Bowel Obstruction Detection and Localization on Abdominal CT with Deep Learning

Moritz Vandenhirtz, Andrea Agostini, Dana Belde, Mélanie Roschewitz, Ismaiel Chikh Bakri, Tilo Niemann, André Euler, Julia E Vogt
Proceedings of the 11th Machine Learning for Healthcare Conference, PMLR 340:2030-2054, 2026.

Abstract

Bowel obstruction is a common and potentially life-threatening gastrointestinal condition. In the face of rising diagnostic workloads, the automated diagnosis of bowel obstruction on CT scans supports radiologists by accelerating detection and improving patient outcomes. In this work, we propose a deep learning framework with a multi-task objective that jointly detects bowel obstruction and localizes its transition zone. Additionally, we extend the method with an inherently interpretable classification method that locates the suspected transition point within a slice. It does so by learning a probabilistic selection mask that faithfully bases the classifier’s prediction solely on a small image region. The proposed method is evaluated on an internal dataset comprising 1,427 abdominal CTs. Here, the model achieves an obstruction detection test accuracy of 93% and a Hit@10 transition zone localization of 95%. As the first method to reliably localize the transition zone, this marks a significant step towards the automated identification of this critical clinical landmark.

Cite this Paper

BibTeX
@InProceedings{pmlr-v340-vandenhirtz26a, title = {Bowel Obstruction Detection and Localization on Abdominal CT with Deep Learning}, author = {Vandenhirtz, Moritz and Agostini, Andrea and Belde, Dana and Roschewitz, M\'{e}lanie and Bakri, Ismaiel Chikh and Niemann, Tilo and Euler, Andr\'{e} and Vogt, Julia E}, booktitle = {Proceedings of the 11th Machine Learning for Healthcare Conference}, pages = {2030--2054}, year = {2026}, editor = {Krishnan, Rahul G. and van Amsterdam, Wouter A. C. and Chopra, Sumit and Overgaard, Shauna and Hughes, Michael and Ötleş, Erkin and Shen, Yiqiu and Shanmugam, Divya and Nayan, Madhur and Engelhard, Matthew and Fackler, Jim and Oberst, Michael}, volume = {340}, series = {Proceedings of Machine Learning Research}, month = {12--14 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v340/main/assets/vandenhirtz26a/vandenhirtz26a.pdf}, url = {https://proceedings.mlr.press/v340/vandenhirtz26a.html}, abstract = {Bowel obstruction is a common and potentially life-threatening gastrointestinal condition. In the face of rising diagnostic workloads, the automated diagnosis of bowel obstruction on CT scans supports radiologists by accelerating detection and improving patient outcomes. In this work, we propose a deep learning framework with a multi-task objective that jointly detects bowel obstruction and localizes its transition zone. Additionally, we extend the method with an inherently interpretable classification method that locates the suspected transition point within a slice. It does so by learning a probabilistic selection mask that faithfully bases the classifier’s prediction solely on a small image region. The proposed method is evaluated on an internal dataset comprising 1,427 abdominal CTs. Here, the model achieves an obstruction detection test accuracy of 93% and a Hit@10 transition zone localization of 95%. As the first method to reliably localize the transition zone, this marks a significant step towards the automated identification of this critical clinical landmark.} }
Endnote
%0 Conference Paper %T Bowel Obstruction Detection and Localization on Abdominal CT with Deep Learning %A Moritz Vandenhirtz %A Andrea Agostini %A Dana Belde %A Mélanie Roschewitz %A Ismaiel Chikh Bakri %A Tilo Niemann %A André Euler %A Julia E Vogt %B Proceedings of the 11th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2026 %E Rahul G. Krishnan %E Wouter A. C. van Amsterdam %E Sumit Chopra %E Shauna Overgaard %E Michael Hughes %E Erkin Ötleş %E Yiqiu Shen %E Divya Shanmugam %E Madhur Nayan %E Matthew Engelhard %E Jim Fackler %E Michael Oberst %F pmlr-v340-vandenhirtz26a %I PMLR %P 2030--2054 %U https://proceedings.mlr.press/v340/vandenhirtz26a.html %V 340 %X Bowel obstruction is a common and potentially life-threatening gastrointestinal condition. In the face of rising diagnostic workloads, the automated diagnosis of bowel obstruction on CT scans supports radiologists by accelerating detection and improving patient outcomes. In this work, we propose a deep learning framework with a multi-task objective that jointly detects bowel obstruction and localizes its transition zone. Additionally, we extend the method with an inherently interpretable classification method that locates the suspected transition point within a slice. It does so by learning a probabilistic selection mask that faithfully bases the classifier’s prediction solely on a small image region. The proposed method is evaluated on an internal dataset comprising 1,427 abdominal CTs. Here, the model achieves an obstruction detection test accuracy of 93% and a Hit@10 transition zone localization of 95%. As the first method to reliably localize the transition zone, this marks a significant step towards the automated identification of this critical clinical landmark.
APA
Vandenhirtz, M., Agostini, A., Belde, D., Roschewitz, M., Bakri, I.C., Niemann, T., Euler, A. & Vogt, J.E.. (2026). Bowel Obstruction Detection and Localization on Abdominal CT with Deep Learning. Proceedings of the 11th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 340:2030-2054 Available from https://proceedings.mlr.press/v340/vandenhirtz26a.html.

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