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

Abstract 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) imag...

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Autores principales:	Xiang Liu, Zhaonan Sun, Chao Han, Yingpu Cui, Jiahao Huang, Xiangpeng Wang, Xiaodong Zhang, Xiaoying Wang
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Prostate cancer Lymph nodes Segmentation Detection Diffusion-weighted imaging Deep learning Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/f85ccd718e04449ab01c7119dae24c6f
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spelling	oai:doaj.org-article:f85ccd718e04449ab01c7119dae24c6f2021-11-14T12:33:37ZDevelopment and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images10.1186/s12880-021-00703-31471-2342https://doaj.org/article/f85ccd718e04449ab01c7119dae24c6f2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12880-021-00703-3https://doaj.org/toc/1471-2342Abstract 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.Xiang LiuZhaonan SunChao HanYingpu CuiJiahao HuangXiangpeng WangXiaodong ZhangXiaoying WangBMCarticleProstate cancerLymph nodesSegmentationDetectionDiffusion-weighted imagingDeep learningMedical technologyR855-855.5ENBMC Medical Imaging, Vol 21, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Prostate cancer Lymph nodes Segmentation Detection Diffusion-weighted imaging Deep learning Medical technology R855-855.5
spellingShingle	Prostate cancer Lymph nodes Segmentation Detection Diffusion-weighted imaging Deep learning Medical technology R855-855.5 Xiang Liu Zhaonan Sun Chao Han Yingpu Cui Jiahao Huang Xiangpeng Wang Xiaodong Zhang Xiaoying Wang Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images
description	Abstract 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.
format	article
author	Xiang Liu Zhaonan Sun Chao Han Yingpu Cui Jiahao Huang Xiangpeng Wang Xiaodong Zhang Xiaoying Wang
author_facet	Xiang Liu Zhaonan Sun Chao Han Yingpu Cui Jiahao Huang Xiangpeng Wang Xiaodong Zhang Xiaoying Wang
author_sort	Xiang Liu
title	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_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_sort	development and validation of the 3d u-net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images
publisher	BMC
publishDate	2021
url	https://doaj.org/article/f85ccd718e04449ab01c7119dae24c6f
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Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images

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