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. 2022;52(6):6340-6353.
doi: 10.1007/s10489-021-02691-x. Epub 2021 Sep 7.

Unsupervised domain adaptation based COVID-19 CT infection segmentation network

Han Chen  1 , Yifan Jiang  1 , Murray Loew  2 , Hanseok Ko  1
Affiliations

Unsupervised domain adaptation based COVID-19 CT infection segmentation network

Han Chen et al. Appl Intell (Dordr). 2022.

Abstract

Automatic segmentation of infection areas in computed tomography (CT) images has proven to be an effective diagnostic approach for COVID-19. However, due to the limited number of pixel-level annotated medical images, accurate segmentation remains a major challenge. In this paper, we propose an unsupervised domain adaptation based segmentation network to improve the segmentation performance of the infection areas in COVID-19 CT images. In particular, we propose to utilize the synthetic data and limited unlabeled real COVID-19 CT images to jointly train the segmentation network. Furthermore, we develop a novel domain adaptation module, which is used to align the two domains and effectively improve the segmentation network's generalization capability to the real domain. Besides, we propose an unsupervised adversarial training scheme, which encourages the segmentation network to learn the domain-invariant feature, so that the robust feature can be used for segmentation. Experimental results demonstrate that our method can achieve state-of-the-art segmentation performance on COVID-19 CT images.

Keywords: Adversarial training; Automatic segmentation; COVID-19; Computed tomography; Domain adaptation.

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Figures

Fig. 1
Fig. 1
Overview of the proposed network. Our network consists of two parts: the segmentation network including a feature extractor, a pixel-wise classifier, as well as the domain adaptation module (DA) including a generator and a discriminator. The black solid lines with one-way arrow indicate the data flow and the dashed lines denote reconstruction and adversarial loss. The feature extractor and pixel-wise classifier together perform the segmentation task. The DA module is introduced to overcome the domain shift through adversarial training in image space
Fig. 2
Fig. 2
Training process for the proposed network. The solid lines indicate the data flow, and the dashed lines indicate the gradient flow
Fig. 3
Fig. 3
Qualitative results for two-class segmentation task. Columns 1 and 2 present the input real COVID-19 CT images and corresponding ground truth, while Column 3 to 6 are segmentation results of Source-only, Self-ensembling [51], SSL [28], and our proposed method. The first to last rows are the results when taking ground-glass opacity (a), consolidation (b), infection (c) and the lung (d) as the segmentation object, respectively
Fig. 4
Fig. 4
Qualitative results for multi-class segmentation task. Columns 1 and 2 show the input real COVID-19 CT images and corresponding ground truth, in which the ground-glass opacity is marked in blue, consolidation is marked in green, and the lung is marked in red. Columns 3 to 7 are the segmentation results for the Source-only, MinEnt [47], AdvEnt [47], IntraDA [49], and our proposed method, respectively
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