Weakly Supervised Medical Image Segmentation via Superpixel-Guided Scribble Walking and Class-Wise Contrastive Regularization

Deep learning-based segmentation typically requires a large amount of data with dense manual delineation, which is both time-consuming and expensive to obtain for medical images. Consequently, weakly supervised learning, which attempts to utilize sparse annotations such as scribbles for effective training, has garnered considerable attention. However, such scribble-supervision inherently lacks sufficient structural information, leading to two critical challenges: (i) while achieving good performance in overall overlap metrics such as Dice score, the existing methods struggle to perform satisfactory local prediction because no desired structural priors are accessible during training; (ii) the class feature distributions are inevitably less-compact due to sparse and extremely incomplete supervision, leading to poor generalizability. To address these, in this paper, we propose the SC-Net, a new scribble-supervised approach that combines Superpixel-guided scribble walking with Class-wise contrastive regularization. Specifically, the framework is built upon the recent dual-decoder backbone design, where predictions from two slightly different decoders are randomly mixed to provide auxiliary pseudo-label supervision. Besides the sparse and pseudo supervision, the scribbles walk towards unlabeled pixels guided by superpixel connectivity and image content to offer as much dense supervision as possible. Then, the class-wise contrastive regularization disconnects the feature manifolds of different classes to encourage the compactness of class feature distributions. We evaluate our approach on the public cardiac dataset ACDC and demonstrate the superiority of our method compared to recent scribble-supervised and semi-supervised learning methods with similar labeling efforts.