A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development

被引:28
作者
Ai, Shiliang [1 ]
Li, Chen [1 ]
Li, Xiaoyan [2 ]
Jiang, Tao [3 ]
Grzegorzek, Marcin [4 ]
Sun, Changhao [1 ,4 ,5 ]
Rahaman, Md Mamunur [1 ]
Zhang, Jinghua [1 ,4 ]
Yao, Yudong [6 ]
Li, Hong [1 ]
机构
[1] Northeastern Univ, Coll Med & Biol Informat Engn, Microscop Image & Med Image Anal Grp, Shenyang 110169, Peoples R China
[2] China Med Univ, Liaoning Canc Hosp & Inst, Canc Hosp, Shenyang 110042, Peoples R China
[3] Chengdu Univ Informat Technol, Control Engn Coll, Chengdu 610103, Peoples R China
[4] Univ Lubeck, Inst Med Informat, Lubeck, Germany
[5] Chinese Acad Sci, Shenyang Inst Automat, Shenyang 110169, Peoples R China
[6] Stevens Inst Technol, Dept Elect & Comp Engn, Hoboken, NJ 07030 USA
基金
中国国家自然科学基金;
关键词
COMPUTER-AIDED DIAGNOSIS; WHOLE SLIDE IMAGES; CANCER; CLASSIFICATION; SEGMENTATION; PROSTATE;
D O I
10.1155/2021/6671417
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Gastric cancer is a common and deadly cancer in the world. The gold standard for the detection of gastric cancer is the histological examination by pathologists, where Gastric Histopathological Image Analysis (GHIA) contributes significant diagnostic information. The histopathological images of gastric cancer contain sufficient characterization information, which plays a crucial role in the diagnosis and treatment of gastric cancer. In order to improve the accuracy and objectivity of GHIA, Computer-Aided Diagnosis (CAD) has been widely used in histological image analysis of gastric cancer. In this review, the CAD technique on pathological images of gastric cancer is summarized. Firstly, the paper summarizes the image preprocessing methods, then introduces the methods of feature extraction, and then generalizes the existing segmentation and classification techniques. Finally, these techniques are systematically introduced and analyzed for the convenience of future researchers.
引用
收藏
页数:19
相关论文
共 97 条
  • [1] gastric segmenting
    不详
    [J]. ELECTRONICS LETTERS, 2020, 56 (15) : 747 - 747
  • [2] [Anonymous], 2008, FEATURE EXTRACTION F
  • [3] Bakator Mihalj, 2018, Multimodal Technologies and Interaction, V2, DOI 10.3390/mti2030047
  • [4] Balakrishnama Suresh., 1998, Linear discriminant analysis-a brief tutorial, V18
  • [5] Bayramoglu N, 2016, INT C PATT RECOG, P2440, DOI 10.1109/ICPR.2016.7900002
  • [6] Belsare A.D., 2016, P TENCON 2015 2015 I, P1, DOI [DOI 10.1109/TENCON.2015.7372809, 10.1109/tencon.2015.7372809]
  • [7] Screening for Cervical Cancer Using Automated Analysis of PAP-Smears
    Bengtsson, Ewert
    Malm, Patrik
    [J]. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2014, 2014
  • [8] Random forests
    Breiman, L
    [J]. MACHINE LEARNING, 2001, 45 (01) : 5 - 32
  • [9] Bugdayci G., 2019, Exp. Biomed. Res, V1, P37, DOI [DOI 10.30714/J-EBR.2019147582, 10.30714/jebr.2019147582]
  • [10] Traffic Density Classification Using Sound Datasets: An Empirical Study on Traffic Flow at Asymmetric Roads
    Bui, Khac-Hoai Nam
    Oh, Hyeonjeong
    Yi, Hongsuk
    [J]. IEEE ACCESS, 2020, 8 : 125671 - 125679