Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19

by   Liansheng Wang, et al.

Automated detecting lung infections from computed tomography (CT) data plays an important role for combating COVID-19. However, there are still some challenges for developing AI system. 1) Most current COVID-19 infection segmentation methods mainly relied on 2D CT images, which lack 3D sequential constraint. 2) Existing 3D CT segmentation methods focus on single-scale representations, which do not achieve the multiple level receptive field sizes on 3D volume. 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 multi-scale information along different dimension of input feature maps and impose supervision on multiple predictions from different CNN layers. Second, we assign this MDA-CNN as a basic network into a novel dual multi-scale mean teacher network (DM^2T-Net) for semi-supervised COVID-19 lung infection segmentation on CT volumes by leveraging unlabeled data and exploring the multi-scale information. Our DM^2T-Net encourages multiple predictions at different CNN layers from the student and teacher networks to be consistent for computing a multi-scale 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.


page 1

page 3

page 4

page 7

page 8

page 9

page 10


Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans

Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causi...

Mixed Attention with Deep Supervision for Delineation of COVID Infection in Lung CT

The COVID-19 pandemic, with its multiple variants, has placed immense pr...

COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty

This paper proposes a segmentation method of infection regions in the lu...

Towards Efficient COVID-19 CT Annotation: A Benchmark for Lung and Infection Segmentation

Accurate segmentation of lung and infection in COVID-19 CT scans plays a...

D-TrAttUnet: Dual-Decoder Transformer-Based Attention Unet Architecture for Binary and Multi-classes Covid-19 Infection Segmentation

In the last three years, the world has been facing a global crisis cause...

Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images

The novel coronavirus disease 2019 (COVID-19) characterized by atypical ...

Please sign up or login with your details

Forgot password? Click here to reset