SRPA: ScribbleMatch and Reliable-Guided Pixel Alignment for Scribble-Supervised Medical Image Segmentation

Medical image segmentation is a critical task in the field of medical image analysis. Recently, there has been increasing attention on scribble-supervised medical image segmentation due to its simplicity for label generation. However, the performance of a scribble-based task highly relies on the quality of learning from inconsistent annotations and the classification of pixels in high-entropy regions. In this paper, we propose a novel framework called SRPA that combines both ScribbleMatch and Reliable-Guided Pixel Alignment to enhance the performance of the scribble-based task. The ScribbleMatch technique utilizes the pseudo label incorporated from two different weakly perturbed views of the same image to supervise a strongly perturbed view, which assists in boosting the quality of the shape information learning as the scribble-based task lacks the prototypes to consistently capture shape prior to model training. The Reliable-Guided Pixel Alignment technique employs reliable pixels selected by contrasting two weakly perturbed views, which serve as the standard for blurred pixels in strongly perturbed images to maximally align. This ensures the reliable classification of high-entropy pixels. Our method is evaluated on the public ACDC and MSCMRseg datasets, and the results demonstrate that our approach surpasses current scribble-supervised segmentation methods. Code will be available at https://github.com/RheinSXY/SRPA.