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GAN-based disentanglement learning for chest X-ray rib suppression

文献类型：期刊论文

作者Han, Luyi2,3; Lyu, Yuanyuan4; Peng, Cheng5; Zhou, S. Kevin1,6,7
刊名MEDICAL IMAGE ANALYSIS
出版日期2022-04-01
卷号77页码:14
关键词CXR Rib suppression Domain adaptation Disentanglement learning
ISSN号1361-8415
DOI10.1016/j.media.2022.102369
英文摘要Clinical evidence has shown that rib-suppressed chest X-rays (CXRs) can improve the reliability of pulmonary disease diagnosis. However, previous approaches on generating rib-suppressed CXR face challenges in preserving details and eliminating rib residues. We hereby propose a GAN-based disentanglement learning framework called Rib Suppression GAN, or RSGAN, to perform rib suppression by utilizing the anatomical knowledge embedded in unpaired computed tomography (CT) images. In this approach, we employ a residual map to characterize the intensity difference between CXR and the corresponding rib-suppressed result. To predict the residual map in CXR domain, we disentangle the image into structure-and contrast-specific features and transfer the rib structural priors from digitally reconstructed radiographs (DRRs) computed by CT. Furthermore, we employ additional adaptive loss to suppress rib residue and preserve more details. We conduct extensive experiments based on 1673 CT volumes, and four benchmarking CXR datasets, totaling over 120K images, to demonstrate that (i) our proposed RSGAN achieves superior image quality compared to the state-of-the-art rib suppression methods; (ii) combining CXR with our rib-suppressed result leads to better performance in lung disease classification and tuberculosis area detection. (c) 2022 Elsevier B.V. All rights reserved.
WOS研究方向Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000793655000002
出版者ELSEVIER
源URL[http://119.78.100.204/handle/2XEOYT63/19548]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhou, S. Kevin
作者单位1.Univ Sci & Technol China, Suzhou Inst Adv Res, Suzhou 215123, Peoples R China
2.Radboud Univ Nijmegen Med Ctr, Dept Radiol & Nucl Med, Geert Grootepl 10, NL-6525 GA Nijmegen, Netherlands
3.Netherlands Canc Inst NKI, Dept Radiol, Plesmanlaan 121, NL-1066 CX Amsterdam, Netherlands
4.Z2Sky Technol Inc, Suzhou 215123, Peoples R China
5.Johns Hopkins Univ, Artificial Intelligence Engn & Med Lab, Baltimore, MD 21218 USA
6.Univ Sci & Technol China, Sch Biomed Engn, Suzhou 215123, Peoples R China
7.Chinese Acad Sci, Inst Comp Technol, CAS, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Han, Luyi,Lyu, Yuanyuan,Peng, Cheng,et al. GAN-based disentanglement learning for chest X-ray rib suppression[J]. MEDICAL IMAGE ANALYSIS,2022,77:14.
APA Han, Luyi,Lyu, Yuanyuan,Peng, Cheng,&Zhou, S. Kevin.(2022).GAN-based disentanglement learning for chest X-ray rib suppression.MEDICAL IMAGE ANALYSIS,77,14.
MLA Han, Luyi,et al."GAN-based disentanglement learning for chest X-ray rib suppression".MEDICAL IMAGE ANALYSIS 77(2022):14.

入库方式： OAI收割

来源：计算技术研究所

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