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Hooft, D.* ; Fischer, S.M. ; Bercea, C.-I. ; Peeken, J.* ; Schnabel, J.A.

LOCBAM: Advancing 3D Patch-Based Image Segmentation by Integrating Location Context.

In: (23rd IEEE International Symposium on Biomedical Imaging, ISBI 2026, 8-11 April 2026, London). 2026. (Proceedings International Symposium on Biomedical Imaging ; 2026-April)
DOI
Patch-based methods are widely used in 3D medical image segmentation to address memory constraints in processing highresolution volumetric data. However, these approaches often neglect the patch's location within the global volume, which can limit segmentation performance when anatomical context is important. In this paper, we investigate the role of location context in patch-based 3D segmentation and propose a novel attention mechanism, LocBAM, that explicitly processes spatial information. Experiments on BTCV, AMOS22, and KiTS23 demonstrate that incorporating location context stabilizes training and improves segmentation performance, particularly under low patch-to-volume coverage where global context is missing. Furthermore, LocBAM consistently outperforms classical coordinate encoding via CoordConv. Code is publicly available at https://github.com/compai-lab/2026-ISBI-hooft.
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Publication type Article: Conference contribution
ISSN (print) / ISBN 1945-7928
e-ISSN 1945-8452
Conference Title 23rd IEEE International Symposium on Biomedical Imaging, ISBI 2026
Conference Date 8-11 April 2026
Conference Location London
Quellenangaben Volume: 2026-April Issue: , Pages: , Article Number: , Supplement: ,
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)