SCU-Net++: A Nested U-Net Based on Sharpening Filter and Channel Attention Mechanism

Abstract

U-Net++ is one of the most prominent deep convolutional neural networks in the field of medical image segmentation after U-Net. However, the semantic gaps between the encoder and decoder subnets are still large, which will lead to fuzzy feature maps and even target regions of segmentation. To solve this problem, we propose an improved semantic segmentation model utilizing channel attention mechanism and Laplacian sharpening filter, SCU-Net++: dense skip connections are redesigned with sharpening filters to ease the semantic gaps, and channel attention modules are used to make the model pay more attention on the feature maps that are useful for our pixel-level classification task. Compared with U-Net++, the proposed model obtains a more competitive performance on the Pancreas Segmentation dataset and Liver Tumor Segmentation dataset, while increases a very small number of learnable parameters and thus almost does not make additional training and reasoning costs. The training of the proposed method is carried out in deep supervision mode, which alleviates the problem of gradient disappearance, and pruning mechanism can be activated to accelerate the reasoning speed.