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Hippocampus Substructure Segmentation Using Morphological Vision Transformer Learning
BACKGROUND: The hippocampus plays a crucial role in memory and cognition. Because of the associated toxicity from whole brain radiotherapy, more advanced treatment planning techniques prioritize hippocampal avoidance, which depends on an accurate segmentation of the small and complexly shaped hippoc...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cornell University
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312910/ https://www.ncbi.nlm.nih.gov/pubmed/37396614 |
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author | Lei, Yang Ding, Yifu Qiu, Richard L.J. Wang, Tonghe Roper, Justin Fu, Yabo Shu, Hui-Kuo Mao, Hui Yang, Xiaofeng |
author_facet | Lei, Yang Ding, Yifu Qiu, Richard L.J. Wang, Tonghe Roper, Justin Fu, Yabo Shu, Hui-Kuo Mao, Hui Yang, Xiaofeng |
author_sort | Lei, Yang |
collection | PubMed |
description | BACKGROUND: The hippocampus plays a crucial role in memory and cognition. Because of the associated toxicity from whole brain radiotherapy, more advanced treatment planning techniques prioritize hippocampal avoidance, which depends on an accurate segmentation of the small and complexly shaped hippocampus. Purpose: To achieve accurate segmentation of the anterior and posterior regions of the hippocampus from T1 weighted (T1w) MRI images, we developed a novel model, Hippo-Net, which uses a mutually enhanced strategy. METHODS: The proposed model consists of two major parts: 1) a localization model is used to detect the volume-of-interest (VOI) of hippocampus. 2) An end-to-end morphological vision transformer network is used to perform substructures segmentation within the hippocampus VOI. The substructures include the anterior and posterior regions of the hippocampus, which are defined as the hippocampus proper and parts of the subiculum. The vision transformer incorporates the dominant features extracted from MRI images, which are further improved by learning-based morphological operators. The integration of these morphological operators into the vision transformer increases the accuracy and ability to separate hippocampus structure into its two distinct substructures. A total of 260 T1w MRI datasets from Medical Segmentation Decathlon dataset were used in this study. We conducted a five-fold cross-validation on the first 200 T1w MR images and then performed a hold-out test on the remaining 60 T1w MR images with the model trained on the first 200 images. The segmentations were evaluated with two indicators, 1) multiple metrics including the Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), mean surface distance (MSD), volume difference (VD) and center-of-mass distance (COMD); 2) Volumetric Pearson correlation analysis. RESULTS: In five-fold cross-validation, the DSCs were 0.900±0.029 and 0.886±0.031 for the hippocampus proper and parts of the subiculum, respectively. The MSD were 0.426±0.115mm and 0.401±0.100 mm for the hippocampus proper and parts of the subiculum, respectively. Conclusions: The proposed method showed great promise in automatically delineating hippocampus substructures on T1w MRI images. It may facilitate the current clinical workflow and reduce the physicians’ effort. |
format | Online Article Text |
id | pubmed-10312910 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cornell University |
record_format | MEDLINE/PubMed |
spelling | pubmed-103129102023-07-01 Hippocampus Substructure Segmentation Using Morphological Vision Transformer Learning Lei, Yang Ding, Yifu Qiu, Richard L.J. Wang, Tonghe Roper, Justin Fu, Yabo Shu, Hui-Kuo Mao, Hui Yang, Xiaofeng ArXiv Article BACKGROUND: The hippocampus plays a crucial role in memory and cognition. Because of the associated toxicity from whole brain radiotherapy, more advanced treatment planning techniques prioritize hippocampal avoidance, which depends on an accurate segmentation of the small and complexly shaped hippocampus. Purpose: To achieve accurate segmentation of the anterior and posterior regions of the hippocampus from T1 weighted (T1w) MRI images, we developed a novel model, Hippo-Net, which uses a mutually enhanced strategy. METHODS: The proposed model consists of two major parts: 1) a localization model is used to detect the volume-of-interest (VOI) of hippocampus. 2) An end-to-end morphological vision transformer network is used to perform substructures segmentation within the hippocampus VOI. The substructures include the anterior and posterior regions of the hippocampus, which are defined as the hippocampus proper and parts of the subiculum. The vision transformer incorporates the dominant features extracted from MRI images, which are further improved by learning-based morphological operators. The integration of these morphological operators into the vision transformer increases the accuracy and ability to separate hippocampus structure into its two distinct substructures. A total of 260 T1w MRI datasets from Medical Segmentation Decathlon dataset were used in this study. We conducted a five-fold cross-validation on the first 200 T1w MR images and then performed a hold-out test on the remaining 60 T1w MR images with the model trained on the first 200 images. The segmentations were evaluated with two indicators, 1) multiple metrics including the Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), mean surface distance (MSD), volume difference (VD) and center-of-mass distance (COMD); 2) Volumetric Pearson correlation analysis. RESULTS: In five-fold cross-validation, the DSCs were 0.900±0.029 and 0.886±0.031 for the hippocampus proper and parts of the subiculum, respectively. The MSD were 0.426±0.115mm and 0.401±0.100 mm for the hippocampus proper and parts of the subiculum, respectively. Conclusions: The proposed method showed great promise in automatically delineating hippocampus substructures on T1w MRI images. It may facilitate the current clinical workflow and reduce the physicians’ effort. Cornell University 2023-06-14 /pmc/articles/PMC10312910/ /pubmed/37396614 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Lei, Yang Ding, Yifu Qiu, Richard L.J. Wang, Tonghe Roper, Justin Fu, Yabo Shu, Hui-Kuo Mao, Hui Yang, Xiaofeng Hippocampus Substructure Segmentation Using Morphological Vision Transformer Learning |
title | Hippocampus Substructure Segmentation Using Morphological Vision Transformer Learning |
title_full | Hippocampus Substructure Segmentation Using Morphological Vision Transformer Learning |
title_fullStr | Hippocampus Substructure Segmentation Using Morphological Vision Transformer Learning |
title_full_unstemmed | Hippocampus Substructure Segmentation Using Morphological Vision Transformer Learning |
title_short | Hippocampus Substructure Segmentation Using Morphological Vision Transformer Learning |
title_sort | hippocampus substructure segmentation using morphological vision transformer learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312910/ https://www.ncbi.nlm.nih.gov/pubmed/37396614 |
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