Automated deep learning-based segmentation of COVID-19 lesions from chest computed tomography images

Mohammad Salehi; Mahdieh Afkhami Ardekani; Alireza Bashari Taramsari; Hamed Ghaffari; Mohammad Haghparast

doi:10.5114/pjr.2022.119027

Automated deep learning-based segmentation of COVID-19 lesions from chest computed tomography images

Pol J Radiol. 2022 Aug 26:87:e478-e486. doi: 10.5114/pjr.2022.119027. eCollection 2022.

Authors

Mohammad Salehi¹, Mahdieh Afkhami Ardekani^{2

3}, Alireza Bashari Taramsari⁴, Hamed Ghaffari¹, Mohammad Haghparast^{1

3}

Affiliations

¹ Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
² Clinical Research Development Center, Shahid Mohammadi Hospital, Hormozgan University of Medical Sciences, Bandar-Abbas, Iran.
³ Department of Radiology, Faculty of Paramedicine, Hormozgan University of Medical Sciences, Bandar-Abbas, Iran.
⁴ Department of Radiology, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Abstract

Purpose: The novel coronavirus COVID-19, which spread globally in late December 2019, is a global health crisis. Chest computed tomography (CT) has played a pivotal role in providing useful information for clinicians to detect COVID-19. However, segmenting COVID-19-infected regions from chest CT results is challenging. Therefore, it is desirable to develop an efficient tool for automated segmentation of COVID-19 lesions using chest CT. Hence, we aimed to propose 2D deep-learning algorithms to automatically segment COVID-19-infected regions from chest CT slices and evaluate their performance.

Material and methods: Herein, 3 known deep learning networks: U-Net, U-Net++, and Res-Unet, were trained from scratch for automated segmenting of COVID-19 lesions using chest CT images. The dataset consists of 20 labelled COVID-19 chest CT volumes. A total of 2112 images were used. The dataset was split into 80% for training and validation and 20% for testing the proposed models. Segmentation performance was assessed using Dice similarity coefficient, average symmetric surface distance (ASSD), mean absolute error (MAE), sensitivity, specificity, and precision.

Results: All proposed models achieved good performance for COVID-19 lesion segmentation. Compared with Res-Unet, the U-Net and U-Net++ models provided better results, with a mean Dice value of 85.0%. Compared with all models, U-Net gained the highest segmentation performance, with 86.0% sensitivity and 2.22 mm ASSD. The U-Net model obtained 1%, 2%, and 0.66 mm improvement over the Res-Unet model in the Dice, sensitivity, and ASSD, respectively. Compared with Res-Unet, U-Net++ achieved 1%, 2%, 0.1 mm, and 0.23 mm improvement in the Dice, sensitivity, ASSD, and MAE, respectively.

Conclusions: Our data indicated that the proposed models achieve an average Dice value greater than 84.0%. Two-dimensional deep learning models were able to accurately segment COVID-19 lesions from chest CT images, assisting the radiologists in faster screening and quantification of the lesion regions for further treatment. Nevertheless, further studies will be required to evaluate the clinical performance and robustness of the proposed models for COVID-19 semantic segmentation.

Keywords: COVID-19; computed tomography; deep learning; image segmentation; infection segmentation.