Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 6
1850-1856,1869

Error prediction for lung CT images nonrigid registration based on machine learning

Liu Yuhang1,2
Hu Jisu2
Chen Wenjian1
Qian Xusheng2
Dai Yakang2
Zhou Zhiyong2
1. School of Electronic & Optical Engineering, Nanjing University of Science & Technology, Nanjing 210094, China
2. Suzhou Institute of Biomedical Engineering & Technology, Chinese Academy of Science, Suzhou Jiangsu 215163, China
DOI: 10.19734/j.issn.1001-3695.2022.09.0488

Abstract

The registration quality assessment is usually given to human experts, which is time-consuming. The commonly used Dice score only focuses on the error at the edge of the tissue, which is difficult to assess the registration result within the tissue. To address these issues, this paper proposed a method to predict registration errors based on machine learning(PREML) in lung CT images. This method firstly constructed three types of features, such as deformation field statistical features, deformation field physiologically realistic features and image similarity features, then expanded the number of features by pooling, and finally used random forest regression to predict non-rigid registration errors. Moreover, it used an adaptive random perturbation to simulate the spatial distribution of lung registration errors to further improve the capability of error characterization of statistical features. The proposed method achieved a mean absolute error of 1.245±2.500 mm from ground truth on lung CT image datasets, outperforming the baseline method. The results show that PREML method has the advantages of high accuracy and robustness, enhancing the safety and effectiveness of registration algorithms in clinical applications.

Foundation Support

中国科学院青年创新促进会资助项目(2021324)
江苏省重点研发项目(BE2022049-2,BE2021053,BE2020625)
丽水市科技计划资助项目(2020ZDYF09)
苏州市科技计划资助项目(SS202054)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.09.0488
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 6
Section: Technology of Graphic & Image
Pages: 1850-1856,1869
Serial Number: 1001-3695(2023)06-040-1850-07

Publish History

[2022-12-12] Accepted Paper
[2023-06-05] Printed Article

Cite This Article

刘宇航, 胡冀苏, 陈文建, 等. 基于机器学习的肺部CT图像非刚性配准误差预测方法 [J]. 计算机应用研究, 2023, 40 (6): 1850-1856,1869. (Liu Yuhang, Hu Jisu, Chen Wenjian, et al. Error prediction for lung CT images nonrigid registration based on machine learning [J]. Application Research of Computers, 2023, 40 (6): 1850-1856,1869. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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