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MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification
The outbreak of COVID-19 has caused a severe shortage of healthcare resources. Ground Glass Opacity (GGO) and consolidation of chest CT scans have been an essential basis for imaging diagnosis since 2020. The similarity of imaging features between COVID-19 and other pneumonia makes it challenging to...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8912982/ https://www.ncbi.nlm.nih.gov/pubmed/35305504 http://dx.doi.org/10.1016/j.compbiomed.2022.105340 |
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author | Li, Cheng-Fan Xu, Yi-Duo Ding, Xue-Hai Zhao, Jun-Juan Du, Rui-Qi Wu, Li-Zhong Sun, Wen-Ping |
author_facet | Li, Cheng-Fan Xu, Yi-Duo Ding, Xue-Hai Zhao, Jun-Juan Du, Rui-Qi Wu, Li-Zhong Sun, Wen-Ping |
author_sort | Li, Cheng-Fan |
collection | PubMed |
description | The outbreak of COVID-19 has caused a severe shortage of healthcare resources. Ground Glass Opacity (GGO) and consolidation of chest CT scans have been an essential basis for imaging diagnosis since 2020. The similarity of imaging features between COVID-19 and other pneumonia makes it challenging to distinguish between them and affects radiologists' diagnosis. Recently, deep learning in COVID-19 has been mainly divided into disease classification and lesion segmentation, yet little work has focused on the feature correlation between the two tasks. To address these issues, in this study, we propose MultiR-Net, a 3D deep learning model for combined COVID-19 classification and lesion segmentation, to achieve real-time and interpretable COVID-19 chest CT diagnosis. Precisely, the proposed network consists of two subnets: a multi-scale feature fusion UNet-like subnet for lesion segmentation and a classification subnet for disease diagnosis. The features between the two subnets are fused by the reverse attention mechanism and the iterable training strategy. Meanwhile, we proposed a loss function to enhance the interaction between the two subnets. Individual metrics can not wholly reflect network effectiveness. Thus we quantify the segmentation results with various evaluation metrics such as average surface distance, volume Dice, and test on the dataset. We employ a dataset containing 275 3D CT scans for classifying COVID-19, Community-acquired Pneumonia (CAP), and healthy people and segmented lesions in pneumonia patients. We split the dataset into 70% and 30% for training and testing. Extensive experiments showed that our multi-task model framework obtained an average recall of 93.323%, an average precision of 94.005% on the classification test set, and a 69.95% Volume Dice score on the segmentation test set of our dataset. |
format | Online Article Text |
id | pubmed-8912982 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89129822022-03-11 MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification Li, Cheng-Fan Xu, Yi-Duo Ding, Xue-Hai Zhao, Jun-Juan Du, Rui-Qi Wu, Li-Zhong Sun, Wen-Ping Comput Biol Med Article The outbreak of COVID-19 has caused a severe shortage of healthcare resources. Ground Glass Opacity (GGO) and consolidation of chest CT scans have been an essential basis for imaging diagnosis since 2020. The similarity of imaging features between COVID-19 and other pneumonia makes it challenging to distinguish between them and affects radiologists' diagnosis. Recently, deep learning in COVID-19 has been mainly divided into disease classification and lesion segmentation, yet little work has focused on the feature correlation between the two tasks. To address these issues, in this study, we propose MultiR-Net, a 3D deep learning model for combined COVID-19 classification and lesion segmentation, to achieve real-time and interpretable COVID-19 chest CT diagnosis. Precisely, the proposed network consists of two subnets: a multi-scale feature fusion UNet-like subnet for lesion segmentation and a classification subnet for disease diagnosis. The features between the two subnets are fused by the reverse attention mechanism and the iterable training strategy. Meanwhile, we proposed a loss function to enhance the interaction between the two subnets. Individual metrics can not wholly reflect network effectiveness. Thus we quantify the segmentation results with various evaluation metrics such as average surface distance, volume Dice, and test on the dataset. We employ a dataset containing 275 3D CT scans for classifying COVID-19, Community-acquired Pneumonia (CAP), and healthy people and segmented lesions in pneumonia patients. We split the dataset into 70% and 30% for training and testing. Extensive experiments showed that our multi-task model framework obtained an average recall of 93.323%, an average precision of 94.005% on the classification test set, and a 69.95% Volume Dice score on the segmentation test set of our dataset. Elsevier Ltd. 2022-05 2022-03-11 /pmc/articles/PMC8912982/ /pubmed/35305504 http://dx.doi.org/10.1016/j.compbiomed.2022.105340 Text en © 2022 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Li, Cheng-Fan Xu, Yi-Duo Ding, Xue-Hai Zhao, Jun-Juan Du, Rui-Qi Wu, Li-Zhong Sun, Wen-Ping MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification |
title | MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification |
title_full | MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification |
title_fullStr | MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification |
title_full_unstemmed | MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification |
title_short | MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification |
title_sort | multir-net: a novel joint learning network for covid-19 segmentation and classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8912982/ https://www.ncbi.nlm.nih.gov/pubmed/35305504 http://dx.doi.org/10.1016/j.compbiomed.2022.105340 |
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