Beyond Pixel-Wise Supervision for Medical Image Segmentation: From Traditional Models to Foundation Models

Understand

Medical image segmentation plays an important role in many image-guided clinical approaches.

• However, existing segmentation algorithms mostly rely on the availability of fully annotated images with pixel-wise annotations for training, which can be both labor-intensive and expertise-demanding, especially in the medical imaging domain where only experts can provide reliable and accurate annotations.
• To alleviate this challenge, there has been a growing focus on developing segmentation methods that can train deep models with weak annotations, such as image-level, bounding boxes, scribbles, and points.
• The emergence of vision foundation models, notably the Segment Anything Model (SAM), has introduced innovative capabilities for segmentation tasks using weak annotations for promptable segmentation enabled by large-scale pre-training.