Dfp-Unet: A Biomedical Image Segmentation Method Based on Deformable Convolution and Feature Pyramid

U-net is a classic deep network framework in the field of biomedical image segmentation, which uses a U-shaped encoder and decoder structure to realize the recognition and segmentation of semantic features, but only uses the last layer of the decoder structure for the final prediction, ignoring the feature maps of different levels of semantic strength. In addition, the convolution kernel size used by U-net is fixed, which is poorly adaptable to unknown changes. Therefore, we propose Dfp-Unet based on deformable convolution and feature pyramid for biomedical image segmentation. Dfp-Unet uses the idea of feature pyramid to respectively add an additional independent path including convolution and up-sampling operations to each level of the decoder. Then, the output feature maps of all levels are concatenated to obtain the final feature map containing multiple levels of semantic information for final prediction. Besides, Dfp-Unet replaces the convolution in the down-sampling modules with a deformable convolution on the basis of U-net. To verify the performance of Dfp-Unet, four image segmentation data sets including Sunnybrook, ISIC2017, Covid19-ct-scans, and ISBI2012 are used to compare Dfp-Unet with the existing convolutional neural networks (U-net and U-net++), and the experimental results show that Dfp-Unet has high segmentation accuracy and generalization performance.