Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations

Understand

Deep-learning has proved in recent years to be a powerful tool for image analysis and is now widely used to segment both 2D and 3D medical images.

• Deep-learning segmentation frameworks rely not only on the choice of network architecture but also on the choice of loss function.
• When the segmentation process targets rare observations, a severe class imbalance is likely to occur between candidate labels, thus resulting in sub-optimal performance.
• In order to mitigate this issue, strategies such as the weighted cross-entropy function, the sensitivity function or the Dice loss function, have been proposed.