Automatic segmentation of rectal tumor on diffusion‐weighted images by deep learning with U‐Net
Hai‐Tao Zhu, Xiaoyan Zhang, Yan‐Jie Shi, Xiao‐Ting Li, Ying‐Shi Sun
Abstract
PURPOSE: Manual delineation of a rectal tumor on a volumetric image is time-consuming and subjective. Deep learning has been used to segment rectal tumors automatically on T2-weighted images, but automatic segmentation on diffusion-weighted imaging is challenged by noise, artifact, and low resolution. In this study, a volumetric U-shaped neural network (U-Net) is proposed to automatically segment rectal tumors on diffusion-weighted images. METHODS: Three hundred patients of locally advanced rectal cancer were enrolled in this study and divided into a training group, a validation group, and a test group. The region of rectal tumor was delineated on the diffusion-weighted images by experienced radiologists as the ground truth. A U-Net was designed with a volumetric input of the diffusion-weighted images and an output of segmentation with the same size. A semi-automatic segmentation method was used for comparison by manually choosing a threshold of gray level and automatically selecting the largest connected region. Dice similarity coefficient (DSC) was calculated to evaluate the methods. RESULTS: On the test group, deep learning method (DSC = 0.675 ± 0.144, median DSC is 0.702, maximum DSC is 0.893, and minimum DSC is 0.297) showed higher segmentation accuracy than the semi-automatic method (DSC = 0.614 ± 0.225, median DSC is 0.685, maximum DSC is 0.869, and minimum DSC is 0.047). Paired t-test shows significant difference (T = 2.160, p = 0.035) in DSC between the deep learning method and the semi-automatic method in the test group. CONCLUSION: Volumetric U-Net can automatically segment rectal tumor region on DWI images of locally advanced rectal cancer.