HALOS: Hallucination-Free Organ Segmentation After Organ Resection Surgery

Rickmann, Anne-Marie ; Xu, Murong; Wolf, Tom Nuno; Kovalenko, Oksana und Wachinger, Christian (2023): HALOS: Hallucination-Free Organ Segmentation After Organ Resection Surgery. IPMI 2023 : Information Processing in Medical Imaging, San Carlos De Bariloche, Argentina, 18-23 June 2023. Frangi, Alejandro; de Bruijne, Marleen; Wassermann, Demian und Navab, Nassir (Hrsg.): In: Information Processing in Medical Imaging : 28th International Conference, IPMI 2023, San Carlos de Bariloche, Argentina, June 18–23, 2023, Proceedings, Bd. 13939 Cham: Springer. S. 667-678

Abstract

The wide range of research in deep learning-based medical image segmentation pushed the boundaries in a multitude of applications. A clinically relevant problem that received less attention is the handling of scans with irregular anatomy, e.g., after organ resection. State-of-the-art segmentation models often lead to organ hallucinations, i.e., false-positive predictions of organs, which cannot be alleviated by oversampling or post-processing. Motivated by the increasing need to develop robust deep learning models, we propose HALOS for abdominal organ segmentation in MR images that handles cases after organ resection surgery. To this end, we combine missing organ classification and multi-organ segmentation tasks into a multi-task model, yielding a classification-assisted segmentation pipeline. The segmentation network learns to incorporate knowledge about organ existence via feature fusion modules. Extensive experiments on a small labeled test set and large-scale UK Biobank data demonstrate the effectiveness of our approach in terms of higher segmentation Dice scores and near-to-zero false positive prediction rate.

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Medizin > Klinikum der LMU München > Klinik und Poliklinik für Kinder- und Jugendpsychiatrie, Psychosomatik und Psychotherapie
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik ; 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISBN: 978-3-031-34047-5 ; 978-3-031-34048-2
• Ort: Cham
• Sprache: Englisch