ZScribbleSeg: Zen and the Art of Scribble Supervised Medical Image Segmentation

Understand

Curating a large scale fully-annotated dataset can be both labour-intensive and expertise-demanding, especially for medical images.

• To alleviate this problem, we propose to utilize solely scribble annotations for weakly supervised segmentation.
• Existing solutions mainly leverage selective losses computed solely on annotated areas and generate pseudo gold standard segmentation by propagating labels to adjacent areas.
• However, these methods could suffer from the inaccurate and sometimes unrealistic pseudo segmentation due to the insufficient supervision and incomplete shape features.