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Deep Network for the Automatic Segmentation and Quantification of Intracranial Hemorrhage on CT
BACKGROUND: The ABC/2 method is usually applied to evaluate intracerebral hemorrhage (ICH) volume on computed tomography (CT), although it might be inaccurate and not applicable in estimating extradural or subdural hemorrhage (EDH, SDH) volume due to their irregular hematoma shapes. This study aimed...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832216/ https://www.ncbi.nlm.nih.gov/pubmed/33505231 http://dx.doi.org/10.3389/fnins.2020.541817 |
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author | Xu, Jun Zhang, Rongguo Zhou, Zijian Wu, Chunxue Gong, Qiang Zhang, Huiling Wu, Shuang Wu, Gang Deng, Yufeng Xia, Chen Ma, Jun |
author_facet | Xu, Jun Zhang, Rongguo Zhou, Zijian Wu, Chunxue Gong, Qiang Zhang, Huiling Wu, Shuang Wu, Gang Deng, Yufeng Xia, Chen Ma, Jun |
author_sort | Xu, Jun |
collection | PubMed |
description | BACKGROUND: The ABC/2 method is usually applied to evaluate intracerebral hemorrhage (ICH) volume on computed tomography (CT), although it might be inaccurate and not applicable in estimating extradural or subdural hemorrhage (EDH, SDH) volume due to their irregular hematoma shapes. This study aimed to evaluate deep framework optimized for the segmentation and quantification of ICH, EDH, and SDH. METHODS: The training datasets were 3,000 images retrospectively collected from a collaborating hospital (Hospital A) and segmented by the Dense U-Net framework. Three experienced radiologists determined the ground truth by marking the pixels as hemorrhage area. We utilized the Dice and intra-class correlation coefficients (ICC) to test the reliability of the ground truth. Moreover, the testing datasets consisted of 211 images (internal test) from Hospital A, and 86 ICH images (external test) from another hospital (Hospital B). In this study, we chose scatter plots, ICC, and Pearson correlation coefficients (PCC) with ground truth to evaluate the performance of the deep framework. Furthermore, to validate the effectiveness of the deep framework, we did a comparative analysis of the hemorrhage volume estimation between the deep model and the ABC/2 method. RESULTS: The high Dice (0.89–0.95) and ICC (0.985–0.997) showed the consistency of the manual segmentations among the radiologists and the reliability of the ground truth. For the internal test, the Dice coefficients of ICH, EDH, and SDH were 0.90 ± 0.06, 0.88 ± 0.12, and 0.82 ± 0.16, respectively. For the external test, the segmentation Dice was 0.86 ± 0.09. Comparatively, the ICC and PCC of ICH volume estimations were 0.99 performed by Dense U-Net that overmatched the ABC/2 method. CONCLUSION: This study revealed the excellent performance of hematoma segmentation and volume evaluation based on Dense U-Net, which indicated our deep framework might contribute to efficiently developing treatment strategies for intracranial hemorrhage in clinics. |
format | Online Article Text |
id | pubmed-7832216 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78322162021-01-26 Deep Network for the Automatic Segmentation and Quantification of Intracranial Hemorrhage on CT Xu, Jun Zhang, Rongguo Zhou, Zijian Wu, Chunxue Gong, Qiang Zhang, Huiling Wu, Shuang Wu, Gang Deng, Yufeng Xia, Chen Ma, Jun Front Neurosci Neuroscience BACKGROUND: The ABC/2 method is usually applied to evaluate intracerebral hemorrhage (ICH) volume on computed tomography (CT), although it might be inaccurate and not applicable in estimating extradural or subdural hemorrhage (EDH, SDH) volume due to their irregular hematoma shapes. This study aimed to evaluate deep framework optimized for the segmentation and quantification of ICH, EDH, and SDH. METHODS: The training datasets were 3,000 images retrospectively collected from a collaborating hospital (Hospital A) and segmented by the Dense U-Net framework. Three experienced radiologists determined the ground truth by marking the pixels as hemorrhage area. We utilized the Dice and intra-class correlation coefficients (ICC) to test the reliability of the ground truth. Moreover, the testing datasets consisted of 211 images (internal test) from Hospital A, and 86 ICH images (external test) from another hospital (Hospital B). In this study, we chose scatter plots, ICC, and Pearson correlation coefficients (PCC) with ground truth to evaluate the performance of the deep framework. Furthermore, to validate the effectiveness of the deep framework, we did a comparative analysis of the hemorrhage volume estimation between the deep model and the ABC/2 method. RESULTS: The high Dice (0.89–0.95) and ICC (0.985–0.997) showed the consistency of the manual segmentations among the radiologists and the reliability of the ground truth. For the internal test, the Dice coefficients of ICH, EDH, and SDH were 0.90 ± 0.06, 0.88 ± 0.12, and 0.82 ± 0.16, respectively. For the external test, the segmentation Dice was 0.86 ± 0.09. Comparatively, the ICC and PCC of ICH volume estimations were 0.99 performed by Dense U-Net that overmatched the ABC/2 method. CONCLUSION: This study revealed the excellent performance of hematoma segmentation and volume evaluation based on Dense U-Net, which indicated our deep framework might contribute to efficiently developing treatment strategies for intracranial hemorrhage in clinics. Frontiers Media S.A. 2021-01-11 /pmc/articles/PMC7832216/ /pubmed/33505231 http://dx.doi.org/10.3389/fnins.2020.541817 Text en Copyright © 2021 Xu, Zhang, Zhou, Wu, Gong, Zhang, Wu, Wu, Deng, Xia and Ma. http://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 | Neuroscience Xu, Jun Zhang, Rongguo Zhou, Zijian Wu, Chunxue Gong, Qiang Zhang, Huiling Wu, Shuang Wu, Gang Deng, Yufeng Xia, Chen Ma, Jun Deep Network for the Automatic Segmentation and Quantification of Intracranial Hemorrhage on CT |
title | Deep Network for the Automatic Segmentation and Quantification of Intracranial Hemorrhage on CT |
title_full | Deep Network for the Automatic Segmentation and Quantification of Intracranial Hemorrhage on CT |
title_fullStr | Deep Network for the Automatic Segmentation and Quantification of Intracranial Hemorrhage on CT |
title_full_unstemmed | Deep Network for the Automatic Segmentation and Quantification of Intracranial Hemorrhage on CT |
title_short | Deep Network for the Automatic Segmentation and Quantification of Intracranial Hemorrhage on CT |
title_sort | deep network for the automatic segmentation and quantification of intracranial hemorrhage on ct |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832216/ https://www.ncbi.nlm.nih.gov/pubmed/33505231 http://dx.doi.org/10.3389/fnins.2020.541817 |
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