ARIFNet: an asymmetric reweighted information aggregation network for cellular lung segmentation

Aiming at the problem of cellular lung CT image segmentation difficulty caused by complex backgrounds, different shapes, and blurred tissue boundaries, an ARIFNet model with a codec structure is proposed. Firstly, depthwise separable convolution with fewer parameters is used as the decoder to reduce the complexity of the model and improve the segmentation efficiency. Second, a reweighted jump connection module RWC acting on jump connections is constructed to adjust the pixel weights according to the spatial locations of the features to suppress the expression of irrelevant information in the high-level features. Finally, the multi-scale information fusion module MIF is added to fuse multi-scale information to provide richer contextual information. Experiments showed that the ARIFNet model could segment the lung lesion region more accurately, and the segmentation indexes IoU, mIoU, Dice, and mDice reached 89.75%, 94.76%, 94.09%, and 96.99%, respectively.