Multi-organ Segmentation in CT from Partially Annotated Datasets using Disentangled Learning

Wang T, Liu C, Rist L, Maier A (2024)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2024

Journal

Informatik aktuell Gesellschaft für Informatik (GI)

Publisher: Springer Vieweg

Series: Informatik aktuell

City/Town: Wiesbaden

Pages Range: 291-296

Conference Proceedings Title: Bildverarbeitung für die Medizin 2024. BVM 2024

Event location: Erlangen DE

ISBN: 9783658440367

DOI: 10.1007/978-3-658-44037-4_76

Abstract

While deep learning models are known to be able to solve the task of multi-organ segmentation, the scarcity of fully annotated multi-organ datasets poses a significant obstacle during training. The 3D volume annotation of such datasets is expensive, time-consuming and varies greatly in the variety of labeled structures. To this end, we propose a solution that leverages multiple partially annotated datasets using disentangled learning for a single segmentation model. Dataset-specific encoder and decoder networks are trained, while a joint decoder network gathers the encoders’ features to generate a complete segmentation mask. We evaluated our method using two simulated partially annotated datasets: one including the liver, lungs and kidneys, the other bones and bladder. Our method is trained to segment all five organs achieving a dice score of 0.78 and an IoU of 0.67. Notably, this performance is close to a model trained on the fully annotated dataset, scoring 0.80 in dice score and 0.70 in IoU respectively.

Authors with CRIS profile

Chang Liu Lehrstuhl für Informatik 5 (Mustererkennung) Leonhard Rist Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

How to cite

APA:

Wang, T., Liu, C., Rist, L., & Maier, A. (2024). Multi-organ Segmentation in CT from Partially Annotated Datasets using Disentangled Learning. In Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2024. BVM 2024 (pp. 291-296). Erlangen, DE: Wiesbaden: Springer Vieweg.

MLA:

Wang, Tianyi, et al. "Multi-organ Segmentation in CT from Partially Annotated Datasets using Disentangled Learning." Proceedings of the German Conference on Medical Image Computing, BVM 2024, Erlangen Ed. Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff, Wiesbaden: Springer Vieweg, 2024. 291-296.

BibTeX: Download