ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding
This work addresses the problem of reducing annotation burden for medical image segmentation, which is incremental as it builds on existing scribble-supervised approaches.
The paper tackles the challenge of accurate medical image segmentation with limited annotations by proposing ScribbleVC, a scribble-supervised framework that leverages vision and class embeddings, achieving improved performance over state-of-the-art methods on three benchmark datasets.
Medical image segmentation plays a critical role in clinical decision-making, treatment planning, and disease monitoring. However, accurate segmentation of medical images is challenging due to several factors, such as the lack of high-quality annotation, imaging noise, and anatomical differences across patients. In addition, there is still a considerable gap in performance between the existing label-efficient methods and fully-supervised methods. To address the above challenges, we propose ScribbleVC, a novel framework for scribble-supervised medical image segmentation that leverages vision and class embeddings via the multimodal information enhancement mechanism. In addition, ScribbleVC uniformly utilizes the CNN features and Transformer features to achieve better visual feature extraction. The proposed method combines a scribble-based approach with a segmentation network and a class-embedding module to produce accurate segmentation masks. We evaluate ScribbleVC on three benchmark datasets and compare it with state-of-the-art methods. The experimental results demonstrate that our method outperforms existing approaches in terms of accuracy, robustness, and efficiency. The datasets and code are released on GitHub.