Contour Dice loss for structures with Fuzzy and Complex Boundaries in Fetal MRI
This work addresses the problem of time-consuming and error-prone volumetric measurements in fetal MRI for clinicians, but it is incremental as it focuses on loss function comparisons without introducing a new paradigm.
The paper tackled automatic segmentation of fetal structures in MRI, specifically placenta and fetal brain, by evaluating the Contour Dice loss against other boundary losses and combined losses, finding that combining Dice with Contour Dice yielded the best performance for placenta segmentation, while Dice with Cross-Entropy was best for fetal brain segmentation.
Volumetric measurements of fetal structures in MRI are time consuming and error prone and therefore require automatic segmentation. Placenta segmentation and accurate fetal brain segmentation for gyrification assessment are particularly challenging because of the placenta fuzzy boundaries and the fetal brain cortex complex foldings. In this paper, we study the use of the Contour Dice loss for both problems and compare it to other boundary losses and to the combined Dice and Cross-Entropy loss. The loss is computed efficiently for each slice via erosion, dilation and XOR operators. We describe a new formulation of the loss akin to the Contour Dice metric. The combination of the Dice loss and the Contour Dice yielded the best performance for placenta segmentation. For fetal brain segmentation, the best performing loss was the combined Dice with Cross-Entropy loss followed by the Dice with Contour Dice loss, which performed better than other boundary losses.