Untangling and segmenting the small intestine in 3D cine-MRI using deep learning

Abstract

Cine-MRI of the abdomen is a non-invasive imaging technique allowing assessment of small intestinal motility. This is valuable for the evaluation of gastrointestinal disorders. While 2D cine-MRI is increasingly used for this purpose in both clinical practice and in research settings, the potential of 3D cine-MRI has been largely underexplored. In the absence of image analysis tools enabling investigation of the intestines as 3D structures, the assessment of motility in 3D cine-images is generally limited to the evaluation of movement in separate 2D slices. Furthermore, while a segmentation map of the small intestine would be required for a number of automatic analysis tasks, deep learning based segmentation of the small intestine generally performs poorly due to the large variety in shapes, sizes and locations in the abdomen among different patients. Using a data set of 3D cine-MRI scans from 14 healthy volunteers, we developed a multi-task method that automatically tracks individual segments of the small intestine in a time-point from 3D cine-MRI scans, using a stochastic tracker built on top of a CNN-based orientation classifier. The method additionally performs segmentation, conditioned on the locations of intestinal centerlines. We demonstrate the benefit of our stochastic tracking strategy and we show that our proposed segmentation method performs significantly better than an identical network without centerline conditioning. Furthermore, we assess the robustness of the method through evaluation on a set of patients with severe bowel disease. In terms of centerline tracking, our method achieves a recall of 0.74±0.07, a precision of 0.80±0.06 and an F1 score of 0.77±0.05 in the set of healthy volunteers. In the set of patients, it achieves a recall of 0.76±0.12, a precision of 0.86±0.11 and an F1 score of 0.80±0.08. Segmentation achieves a Dice coefficient of 0.88±0.03 in the set of healthy volunteers and 0.79±0.09 in the set of patients. By extracting a structural representation of the small intestine, the presented method provides a major first step towards automatic detailed quantitative assessment of small intestinal motility in abdominal 3D cine-MRI.

Keywords: Abdominal cine-MRI; Centerline extraction; Convolutional neural networks; Gastrointestinal motility; Small intestine segmentation.