Segmentation Loss

Segmentation loss functions are crucial components of deep learning models used for image segmentation, aiming to optimize the accuracy and efficiency of separating different objects or regions within an image. Current research focuses on developing loss functions that address challenges like handling imbalanced datasets, preserving topological information (especially in medical imaging), and improving robustness to noise and domain shifts. These advancements are improving the performance of segmentation models across various applications, including medical image analysis, remote sensing, and autonomous driving, by enabling more accurate and reliable segmentations with less labeled data.