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An End-To-End Pipeline for Fully Automatic Morphological Quantification of Mouse Brain Structures From MRI Imagery

Segmentation of mouse brain magnetic resonance images (MRI) based on anatomical and/or functional features is an important step towards morphogenetic brain structure characterization of murine models in neurobiological studies. State-of-the-art image segmentation methods register image volumes to st...

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Autores principales: Alam, Shahinur, Eom, Tae-Yeon, Steinberg, Jeffrey, Ackerman, David, Schmitt, J. Eric, Akers, Walter J., Zakharenko, Stanislav S., Khairy, Khaled
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580949/
https://www.ncbi.nlm.nih.gov/pubmed/36304320
http://dx.doi.org/10.3389/fbinf.2022.865443
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author Alam, Shahinur
Eom, Tae-Yeon
Steinberg, Jeffrey
Ackerman, David
Schmitt, J. Eric
Akers, Walter J.
Zakharenko, Stanislav S.
Khairy, Khaled
author_facet Alam, Shahinur
Eom, Tae-Yeon
Steinberg, Jeffrey
Ackerman, David
Schmitt, J. Eric
Akers, Walter J.
Zakharenko, Stanislav S.
Khairy, Khaled
author_sort Alam, Shahinur
collection PubMed
description Segmentation of mouse brain magnetic resonance images (MRI) based on anatomical and/or functional features is an important step towards morphogenetic brain structure characterization of murine models in neurobiological studies. State-of-the-art image segmentation methods register image volumes to standard presegmented templates or well-characterized highly detailed image atlases. Performance of these methods depends critically on the quality of skull-stripping, which is the digital removal of tissue signal exterior to the brain. This is, however, tedious to do manually and challenging to automate. Registration-based segmentation, in addition, performs poorly on small structures, low resolution images, weak signals, or faint boundaries, intrinsic to in vivo MRI scans. To address these issues, we developed an automated end-to-end pipeline called DeepBrainIPP (deep learning-based brain image processing pipeline) for 1) isolating brain volumes by stripping skull and tissue from T2w MRI images using an improved deep learning-based skull-stripping and data augmentation strategy, which enables segmentation of large brain regions by atlas or template registration, and 2) address segmentation of small brain structures, such as the paraflocculus, a small lobule of the cerebellum, for which DeepBrainIPP performs direct segmentation with a dedicated model, producing results superior to the skull-stripping/atlas-registration paradigm. We demonstrate our approach on data from both in vivo and ex vivo samples, using an in-house dataset of 172 images, expanded to 4,040 samples through data augmentation. Our skull stripping model produced an average Dice score of 0.96 and residual volume of 2.18%. This facilitated automatic registration of the skull-stripped brain to an atlas yielding an average cross-correlation of 0.98. For small brain structures, direct segmentation yielded an average Dice score of 0.89 and 5.32% residual volume error, well below the tolerance threshold for phenotype detection. Full pipeline execution is provided to non-expert users via a Web-based interface, which exposes analysis parameters, and is powered by a service that manages job submission, monitors job status and provides job history. Usability, reliability, and user experience of DeepBrainIPP was measured using the Customer Satisfaction Score (CSAT) and a modified PYTHEIA Scale, with a rating of excellent. DeepBrainIPP code, documentation and network weights are freely available to the research community.
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spelling pubmed-95809492022-10-26 An End-To-End Pipeline for Fully Automatic Morphological Quantification of Mouse Brain Structures From MRI Imagery Alam, Shahinur Eom, Tae-Yeon Steinberg, Jeffrey Ackerman, David Schmitt, J. Eric Akers, Walter J. Zakharenko, Stanislav S. Khairy, Khaled Front Bioinform Bioinformatics Segmentation of mouse brain magnetic resonance images (MRI) based on anatomical and/or functional features is an important step towards morphogenetic brain structure characterization of murine models in neurobiological studies. State-of-the-art image segmentation methods register image volumes to standard presegmented templates or well-characterized highly detailed image atlases. Performance of these methods depends critically on the quality of skull-stripping, which is the digital removal of tissue signal exterior to the brain. This is, however, tedious to do manually and challenging to automate. Registration-based segmentation, in addition, performs poorly on small structures, low resolution images, weak signals, or faint boundaries, intrinsic to in vivo MRI scans. To address these issues, we developed an automated end-to-end pipeline called DeepBrainIPP (deep learning-based brain image processing pipeline) for 1) isolating brain volumes by stripping skull and tissue from T2w MRI images using an improved deep learning-based skull-stripping and data augmentation strategy, which enables segmentation of large brain regions by atlas or template registration, and 2) address segmentation of small brain structures, such as the paraflocculus, a small lobule of the cerebellum, for which DeepBrainIPP performs direct segmentation with a dedicated model, producing results superior to the skull-stripping/atlas-registration paradigm. We demonstrate our approach on data from both in vivo and ex vivo samples, using an in-house dataset of 172 images, expanded to 4,040 samples through data augmentation. Our skull stripping model produced an average Dice score of 0.96 and residual volume of 2.18%. This facilitated automatic registration of the skull-stripped brain to an atlas yielding an average cross-correlation of 0.98. For small brain structures, direct segmentation yielded an average Dice score of 0.89 and 5.32% residual volume error, well below the tolerance threshold for phenotype detection. Full pipeline execution is provided to non-expert users via a Web-based interface, which exposes analysis parameters, and is powered by a service that manages job submission, monitors job status and provides job history. Usability, reliability, and user experience of DeepBrainIPP was measured using the Customer Satisfaction Score (CSAT) and a modified PYTHEIA Scale, with a rating of excellent. DeepBrainIPP code, documentation and network weights are freely available to the research community. Frontiers Media S.A. 2022-06-08 /pmc/articles/PMC9580949/ /pubmed/36304320 http://dx.doi.org/10.3389/fbinf.2022.865443 Text en Copyright © 2022 Alam, Eom, Steinberg, Ackerman, Schmitt, Akers, Zakharenko and Khairy. 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 Bioinformatics
Alam, Shahinur
Eom, Tae-Yeon
Steinberg, Jeffrey
Ackerman, David
Schmitt, J. Eric
Akers, Walter J.
Zakharenko, Stanislav S.
Khairy, Khaled
An End-To-End Pipeline for Fully Automatic Morphological Quantification of Mouse Brain Structures From MRI Imagery
title An End-To-End Pipeline for Fully Automatic Morphological Quantification of Mouse Brain Structures From MRI Imagery
title_full An End-To-End Pipeline for Fully Automatic Morphological Quantification of Mouse Brain Structures From MRI Imagery
title_fullStr An End-To-End Pipeline for Fully Automatic Morphological Quantification of Mouse Brain Structures From MRI Imagery
title_full_unstemmed An End-To-End Pipeline for Fully Automatic Morphological Quantification of Mouse Brain Structures From MRI Imagery
title_short An End-To-End Pipeline for Fully Automatic Morphological Quantification of Mouse Brain Structures From MRI Imagery
title_sort end-to-end pipeline for fully automatic morphological quantification of mouse brain structures from mri imagery
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580949/
https://www.ncbi.nlm.nih.gov/pubmed/36304320
http://dx.doi.org/10.3389/fbinf.2022.865443
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