SEGMENTATION METHOD BASED ON PYRAMID CONVOLUTION AND PIXEL ATTENTION

To address the problems of large variation in size and complex structure of segmentation targets and poor learning of target edge details by neural networks in medical image segmentation tasks, we propose a pyramidal dilated convolution and pixel-level attention network (DP-Net) based on the U-Net network. The dilated convolution pyramid module was constructed and designed to replace the traditional convolution operation, which extended the network perceptual field and encoded the global contextual information through the combination of multiple dilated convolutions. A pixel-level attention module was proposed to further encode inter-pixel dependencies based on the channel attention mechanism enabling the network to learn richer local contextual information from the features of different channels. Through experimental evaluation on the open lung dataset LIDC and private liver tumor dataset, the proposed DP-Net obtains better performance than current methods on all three kind of evaluation metrics, demonstrating the effectiveness of the proposed network improvement in terms of segmentation accuracy.