TY - JOUR
T1 - Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional long short-term memory networks
AU - Killekar, Aditya
AU - Grodecki, Kajetan
AU - Lin, Andrew
AU - Cadet, Sebastien
AU - McElhinney, Priscilla
AU - Razipour, Aryabod
AU - Chan, Cato
AU - Pressman, Barry D.
AU - Julien, Peter
AU - Chen, Peter
AU - Simon, Judit
AU - Maurovich-Horvat, Pal
AU - Gaibazzi, Nicola
AU - Thakur, Udit
AU - Mancini, Elisabetta
AU - Agalbato, Cecilia
AU - Munechika, Jiro
AU - Matsumoto, Hidenari
AU - Menè, Roberto
AU - Parati, Gianfranco
AU - Cernigliaro, Franco
AU - Nerlekar, Nitesh
AU - Torlasco, Camilla
AU - Pontone, Gianluca
AU - Dey, Damini
AU - Slomka, Piotr
N1 - Funding Information:
This research was supported by Cedars-Sinai COVID-19 funding. This research was also supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health (NIH; R01HL133616). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Kajetan Grodecki was supported by the Foundation for Polish Science (FNP). IRCCS Istituto Auxologico Italiano research was supported by the Italian Ministry of Health. We thank the National Lung Screening Trial (NLST) consortium for supporting our research by providing us with valuable data. A preliminary version50 of this work with a subset of patients was presented at SPIE Medical Imaging 2022.
Publisher Copyright:
© 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)
PY - 2022/9
Y1 - 2022/9
N2 - Purpose: Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in coronavirus disease 2019 (COVID-19) patients but are not part of clinical routine because the required manual segmentation of lung lesions is prohibitively time consuming. We aim to automatically segment ground-glass opacities and high opacities (comprising consolidation and pleural effusion). Approach: We propose a new fully automated deep-learning framework for fast multi-class segmentation of lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional long short-term memory (ConvLSTM) networks. Utilizing the expert annotations, model training was performed using five-fold cross-validation to segment COVID-19 lesions. The performance of the method was evaluated on CT datasets from 197 patients with a positive reverse transcription polymerase chain reaction test result for SARSCoV-2, 68 unseen test cases, and 695 independent controls. Results: Strong agreement between expert manual and automatic segmentation was obtained for lung lesions with a Dice score of 0.89 ± 0.07; excellent correlations of 0.93 and 0.98 for ground-glass opacity (GGO) and high opacity volumes, respectively, were obtained. In the external testing set of 68 patients, we observed a Dice score of 0.89 ± 0.06 as well as excellent correlations of 0.99 and 0.98 for GGO and high opacity volumes, respectively. Computations for a CT scan comprising 120 slices were performed under 3 s on a computer equipped with an NVIDIA TITAN RTX GPU. Diagnostically, the automated quantification of the lung burden % discriminate COVID-19 patients from controls with an area under the receiver operating curve of 0.96 (0.95–0.98). Conclusions: Our method allows for the rapid fully automated quantitative measurement of the pneumonia burden from CT, which can be used to rapidly assess the severity of COVID-19 pneumonia on chest CT.
AB - Purpose: Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in coronavirus disease 2019 (COVID-19) patients but are not part of clinical routine because the required manual segmentation of lung lesions is prohibitively time consuming. We aim to automatically segment ground-glass opacities and high opacities (comprising consolidation and pleural effusion). Approach: We propose a new fully automated deep-learning framework for fast multi-class segmentation of lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional long short-term memory (ConvLSTM) networks. Utilizing the expert annotations, model training was performed using five-fold cross-validation to segment COVID-19 lesions. The performance of the method was evaluated on CT datasets from 197 patients with a positive reverse transcription polymerase chain reaction test result for SARSCoV-2, 68 unseen test cases, and 695 independent controls. Results: Strong agreement between expert manual and automatic segmentation was obtained for lung lesions with a Dice score of 0.89 ± 0.07; excellent correlations of 0.93 and 0.98 for ground-glass opacity (GGO) and high opacity volumes, respectively, were obtained. In the external testing set of 68 patients, we observed a Dice score of 0.89 ± 0.06 as well as excellent correlations of 0.99 and 0.98 for GGO and high opacity volumes, respectively. Computations for a CT scan comprising 120 slices were performed under 3 s on a computer equipped with an NVIDIA TITAN RTX GPU. Diagnostically, the automated quantification of the lung burden % discriminate COVID-19 patients from controls with an area under the receiver operating curve of 0.96 (0.95–0.98). Conclusions: Our method allows for the rapid fully automated quantitative measurement of the pneumonia burden from CT, which can be used to rapidly assess the severity of COVID-19 pneumonia on chest CT.
KW - computed tomography imaging
KW - coronavirus disease 2019
KW - deep learning
KW - image processing
KW - lesion segmentation
KW - supervised learning
UR - https://www.scopus.com/pages/publications/85177778253
U2 - 10.1117/1.JMI.9.5.054001
DO - 10.1117/1.JMI.9.5.054001
M3 - Article
C2 - 36090960
AN - SCOPUS:85177778253
SN - 2329-4302
VL - 9
JO - Journal of Medical Imaging
JF - Journal of Medical Imaging
IS - 5
M1 - 054001
ER -