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

Louis van Harten, Catharina de Jonge, Jaap Stoker, Ivana Isgum
Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, PMLR 143:802-812, 2021.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v143-harten21a, title = {Untangling the Small Intestine in 3D cine-{MRI} using Deep Stochastic Tracking}, author = {van Harten, Louis and de Jonge, Catharina and Stoker, Jaap and Isgum, Ivana}, booktitle = {Proceedings of the Fourth Conference on Medical Imaging with Deep Learning}, pages = {802--812}, year = {2021}, editor = {Heinrich, Mattias and Dou, Qi and de Bruijne, Marleen and Lellmann, Jan and Schläfer, Alexander and Ernst, Floris}, volume = {143}, series = {Proceedings of Machine Learning Research}, month = {07--09 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v143/harten21a/harten21a.pdf}, url = {https://proceedings.mlr.press/v143/harten21a.html}, 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.} }
Endnote
%0 Conference Paper %T Untangling the Small Intestine in 3D cine-MRI using Deep Stochastic Tracking %A Louis van Harten %A Catharina de Jonge %A Jaap Stoker %A Ivana Isgum %B Proceedings of the Fourth Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2021 %E Mattias Heinrich %E Qi Dou %E Marleen de Bruijne %E Jan Lellmann %E Alexander Schläfer %E Floris Ernst %F pmlr-v143-harten21a %I PMLR %P 802--812 %U https://proceedings.mlr.press/v143/harten21a.html %V 143 %X 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.
APA
van Harten, L., de Jonge, C., Stoker, J. & Isgum, I.. (2021). Untangling the Small Intestine in 3D cine-MRI using Deep Stochastic Tracking. Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 143:802-812 Available from https://proceedings.mlr.press/v143/harten21a.html.

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