Cargando…
Anatomically curated segmentation of human subcortical structures in high resolution magnetic resonance imaging: An open science approach
Magnetic resonance imaging (MRI)-based brain segmentation has recently been revolutionized by deep learning methods. These methods use large numbers of annotated segmentations to train algorithms that have the potential to perform brain segmentations reliably and quickly. However, training data for...
Autores principales: | , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9562126/ https://www.ncbi.nlm.nih.gov/pubmed/36249866 http://dx.doi.org/10.3389/fnana.2022.894606 |
_version_ | 1784808100785553408 |
---|---|
author | Rushmore, R. Jarrett Sunderland, Kyle Carrington, Holly Chen, Justine Halle, Michael Lasso, Andras Papadimitriou, G. Prunier, N. Rizzoni, Elizabeth Vessey, Brynn Wilson-Braun, Peter Rathi, Yogesh Kubicki, Marek Bouix, Sylvain Yeterian, Edward Makris, Nikos |
author_facet | Rushmore, R. Jarrett Sunderland, Kyle Carrington, Holly Chen, Justine Halle, Michael Lasso, Andras Papadimitriou, G. Prunier, N. Rizzoni, Elizabeth Vessey, Brynn Wilson-Braun, Peter Rathi, Yogesh Kubicki, Marek Bouix, Sylvain Yeterian, Edward Makris, Nikos |
author_sort | Rushmore, R. Jarrett |
collection | PubMed |
description | Magnetic resonance imaging (MRI)-based brain segmentation has recently been revolutionized by deep learning methods. These methods use large numbers of annotated segmentations to train algorithms that have the potential to perform brain segmentations reliably and quickly. However, training data for these algorithms are frequently obtained from automated brain segmentation systems, which may contain inaccurate neuroanatomy. Thus, the neuroimaging community would benefit from an open source database of high quality, neuroanatomically curated and manually edited MRI brain images, as well as the publicly available tools and detailed procedures for generating these curated data. Manual segmentation approaches are regarded as the gold standard for brain segmentation and parcellation. These approaches underpin the construction of neuroanatomically accurate human brain atlases. In addition, neuroanatomically precise definitions of MRI-based regions of interest (ROIs) derived from manual brain segmentation are essential for accuracy in structural connectivity studies and in surgical planning for procedures such as deep brain stimulation. However, manual segmentation procedures are time and labor intensive, and not practical in studies utilizing very large datasets, large cohorts, or multimodal imaging. Automated segmentation methods were developed to overcome these issues, and provide high data throughput, increased reliability, and multimodal imaging capability. These methods utilize manually labeled brain atlases to automatically parcellate the brain into different ROIs, but do not have the anatomical accuracy of skilled manual segmentation approaches. In the present study, we developed a custom software module for manual editing of brain structures in the freely available 3D Slicer software platform that employs principles and tools based on pioneering work from the Center for Morphometric Analysis (CMA) at Massachusetts General Hospital. We used these novel 3D Slicer segmentation tools and techniques in conjunction with well-established neuroanatomical definitions of subcortical brain structures to manually segment 50 high resolution T1w MRI brains from the Human Connectome Project (HCP) Young Adult database. The structural definitions used herein are associated with specific neuroanatomical ontologies to systematically interrelate histological and MRI-based morphometric definitions. The resulting brain datasets are publicly available and will provide the basis for a larger database of anatomically curated brains as an open science resource. |
format | Online Article Text |
id | pubmed-9562126 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95621262022-10-15 Anatomically curated segmentation of human subcortical structures in high resolution magnetic resonance imaging: An open science approach Rushmore, R. Jarrett Sunderland, Kyle Carrington, Holly Chen, Justine Halle, Michael Lasso, Andras Papadimitriou, G. Prunier, N. Rizzoni, Elizabeth Vessey, Brynn Wilson-Braun, Peter Rathi, Yogesh Kubicki, Marek Bouix, Sylvain Yeterian, Edward Makris, Nikos Front Neuroanat Neuroanatomy Magnetic resonance imaging (MRI)-based brain segmentation has recently been revolutionized by deep learning methods. These methods use large numbers of annotated segmentations to train algorithms that have the potential to perform brain segmentations reliably and quickly. However, training data for these algorithms are frequently obtained from automated brain segmentation systems, which may contain inaccurate neuroanatomy. Thus, the neuroimaging community would benefit from an open source database of high quality, neuroanatomically curated and manually edited MRI brain images, as well as the publicly available tools and detailed procedures for generating these curated data. Manual segmentation approaches are regarded as the gold standard for brain segmentation and parcellation. These approaches underpin the construction of neuroanatomically accurate human brain atlases. In addition, neuroanatomically precise definitions of MRI-based regions of interest (ROIs) derived from manual brain segmentation are essential for accuracy in structural connectivity studies and in surgical planning for procedures such as