Dr. Chenglong Wang Profile

Dr. Chenglong Wang

at East China Normal Univ

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Unsupervised segmentation of COVID-19 infected lung clinical CT volumes using image inpainting and representation learning

Tong Zheng, Masahiro Oda, Chenglong Wang, Takayasu Moriya, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Masaki Mori, Hirotsugu Takabatake, Hiroshi Natori, Kensaku Mori

Proceedings Volume 11596, 115963F (2021) https://doi.org/10.1117/12.2580641

KEYWORDS: Image segmentation, Computed tomography, Chest, Arteries, Spherical lenses, Lung, Tissues, Opacity, Glasses

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This paper newly proposes a segmentation method of infected area for COVID-19 (Coronavirus Disease 2019) infected lung clinical CT volumes. COVID-19 spread globally from 2019 to 2020, causing the world to face a globally health crisis. It is desired to estimate severity of COVID-19, based on observing the infected area segmented from clinical computed tomography (CT) volume of COVID-19 patients. Given the lung field from a COVID-19 lung clinical CT volume as input, we desire an automated approach that could perform segmentation of infected area. Since labeling infected area for supervised segmentation needs a lot of labor, we propose a segmentation method without labeling of infected area. Our method refers to a baseline method utilizing representation learning and clustering. However, the baseline method is likely to segment anatomical structures with high H.U. (Houns field) intensity such as blood vessel into infected area. Aiming to solve this problem, we propose a novel pre-processing method that could transform high intensity anatomical structures into low intensity structures. This pre-processing method avoids high intensity anatomical structures to be mis-segmented into infected area. Given the lung field extracted from a CT volume, our method segment the lung field into normal tissue, ground GGO (ground glass opacity), and consolidation. Our method consists of three steps: 1) pulmonary blood vessel segmentation, 2) image inpainting of pulmonary blood vessel based on blood vessel segmentation result, and 3) segmentation of infected area. Compared to the baseline method, experimental results showed that our method contributes to the segmentation accuracy, especially on tubular structures such as blood vessels. Our method improved normalized mutual information score from 0.280 (the baseline method) to 0.394.

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