Multi-scale feature fusion network with local attention for lung segmentation

Computer-assisted medical care can benefit from the lung region segmentation method. Numerous methods provide end-to-end solutions, these methods employ convolution neural networks to segment lung regions from images. The low contrast, unpredictable appearance, and other problems in medical images have an effect on the accuracy of existing methods. In order to overcome the aforementioned issues, the MSDC (multi-scale dilated convolution) module is added to the short-cut connection, so as to fuse multi-scale features with various receptive fields to obtain more global information of lung area. Moreover, a local attention module which includes channel attention and spatial attention is suggested to give more weight to the lung area to lower the influence of background. Several lung segmentation datasets are employed to evaluate the segmentation performance of images qualitatively and quantitatively. From the experimental results, we can see that the segmentation accuracy of our model outperforms many recent image segmentation methods.