Shen Tongping, Li Xueguang
School of Information Engineering, Anhui University of Chinese Medicine, Hefei, China.
Graduate School, Angeles University Foundation, Angeles, Philippines.
Front Oncol. 2023 Jan 12;12:1087438. doi: 10.3389/fonc.2022.1087438. eCollection 2022.
The similar shape and texture of colonic polyps and normal mucosal tissues lead to low accuracy of medical image segmentation algorithms. To solve these problems, we proposed a polyp image segmentation algorithm based on deep learning technology, which combines a HarDNet module, attention module, and multi-scale coding module with the U-Net network as the basic framework, including two stages of coding and decoding. In the encoder stage, HarDNet68 is used as the main backbone network to extract features using four null space convolutional pooling pyramids while improving the inference speed and computational efficiency; the attention mechanism module is added to the encoding and decoding network; then the model can learn the global and local feature information of the polyp image, thus having the ability to process information in both spatial and channel dimensions, to solve the problem of information loss in the encoding stage of the network and improving the performance of the segmentation network. Through comparative analysis with other algorithms, we can find that the network of this paper has a certain degree of improvement in segmentation accuracy and operation speed, which can effectively assist physicians in removing abnormal colorectal tissues and thus reduce the probability of polyp cancer, and improve the survival rate and quality of life of patients. Also, it has good generalization ability, which can provide technical support and prevention for colon cancer.
结肠息肉与正常黏膜组织相似的形状和纹理导致医学图像分割算法的准确率较低。为了解决这些问题，我们提出了一种基于深度学习技术的息肉图像分割算法，该算法以U-Net网络为基本框架，将HarDNet模块、注意力模块和多尺度编码模块相结合，包括编码和解码两个阶段。在编码器阶段，使用HarDNet68作为主要骨干网络，通过四个零空间卷积池化金字塔提取特征，同时提高推理速度和计算效率；在编码和解码网络中添加注意力机制模块；这样模型就能学习息肉图像的全局和局部特征信息，从而具备在空间和通道维度上处理信息的能力，解决网络编码阶段的信息丢失问题，提高分割网络的性能。通过与其他算法的对比分析发现，本文网络在分割准确率和运算速度上有一定程度的提升，能够有效辅助医生切除结直肠异常组织，从而降低息肉癌变概率，提高患者生存率和生活质量。此外，它具有良好的泛化能力，可为结肠癌提供技术支持和预防手段。
