面向模糊医学图像边缘检测的卷积网络

考虑到传统边缘检测算法难以处理模糊的医学图像, 提出一种基于深度学习的边缘检测网络ECENet. 首先, 本文网络基于CHRNet模型, 对其最后两层进行剪枝, 使模型更加高效和轻量化. 其次, 在网络的特征提取阶段加入注意力模块SKSAM, 优化图像特征的自适应提取, 并降低噪声的影响. 最后, 在多尺度的网络输出上采用上下文感知融合块进行连接, 帮助模型更好地理解图像的结构和语义信息. 此外, 综合考虑像素级别的准确性和边界的平滑性, 优化了损失函数, 为模型训练提供更好的梯度信号. 实验结果表明: 本文算法在最佳数据集规模(ODS)和最佳图像比例(OIS)指标分别提高到0.816和0.823; 相关边缘指标参数显著提高, PSNR提高了16.8%, SSIM提高了37.6%.

Convolutional Network for Edge Detection in Blurred Medical Images

Considering that traditional edge detection algorithms are difficult to handle blurred medical images, this study proposes an edge detection network ECENet based on deep learning. First, the network is based on the CHRNet model, and its last two layers are pruned to make the model more efficient and lightweight. Secondly, the attention module SKSAM is added to the feature extraction stage of the network to optimize the adaptive extraction of image features and reduce the impact of noise. Finally, context-aware fusion blocks are applied to connect multi-scale network outputs to help the model better understand the structure and semantic information of the image. In addition, considering the pixel-level accuracy and the smoothness of the boundary, the loss function is optimized to provide better gradient signals for model training. Experimental results show that the proposed algorithm increases optimal data set size (ODS) and optimal image ratio (OIS) indicators to 0.816 and 0.823 respectively; the relevant edge indicator parameters were significantly improved, with PSNR increased by 16.8% and SSIM by 37.6%.