Technology of Graphic & Image
|
App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 7
2237-2240

Automated segmentation of cystic kidney in CT images using residual double attention motivated U-Net model

Xu Hongwei1
Yan Peixin2
Wu Min3a
Xu Zhenyu3b
Sun Yubao1
1. Jiangsu Collaborative Innovation Center on Atmospheric Environment & Equipment Technology, School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China
2. No. 63936 Unit, People's Liberation Army, Beijing 102202, China
3. a. Dept. of Medical Engineering, b. Dept. of Urinary Surgery, General Hospital of Eastern Theater Command, PLA, Nanjing 210044, China
DOI: 10.19734/j.issn.1001-3695.2019.03.0092

Abstract

Human kidneys have the variety of shapes and the complexity of anatomy. Cyst lesions can also cause large changes in kidney shape. This paper proposed a new deep network segmentation model to cope with the many challenges of automatic segmentation of CT image cysts. The proposed model deployed a dual attention module with residual connection. Based on the residual structure, it adopted the joint spatial attention and channel attention mechanism to learn more effective feature expression. According to the U-Net architecture, it built the encoder and decoder with the residual dual attention module as the building block, and also set the jump connections between the layers, so that the network could pay more attention to the characteristics of the kidney region and cope well with the changes in kidney shape. In order to verify the validity of the proposed model, it collected CT images of 79 patients with renal cysts from the hospital for training and testing. The experimental results show that the model can accurately segment the kidney regions in CT image slices, and the segmentation indicators are better than some classic segmentation network models.

Foundation Support

国家自然科学基金资助项目(61672292)
江苏省高等学校自然科学研究重大资助项目(18KJA52007)
江苏省“六大人才高峰”资助项目(DZXX-037)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.03.0092
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 7
Section: Technology of Graphic & Image
Pages: 2237-2240
Serial Number: 1001-3695(2020)07-066-2237-04

Publish History

[2020-07-05] Printed Article

Cite This Article

徐宏伟, 闫培新, 吴敏, 等. 基于残差双注意力U-Net模型的CT图像囊肿肾脏自动分割 [J]. 计算机应用研究, 2020, 37 (7): 2237-2240. (Xu Hongwei, Yan Peixin, Wu Min, et al. Automated segmentation of cystic kidney in CT images using residual double attention motivated U-Net model [J]. Application Research of Computers, 2020, 37 (7): 2237-2240. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)