3D V-Net deep learning model for automatic segmentation of prostate on T2WI and apparent diffusion coefficient maps
10.13929/j.issn.1003-3289.2024.09.031
- VernacularTitle:3D V-Net深度学习模型用于自动分割T2WI及表观弥散系数图所示前列腺
- Author:
Zhaonan SUN
1
;
Jiangkai HE
;
Kexin WANG
;
Wenpeng HUANG
;
Pengsheng WU
;
Xiaodong ZHANG
;
Xiaoying WANG
Author Information
1. 北京大学第一医院医学影像科,北京 100034
- Keywords:
prostate;
magnetic resonance imaging;
artificial intelligence;
automated segmentation
- From:
Chinese Journal of Medical Imaging Technology
2024;40(9):1426-1431
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop a 3D V-Net deep learning segmentation model based on multi-center data,and to evaluate its value for automatic segmentation of prostate on T2WI and apparent diffusion coefficient(ADC)maps.Methods Totally 2 894 sets of multi-parametric MRI data of 2 673 patients with clinically suspected prostate cancer from 3 medical centers within 1 month before biopsy were retrospectively collected.Finally 5 974 sets axial images were enrolled,including 3 654 sets of T2WI and 2 320 sets of ADC maps.Prostate contours were manually annotated layer by layer on axial T2WI and ADC maps,and the left-to-right,anterior-to-posterior,superior-to-inferior diameters and volume of prostate were measured and taken as reference standards.The images were divided into training set(n=4 780,including 2 907 sets of T2WI and 1 873 sets of ADC map),verification set(n=601,including 384 sets of T2WI and 217 sets of ADC map)and test set(n=593,including 363 sets of T2WI and 230 sets of ADC map)at the ratio of 8:1:1.After preprocessing and augmentation,3D V-Net was used to construct and train the segmentation model based on training and verification sets,and the segmentation performance of the model was evaluated in test set using Dice similarity coefficient(DSC),Jaccard coefficient(JACARD)and volume similarity(VS),respectively.The parameters measured with the model were compared with the reference standards,and the correlations were explored.Results Compared with the corresponding ADC maps,DSC,JACARD and VS of the model for automatic segmentation of prostate on T2WI in test set were all higher(all P＜0.001).The left-to-right,anterior-to-posterior and superior-to-inferior diameters of prostate measured with the model on both T2WI and ADC maps were all larger than the reference standards(all P＜0.001),while no significant difference of the volume was found(both P＞0.05).All parameters measured with the model on T2WI and ADC maps were positively correlated with reference standards(rs=0.794-0.985).Conclusion 3D V-Net deep learning model could automatically segment prostate on T2WI and ADC maps with high accuracy,and its efficiency based on T2WI was better than that based on ADC maps.
