Segmentation Target

Segmentation target research focuses on improving the accuracy and efficiency of image segmentation, particularly in challenging domains like medical imaging and microscopy. Current efforts concentrate on developing flexible and universal segmentation models capable of handling diverse targets and granularities, often leveraging large language models or advanced architectures like U-Nets and transformers to achieve this. These advancements are crucial for improving the reliability and applicability of automated image analysis across various scientific fields and clinical applications, such as radiotherapy planning and disease diagnosis. The development of robust uncertainty quantification methods further enhances the trustworthiness and interpretability of segmentation results.