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### 基于结构重参数化与多尺度深度监督的COVID-19胸部CT图像自动分割

1. 湖南师范大学信息科学与工程学院，湖南长沙410006
• 收稿日期:2022-04-07 修回日期:2022-11-22 出版日期:2023-01-28
• 通讯作者:
• 徐鹏飞
• 作者简介:
• 刘金平　男，1983年9月出生于湖南省邵阳市.现为湖南师范大学信息科学与工程学院教授、博士生导师，主要从事智能信息处理、过程检测、故障诊断等相关研究.E-mail: ljp202518@163.com
吴娟娟　女，1996年5月出生于湖南省株洲市.目前在湖南师范大学信息科学与工程学院攻读硕士学位.研究方向为生物医学图像处理. E-mail: Ajuan0527@hunnu.edu.cn
张荣　女，1999年12月出生于湖南省长沙市.目前在湖南师范大学信息科学与工程学院攻读硕士学位.研究方向为生物医学图像处理. E-mail: cpstzr@163.com
徐鹏飞（通讯作者）　男，1977年9月出生于湖南省岳阳市.现为湖南师范大学信息科学与工程学院副教授，主要从事智能信息处理方面的研究. Email: xupf@hunnu.edu.cn
• 基金资助:
• 国家自然科学基金 (61971188)

### Toward Automated Segmentation of COVID-19 Chest CT Images Based on Structural Reparameterization and Multi-Scale Deep Supervision

LIU Jin-ping, WU Juan-juan, ZHANG Rong, XU Peng-fei

1. College of Information Science and Engineering，Hunan Normal University，Changsha，Hunan 410081，China
• Received:2022-04-07 Revised:2022-11-22 Online:2023-01-28
• Corresponding author:
• XU Peng-fei
• Supported by:
• National Natural Science Foundation of China (61971188)

Abstract:

Automatic segmentation of chest CT(Computed Tomography) images based on deep learning models is helpful to assist doctors in diagnosis and treatment. However, with the deepening of the network width and depth, the network training is difficult and the inference slows down. In order to improve the learning ability of the hidden layer, the deep supervision mechanism is used in network training. However, previous deep supervision methods did not considerthe layered representation of multi-scale feature maps in the segmentation model and the influence of up-sampling on the quality of feature maps involved in loss calculation. In order to improve the directness of the hidden layer learning process and speed up the reasoning. This article proposes a structural reparameterization and multi-scale deep supervision network(SR&MDS-Net) for the purpose of achieving the efficient and accurate segmentation results of lesions in COVID-19(COrona VIrus Disease 2019) pneumonia chest CT images. Structure reparameterized featurevariation(SRFV) module was constructed to decoupage the network training and reasoning, which improved the model expression ability and accelerated the reasoning speed. In order to strengthen the effect of network supervision and improve the performance of network, a novel multi-scale deep supervision mechanism is proposed. The experiment was carried out on the public COVID-19 pneumonia chest CT image dataset. The sensitivity, specificity, accuracy, and Dice of SR&MDS-Net reached 91.5%, 99.5%, 72.8%, and 80.1%, respectively, which had better performance compared with other similar methods.