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CHS-Net: A Deep Learning Approach for Hierarchical Segmentation of COVID-19 via CT Images
The pandemic of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19 has been spreading worldwide, causing rampant loss of lives. Medical imaging such as computed tomography (CT), X-ray, etc., plays a significant role in diagnosing the patients by presenting the...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924740/ https://www.ncbi.nlm.nih.gov/pubmed/35310011 http://dx.doi.org/10.1007/s11063-022-10785-x |
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author | Punn, Narinder Singh Agarwal, Sonali |
author_facet | Punn, Narinder Singh Agarwal, Sonali |
author_sort | Punn, Narinder Singh |
collection | PubMed |
description | The pandemic of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19 has been spreading worldwide, causing rampant loss of lives. Medical imaging such as computed tomography (CT), X-ray, etc., plays a significant role in diagnosing the patients by presenting the visual representation of the functioning of the organs. However, for any radiologist analyzing such scans is a tedious and time-consuming task. The emerging deep learning technologies have displayed its strength in analyzing such scans to aid in the faster diagnosis of the diseases and viruses such as COVID-19. In the present article, an automated deep learning based model, COVID-19 hierarchical segmentation network (CHS-Net) is proposed that functions as a semantic hierarchical segmenter to identify the COVID-19 infected regions from lungs contour via CT medical imaging using two cascaded residual attention inception U-Net (RAIU-Net) models. RAIU-Net comprises of a residual inception U-Net model with spectral spatial and depth attention network (SSD) that is developed with the contraction and expansion phases of depthwise separable convolutions and hybrid pooling (max and spectral pooling) to efficiently encode and decode the semantic and varying resolution information. The CHS-Net is trained with the segmentation loss function that is the defined as the average of binary cross entropy loss and dice loss to penalize false negative and false positive predictions. The approach is compared with the recently proposed approaches and evaluated using the standard metrics like accuracy, precision, specificity, recall, dice coefficient and Jaccard similarity along with the visualized interpretation of the model prediction with GradCam++ and uncertainty maps. With extensive trials, it is observed that the proposed approach outperformed the recently proposed approaches and effectively segments the COVID-19 infected regions in the lungs. |
format | Online Article Text |
id | pubmed-8924740 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-89247402022-03-16 CHS-Net: A Deep Learning Approach for Hierarchical Segmentation of COVID-19 via CT Images Punn, Narinder Singh Agarwal, Sonali Neural Process Lett Article The pandemic of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19 has been spreading worldwide, causing rampant loss of lives. Medical imaging such as computed tomography (CT), X-ray, etc., plays a significant role in diagnosing the patients by presenting the visual representation of the functioning of the organs. However, for any radiologist analyzing such scans is a tedious and time-consuming task. The emerging deep learning technologies have displayed its strength in analyzing such scans to aid in the faster diagnosis of the diseases and viruses such as COVID-19. In the present article, an automated deep learning based model, COVID-19 hierarchical segmentation network (CHS-Net) is proposed that functions as a semantic hierarchical segmenter to identify the COVID-19 infected regions from lungs contour via CT medical imaging using two cascaded residual attention inception U-Net (RAIU-Net) models. RAIU-Net comprises of a residual inception U-Net model with spectral spatial and depth attention network (SSD) that is developed with the contraction and expansion phases of depthwise separable convolutions and hybrid pooling (max and spectral pooling) to efficiently encode and decode the semantic and varying resolution information. The CHS-Net is trained with the segmentation loss function that is the defined as the average of binary cross entropy loss and dice loss to penalize false negative and false positive predictions. The approach is compared with the recently proposed approaches and evaluated using the standard metrics like accuracy, precision, specificity, recall, dice coefficient and Jaccard similarity along with the visualized interpretation of the model prediction with GradCam++ and uncertainty maps. With extensive trials, it is observed that the proposed approach outperformed the recently proposed approaches and effectively segments the COVID-19 infected regions in the lungs. Springer US 2022-03-16 2022 /pmc/articles/PMC8924740/ /pubmed/35310011 http://dx.doi.org/10.1007/s11063-022-10785-x Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Punn, Narinder Singh Agarwal, Sonali CHS-Net: A Deep Learning Approach for Hierarchical Segmentation of COVID-19 via CT Images |
title | CHS-Net: A Deep Learning Approach for Hierarchical Segmentation of COVID-19 via CT Images |
title_full | CHS-Net: A Deep Learning Approach for Hierarchical Segmentation of COVID-19 via CT Images |
title_fullStr | CHS-Net: A Deep Learning Approach for Hierarchical Segmentation of COVID-19 via CT Images |
title_full_unstemmed | CHS-Net: A Deep Learning Approach for Hierarchical Segmentation of COVID-19 via CT Images |
title_short | CHS-Net: A Deep Learning Approach for Hierarchical Segmentation of COVID-19 via CT Images |
title_sort | chs-net: a deep learning approach for hierarchical segmentation of covid-19 via ct images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924740/ https://www.ncbi.nlm.nih.gov/pubmed/35310011 http://dx.doi.org/10.1007/s11063-022-10785-x |
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