A novel CT image segmentation model

Convolutional neural networks (CNNs) can be used for clinical medical image segmentation to improve detection efficiency and accuracy. However, because of the fixed size of convolution kernel and receptive field for existing CNN model, some associated pixel features are ignored and segmentation performance is impaired. Therefore, we propose an effective image segmentation model, COPANet, which uses RepVGG as the backbone and replaces the standard convolution with dilated convolution of multiple kernels to increase the receptive field size, so that COPANet can make full use of pixel correlations and acquire contexture semantic information. We also build skip-connections between global and local features, where a parallel attention (PANet) is employed to extract important location information in the downsampling. PANet is a fused mechanism integrating channel attention with spatial attention in parallel, which can fully extract more global information. We also apply weighted combined loss to reduce the effect of class imbalance of foreground and background pixels on segmentation performance and speed up convergence. In addition, we conduct experiments on 480 cases of clinical CT sinus from Shanghai Tongji Hospital and 236 cases of CT patella fracture from Shanghai Sixth People's Hospital. The evaluation indexes after 5-Fold cross-validation are as follows: Precision is 92.76% and 96.55%, Recall is 88.25% and 57.58%, Specificity is 99.85% and 96.55%, IOU is 91.08% and 77.38%, Hausdorff Distance is 2.8135 and 11.1879, respectively. Compared with the state-of-the-art models, COPANet has higher segmentation accuracy and better generalization performance, which can assist the clinical diagnosis.