Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 136, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2021.104689
Keywords
COVID-19; SARS-CoV-2; Convolutional neural network; Segmentation; Detection
Categories
Funding
- Catalan Government [TIN2018-095232-B-C21, SGR-2017 1742]
- EIT Digital program
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This study introduces a fully-automated COVID-19 detection and lesion segmentation system powered by deep learning, achieving high accuracy with no false positives observed, supporting decision-making process for COVID-19 diagnosis.
Background and objective: The most common tool for population-wide COVID-19 identification is the Reverse Transcription-Polymerase Chain Reaction test that detects the presence of the virus in the throat (or sputum) in swab samples. This test has a sensitivity between 59% and 71%. However, this test does not provide precise information regarding the extension of the pulmonary infection. Moreover, it has been proven that through the reading of a computed tomography (CT) scan, a clinician can provide a more complete perspective of the severity of the disease. Therefore, we propose a comprehensive system for fully-automated COVID-19 detection and lesion segmentation from CT scans, powered by deep learning strategies to support decision-making process for the diagnosis of COVID-19. Methods: In the workflow proposed, the input CT image initially goes through lung delineation, then COVID-19 detection and finally lesion segmentation. The chosen neural network has a U-shaped architecture using a newly introduced Multiple Convolutional Layers structure, that produces a lung segmentation mask within a novel pipeline for direct COVID-19 detection and segmentation. In addition, we propose a customized loss function that guarantees an optimal balance on average between sensitivity and precision. Results: Lungs' segmentation results show a sensitivity near 99% and Dice-score of 97%. No false positives were observed in the detection network after 10 different runs with an average accuracy of 97.1%. The average accuracy for lesion segmentation was approximately 99%. Using UNet as a benchmark, we compared our results with several other techniques proposed in the literature, obtaining the largest improvement over the UNet outcomes. Conclusions: The method proposed in this paper outperformed the state-of-the-art methods for COVID-19 lesion segmentation from CT images, and improved by 38.2% the results for F1-score of UNet. The high accuracy observed in this work opens up a wide range of possible applications of our algorithm in other fields related to medical image segmentation.
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