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

• URL: http://arxiv.org/abs/2511.17146v1
• Date: Fri, 21 Nov 2025 11:10:05 GMT
• Title: Learning to Look Closer: A New Instance-Wise Loss for Small Cerebral Lesion Segmentation
• Authors: Luc Bouteille, Alexander Jaus, Jens Kleesiek, Rainer Stiefelhagen, Lukas Heine,
• Abstract summary: We introduce CC-DiceCE, a loss function based on the CC-Metrics framework, and compare it with the existing blob loss.<n>We find that CC-DiceCE loss increases detection (recall) with minimal to no degradation in segmentation performance, albeit at the cost of slightly more false positives.
• Score: 64.7502063982282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, albeit at the cost of slightly more false positives. Furthermore, our multi-dataset study shows that CC-DiceCE generally outperforms blob loss.

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