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.

翻译：医学图像分割在临床决策、治疗规划和疾病监测中扮演着关键角色。然而，由于高质量标注的缺乏、成像噪声以及患者间解剖差异等因素，医学图像的精确分割仍面临挑战。此外，现有标签高效方法与全监督方法之间仍存在显著的性能差距。为解决上述挑战，我们提出了ScribbleVC——一种新颖的涂鸦监督医学图像分割框架，通过多模态信息增强机制利用视觉和类别嵌入。同时，ScribbleVC统一利用CNN特征与Transformer特征以实现更优的视觉特征提取。该方法结合涂鸦式方法、分割网络和类别嵌入模块，生成精确的分割掩膜。我们在三个基准数据集上评估ScribbleVC，并与最新方法进行对比。实验结果表明，本方法在准确性、鲁棒性和效率方面均优于现有方法。数据集和代码已在GitHub上发布。