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Abstract #2997

Fully Automated Pelvic Bones Segmentation in Multiparameter MRI Using a 3D Convolutional Neural Network

xiang liu1, chao han1, and xiaoying wang1
1department of radiology, peking university first hospital, Beijing, China

This retrospective study aims to perform automated pelvic bones segmentation in multiparametric MRI (mpMRI) using 3D convolutional neural network (CNN). 264 pelvic DWI images and corresponding ADC maps obtained from three MRI vendors from 2018 to 2019 were used for the 3D U-Net CNN development. 60 independent mpMRI data from 2020 were used to externally evaluate the segmentation model using quantitative criteria (Dice similarity coefficient) and qualitative assessment (SCORE system). The results demonstrated that the 3D CNN can achieve fully automated pelvic bone segmentation on multi-vendor DWI and ADC images with good quantitative and qualitative performances.

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