🤖 AI Summary
Longitudinal segmentation of multiple sclerosis (MS) lesions faces severe data and output imbalance challenges, particularly degrading performance on small lesions, boundary regions, and distance-sensitive metrics. To address this, we propose HyTver, a novel hybrid loss function that integrates an enhanced Dice loss with weighted cross-entropy and incorporates distance-aware regularization to jointly optimize segmentation accuracy and geometric consistency. HyTver requires no complex hyperparameter tuning and demonstrates superior training stability when applied to pre-trained models. Evaluated on the public MSLesion dataset, our method achieves a Dice score of 0.659 and significantly outperforms mainstream loss functions in distance-based metrics—including Hausdorff Distance (HD) and Average Symmetric Surface Distance (ASSD)—demonstrating its effectiveness in multi-objective optimization and robustness to lesion-scale variability and boundary ambiguity.
📝 Abstract
Longitudinal Multiple Sclerosis Lesion Segmentation is a particularly challenging problem that involves both input and output imbalance in the data and segmentation. Therefore in order to develop models that are practical, one of the solutions is to develop better loss functions. Most models naively use either Dice loss or Cross-Entropy loss or their combination without too much consideration. However, one must select an appropriate loss function as the imbalance can be mitigated by selecting a proper loss function. In order to solve the imbalance problem, multiple loss functions were proposed that claimed to solve it. They come with problems of their own which include being too computationally complex due to hyperparameters as exponents or having detrimental performance in metrics other than region-based ones. We propose a novel hybrid loss called HyTver that achieves good segmentation performance while maintaining performance in other metrics. We achieve a Dice score of 0.659 while also ensuring that the distance-based metrics are comparable to other popular functions. In addition, we also evaluate the stability of the loss functions when used on a pre- trained model and perform extensive comparisons with other popular loss functions