Abstract
Accurate segmentation of COVID-19 CT images is crucial for reducing the severity and mortality rates associated with COVID-19 infections. In response to blurred boundaries and high variability characteristic of lesion areas in COVID-19 CT images, we introduce CDSE-UNet: a novel UNet-based segmentation model that integrates Canny operator edge detection and a dual-path SENet feature fusion mechanism. This model enhances the standard UNet architecture by employing the Canny operator for edge detection in sample images, paralleling this with a similar network structure for semantic feature extraction. A key innovation is the Double SENet Feature Fusion Block, applied across corresponding network layers to effectively combine features from both image paths. Moreover, we have developed a Multiscale Convolution approach, replacing the standard Convolution in UNet, to adapt to the varied lesion sizes and shapes. This addition not only aids in accurately classifying lesion edge pixels but also significantly improves channel differentiation and expands the capacity of the model. Our evaluations on public datasets demonstrate CDSE-UNet's superior performance over other leading models, particularly in segmenting large and small lesion areas, accurately delineating lesion edges, and effectively suppressing noise
Abstract (translated)
准确分割 COVID-19 CT 图像对降低由 COVID-19 感染引起的严重程度和死亡率至关重要。为应对 COVID-19 CT 图像中模糊的边界和高变异性特征区域，我们引入了 CDSE-UNet：一种新颖的 UNet 分割模型，它整合了 Canny 操作符边缘检测和双路径 SENet 特征融合机制。通过在样本图像中使用 Canny 操作符进行边缘检测，并与同构网络结构进行 semantic 特征提取，从而增强 standard UNet 架构。关键创新是应用 Double SENet Feature Fusion Block，跨相应网络层有效地结合图像路径的特征。此外，我们还开发了多尺度卷积方法，用 UNet 的标准卷积替换，以适应各种病变尺寸和形状。这一改进不仅有助于准确分类病变边缘像素，而且显著提高了通道差异，扩展了模型的容量。我们在公共数据集上的评估表明，CDSE-UNet 的性能超过其他领先模型，尤其是在分割大和小型病变区域、准确描绘病变边缘和有效抑制噪声方面。
URL
https://arxiv.org/abs/2403.01513
