TDD-UNet:Transformer with double decoder UNet for COVID-19 lesions segmentation. (December 2022)

• Record Type: Journal Article
• Authors: Huang, Xuping; Chen, Junxi; Chen, Mingzhi; Chen, Lingna; Wan, Yaping
• Is Part Of: Computers in biology and medicine. Volume 151:Part A(2022)
• Publication Date: 2022-12
• Subjects: COVID-19 -- UNet -- Transformer -- Dual decoding module -- Self-attention; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106306
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The outbreak of new coronary pneumonia has brought severe health risks to the world. Detection of COVID-19 based on the UNet network has attracted widespread attention in medical image segmentation. However, the traditional UNet model is challenging to capture the long-range dependence of the image due to the limitations of the convolution kernel with a fixed receptive field. The Transformer Encoder overcomes the long-range dependence problem. However, the Transformer-based segmentation approach cannot effectively capture the fine-grained details. We propose a transformer with a double decoder UNet for COVID-19 lesions segmentation to address this challenge, TDD-UNet. We introduce the multi-head self-attention of the Transformer to the UNet encoding layer to extract global context information. The dual decoder structure is used to improve the result of foreground segmentation by predicting the background and applying deep supervision. We performed quantitative analysis and comparison for our proposed method on four public datasets with different modalities, including CT and CXR, to demonstrate its effectiveness and generality in segmenting COVID-19 lesions. We also performed ablation studies on the COVID-19-CT-505 dataset to verify the effectiveness of the key components of our proposed model. The proposed TDD-UNet also achieves higher Dice and Jaccard mean scores and the lowest standard deviation compared to competitors. Our proposed method achieves better segmentation results than other state-of-the-art methods. Highlights: We combine UNet's encoder with MHSA in Transformer to gain a larger receptive field. We apply deep supervision and a dual decoder to promote final segmentation. We present the evaluation of our method on four datasets with different modalities.