Optimization with soft Dice can lead to a volumetric bias
This is an incremental finding that addresses a specific issue in medical image segmentation for clinical applications.
The paper tackles the problem that using soft Dice as a loss function in medical image segmentation can introduce a volumetric bias, especially in tasks with high uncertainty, potentially limiting clinical applicability, as demonstrated theoretically and empirically on four medical tasks.
Segmentation is a fundamental task in medical image analysis. The clinical interest is often to measure the volume of a structure. To evaluate and compare segmentation methods, the similarity between a segmentation and a predefined ground truth is measured using metrics such as the Dice score. Recent segmentation methods based on convolutional neural networks use a differentiable surrogate of the Dice score, such as soft Dice, explicitly as the loss function during the learning phase. Even though this approach leads to improved Dice scores, we find that, both theoretically and empirically on four medical tasks, it can introduce a volumetric bias for tasks with high inherent uncertainty. As such, this may limit the method's clinical applicability.