Segmentation Dice

Segmentation Dice, a metric assessing the accuracy of image segmentation, is crucial for evaluating the performance of algorithms in various fields, particularly medical imaging. Current research focuses on improving segmentation accuracy and robustness across diverse datasets and imaging modalities, employing techniques like prompt adaptation for pre-trained models (e.g., Segment Anything Model) and novel loss functions to handle partially labeled data or unsupervised learning scenarios. These advancements aim to enhance the generalizability and efficiency of segmentation models, leading to improved diagnostic tools and wider accessibility of AI-powered image analysis in healthcare and beyond.