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Communication Dans Un Congrès Année : 2024

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

Résumé

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.
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hal-04454942 , version 1 (13-02-2024)

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  • HAL Id : hal-04454942 , version 1

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Rosana El Jurdi, Lucia Nichelli, Agusti Alentorn, Ghislain Vaillant, Guanghui Fu, et al.. Border irregularity loss for automated segmentation of primary brain lymphomas on post-contrast MRI. SPIE Medical Imaging 2024, Feb 2024, San Diego, CA, United States. ⟨hal-04454942⟩
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