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arXiv · 2511.17146

Learning to Look Closer: A New Instance-Wise Loss for Small Cerebral Lesion Segmentation

Luc Bouteille·Alexander Jaus·Jens Kleesiek·Rainer Stiefelhagen·Lukas Heine
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Abstract

Traditional loss functions in medical image segmentation, such as Dice, often under-segment small lesions because their small relative volume contributes negligibly to the overall loss. To address this, instance-wise loss functions and metrics have been proposed to evaluate segmentation quality on a per-lesion basis. We introduce CC-DiceCE, a loss function based on the CC-Metrics framework, and compare it with the existing blob loss. Both are benchmarked against a DiceCE baseline within the nnU-Net framework, which provides a robust and standardized setup. We find that CC-DiceCE loss increases detection (recall) with minimal to no degradation in segmentation performance, though with dataset-dependent trade-offs in precision. Furthermore, our multi-dataset study shows that CC-DiceCE generally outperforms blob loss.

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Luc Bouteille, Alexander Jaus, Jens Kleesiek, Rainer Stiefelhagen, Lukas Heine. 2025-11-21. Learning to Look Closer: A New Instance-Wise Loss for Small Cerebral Lesion Segmentation. https://arxiv.org/abs/2511.17146

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