U-net-based enhanced COVID-19 lung automatic segmentation model for infected regions of CT images

COVID-19 outbreak has so far caused us much inconvenience, resulting in significant economic losses and casualties. CT scanning, as a fast and sensitive detection tool, plays an essential role in delineating infected regions. However, CT images of COVID-19 exhibit complex variations in infected regions and have limited expert annotation data. In this research, a novel Dilated Convolution (DC) module, incorporating a residual structure, is introduced to systematically broaden the sensory field of the model. Following this, the Pyramid Pooling Module (PPM) and Multiscale Attention Module (MSA) are synergistically fused to create an advanced multilayer attention module. Finally, the proposed Enhanced Decoder Path (EDPath) module serves the crucial role of bridging the gap between the encoder and decoder. This module effectively resolves challenges associated with the loss of high-resolution information and the issue of gradient vanishing during decoder transmission. A large number of ablation experiments and comparative experiments have demonstrated that each of the proposed modules is effective in improving the model performance, and the model outperforms the more advanced models in terms of DICE, IOU and SEN. This experimental study demonstrates that an improved segmentation network model based on Unet and attention mechanisms has high accuracy in the COVID-19 lesion segmentation task.