School of Electrical and Information Engineering, Zhengzhou University, Henan Province, 450001, China; Robot Perception and Control Engineering Laboratory of Henan Province, 450001, China.
School of Electrical and Information Engineering, Zhengzhou University, Henan Province, 450001, China; Robot Perception and Control Engineering Laboratory of Henan Province, 450001, China.
Comput Biol Med. 2023 Sep;164:107301. doi: 10.1016/j.compbiomed.2023.107301. Epub 2023 Aug 7.
Colorectal cancer is a prevalent disease in modern times, with most cases being caused by polyps. Therefore, the segmentation of polyps has garnered significant attention in the field of medical image segmentation. In recent years, the variant network derived from the U-Net network has demonstrated a good segmentation effect on polyp segmentation challenges. In this paper, a polyp segmentation model, called CFHA-Net, is proposed, that combines a cross-scale feature fusion strategy and a hybrid attention mechanism. Inspired by feature learning, the encoder unit incorporates a cross-scale context fusion (CCF) module that performs cross-layer feature fusion and enhances the feature information of different scales. The skip connection is optimized by proposed triple hybrid attention (THA) module that aggregates spatial and channel attention features from three directions to improve the long-range dependence between features and help identify subsequent polyp lesion boundaries. Additionally, a dense-receptive feature fusion (DFF) module, which combines dense connections and multi-receptive field fusion modules, is added at the bottleneck layer to capture more comprehensive context information. Furthermore, a hybrid pooling (HP) module and a hybrid upsampling (HU) module are proposed to help the segmentation network acquire more contextual features. A series of experiments have been conducted on three typical datasets for polyp segmentation (CVC-ClinicDB, Kvasir-SEG, EndoTect) to evaluate the effectiveness and generalization of the proposed CFHA-Net. The experimental results demonstrate the validity and generalization of the proposed method, with many performance metrics surpassing those of related advanced segmentation networks. Therefore, proposed CFHA-Net could present a promising solution to the challenges of polyp segmentation in medical image analysis. The source code of proposed CFHA-Net is available at https://github.com/CXzhai/CFHA-Net.git.
结直肠癌是现代社会的一种常见疾病，大多数病例是由息肉引起的。因此，息肉的分割在医学图像分割领域引起了广泛关注。近年来，从 U-Net 网络衍生出的变体网络在息肉分割挑战中表现出了很好的分割效果。本文提出了一种名为 CFHA-Net 的息肉分割模型，该模型结合了跨尺度特征融合策略和混合注意力机制。受特征学习的启发，编码器单元采用了跨尺度上下文融合（CCF）模块，该模块执行跨层特征融合，增强了不同尺度的特征信息。通过提出的三重混合注意力（THA）模块对跳连接进行了优化，该模块从三个方向聚合空间和通道注意力特征，提高了特征之间的长程依赖关系，有助于识别后续的息肉病变边界。此外，在瓶颈层添加了密集接收特征融合（DFF）模块，该模块结合了密集连接和多接收场融合模块，以捕获更全面的上下文信息。此外，提出了混合池化（HP）模块和混合上采样（HU）模块，以帮助分割网络获取更多的上下文特征。在三个典型的息肉分割数据集（CVC-ClinicDB、Kvasir-SEG、EndoTect）上进行了一系列实验，以评估所提出的 CFHA-Net 的有效性和泛化能力。实验结果表明，所提出的方法是有效的和具有泛化性的，许多性能指标都超过了相关的先进分割网络。因此，所提出的 CFHA-Net 可以为医学图像分析中的息肉分割挑战提供一种有前途的解决方案。所提出的 CFHA-Net 的源代码可在 https://github.com/CXzhai/CFHA-Net.git 上获得。
