Semi-Automated Extraction of Crohns Disease MR Imaging Markers using a 3D Residual CNN with Distance Prior.

Abstract

We propose a 3D residual convolutional neural network (CNN) algorithm with an integrated distance prior for segmenting the small bowel lumen and wall to enable extraction of pediatric Crohns disease (pCD) imaging markers from T1-weighted contrast-enhanced MR images. Our proposed segmentation framework enables, for the first time, to quantitatively assess luminal narrowing and dilation in CD aimed at optimizing surgical decisions as well as analyzing bowel wall thickness and tissue enhancement for assessment of response to therapy. Given seed points along the bowel lumen, the proposed algorithm automatically extracts 3D image patches centered on these points and a distance map from the interpolated centerline. These 3D patches and corresponding distance map are jointly used by the proposed residual CNN architecture to segment the lumen and the wall, and to extract imaging markers. Due to lack of available training data, we also propose a novel and efficient semi-automated segmentation algorithm based on graph-cuts technique as well as a software tool for quickly editing labeled data that was used to train our proposed CNN model. The method which is based on curved planar reformation of the small bowel is also useful for visualizing, manually refining, and measuring pCD imaging markers. In preliminary experiments, our CNN network obtained Dice coefficients of 75 ± 18%, 81 ± 8% and 97 ± 2% for the lumen, wall and background, respectively.