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SC-Net: Symmetrical conical network for colorectal pathology image segmentation 期刊论文

编号：

BC337E87E06039B362E14B8294FAE30D
作者：

Zhang, Gang#^[1]He, Zifen*^[1]Zhang, Yinhui^[1];Li, Zhenhui(李振辉)^[2]Wu, Lin^[2];
语种：

英文
期刊：

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE ISSN：0169-2607 2024 年 248 卷 ; MAY
收录：
关键词：

Pathology image segmentation Symmetric conical network Multi-branch Colorectal cancer
摘要：

Background and Objective: Image segmentation of histopathology of colorectal cancer is a core task of computer aided medical image diagnosis system. Existing convolutional neural networks generally extract multi-scale information in linear flow structures by inserting multi-branch modules, which is difficult to extract heterogeneous semantic information under multi-level and different receptive field and tough to establish context dependency among different receptive field features. Methods: To address these issues, we propose a symmetric spiral progressive feature fusion encoder-decoder network called the Symmetric Conical Network (SC-Net). First, we design a Multi-scale Feature Extraction Block (MFEB) matching with the Symmetric Conical Network to obtain multi-branch heterogeneous semantic information under different receptive fields, so as to enrich the diversity of extracted feature information. The encoder is composed of MFEB through spiral and multi-branch arrangement to enhance context dependence between different information flow. Secondly, the information loss of contour, color and others in high-level semantic information through causally stacking MFEB, the Feature Mapping Layer (FML) is designed to map low-level features to high-level semantic features along the down-sampling branch and solve the problem of insufficient global feature extraction in deep levels. Results: The SC-Net was evaluated on our self-constructed colorectal cancer dataset, a publicly available breast cancer dataset and a polyp dataset. The results revealed that the mDice of segmentation reached 0.8611, 0.7259 and 0.7144. We compare our model with the state-of-art semantic segmentation UNet++, PSPNet, Attention U-Net, R2U-Net and other advanced segmentation networks. The experimental results demonstrate that we achieve the most advanced performance. Conclusions: The results indicate that the proposed SC-Net excels in segmenting H & E stained pathology images, effectively preserving morphological features and spatial information even in scenarios with weak texture, poor contrast, and variations in appearance.
推荐引用方式
GB/T 7714：

Zhang Gang,He Zifen,Zhang Yinhui, et al. SC-Net: Symmetrical conical network for colorectal pathology image segmentation [J].COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,2024,248.
APA：

Zhang Gang,He Zifen,Zhang Yinhui,Li Zhenhui,&Wu Lin.(2024).SC-Net: Symmetrical conical network for colorectal pathology image segmentation .COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,248.
MLA：

Zhang Gang, et al. "SC-Net: Symmetrical conical network for colorectal pathology image segmentation" .COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 248(2024).
入库时间：

2024/10/31 9:47:33
更新时间：

2024/10/31 9:47:33
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