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Medical image segmentation plays a critical role in clinical decision-making,treatment planning, and disease monitoring. However, accurate segmentation ofmedical images is challenging due to several factors, such as the lack ofhigh-quality annotation, imaging noise, and anatomical differences acrosspatients. In addition, there is still a considerable gap in performance betweenthe existing label-efficient methods and fully-supervised methods. To addressthe above challenges, we propose ScribbleVC, a novel framework forscribble-supervised medical image segmentation that leverages vision and classembeddings via the multimodal information enhancement mechanism. In addition,ScribbleVC uniformly utilizes the CNN features and Transformer features toachieve better visual feature extraction. The proposed method combines ascribble-based approach with a segmentation network and a class-embeddingmodule to produce accurate segmentation masks. We evaluate ScribbleVC on threebenchmark datasets and compare it with state-of-the-art methods. Theexperimental results demonstrate that our method outperforms existingapproaches in terms of accuracy, robustness, and efficiency. The datasets andcode are released on GitHub.

ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding