Automatic segmentation of rectal tumor on diffusion‐weighted images by deep learning with U‐Net

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.