Trans-UNeter: A new Decoder of TransUNet for Medical Image Segmentation

Recently, how to integrate convolutional neural networks and transformers into a U-Net-like encoder-decoder structure has drawn growing interest in medical image segmentation, as transformer is more efficient in capturing longrange relations. Following this line of research, TransUNet is one representative work. However, it still insufficiently explores the rich relations of features from the encoder layer and the decoder layer with just a simple concatenation, which weakens their effectiveness to some extent. To address this issue, we propose two important design improvements to strengthen TransUNet: 1) a novel skip connection module, which upsamples the high-level semantic features and fuse it with low-level features, producing comprehensive semantic-aware features for the decoder. 2) an improved decoder network cascades reverse attention and spatial attention to adaptively combines features from the corresponding encoder layer and the previously decoded outputs.The results of the abdominal multi-organ segmentation experiment on the Synapse multi-organ segmentation dataset indicated that Trans-UNeter improved the mean similarity coefficient(DSC) by 3.71% compared to TransUNet. Code and models are available at https://github.com/iaoqin/Trans-UNeter.