Unsupervised COVID-19 Lesion Segmentation in CT Using Cycle Consistent Generative Adversarial Network

TL;DR

This paper introduces an unsupervised cycle-GAN based method for COVID-19 lesion segmentation in CT scans, achieving high accuracy without requiring manual annotations, thus enabling faster and more consistent diagnosis support.

Contribution

The study presents a novel unsupervised lesion segmentation approach using cycle-GAN that outperforms previous unsupervised methods and matches supervised network performance.

Findings

Dice coefficient of 0.748 and 0.730 on two datasets

Precision and sensitivity around 0.77-0.81

Comparable to supervised segmentation networks

Abstract

COVID-19 has become a global pandemic and is still posing a severe health risk to the public. Accurate and efficient segmentation of pneumonia lesions in CT scans is vital for treatment decision-making. We proposed a novel unsupervised approach using cycle consistent generative adversarial network (cycle-GAN) which automates and accelerates the process of lesion delineation. The workflow includes lung volume segmentation, "synthetic" healthy lung generation, infected and healthy image subtraction, and binary lesion mask creation. The lung volume volume was firstly delineated using a pre-trained U-net and worked as the input for the later network. The cycle-GAN was developed to generate synthetic "healthy" lung CT images from infected lung images. After that, the pneumonia lesions are extracted by subtracting the synthetic "healthy" lung CT images from the "infected" lung CT images. A median filter and K-means clustering were then applied to contour the lesions. The auto segmentation approach was validated on two public datasets (Coronacases and Radiopedia). The Dice coefficients reached 0.748 and 0.730, respectively, for the Coronacases and Radiopedia datasets. Meanwhile, the precision and sensitivity for lesion segmentationdetection are 0.813 and 0.735 for the Coronacases dataset, and 0.773 and 0.726 for the Radiopedia dataset. The performance is comparable to existing supervised segmentation networks and outperforms previous unsupervised ones. The proposed unsupervised segmentation method achieved high accuracy and efficiency in automatic COVID-19 lesion delineation. The segmentation result can serve as a baseline for further manual modification and a quality assurance tool for lesion diagnosis. Furthermore, due to its unsupervised nature, the result is not influenced by physicians' experience which otherwise is crucial for supervised methods.