Introducing soft topology constraints in deep learning-based segmentation using projected pooling loss

Guanghui Fu; Rosana El Jurdi; Lydia Chougar; Didier Dormont; Romain Valabregue; Stéphane Lehéricy; Olivier Colliot

doi:10.1117/12.2651576

3 April 2023 Introducing soft topology constraints in deep learning-based segmentation using projected pooling loss

Guanghui Fu, Rosana El Jurdi, Lydia Chougar, Didier Dormont, Romain Valabregue, Stéphane Lehéricy, Olivier Colliot

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 124641J (2023) https://doi.org/10.1117/12.2651576
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

When performing manual segmentations, experts heavily rely on prior anatomical knowledge. Topology is an important prior information due to its stability across patients. Recently, several losses based on persistent homology were proposed to constrain topology. However, such approaches are computationally expensive and complex to implement, in particular in 3D. In this paper, we propose a novel loss function to introduce topological priors in deep learning-based segmentation, which is fast to compute and easy to implement. Our approach was evaluated in several medical datasets (spleen, heart, hippocampus, red nucleus). It allowed reducing topological errors and, in some cases, improving voxel-level accuracy.

Citation Download Citation

Guanghui Fu, Rosana El Jurdi, Lydia Chougar, Didier Dormont, Romain Valabregue, Stéphane Lehéricy, and Olivier Colliot "Introducing soft topology constraints in deep learning-based segmentation using projected pooling loss", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124641J (3 April 2023); https://doi.org/10.1117/12.2651576

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