Automatic Detection of Free Intra-Abdominal Air in Computed Tomography

Taubmann O, Li J, Denzinger F, Eibenberger E, Müller F, Brejnebøl M, Maier A (2020)

Publication Type: Conference contribution, Conference Contribution

Publication year: 2020

Publisher: Springer

City/Town: Cham

Conference Proceedings Title: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Event location: Lima PE

ISBN: 978-3-030-59713-9

DOI: 10.1007/978-3-030-59713-9

Abstract

Pneumoperitoneum, the presence of air within the peritoneal cavity, is a comparatively rare but potentially urgent critical finding in patients presenting with acute abdominal pain. When prior laparoscopic treatment can be ruled out as a cause, it can indicate perforation of the wall of a hollow organ, which typically necessitates immediate surgery. Computed tomography (CT) is the gold standard for detecting free intraabdominal air, yet subtle cases are easy to miss. More crucially though, if there is no initial suspicion of pneumoperitoneum, the scans may not be read immediately as other emergency patients take precedence. Therefore, fully automatic detection would provide a direct clinical benefit. In this work, an algorithm for this purpose is proposed which follows a sliding-window approach and has a deep-learning based classifier at its core. In addition to the baseline method, variants that rely on multi-scale inputs and recurrent layers to increase robustness are presented. In a fivefold cross validation on the training data, consisting in abdominal CT scans of 110 affected patients and 29 controls, our method achieved an area under the receiver-operating characteristic curve of 89 % for caselevel classification. Due to its high specificity at reasonable detection rates, it shows potential for use in triage, where false alerts are considered particularly harmful as they may disrupt the clinical workflow.

Authors with CRIS profile

Jingpeng Li Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Felix Denzinger Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Additional Organisation(s)

Machine Learning and Data Analytics Lab

Involved external institutions

Siemens Healthineers DE Germany (DE) Herlev Hospital DK Denmark (DK)

How to cite

APA:

Taubmann, O., Li, J., Denzinger, F., Eibenberger, E., Müller, F., Brejnebøl, M., & Maier, A. (2020). Automatic Detection of Free Intra-Abdominal Air in Computed Tomography. In Martel AL, Abolmaesumi P, Stoyanov D, Mateus D, Zuluaga MA, Zhou SK, Racoceanu D, Joskowicz L (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Lima, PE: Cham: Springer.

MLA:

Taubmann, Oliver, et al. "Automatic Detection of Free Intra-Abdominal Air in Computed Tomography." Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, Lima Ed. Martel AL, Abolmaesumi P, Stoyanov D, Mateus D, Zuluaga MA, Zhou SK, Racoceanu D, Joskowicz L, Cham: Springer, 2020.

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