Segment Anything Model-guided Collaborative Learning Network for Scribble-supervised Polyp Segmentation

• URL: http://arxiv.org/abs/2312.00312v1
• Date: Fri, 1 Dec 2023 03:07:13 GMT
• Title: Segment Anything Model-guided Collaborative Learning Network for Scribble-supervised Polyp Segmentation
• Authors: Yiming Zhao, Tao Zhou, Yunqi Gu, Yi Zhou, Yizhe Zhang, Ye Wu, Huazhu Fu
• Abstract summary: Polyp segmentation plays a vital role in accurately locating polyps at an early stage. pixel-wise annotation for polyp images by physicians during the diagnosis is both time-consuming and expensive. We propose a novel SAM-guided Collaborative Learning Network (SAM-CLNet) for scribble-supervised polyp segmentation.
• Score: 45.15517909664628
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Polyp segmentation plays a vital role in accurately locating polyps at an early stage, which holds significant clinical importance for the prevention of colorectal cancer. Various polyp segmentation methods have been developed using fully-supervised deep learning techniques. However, pixel-wise annotation for polyp images by physicians during the diagnosis is both time-consuming and expensive. Moreover, visual foundation models such as the Segment Anything Model (SAM) have shown remarkable performance. Nevertheless, directly applying SAM to medical segmentation may not produce satisfactory results due to the inherent absence of medical knowledge. In this paper, we propose a novel SAM-guided Collaborative Learning Network (SAM-CLNet) for scribble-supervised polyp segmentation, enabling a collaborative learning process between our segmentation network and SAM to boost the model performance. Specifically, we first propose a Cross-level Enhancement and Aggregation Network (CEA-Net) for weakly-supervised polyp segmentation. Within CEA-Net, we propose a Cross-level Enhancement Module (CEM) that integrates the adjacent features to enhance the representation capabilities of different resolution features. Additionally, a Feature Aggregation Module (FAM) is employed to capture richer features across multiple levels. Moreover, we present a box-augmentation strategy that combines the segmentation maps generated by CEA-Net with scribble annotations to create more precise prompts. These prompts are then fed into SAM, generating segmentation SAM-guided masks, which can provide additional supervision to train CEA-Net effectively. Furthermore, we present an Image-level Filtering Mechanism to filter out unreliable SAM-guided masks. Extensive experimental results show that our SAM-CLNet outperforms state-of-the-art weakly-supervised segmentation methods.

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