Fully Automated Colon Delineation and Volume Estimation in T2-Weighted MRI with a 2D U-Net.

Artificial intelligence has the potential to provide an objective, accurate and fast evaluation of the colon for clinical assessment of gastrointestinal diseases, such as chronic constipation. Accurate colon segmentation is a labor-intensive task, prone to interobserver variability. This study presents a method for automatic colon segmentation in T2-weighted MRI images, using 297 MRI scans from 72 healthy subjects and a neural network based on the 2D U-Net architecture. The presented network provided a Dice similarity coefficient (DSC) of 0.82 ± 0.04 when compared to ground truth data (p=0.722), allowing for accurate evaluations of colonic physiology. The estimated colonic volumes were significantly correlated with the ground truth volumes with no tendency for over- or underestimation (p<0.001). This study is, to our knowledge, the first to fully automatically segment the colon using MRI data, thus providing a method for objective colon evaluation without radiation exposure or disturbance of its physiology.