DSGA-Net: Deeply separable gated transformer and attention strategy for medical image segmentation network

Abstract

To address the problems of under-segmentation and over-segmentation of small organs in medical image segmentation. We present a novel medical image segmentation network model with Depth Separable Gating Transformer and a Three-branch Attention module (DSGA-Net). Firstly, the model adds a Depth Separable Gated Visual Transformer (DSG-ViT) module into its Encoder to enhance (i) the contextual links among global, local, and channels and (ii) the sensitivity to location information. Secondly, a Mixed Three-branch Attention (MTA) module is proposed to increase the number of features in the up-sampling process. Meanwhile, the loss of feature information is reduced when restoring the feature image to the original image size. By validating Synapse, BraTs2020, and ACDC public datasets, the Dice Similarity Coefficient (DSC) of the results of DSGA-Net reached 81.24%,85.82%, and 91.34%, respectively. Moreover, the Hausdorff Score (HD) decreased to 20.91% and 5.27% on the Synapse and BraTs2020. There are 10.78% and 0.69% decreases compared to the Baseline TransUNet. The experimental results indicate that DSGA-Net achieves better segmentation than most advanced methods.

Keywords: Depth separable; Gated attention mechanism; Medical image segmentation; Transformer.

Fig. 1. Spatial feature pyramid attention mechanism module.

Fig. 2. DSG-ViT.

Fig. 3. MTA Block.

Fig. 4. Structure of the proposed DSGA-Net.

Fig. 5. Structure of the Encoder.

Fig. 6. Comparison of DSGA-Net modules segmentation.

Fig. 7. Study of the number of skip connections added to DSGA-Net.

Fig. 8. Segmentation results of different CNN-based models on Synapse Dataset.

Fig. 9. Segmentation Results of Different Variants of CNN and ViT on Synapse Dataset.