Dual multiscale mean teacher network for semi-supervised infection segmentation in chest CT volume for COVID-19

Liansheng WANG, Jiacheng WANG, Lei ZHU, Huazhu FU, Ping LI, Kwok Shing CHENG, Zhipeng FENG, Shuo LI, Pheng-Ann HENG

Research output: Contribution to journalArticlespeer-review

8 Citations (Scopus)

Abstract

Automated detecting lung infections from computed tomography (CT) data plays an important role for combating coronavirus 2019 (COVID-19). However, there are still some challenges for developing AI system: 1) most current COVID-19 infection segmentation methods mainly relied on 2-D CT images, which lack 3-D sequential constraint; 2) existing 3-D CT segmentation methods focus on single-scale representations, which do not achieve the multiple level receptive field sizes on 3-D volume; and 3) the emergent breaking out of COVID-19 makes it hard to annotate sufficient CT volumes for training deep model. To address these issues, we first build a multiple dimensional-attention convolutional neural network (MDA-CNN) to aggregate multiscale information along different dimension of input feature maps and impose supervision on multiple predictions from different convolutional neural networks (CNNs) layers. Second, we assign this MDA-CNN as a basic network into a novel dual multiscale mean teacher network (DM² T-Net) for semi-supervised COVID-19 lung infection segmentation on CT volumes by leveraging unlabeled data and exploring the multiscale information. Our DM² T-Net encourages multiple predictions at different CNN layers from the student and teacher networks to be consistent for computing a multiscale consistency loss on unlabeled data, which is then added to the supervised loss on the labeled data from multiple predictions of MDA-CNN. Third, we collect two COVID-19 segmentation datasets to evaluate our method. The experimental results show that our network consistently outperforms the compared state-of-the-art methods. Copyright © 2022 IEEE.
Original languageEnglish
Pages (from-to)6363-6375
JournalIEEE Transactions on Cybernetics
Volume53
Issue number10
Early online dateDec 2022
DOIs
Publication statusPublished - Oct 2023

Citation

Wang, L., Wang, J., Zhu, L., Fu, H., Li, P., Cheng, G., Feng, Z., Li, S., & Heng, P.-A. (2023). Dual multiscale mean teacher network for semi-supervised infection segmentation in chest CT volume for COVID-19. IEEE Transactions on Cybernetics, 53(10), 6363-6375. https://doi.org/10.1109/TCYB.2022.3223528

Fingerprint

Dive into the research topics of 'Dual multiscale mean teacher network for semi-supervised infection segmentation in chest CT volume for COVID-19'. Together they form a unique fingerprint.