Medical Image Segmentation Model

Medical image segmentation models automatically delineate anatomical structures or regions of interest within medical images, aiding diagnosis and treatment planning. Current research emphasizes improving model generalizability across diverse imaging modalities and datasets, with a focus on lightweight architectures like Swin Transformers and adaptations of the Segment Anything Model (SAM) to reduce computational demands. Evaluation methods are evolving to incorporate volumetric accuracy and confidence intervals, addressing limitations of traditional metrics and enhancing the clinical relevance of segmentation results. These advancements aim to improve the reliability and usability of these models in real-world healthcare settings.