Unsupervised COVID-19 Lesion Segmentation in CT Using Cycle Consistent
Generative Adversarial Network
- URL: http://arxiv.org/abs/2111.11602v1
- Date: Tue, 23 Nov 2021 01:47:34 GMT
- Title: Unsupervised COVID-19 Lesion Segmentation in CT Using Cycle Consistent
Generative Adversarial Network
- Authors: Chengyijue Fang, Yingao Liu, Mengqiu Liu, Xiaohui Qiu, Ying Liu, Yang
Li, Jie Wen, Yidong Yang
- Abstract summary: COVID-19 has become a global pandemic and is still posing a severe health risk to the public.
We proposed a novel unsupervised approach using cycle consistent generative adversarial network (cycle-GAN) which automates and accelerates the process of lesion delineation.
The proposed unsupervised segmentation method achieved high accuracy and efficiency in automatic COVID-19 lesion delineation.
- Score: 9.845581652243583
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: COVID-19 has become a global pandemic and is still posing a severe health
risk to the public. Accurate and efficient segmentation of pneumonia lesions in
CT scans is vital for treatment decision-making. We proposed a novel
unsupervised approach using cycle consistent generative adversarial network
(cycle-GAN) which automates and accelerates the process of lesion delineation.
The workflow includes lung volume segmentation, "synthetic" healthy lung
generation, infected and healthy image subtraction, and binary lesion mask
creation. The lung volume volume was firstly delineated using a pre-trained
U-net and worked as the input for the later network. The cycle-GAN was
developed to generate synthetic "healthy" lung CT images from infected lung
images. After that, the pneumonia lesions are extracted by subtracting the
synthetic "healthy" lung CT images from the "infected" lung CT images. A median
filter and K-means clustering were then applied to contour the lesions. The
auto segmentation approach was validated on two public datasets (Coronacases
and Radiopedia). The Dice coefficients reached 0.748 and 0.730, respectively,
for the Coronacases and Radiopedia datasets. Meanwhile, the precision and
sensitivity for lesion segmentationdetection are 0.813 and 0.735 for the
Coronacases dataset, and 0.773 and 0.726 for the Radiopedia dataset. The
performance is comparable to existing supervised segmentation networks and
outperforms previous unsupervised ones. The proposed unsupervised segmentation
method achieved high accuracy and efficiency in automatic COVID-19 lesion
delineation. The segmentation result can serve as a baseline for further manual
modification and a quality assurance tool for lesion diagnosis. Furthermore,
due to its unsupervised nature, the result is not influenced by physicians'
experience which otherwise is crucial for supervised methods.
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