Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs
Kim ST, Goli L, Paschali M, Khakzar A, Keicher M, Czempiel T, Burian E, Braren R, Navab N, Wendler T (2021)
Publication Type: Conference contribution
Publication year: 2021
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12907 LNCS
Pages Range: 273-282
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Virtual, Online
ISBN: 9783030872335
DOI: 10.1007/978-3-030-87234-2_26
Abstract
Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation. Image segmentation methods have proven to help quantify the disease and even help predict the outcome. The availability of longitudinal CT series may also result in an efficient and effective method to reliably assess the progression of COVID-19, monitor the healing process and the response to different therapeutic strategies. In this paper, we propose a new framework to identify infection at a voxel level (identification of healthy lung, consolidation, and ground-glass opacity) and visualize the progression of COVID-19 using sequential low-dose non-contrast CT scans. In particular, we devise a longitudinal segmentation network that utilizes the reference scan information to improve the performance of disease identification. Experimental results on a clinical longitudinal dataset collected in our institution show the effectiveness of the proposed method compared to the static deep neural networks for disease quantification.
Involved external institutions
Kyung Hee University
Korea, Republic of (KR)
Sharif University of Technology / دانشگاه صنعتی شریف (SUT)
Iran, Islamic Republic of (IR)
Technische Universität München (TUM)
Germany (DE)
Korea, Republic of (KR)
Sharif University of Technology / دانشگاه صنعتی شریف (SUT)
Iran, Islamic Republic of (IR)
Technische Universität München (TUM)
Germany (DE)
How to cite
APA:
Kim, S.T., Goli, L., Paschali, M., Khakzar, A., Keicher, M., Czempiel, T.,... Wendler, T. (2021). Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs. In Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 273-282). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
MLA:
Kim, Seong Tae, et al. "Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs." Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online Ed. Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert, Springer Science and Business Media Deutschland GmbH, 2021. 273-282.
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