Virtual monochromatic image-based automatic segmentation strategy using deep learning method

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成果类型：

期刊论文

作者：

Chen, Lekang;Yu, Shutong;Chen, Yan;Wei, Xiang;Yang, Junqian;...

通讯作者：

Le, XY;Zhang, YB

作者机构：

[Le, Xiaoyun; Le, XY; Yang, Junqian; Chen, Lekang] Beihang Univ, Sch Phys, Beijing 102206, Peoples R China.

[Zhang, Yibao; Yu, Shutong] Peking Univ Canc Hosp & Inst, Minist Educ Beijing, Dept Radiat Oncol, Key Lab Carcinogenesis & Translat Res, Beijing 100142, Peoples R China.

[Chen, Yan; Wei, Xiang] Third Hosp Mianyang, Sichuan Mental Hlth Ctr, Mianyang, Peoples R China.

[Zeng, Wenjie; Guo, Chong] Univ South China, Sch Nucl Sci & Technol, Hengyang City 421001, Peoples R China.

[Yang, Chao] CAS Ion Med Technol Co Ltd, Dept Technol, Beijing 100190, Peoples R China.

通讯机构：

[Zhang, YB ] P

[Le, XY ] B

Beihang Univ, Sch Phys, Beijing 102206, Peoples R China.

Peking Univ Canc Hosp & Inst, Minist Educ Beijing, Dept Radiat Oncol, Key Lab Carcinogenesis & Translat Res, Beijing 100142, Peoples R China.

语种：

英文

关键词：

Automatic segmentation;Deep learning;Dual-energy CT;Virtual monochromatic image

期刊：

Physica Medica

ISSN：

1120-1797

年：

2025

卷：

134

页码：

104986

DOI：

10.1016/j.ejmp.2025.104986

基金类别：

Beijing Natural Science Foundation [Z210008]; National Natural Science Foundation of China [12475309, 12275012, 12411530076, 82202941]; China International Talent Exchange Foundation [JC202502001F]; Fundamental Research Funds for the Central Universities/Clinical Medicine Plus X-Young Scholars Project of Peking University [PKU2025PKULCXQ014]; Ministry of Education Exchange Program for Teachers and Students of Higher Education Institutions in the Chinese Mainland/Hong Kong/Macao [7111400072]; National Key R & D Program of China [2019YFF01014405]; Inner Mongolia Science & Technology Project Plan [2022YFSH0064]

机构署名：

本校为其他机构

院系归属：

核科学技术学院

摘要：

Background and purpose The image quality of single-energy CT (SECT) limited the accuracy of automatic segmentation. Dual-energy CT (DECT) may potentially improve automatic segmentation yet the performance and strategy have not been investigated thoroughly. Based on DECT-generated virtual monochromatic images (VMIs), this study proposed a novel deep learning model (MIAU-Net) and evaluated the segmentation performance on the head organs-at-risk (OARs). The image quality of single-energy CT (SECT) limited the accuracy of automatic segmentation. Dual-energy CT (DECT) may potentially improve automa...

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Virtual monochromatic image-based automatic segmentation strategy using deep learning method

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