obtained via different sensor types or imaging protocols. In this paper, we present a generative probabilistic model that yields an algorithm for solving the atlas-to-target registrations and label fusion steps simultaneously. The proposed model does not directly rely on the similarity of image intensities. Instead, it exploits the consistency of voxel intensities within the target scan to drive the registration and label fusion, hence the atlases and target image can be of different modalities. Furthermore, the framework models the joint warp of all the atlases, introducing interdependence between the registrations. We use variational expectation maximization and the Demons registration framework in order to efficiently identify the most probable segmentation and registrations. We use two sets of experiments to illustrate the approach, where proton density (PD) MRI atlases are used to segment T1-weighted brain scans and vice versa. Our results clearly demonstrate the accuracy gain due to exploiting within-target intensity consistency and integrating registration into label fusion.
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Affiliation(s)
- Juan Eugenio Iglesias
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA.
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14
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Bai W, Shi W, O'Regan DP, Tong T, Wang H, Jamil-Copley S, Peters NS, Rueckert D. A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: application to cardiac MR images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2013; 32:1302-1315. [PMID: 23568495 DOI: 10.1109/tmi.2013.2256922] [Citation(s) in RCA: 116] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
The evaluation of ventricular function is important for the diagnosis of cardiovascular diseases. It typically involves measurement of the left ventricular (LV) mass and LV cavity volume. Manual delineation of the myocardial contours is time-consuming and dependent on the subjective experience of the expert observer. In this paper, a multi-atlas method is proposed for cardiac magnetic resonance (MR) image segmentation. The proposed method is novel in two aspects. First, it formulates a patch-based label fusion model in a Bayesian framework. Second, it improves image registration accuracy by utilizing label information, which leads to improvement of segmentation accuracy. The proposed method was evaluated on a cardiac MR image set of 28 subjects. The average Dice overlap metric of our segmentation is 0.92 for the LV cavity, 0.89 for the right ventricular cavity and 0.82 for the myocardium. The results show that the proposed method is able to provide accurate information for clinical diagnosis.
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Affiliation(s)
- Wenjia Bai
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, SW7 2RH London, UK
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15
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Iglesias JE, Sabuncu MR, Van Leemput K. A Generative Model for Probabilistic Label Fusion of Multimodal Data. ACTA ACUST UNITED AC 2012; 7509:115-133. [PMID: 25685856 DOI: 10.1007/978-3-642-33530-3_10] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
Abstract
The maturity of registration methods, in combination with the increasing processing power of computers, has made multi-atlas segmentation methods practical. The problem of merging the deformed label maps from the atlases is known as label fusion. Even though label fusion has been well studied for intramodality scenarios, it remains relatively unexplored when the nature of the target data is multimodal or when its modality is different from that of the atlases. In this paper, we review the literature on label fusion methods and also present an extension of our previously published algorithm to the general case in which the target data are multimodal. The method is based on a generative model that exploits the consistency of voxel intensities within the target scan based on the current estimate of the segmentation. Using brain MRI scans acquired with a multiecho FLASH sequence, we compare the method with majority voting, statistical-atlas-based segmentation, the popular package FreeSurfer and an adaptive local multi-atlas segmentation method. The results show that our approach produces highly accurate segmentations (Dice 86.3% across 22 brain structures of interest), outperforming the competing methods.
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Affiliation(s)
| | | | - Koen Van Leemput
- Departments of Information and Computer Science and of Biomedical Engineering and Computational Science, Aalto University, Finland
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