High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss. (arXiv:2211.02419v1 [eess.IV])

Deep learning methods have contributed substantially to the rapid advancement
of medical image segmentation, the quality of which relies on the suitable
design of loss functions. Popular loss functions, including the cross-entropy
and dice losses, often fall short of boundary detection, thereby limiting
high-resolution downstream applications such as automated diagnoses and
procedures. We developed a novel loss function that is tailored to reflect the
boundary information to enhance the boundary detection. As the contrast between
segmentation and background regions along …

arxiv detection image loss medical segmentation test t-test