deep brain stimulation. However, manual segmentation procedures are time and labor intensive, and not practical in studies utilizing very large datasets, large cohorts, or multimodal imaging. Automated segmentation methods were developed to overcome these issues, and provide high data throughput, increased reliability, and multimodal imaging capability. These methods utilize manually labeled brain atlases to automatically parcellate the brain into different ROIs, but do not have the anatomical accuracy of skilled manual segmentation approaches. In the present study, we developed a custom software module for manual editing of brain structures in the freely available 3D Slicer software platform that employs principles and tools based on pioneering work from the Center for Morphometric Analysis (CMA) at Massachusetts General Hospital. We used these novel 3D Slicer segmentation tools and techniques in conjunction with well-established neuroanatomical definitions of subcortical brain structures to manually segment 50 high resolution T1w MRI brains from the Human Connectome Project (HCP) Young Adult database. The structural definitions used herein are associated with specific neuroanatomical ontologies to systematically interrelate histological and MRI-based morphometric definitions. The resulting brain datasets are publicly available and will provide the basis for a larger database of anatomically curated brains as an open science resource. Frontiers Media S.A. 2022-09-30 /pmc/articles/PMC9562126/ /pubmed/36249866 http://dx.doi.org/10.3389/fnana.2022.894606 Text en Copyright © 2022 Rushmore, Sunderland, Carrington, Chen, Halle, Lasso, Papadimitriou, Prunier, Rizzoni, Vessey, Wilson-Braun, Rathi, Kubicki, Bouix, Yeterian and Makris. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroanatomy Rushmore, R. Jarrett Sunderland, Kyle Carrington, Holly Chen, Justine Halle, Michael Lasso, Andras Papadimitriou, G. Prunier, N. Rizzoni, Elizabeth Vessey, Brynn Wilson-Braun, Peter Rathi, Yogesh Kubicki, Marek Bouix, Sylvain Yeterian, Edward Makris, Nikos Anatomically curated segmentation of human subcortical structures in high resolution magnetic resonance imaging: An open science approach |
title | Anatomically curated segmentation of human subcortical structures in high resolution magnetic resonance imaging: An open science approach |
title_full | Anatomically curated segmentation of human subcortical structures in high resolution magnetic resonance imaging: An open science approach |
title_fullStr | Anatomically curated segmentation of human subcortical structures in high resolution magnetic resonance imaging: An open science approach |
title_full_unstemmed | Anatomically curated segmentation of human subcortical structures in high resolution magnetic resonance imaging: An open science approach |
title_short | Anatomically curated segmentation of human subcortical structures in high resolution magnetic resonance imaging: An open science approach |
title_sort | anatomically curated segmentation of human subcortical structures in high resolution magnetic resonance imaging: an open science approach |
topic | Neuroanatomy |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9562126/ https://www.ncbi.nlm.nih.gov/pubmed/36249866 http://dx.doi.org/10.3389/fnana.2022.894606 |
work_keys_str_mv | AT rushmorerjarrett anatomicallycuratedsegmentationofhumansubcorticalstructuresinhighresolutionmagneticresonanceimaginganopenscienceapproach AT sunderlandkyle anatomicallycuratedsegmentationofhumansubcorticalstructuresinhighresolutionmagneticresonanceimaginganopenscienceapproach AT carringtonholly anatomicallycuratedsegmentationofhumansubcorticalstructuresinhighresolutionmagneticresonanceimaginganopenscienceapproach AT chenjustine anatomicallycuratedsegmentationofhumansubcorticalstructuresinhighresolutionmagneticresonanceimaginganopenscienceapproach AT hallemichael anatomicallycuratedsegmentationofhumansubcorticalstructuresinhighresolutionmagneticresonanceimaginganopenscienceapproach AT lassoandras anatomicallycuratedsegmentationofhumansubcorticalstructuresinhighresolutionmagneticresonanceimaginganopenscienceapproach AT papadimitrioug anatomicallycuratedsegmentationofhumansubcorticalstructuresinhighresolutionmagneticresonanceimaginganopenscienceapproach AT pruniern anatomicallycuratedsegmentationofhumansubcorticalstructuresinhighresolutionmagneticresonanceimaginganopenscienceapproach AT rizzonielizabeth anatomicallycuratedsegmentationofhumansubcorticalstructuresinhighresolutionmagneticresonanceimaginganopenscienceapproach AT vesseybrynn anatomicallycuratedsegmentationofhumansubcorticalstructuresinhighresolutionmagneticresonanceimaginganopenscienceapproach AT wilsonbraunpeter anatomicallycuratedsegmentationofhumansubcorticalstructuresinhighresolutionmagneticresonanceimaginganopenscienceapproach AT rathiyogesh anatomicallycuratedsegmentationofhumansubcorticalstructuresinhighresolutionmagneticresonanceimaginganopenscienceapproach AT kubickimarek anatomicallycuratedsegmentationofhumansubcorticalstructuresinhighresolutionmagneticresonanceimaginganopenscienceapproach AT bouixsylvain anatomicallycuratedsegmentationofhumansubcorticalstructuresinhighresolutionmagneticresonanceimaginganopenscienceapproach AT yeterianedward anatomicallycuratedsegmentationofhumansubcorticalstructuresinhighresolutionmagneticresonanceimaginganopenscienceapproach AT makrisnikos anatomicallycuratedsegmentationofhumansubcorticalstructuresinhighresolutionmagneticresonanceimaginganopenscienceapproach |