Scribble Supervised Medical Image Segmentation

Scribble-supervised medical image segmentation aims to train accurate segmentation models using only sparse scribble annotations instead of laborious pixel-wise labeling. Current research focuses on developing novel architectures, often hybrid CNN-Transformer models or dual-branch networks, and sophisticated algorithms that leverage techniques like masked context modeling, continuous pseudo-labels, and positive-unlabeled learning to effectively utilize the limited scribble information. This approach significantly reduces annotation costs, making advanced segmentation techniques more accessible for applications where labeled data is scarce, ultimately improving the efficiency and scalability of medical image analysis.