Border irregularity loss for automated segmentation of primary brain lymphomas on post-contrast MRI

Rosana El Jurdi; Lucia Nichelli; Agusti Alentorn; Ghislain Vaillant; Guanghui Fu; Khê Hoang-Xuan; Caroline Houillier; Stéphane Lehéricy; Olivier Colliot

doi:10.1117/12.3002380

2 April 2024 Border irregularity loss for automated segmentation of primary brain lymphomas on post-contrast MRI

Rosana El Jurdi, Lucia Nichelli, Agusti Alentorn, Ghislain Vaillant, Guanghui Fu, Khê Hoang-Xuan, Caroline Houillier, Stéphane Lehéricy, Olivier Colliot

Author Affiliations +

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129261O (2024) https://doi.org/10.1117/12.3002380
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Unlike for other brain tumors, there has been little work on the automatic segmentation of primary central nervous system (CNS) lymphomas. This is a challenging task due the highly variable pattern of the tumor and its boundaries. In this work, we propose a new loss function that controls border irregularity for deep learning-based automatic segmentation of primary CNS lymphomas. We introduce a border irregularity loss which is based on the comparison of the segmentation and it smoothed version. The border irregularity loss is combined with a previously proposed topological loss to better control the different connected components. The approach is general and can be used with any segmentation network. We studied a population of 99 patients with primary CNS lymphoma. 40 patients were isolated from the very beginning and formed the independent test set. The segmentations were performed on post-contrast T1-weighted MRI. The MRI were acquired in clinical routine and were highly heterogeneous. The proposed approach substantially outperformed the baseline across the various evaluation metrics (by 6 percent points of Dice, 40mm of Hausdorff distance and 6mm of mean average surface distance). However, the overall performance was moderate, highlighting that automatic segmentation of primary CNS lymphomas is a difficult task, especially when dealing with clinical routine MRI. The code is publicly available here: https://github.com/rosanajurdi/LymphSeg.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Rosana El Jurdi, Lucia Nichelli, Agusti Alentorn, Ghislain Vaillant, Guanghui Fu, Khê Hoang-Xuan, Caroline Houillier, Stéphane Lehéricy, and Olivier Colliot "Border irregularity loss for automated segmentation of primary brain lymphomas on post-contrast MRI", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129261O (2 April 2024); https://doi.org/10.1117/12.3002380

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