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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 3
641-650

Review of liver and tumor image segmentation methods

Chen Ying1
Zheng Cheng1
Yi Zhen2a
Hu Fei1
Xu Guohui2b
1. School of Software, Nanchang Hangkong University, Nanchang 330063, China
2. a. Radiology, b. Hepatobiliary Surgery, Jiangxi Provincial Cancer Hospital, Nanchang 330029, China
DOI: 10.19734/j.issn.1001-3695.2021.08.0320

Abstract

The liver tumor is a disease with a high incidence and probability of deterioration, and the rapid diagnosis of liver disease requires accurate segmentation of liver and tumor from computed tomography(CT) scan. To analyze the status and the trend of the liver and tumor image segmentation field, this paper investigated the segmentation methods for liver and tumor images and summarized the segmentation methods for liver and tumor images in recent years. Liver and tumor image segmentation methods included conventional methods and deep learning methods. The conventional methods required more manual involvement and could not be fully automated. Deep learning methods could be divided into 2D, 2.5D and 3D methods from the dimension of segmentation network. These methods had high segmentation accuracy and high hardware requirements. While considering the advantages and disadvantages of deep learning and conventional methods, their combination was constantly explored, and conventional methods such as graph cuts and conditional random fields often used to refine the segmentation results of deep learning methods.

Foundation Support

江西省自然科学基金资助项目(20202BABL202029)
国家自然科学基金资助项目(61762067)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.08.0320
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 3
Section: Survey
Pages: 641-650
Serial Number: 1001-3695(2022)03-001-0641-10

Publish History

[2021-10-29] Accepted Paper
[2022-03-05] Printed Article

Cite This Article

陈英, 郑铖, 易珍, 等. 肝脏及肿瘤图像分割方法综述 [J]. 计算机应用研究, 2022, 39 (3): 641-650. (Chen Ying, Zheng Cheng, Yi Zhen, et al. Review of liver and tumor image segmentation methods [J]. Application Research of Computers, 2022, 39 (3): 641-650. )

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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