Fully-Automated Segmentation algorithm of Rectal Cancer and mesorectum on Multiparametric MR

Motivation: Developing a fast solution to segment rectal tumors and mesorectal tissue instead of the current manual labeling. Goal(s): The goal was to develop an automated segmentation model using nnU-Net for fully-automated segmentation of rectal cancer and mesorectum on MR images. Approach: The dataset was divided into training and testing sets, and pre-processing steps were conducted to minimize computational burden. The nnU-Net deep learning network was employed to train the model. Results: The Dice similarity coefficients for tumor and mesorectum in both the training and testing sets were as follows: 0.91 (training) and 0.88(testing) for tumor, and 0.93 (training) and 0.89 (testing) for mesorectum. Impact: This study proposes an automatic segmentation scheme for rectal tumor and mesentery using deep learning. It can be used to guide the annotation of new medical images, potentially improving the accuracy of rectal cancer treatment response predictions.