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An Ensemble Learning Framework for Multi-class COVID-19 Lesion Segmentation from Chest CT Images

Citation Author(s):
Authors:N. Enshaei, P. Afshar, Sh. Heidarian, A. Mohammadi, MJ. Rafiee, A. Oikonomou, F. Babaki Fard, K. N. Plataniotis, F. Naderkhani
Submitted by:
Nastaran Enshaei
Last updated:
22 August 2021 - 4:32pm
Document Type:
Presentation Slides
Document Year:
Nastaran Enshaei
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The novel Coronavirus disease (COVID-19) has been the most critical global challenge over the past months. Lung involvement quantification and distinguishing the types of infections from chest CT scans can assist in accurate severity assessment of COVID-19 pneumonia, efficient use of limited medical resources, and saving more lives. Nevertheless, visual assessment of chest CT images and evaluating the disease severity by radiologists are expensive and prone to error. This paper proposes an automated deep learning (DL)-based framework for multi-class segmentation of COVID lesions from chest CT images that takes the CT images as the input and generates a mask indicating the infection regions. The infection regions are segmented under two classes of data, GGOs and consolidations, which are the most common CT patterns of COVID-19 pneumonia. The proposed end-to-end framework contains four encoder-decoder-based segmentation networks that exploit the top-performing pre-trained CNNs as the encoder paths and are developed and trained separately. The results then are aggregated using pixel-level Soft Majority Voting to obtain the final class membership probabilities for each pixel of the image. The proposed framework is evaluated using an open-access CT segmentation dataset. The experimental results indicate that our method successfully performs multi-class segmenting of COVID-19 lung infection regions and outperforms previous works.

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