3D ResNet deep learning model for automatically identifying sequences of prostate multi-parametric MRI:A multicenter study
10.13929/j.issn.1003-3289.2024.05.028
- VernacularTitle:3D ResNet深度学习模型自动甄别前列腺多参数MR扫描序列:多中心研究
- Author:
Zhaonan SUN
1
;
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:
prostatic neoplasms;
magnetic resonance imaging;
artificial intelligence
- From:
Chinese Journal of Medical Imaging Technology
2024;40(5):769-773
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct a 3D ResNet deep learning model based on multi-parametric prostate MRI(mpMRI),and to observe its value for automatically identifying the main MR sequences.Methods Totally 1 153 sets pre-biopsy prostate mpMRI data of 1 086 patients who underwent ultrasound-guided prostate biopsy in 3 hospitals were collected and divided into different image datasets,i.e.T2WI,diffusion-weighted imaging(DWI)and apparent diffusion coefficient(ADC)maps with a total of 5 151 images.Then the images were categorized into non-fat-suppressed T2WI(T2WI_nan,n=1 000),fat-suppressed T2WI(T2WI_fs,n=1 188),high b-value DWI(DWI_High,b-value≥500 s/mm2,n=1 045),low b-value DWI(DWI_Low,b-value＜500 s/mm2,n=1 012)or ADC map(n=906),also divided into training set(n=4 122),verification set(n=513)and test set(n=516)at the ratio of 8∶1∶1.After preprocessing and augmentation,a 3D ResNet model for automatically identifying image categories was trained and optimized in the training and verification sets,and its classification efficiency was evaluated in the test set.Results The identifying accuracy,sensitivity,specificity,positive predictive value,negative predictive value,F1 score and Kappa value of the obtained model for automatically identifying categories of images in the test set was 0.995-1.000,0.990-1.000,0.998-1.000,0.990-1.000,0.998-1.000,0.995-1.000 and 0.994-1.000,respectively.Conclusion The obtained 3D ResNet deep learning model could effectively and automatically identify the main sequences of prostate mpMRI.
