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Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images

BACKGROUND: The 3D U-Net model has been proved to perform well in the automatic organ segmentation. The aim of this study is to evaluate the feasibility of the 3D U-Net algorithm for the automated detection and segmentation of lymph nodes (LNs) on pelvic diffusion-weighted imaging (DWI) images. METH...

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Autores principales: Liu, Xiang, Sun, Zhaonan, Han, Chao, Cui, Yingpu, Huang, Jiahao, Wang, Xiangpeng, Zhang, Xiaodong, Wang, Xiaoying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8590773/
https://www.ncbi.nlm.nih.gov/pubmed/34774001
http://dx.doi.org/10.1186/s12880-021-00703-3
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author Liu, Xiang
Sun, Zhaonan
Han, Chao
Cui, Yingpu
Huang, Jiahao
Wang, Xiangpeng
Zhang, Xiaodong
Wang, Xiaoying
author_facet Liu, Xiang
Sun, Zhaonan
Han, Chao
Cui, Yingpu
Huang, Jiahao
Wang, Xiangpeng
Zhang, Xiaodong
Wang, Xiaoying
author_sort Liu, Xiang
collection PubMed
description BACKGROUND: The 3D U-Net model has been proved to perform well in the automatic organ segmentation. The aim of this study is to evaluate the feasibility of the 3D U-Net algorithm for the automated detection and segmentation of lymph nodes (LNs) on pelvic diffusion-weighted imaging (DWI) images. METHODS: A total of 393 DWI images of patients suspected of having prostate cancer (PCa) between January 2019 and December 2020 were collected for model development. Seventy-seven DWI images from another group of PCa patients imaged between January 2021 and April 2021 were collected for temporal validation. Segmentation performance was assessed using the Dice score, positive predictive value (PPV), true positive rate (TPR), and volumetric similarity (VS), Hausdorff distance (HD), the Average distance (AVD), and the Mahalanobis distance (MHD) with manual annotation of pelvic LNs as the reference. The accuracy with which the suspicious metastatic LNs (short diameter > 0.8 cm) were detected was evaluated using the area under the curve (AUC) at the patient level, and the precision, recall, and F1-score were determined at the lesion level. The consistency of LN staging on an hold-out test dataset between the model and radiologist was assessed using Cohen’s kappa coefficient. RESULTS: In the testing set used for model development, the Dice score, TPR, PPV, VS, HD, AVD and MHD values for the segmentation of suspicious LNs were 0.85, 0.82, 0.80, 0.86, 2.02 (mm), 2.01 (mm), and 1.54 (mm) respectively. The precision, recall, and F1-score for the detection of suspicious LNs were 0.97, 0.98 and 0.97, respectively. In the temporal validation dataset, the AUC of the model for identifying PCa patients with suspicious LNs was 0.963 (95% CI: 0.892–0.993). High consistency of LN staging (Kappa = 0.922) was achieved between the model and expert radiologist. CONCLUSION: The 3D U-Net algorithm can accurately detect and segment pelvic LNs based on DWI images.
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spelling pubmed-85907732021-11-15 Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images Liu, Xiang Sun, Zhaonan Han, Chao Cui, Yingpu Huang, Jiahao Wang, Xiangpeng Zhang, Xiaodong Wang, Xiaoying BMC Med Imaging Research BACKGROUND: The 3D U-Net model has been proved to perform well in the automatic organ segmentation. The aim of this study is to evaluate the feasibility of the 3D U-Net algorithm for the automated detection and segmentation of lymph nodes (LNs) on pelvic diffusion-weighted imaging (DWI) images. METHODS: A total of 393 DWI images of patients suspected of having prostate cancer (PCa) between January 2019 and December 2020 were collected for model development. Seventy-seven DWI images from another group of PCa patients imaged between January 2021 and April 2021 were collected for temporal validation. Segmentation performance was assessed using the Dice score, positive predictive value (PPV), true positive rate (TPR), and volumetric similarity (VS), Hausdorff distance (HD), the Average distance (AVD), and the Mahalanobis distance (MHD) with manual annotation of pelvic LNs as the reference. The accuracy with which the suspicious metastatic LNs (short diameter > 0.8 cm) were detected was evaluated using the area under the curve (AUC) at the patient level, and the precision, recall, and F1-score were determined at the lesion level. The consistency of LN staging on an hold-out test dataset between the model and radiologist was assessed using Cohen’s kappa coefficient. RESULTS: In the testing set used for model development, the Dice score, TPR, PPV, VS, HD, AVD and MHD values for the segmentation of suspicious LNs were 0.85, 0.82, 0.80, 0.86, 2.02 (mm), 2.01 (mm), and 1.54 (mm) respectively. The precision, recall, and F1-score for the detection of suspicious LNs were 0.97, 0.98 and 0.97, respectively. In the temporal validation dataset, the AUC of the model for identifying PCa patients with suspicious LNs was 0.963 (95% CI: 0.892–0.993). High consistency of LN staging (Kappa = 0.922) was achieved between the model and expert radiologist. CONCLUSION: The 3D U-Net algorithm can accurately detect and segment pelvic LNs based on DWI images. BioMed Central 2021-11-13 /pmc/articles/PMC8590773/ /pubmed/34774001 http://dx.doi.org/10.1186/s12880-021-00703-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Liu, Xiang
Sun, Zhaonan
Han, Chao
Cui, Yingpu
Huang, Jiahao
Wang, Xiangpeng
Zhang, Xiaodong
Wang, Xiaoying
Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images
title Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images
title_full Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images
title_fullStr Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images
title_full_unstemmed Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images
title_short Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images
title_sort development and validation of the 3d u-net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8590773/
https://www.ncbi.nlm.nih.gov/pubmed/34774001
http://dx.doi.org/10.1186/s12880-021-00703-3
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