MSBP-Net: A multi-scale boundary prediction network for automated polyp segmentation.
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| Title: | MSBP-Net: A multi-scale boundary prediction network for automated polyp segmentation. |
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| Authors: | Pan, Xing-Liang1,2 (AUTHOR), Ding, Ju-Rong1,2 (AUTHOR) jurongding@gmail.com, Li, Xia1,2 (AUTHOR), Liu, Shuo1,2 (AUTHOR), Wang, Jie1,2 (AUTHOR), Hua, Bo1,2 (AUTHOR), Tang, Guo-Zhi1,2 (AUTHOR), Zhong, Chang-Hua1,2 (AUTHOR) |
| Source: | Pattern Recognition. Feb2026, Vol. 170, pN.PAG-N.PAG. 1p. |
| Subjects: | Colon polyps, Image segmentation, Machine learning, Deep learning, Image processing, Colorectal cancer, Edge detection (Image processing) |
| Abstract: | Accurate polyp segmentation is a critical step in the early diagnosis of colorectal cancer. Predicting the polyp boundaries with powerful camouflage properties is an intricate challenge in automatic segmentation tasks. Here, we propose a multi-scale boundary prediction network (MSBP-Net) for effective polyp segmentation with low complexity. The MSBP-Net consists of a pre-trained pyramid vision transformer and a novel lightweight decoder which contains three modified receptive field blocks (mRFBs), three boundary prediction modules (BPMs) and a shallow filtering module (SFM). Firstly, the mRFBs are developed to suppress redundant information and irrelevant background. Secondly, the BMPs are built based on reverse attention and multi-scale criss-cross attention to efficiently explore boundaries and fuse multi-scale information for recovering coarse-grained polyp masks. Then, the SFM is proposed based on Laplacian edge operators, which mines boundary cues in the shallow layers as a supplement to the coarse-grained masks and finally gains fine-grained segmentation masks. We conduct extensive comparative experiments on five public datasets. The ablation results prove the effectiveness of the proposed modules, achieving a highest dice similarity coefficient of 0.940. Compared to existing state-of-the-art methods, the MSBP-Net performs close to the optimal methods on the five test sets, while reducing the complexity by at least 20.2 %. The lightweight design of the MSBP-Net achieves a speed of 41 frames per second on a 3070 GPU (8 GB memory). Therefore, the MSBP-Net is a promising polyp segmentation method with robust performance and low complexity, and has the potential for real-time segmentation tasks. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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