Extraction of lung and lesion regions from COVID-19 CT volumes using 3D fully convolutional networks

Yuichiro Hayashi; Masahiro Oda; Chen Shen; Masahiro Hashimoto; Yoshito Otake; Toshiaki Akashi; Kensaku Mori

doi:10.1117/12.2581818

15 February 2021 Extraction of lung and lesion regions from COVID-19 CT volumes using 3D fully convolutional networks

Yuichiro Hayashi, Masahiro Oda, Chen Shen, Masahiro Hashimoto, Yoshito Otake, Toshiaki Akashi, Kensaku Mori

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 115972A (2021) https://doi.org/10.1117/12.2581818
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

This paper presents a method for extracting the lung and lesion regions from COVID-19 CT volumes using 3D fully convolutional networks. Due to the pandemic of coronavirus disease 2019 (COVID-19), computer aided diagnosis (CAD) system for COVID-19 using CT volume is required. In the development of CAD system, it is important to extract patient anatomical structures in CT volume. Therefore, we develop a method for extracting the lung and lesion regions from COVID-19 CT volumes for the CAD system of COVID-19. We use 3D U-Net type fully convolutional network (FCN) for extraction of the lung and lesion regions. We also use transfer learning to train the 3D U-Net type FCN using the limited data of COVID-19 CT volume. As pre-training, the proposed method trains the 3D U-Net model using abdominal multi-organ regions segmentation dataset which contains a large number of annotated CT volumes. After pre-training, we train the 3D U-Net model from the pre-trained model using a small number of annotated COVID-19 CT volumes. The experimental results showed that the proposed method could extract the lung and lesion regions from COVID-19 CT volumes.

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Yuichiro Hayashi, Masahiro Oda, Chen Shen, Masahiro Hashimoto, Yoshito Otake, Toshiaki Akashi, and Kensaku Mori "Extraction of lung and lesion regions from COVID-19 CT volumes using 3D fully convolutional networks", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115972A (15 February 2021); https://doi.org/10.1117/12.2581818

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