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Multi-organ segmentation of abdominal structures from non-contrast and contrast enhanced CT images
Manually delineating upper abdominal organs at risk (OARs) is a time-consuming task. To develop a deep-learning-based tool for accurate and robust auto-segmentation of these OARs, forty pancreatic cancer patients with contrast-enhanced breath-hold computed tomographic (CT) images were selected. We t...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9646761/ https://www.ncbi.nlm.nih.gov/pubmed/36351987 http://dx.doi.org/10.1038/s41598-022-21206-3 |
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author | Yu, Cenji Anakwenze, Chidinma P. Zhao, Yao Martin, Rachael M. Ludmir, Ethan B. S.Niedzielski, Joshua Qureshi, Asad Das, Prajnan Holliday, Emma B. Raldow, Ann C. Nguyen, Callistus M. Mumme, Raymond P. Netherton, Tucker J. Rhee, Dong Joo Gay, Skylar S. Yang, Jinzhong Court, Laurence E. Cardenas, Carlos E. |
author_facet | Yu, Cenji Anakwenze, Chidinma P. Zhao, Yao Martin, Rachael M. Ludmir, Ethan B. S.Niedzielski, Joshua Qureshi, Asad Das, Prajnan Holliday, Emma B. Raldow, Ann C. Nguyen, Callistus M. Mumme, Raymond P. Netherton, Tucker J. Rhee, Dong Joo Gay, Skylar S. Yang, Jinzhong Court, Laurence E. Cardenas, Carlos E. |
author_sort | Yu, Cenji |
collection | PubMed |
description | Manually delineating upper abdominal organs at risk (OARs) is a time-consuming task. To develop a deep-learning-based tool for accurate and robust auto-segmentation of these OARs, forty pancreatic cancer patients with contrast-enhanced breath-hold computed tomographic (CT) images were selected. We trained a three-dimensional (3D) U-Net ensemble that automatically segments all organ contours concurrently with the self-configuring nnU-Net framework. Our tool’s performance was assessed on a held-out test set of 30 patients quantitatively. Five radiation oncologists from three different institutions assessed the performance of the tool using a 5-point Likert scale on an additional 75 randomly selected test patients. The mean (± std. dev.) Dice similarity coefficient values between the automatic segmentation and the ground truth on contrast-enhanced CT images were 0.80 ± 0.08, 0.89 ± 0.05, 0.90 ± 0.06, 0.92 ± 0.03, 0.96 ± 0.01, 0.97 ± 0.01, 0.96 ± 0.01, and 0.96 ± 0.01 for the duodenum, small bowel, large bowel, stomach, liver, spleen, right kidney, and left kidney, respectively. 89.3% (contrast-enhanced) and 85.3% (non-contrast-enhanced) of duodenum contours were scored as a 3 or above, which required only minor edits. More than 90% of the other organs’ contours were scored as a 3 or above. Our tool achieved a high level of clinical acceptability with a small training dataset and provides accurate contours for treatment planning. |
format | Online Article Text |
id | pubmed-9646761 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96467612022-11-15 Multi-organ segmentation of abdominal structures from non-contrast and contrast enhanced CT images Yu, Cenji Anakwenze, Chidinma P. Zhao, Yao Martin, Rachael M. Ludmir, Ethan B. S.Niedzielski, Joshua Qureshi, Asad Das, Prajnan Holliday, Emma B. Raldow, Ann C. Nguyen, Callistus M. Mumme, Raymond P. Netherton, Tucker J. Rhee, Dong Joo Gay, Skylar S. Yang, Jinzhong Court, Laurence E. Cardenas, Carlos E. Sci Rep Article Manually delineating upper abdominal organs at risk (OARs) is a time-consuming task. To develop a deep-learning-based tool for accurate and robust auto-segmentation of these OARs, forty pancreatic cancer patients with contrast-enhanced breath-hold computed tomographic (CT) images were selected. We trained a three-dimensional (3D) U-Net ensemble that automatically segments all organ contours concurrently with the self-configuring nnU-Net framework. Our tool’s performance was assessed on a held-out test set of 30 patients quantitatively. Five radiation oncologists from three different institutions assessed the performance of the tool using a 5-point Likert scale on an additional 75 randomly selected test patients. The mean (± std. dev.) Dice similarity coefficient values between the automatic segmentation and the ground truth on contrast-enhanced CT images were 0.80 ± 0.08, 0.89 ± 0.05, 0.90 ± 0.06, 0.92 ± 0.03, 0.96 ± 0.01, 0.97 ± 0.01, 0.96 ± 0.01, and 0.96 ± 0.01 for the duodenum, small bowel, large bowel, stomach, liver, spleen, right kidney, and left kidney, respectively. 89.3% (contrast-enhanced) and 85.3% (non-contrast-enhanced) of duodenum contours were scored as a 3 or above, which required only minor edits. More than 90% of the other organs’ contours were scored as a 3 or above. Our tool achieved a high level of clinical acceptability with a small training dataset and provides accurate contours for treatment planning. Nature Publishing Group UK 2022-11-09 /pmc/articles/PMC9646761/ /pubmed/36351987 http://dx.doi.org/10.1038/s41598-022-21206-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Yu, Cenji Anakwenze, Chidinma P. Zhao, Yao Martin, Rachael M. Ludmir, Ethan B. S.Niedzielski, Joshua Qureshi, Asad Das, Prajnan Holliday, Emma B. Raldow, Ann C. Nguyen, Callistus M. Mumme, Raymond P. Netherton, Tucker J. Rhee, Dong Joo Gay, Skylar S. Yang, Jinzhong Court, Laurence E. Cardenas, Carlos E. Multi-organ segmentation of abdominal structures from non-contrast and contrast enhanced CT images |
title | Multi-organ segmentation of abdominal structures from non-contrast and contrast enhanced CT images |
title_full | Multi-organ segmentation of abdominal structures from non-contrast and contrast enhanced CT images |
title_fullStr | Multi-organ segmentation of abdominal structures from non-contrast and contrast enhanced CT images |
title_full_unstemmed | Multi-organ segmentation of abdominal structures from non-contrast and contrast enhanced CT images |
title_short | Multi-organ segmentation of abdominal structures from non-contrast and contrast enhanced CT images |
title_sort | multi-organ segmentation of abdominal structures from non-contrast and contrast enhanced ct images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9646761/ https://www.ncbi.nlm.nih.gov/pubmed/36351987 http://dx.doi.org/10.1038/s41598-022-21206-3 |
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