Untangling the Small Intestine in 3D cine-MRI using Deep Stochastic Tracking

Louis van Harten, Catharina de Jonge, Jaap Stoker, Ivana Isgum

Keywords: Deep learning, cine-MRI, centerline extraction, small intestine, motility

TL;DR: We extract small intestine centerlines in MR images by stochastically sampling a CNN-based directional classifier

Abstract: Motility of the small intestine is a valuable metric in the evaluation of gastrointestinal disorders. Cine-MRI of the abdomen is a non-invasive imaging technique allowing evaluation of this motility. While 2D cine-MR imaging is increasingly used for this purpose in both clinical practice and in research settings, the potential of 3D cine-MR imaging 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. Hence, to obtain an untangled representation of the small intestine in 3D cine-MRI, we propose a method to extract a centerline of the intestine, thereby allowing easier (visual) assessment by human observers, as well as providing a possible starting point for automatic analysis methods quantifying peristaltic bowel movement along intestinal segments. The proposed method automatically tracks individual sections of the small intestine in 3D space, using a stochastic tracker built on top of a CNN-based orientation classifier. We show that the proposed method outperforms a non-stochastic iterative tracking approach.

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Paper Type: methodological development

Primary Subject Area: Application: Radiology

Secondary Subject Area: Learning with Noisy Labels and Limited Data