Abstract:
COVID-19 has severely been threatening human being. The computer aided automatic segmentation of lung CT (computed tomography) images is an important way to help medicine doctors to make fast and accurate diagnostic decisions. Therefore, a lightweight model referred to as COVIDSeg is proposed in this paper for segmenting the lung CT images of COVID-19 patients. This model adopts encoder-decoder structure. The squeeze and extend channel attention block (SECA) and the residual multi-scale channel attention block (RMSCA) are proposed to compose the main components of the encoder subnetwork. The dual-path structure is proposed to connect each module of the encoder network. The features are transferred layer by layer along the path, and features from different layers interact between pathways, so that the feature information can be transferred and expressed between different layers. The feature aggregation module is used as the main component of the decoder network, so as to realize the multipath decoder via multiscale feature decoding. This COVIDSeg model is tested on four public COVID-19 CT image datasets. The experimental results show that the proposed lightweight model COVIDSeg for segmenting the COVID-19 CT images outperforms the current main medical image segmentation models in terms of several popular metrics, and it is so far the best model for segmenting the lung CT images of COVID-19 patients. Furthermore, the ablation experiment was carried out to test the influence over the COVIDSeg model performance from its main modules. The experimental results show that the proposed SECA module and RMSCA module are effective to advance the performance of the COVIDSeg model.The segmentation results are visualized for this COVIDSeg model for the lung CT images of COVID-19 patients, and it is found that the segmentation results are basically identical to the real Mask of the images.
