Polyp semantic segmentation model based on local context fusion
10.3969/j.issn.1005-202X.2025.01.017
- VernacularTitle:基于局部上下文融合的息肉语义分割模型
- Author:
Tijian CAI
1
;
Jiahao JIANG
1
;
Zunxiong LIU
1
;
Shiming ZHAO
1
;
Shengquan YI
1
Author Information
1. 华东交通大学信息与软件工程学院,江西南昌330013
- Publication Type:Journal Article
- Keywords:
colorectal cancer;
polyp segmentation;
deep learning;
dilated convolution;
context information
- From:
Chinese Journal of Medical Physics
2025;42(1):128-134
- CountryChina
- Language:Chinese
-
Abstract:
A local context fusion based segmentation model which uses a local context attention mechanism to filter out irrelevant feature information and enhance the attention to important regions is presented for accurate polyp segmentation. The features at different scales are captured by multi-kernel dilated convolution for improving the accuracy of polyp boundary segmentation. Pyramid context selection module utilizes shallow encoder features to compensate for the low-level information lost by the deeper encoder,enabling the model to adapt to polyps of various sizes. The proposed model achieves accuracies of 97.67％,97.19％ and 99.23％ on Kvasir-SEG,EndoScene and CVC-ClinicDB datasets,respectively,with mIoU of 91.20％,88.31％ and 94.75％,respectively,exhibiting higher accuracy and generalizability than the existing classical methods and validating its superior performance in polyp segmentation. The proposed model can improve polyp segmentation accuracy and provide a more accurate aid for polyp segmentation.
