Learning to Look Closer: A New Instance-Wise Loss for Small Cerebral Lesion Segmentation
This work addresses segmentation challenges for small lesions in medical imaging, offering an incremental improvement over existing methods.
The paper tackled the problem of under-segmenting small cerebral lesions in medical images by introducing CC-DiceCE, a new instance-wise loss function, which increased lesion detection recall with minimal degradation in segmentation performance, though with dataset-dependent precision trade-offs.
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.