Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images

TL;DR

This paper introduces a novel deep learning model combining residual transformations and attention mechanisms for accurate multi-class segmentation of COVID-19 lung infections in CT images, aiding diagnosis.

Contribution

The study presents a new residual attention U-Net architecture specifically designed for COVID-19 CT image segmentation, improving upon existing methods.

Findings

Outperforms competing segmentation methods on public dataset.

Achieves high accuracy in delineating COVID-19 infection regions.

Provides a promising tool for quantitative COVID-19 diagnosis.

Abstract

The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy. However, there is still lack of studies on effectively quantifying the lung infection caused by COVID-19. As a basic but challenging task of the diagnostic framework, segmentation plays a crucial role in accurate quantification of COVID-19 infection measured by computed tomography (CT) images. To this end, we proposed a novel deep learning algorithm for automated segmentation of multiple COVID-19 infection regions. Specifically, we use the Aggregated Residual Transformations to learn a robust and expressive feature representation and apply the soft attention mechanism to improve the capability of the model to distinguish a variety of symptoms of the COVID-19. With a public CT image dataset, we validate the efficacy of the proposed algorithm in comparison with other competing methods. Experimental results demonstrate the outstanding performance of our algorithm for automated segmentation of COVID-19 Chest CT images. Our study provides a promising deep leaning-based segmentation tool to lay a foundation to quantitative diagnosis of COVID-19 lung infection in CT images.