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Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training

The inference of cortical sulcal labels often focuses on deep (primary and secondary) sulcal regions, whereas shallow (tertiary) sulcal regions are largely overlooked in the literature due to the scarcity of manual/well-defined annotations and their large neuroanatomical variability. In this paper,...

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Autores principales: Lyu, Ilwoo, Bao, Shuxing, Hao, Lingyan, Yao, Jewelia, Miller, Jacob A., Voorhies, Willa, Taylor, Warren D., Bunge, Silvia A., Weiner, Kevin S., Landman, Bennett A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8366030/
https://www.ncbi.nlm.nih.gov/pubmed/33497773
http://dx.doi.org/10.1016/j.neuroimage.2021.117758
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author Lyu, Ilwoo
Bao, Shuxing
Hao, Lingyan
Yao, Jewelia
Miller, Jacob A.
Voorhies, Willa
Taylor, Warren D.
Bunge, Silvia A.
Weiner, Kevin S.
Landman, Bennett A.
author_facet Lyu, Ilwoo
Bao, Shuxing
Hao, Lingyan
Yao, Jewelia
Miller, Jacob A.
Voorhies, Willa
Taylor, Warren D.
Bunge, Silvia A.
Weiner, Kevin S.
Landman, Bennett A.
author_sort Lyu, Ilwoo
collection PubMed
description The inference of cortical sulcal labels often focuses on deep (primary and secondary) sulcal regions, whereas shallow (tertiary) sulcal regions are largely overlooked in the literature due to the scarcity of manual/well-defined annotations and their large neuroanatomical variability. In this paper, we present an automated framework for regional labeling of both primary/secondary and tertiary sulci of the dorsal portion of lateral prefrontal cortex (LPFC) using spherical convolutional neural networks. We propose two core components that enhance the inference of sulcal labels to overcome such large neuroanatomical variability: (1) surface data augmentation and (2) context-aware training. (1) To take into account neuroanatomical variability, we synthesize training data from the proposed feature space that embeds intermediate deformation trajectories of spherical data in a rigid to non-rigid fashion, which bridges an augmentation gap in conventional rotation data augmentation. (2) Moreover, we design a two-stage training process to improve labeling accuracy of tertiary sulci by informing the biological associations in neuroanatomy: inference of primary/secondary sulci and then their spatial likelihood to guide the definition of tertiary sulci. In the experiments, we evaluate our method on 13 deep and shallow sulci of human LPFC in two independent data sets with different age ranges: pediatric (N = 60) and adult (N = 36) cohorts. We compare the proposed method with a conventional multi-atlas approach and spherical convolutional neural networks without/with rotation data augmentation. In both cohorts, the proposed data augmentation improves labeling accuracy of deep and shallow sulci over the baselines, and the proposed context-aware training offers further improvement in the labeling of shallow sulci over the proposed data augmentation. We share our tools with the field and discuss applications of our results for understanding neuroanatomical-functional organization of LPFC and the rest of cortex (https://github.com/ilwoolyu/SphericalLabeling).
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spelling pubmed-83660302021-08-16 Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training Lyu, Ilwoo Bao, Shuxing Hao, Lingyan Yao, Jewelia Miller, Jacob A. Voorhies, Willa Taylor, Warren D. Bunge, Silvia A. Weiner, Kevin S. Landman, Bennett A. Neuroimage Article The inference of cortical sulcal labels often focuses on deep (primary and secondary) sulcal regions, whereas shallow (tertiary) sulcal regions are largely overlooked in the literature due to the scarcity of manual/well-defined annotations and their large neuroanatomical variability. In this paper, we present an automated framework for regional labeling of both primary/secondary and tertiary sulci of the dorsal portion of lateral prefrontal cortex (LPFC) using spherical convolutional neural networks. We propose two core components that enhance the inference of sulcal labels to overcome such large neuroanatomical variability: (1) surface data augmentation and (2) context-aware training. (1) To take into account neuroanatomical variability, we synthesize training data from the proposed feature space that embeds intermediate deformation trajectories of spherical data in a rigid to non-rigid fashion, which bridges an augmentation gap in conventional rotation data augmentation. (2) Moreover, we design a two-stage training process to improve labeling accuracy of tertiary sulci by informing the biological associations in neuroanatomy: inference of primary/secondary sulci and then their spatial likelihood to guide the definition of tertiary sulci. In the experiments, we evaluate our method on 13 deep and shallow sulci of human LPFC in two independent data sets with different age ranges: pediatric (N = 60) and adult (N = 36) cohorts. We compare the proposed method with a conventional multi-atlas approach and spherical convolutional neural networks without/with rotation data augmentation. In both cohorts, the proposed data augmentation improves labeling accuracy of deep and shallow sulci over the baselines, and the proposed context-aware training offers further improvement in the labeling of shallow sulci over the proposed data augmentation. We share our tools with the field and discuss applications of our results for understanding neuroanatomical-functional organization of LPFC and the rest of cortex (https://github.com/ilwoolyu/SphericalLabeling). 2021-01-23 2021-04-01 /pmc/articles/PMC8366030/ /pubmed/33497773 http://dx.doi.org/10.1016/j.neuroimage.2021.117758 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) )
spellingShingle Article
Lyu, Ilwoo
Bao, Shuxing
Hao, Lingyan
Yao, Jewelia
Miller, Jacob A.
Voorhies, Willa
Taylor, Warren D.
Bunge, Silvia A.
Weiner, Kevin S.
Landman, Bennett A.
Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training
title Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training
title_full Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training
title_fullStr Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training
title_full_unstemmed Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training
title_short Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training
title_sort labeling lateral prefrontal sulci using spherical data augmentation and context-aware training
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8366030/
https://www.ncbi.nlm.nih.gov/pubmed/33497773
http://dx.doi.org/10.1016/j.neuroimage.2021.117758
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