D-TrAttUnet: Dual-Decoder Transformer-Based Attention Unet Architecture for Binary and Multi-classes Covid-19 Infection Segmentation

TL;DR

This paper introduces D-TrAttUnet, a novel Transformer-CNN based dual-decoder architecture for accurate Covid-19 infection segmentation from CT scans, outperforming existing models in binary and multi-class tasks.

Contribution

The paper presents a new Transformer-CNN encoder with dual CNN decoders and attention gates, specifically designed for Covid-19 segmentation, demonstrating superior performance over existing architectures.

Findings

Outperforms baseline CNN models in segmentation accuracy.

Effective in handling limited data for Covid-19 segmentation.

Achieves higher accuracy in both binary and multi-class segmentation tasks.

Abstract

In the last three years, the world has been facing a global crisis caused by Covid-19 pandemic. Medical imaging has been playing a crucial role in the fighting against this disease and saving the human lives. Indeed, CT-scans has proved their efficiency in diagnosing, detecting, and following-up the Covid-19 infection. In this paper, we propose a new Transformer-CNN based approach for Covid-19 infection segmentation from the CT slices. The proposed D-TrAttUnet architecture has an Encoder-Decoder structure, where compound Transformer-CNN encoder and Dual-Decoders are proposed. The Transformer-CNN encoder is built using Transformer layers, UpResBlocks, ResBlocks and max-pooling layers. The Dual-Decoder consists of two identical CNN decoders with attention gates. The two decoders are used to segment the infection and the lung regions simultaneously and the losses of the two tasks are joined. The proposed D-TrAttUnet architecture is evaluated for both Binary and Multi-classes Covid-19 infection segmentation. The experimental results prove the efficiency of the proposed approach to deal with the complexity of Covid-19 segmentation task from limited data. Furthermore, D-TrAttUnet architecture outperforms three baseline CNN segmentation architectures (Unet, AttUnet and Unet++) and three state-of-the-art architectures (AnamNet, SCOATNet and CopleNet), in both Binary and Mutli-classes segmentation tasks.