SparseMamba-PCL: Scribble-Supervised Medical Image Segmentation via SAM-Guided Progressive Collaborative Learning

• URL: http://arxiv.org/abs/2503.01633v1
• Date: Mon, 03 Mar 2025 15:09:04 GMT
• Title: SparseMamba-PCL: Scribble-Supervised Medical Image Segmentation via SAM-Guided Progressive Collaborative Learning
• Authors: Luyi Qiu, Tristan Till, Xiaobao Guo, Adams Wai-Kin Kong,
• Abstract summary: We propose a Progressive Collaborative Learning framework to enhance information quality during training.<n>We enrich ground truth scribble segmentation labels through a new algorithm, propagating scribbles to estimate object boundaries.<n>We enhance feature representation by optimizing Med-SAM-guided training through the fusion of feature embeddings from Med-SAM and our proposed Sparse Mamba network.
• Score: 9.228586820098723
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scribble annotations significantly reduce the cost and labor required for dense labeling in large medical datasets with complex anatomical structures. However, current scribble-supervised learning methods are limited in their ability to effectively propagate sparse annotation labels to dense segmentation masks and accurately segment object boundaries. To address these issues, we propose a Progressive Collaborative Learning framework that leverages novel algorithms and the Med-SAM foundation model to enhance information quality during training. (1) We enrich ground truth scribble segmentation labels through a new algorithm, propagating scribbles to estimate object boundaries. (2) We enhance feature representation by optimizing Med-SAM-guided training through the fusion of feature embeddings from Med-SAM and our proposed Sparse Mamba network. This enriched representation also facilitates the fine-tuning of the Med-SAM decoder with enriched scribbles. (3) For inference, we introduce a Sparse Mamba network, which is highly capable of capturing local and global dependencies by replacing the traditional sequential patch processing method with a skip-sampling procedure. Experiments on the ACDC, CHAOS, and MSCMRSeg datasets validate the effectiveness of our framework, outperforming nine state-of-the-art methods. Our code is available at \href{https://github.com/QLYCode/SparseMamba-PCL}{SparseMamba-PCL.git}.

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