多标签病毒性肺炎图像分割方法的研究

doi:10.3969/j.issn.0258-8021.2023.06.009

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Abstract Researches on the segmentation algorithm of COVID-19’s lesion are mostly based on the single-label segmentation algorithm, but the accuracy can’t reach the clinical criteria. In this paper, a new method of COVID-19’s lesion segmentation based on multi-label was proposed, training on COVID-19 Lung CT Lesion Segmentation Challenge-2020 dataset in the Grand Challenge. The dataset contains 179 cases, including 139 cases in the training set and the rest 40 cases in both of the validation and prediction set. We conducted lung regions with existing lung region segmentation model, which generated from LUNA16 dataset. The generated lung region label was incorporated with the lesion label to form the multi-label of training dataset. The One-Hot coding principle and improved 3D-UNet network model is used for training. This paper also proposed a new evaluation index, focus-lung ratio which was used to reflect the proportion of lesion regions in the lung and measured the model’s robustness with other indicators. In the end, the prediction’s Dice reached 70.10 %, which is 4.20 % higher than the single-label segmentation method under the same network. Besides, our results were compared with some published data, and ours displayed better performance, the validation’s accuracy of dataset reached 75.70 %. Experimental results showed that the proposed algorithm improved the accuracy of pneumonia lesion segmentation and the robustness of the model, therefore, is of clinical value and potential significance for future studies.

Key words： deep learning image segmentation computerized tomography multi-label viral pneumonia

Received: 25 August 2021

PACS:

R318

Corresponding Authors: ^*E-mail:sunff511@hdu.edu.cn

About author:: ^#Member, Chinese Society of Biomedical Engineering

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	Wu Yihong
	Yang Yong
	Ye Hongwei
	Wang Xiaozhuang
	Sun Fangfang

Cite this article:

Wu Yihong,Yang Yong,Ye Hongwei, et al. Research on Multi-Label Viral Pneumonia Image Segmentation Method[J]. Chinese Journal of Biomedical Engineering, 2023, 42(6): 720-729.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2023.06.009 OR http://cjbme.csbme.org/EN/Y2023/V42/I6/720

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