Automated triage of covid-19 from various lung abnormalities using chest ct features

Dor Amran; Maayan Frid-Adar; Nimrod Sagie; Jannette Nassar; Asher Kabakovitch; Hayit Greenspan

doi:https://doi.org/10.1109/ISBI48211.2021.9433803

Automated triage of covid-19 from various lung abnormalities using chest ct features

Dor Amran, Maayan Frid-Adar, Nimrod Sagie, Jannette Nassar, Asher Kabakovitch, Hayit Greenspan

Department of Bio-Medical Engineering

Tel Aviv University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

outbreak of COVID-19 has led to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 suspected patients is utilized, either as a support or replacement to reverse transcription-polymerase chain reaction (RT-PCR) test. In this paper, we propose a fully automated AI based system that takes as input chest CT scans and triages COVID-19 cases. More specifically, we produce multiple descriptive features, including lung and infections statistics, texture, shape and location, to train a machine learning based classifier that distinguishes between COVID-19 and other lung abnormalities (including community acquired pneumonia). We evaluated our system on a dataset of 2191 CT cases and demonstrated a robust solution with 90.8% sensitivity at 85.4% specificity with 94.0% ROC-AUC. In addition, we present an elaborated feature analysis and ablation study to explore the importance of each feature.

Original language	English
Title of host publication	2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
Publisher	IEEE Computer Society
Pages	155-159
Number of pages	5
ISBN (Electronic)	9781665412469
DOIs	https://doi.org/10.1109/ISBI48211.2021.9433803
State	Published - 13 Apr 2021
Event	18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France Duration: 13 Apr 2021 → 16 Apr 2021

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2021-April

Conference

Conference	18th IEEE International Symposium on Biomedical Imaging, ISBI 2021
Country/Territory	France
City	Nice
Period	13/04/21 → 16/04/21

Keywords

CNN
COVID-19
CT
Chest
Machine learning

All Science Journal Classification (ASJC) codes

Radiology Nuclear Medicine and imaging
Biomedical Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

https://doi.org/10.1109/ISBI48211.2021.9433803

Cite this

Amran, D., Frid-Adar, M., Sagie, N., Nassar, J., Kabakovitch, A., & Greenspan, H. (2021). Automated triage of covid-19 from various lung abnormalities using chest ct features. In 2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021 (pp. 155-159). Article 9433803 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2021-April). IEEE Computer Society. https://doi.org/10.1109/ISBI48211.2021.9433803

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title = "Automated triage of covid-19 from various lung abnormalities using chest ct features",

abstract = "outbreak of COVID-19 has led to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 suspected patients is utilized, either as a support or replacement to reverse transcription-polymerase chain reaction (RT-PCR) test. In this paper, we propose a fully automated AI based system that takes as input chest CT scans and triages COVID-19 cases. More specifically, we produce multiple descriptive features, including lung and infections statistics, texture, shape and location, to train a machine learning based classifier that distinguishes between COVID-19 and other lung abnormalities (including community acquired pneumonia). We evaluated our system on a dataset of 2191 CT cases and demonstrated a robust solution with 90.8% sensitivity at 85.4% specificity with 94.0% ROC-AUC. In addition, we present an elaborated feature analysis and ablation study to explore the importance of each feature.",

keywords = "CNN, COVID-19, CT, Chest, Machine learning",

author = "Dor Amran and Maayan Frid-Adar and Nimrod Sagie and Jannette Nassar and Asher Kabakovitch and Hayit Greenspan",

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year = "2021",

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day = "13",

doi = "https://doi.org/10.1109/ISBI48211.2021.9433803",

language = "الإنجليزيّة",

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Amran, D, Frid-Adar, M, Sagie, N, Nassar, J, Kabakovitch, A & Greenspan, H 2021, Automated triage of covid-19 from various lung abnormalities using chest ct features. in 2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021., 9433803, Proceedings - International Symposium on Biomedical Imaging, vol. 2021-April, IEEE Computer Society, pp. 155-159, 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021, Nice, France, 13/04/21. https://doi.org/10.1109/ISBI48211.2021.9433803

Automated triage of covid-19 from various lung abnormalities using chest ct features. / Amran, Dor; Frid-Adar, Maayan; Sagie, Nimrod et al.
2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021. IEEE Computer Society, 2021. p. 155-159 9433803 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2021-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Automated triage of covid-19 from various lung abnormalities using chest ct features

AU - Amran, Dor

AU - Frid-Adar, Maayan

AU - Sagie, Nimrod

AU - Nassar, Jannette

AU - Kabakovitch, Asher

AU - Greenspan, Hayit

PY - 2021/4/13

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N2 - outbreak of COVID-19 has led to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 suspected patients is utilized, either as a support or replacement to reverse transcription-polymerase chain reaction (RT-PCR) test. In this paper, we propose a fully automated AI based system that takes as input chest CT scans and triages COVID-19 cases. More specifically, we produce multiple descriptive features, including lung and infections statistics, texture, shape and location, to train a machine learning based classifier that distinguishes between COVID-19 and other lung abnormalities (including community acquired pneumonia). We evaluated our system on a dataset of 2191 CT cases and demonstrated a robust solution with 90.8% sensitivity at 85.4% specificity with 94.0% ROC-AUC. In addition, we present an elaborated feature analysis and ablation study to explore the importance of each feature.

AB - outbreak of COVID-19 has led to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 suspected patients is utilized, either as a support or replacement to reverse transcription-polymerase chain reaction (RT-PCR) test. In this paper, we propose a fully automated AI based system that takes as input chest CT scans and triages COVID-19 cases. More specifically, we produce multiple descriptive features, including lung and infections statistics, texture, shape and location, to train a machine learning based classifier that distinguishes between COVID-19 and other lung abnormalities (including community acquired pneumonia). We evaluated our system on a dataset of 2191 CT cases and demonstrated a robust solution with 90.8% sensitivity at 85.4% specificity with 94.0% ROC-AUC. In addition, we present an elaborated feature analysis and ablation study to explore the importance of each feature.

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KW - COVID-19

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KW - Chest

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M3 - منشور من مؤتمر

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T2 - 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021

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ER -

Amran D, Frid-Adar M, Sagie N, Nassar J, Kabakovitch A, Greenspan H. Automated triage of covid-19 from various lung abnormalities using chest ct features. In 2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021. IEEE Computer Society. 2021. p. 155-159. 9433803. (Proceedings - International Symposium on Biomedical Imaging). doi: https://doi.org/10.1109/ISBI48211.2021.9433803

Automated triage of covid-19 from various lung abnormalities using chest ct features

Abstract

Publication series

Conference

Keywords

All Science Journal Classification (ASJC) codes

UN SDGs

Access to Document

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