Lesion segmentation in lung CT scans using unsupervised adversarial learning

Lesion segmentation in medical images is difficult yet crucial for proper diagnosis and treatment. Identifying lesions in medical images is costly and time-consuming and requires highly specialized knowledge. For this reason, supervised and semi-supervised learning techniques have been developed. Nevertheless, the lack of annotated data, which is common in medical imaging, is an issue; in this context, interesting approaches can use unsupervised learning to accurately distinguish between healthy tissues and lesions, training the network without using the annotations. In this work, an unsupervised learning technique is proposed to automatically segment coronavirus disease 2019 (COVID-19) lesions on 2D axial CT lung slices. The proposed approach uses the technique of image translation to generate healthy lung images based on the infected lung image without the need for lesion annotations. Attention masks are used to improve the quality of the segmentation further. Experiments showed the capability of the proposed approaches to segment the lesions, and it outperforms a range of unsupervised lesion detection approaches. The average reported results for the test dataset based on the metrics: Dice Score, Sensitivity, Specificity, Structure Measure, Enhanced-Alignment Measure, and Mean Absolute Error are 0.695, 0.694, 0.961, 0.791, 0.875, and 0.082 respectively. The achieved results are promising compared with the state-of-the-art and could constitute a valuable tool for future developments.