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

被引:30
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.
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页数:19
相关论文
共 97 条
[61]   Artificial intelligence in medicine [J].
Ramesh, AN ;
Kambhampati, C ;
Monson, JRT ;
Drew, PJ .
ANNALS OF THE ROYAL COLLEGE OF SURGEONS OF ENGLAND, 2004, 86 (05) :334-338
[62]  
Rehman A., 2014, ARTIF INTELL REV, V42, DOI [10.1007/s10462-012-9335-1, DOI 10.1007/S10462-012-9335-1]
[63]  
Rejani Y., 2009, EARLY DETECTION BREA
[64]   COMPUTER-AIDED MEDICAL DIAGNOSIS - LITERATURE-REVIEW [J].
ROGERS, W ;
RYACK, B ;
MOELLER, G .
INTERNATIONAL JOURNAL OF BIO-MEDICAL COMPUTING, 1979, 10 (04) :267-289
[65]   U-Net: Convolutional Networks for Biomedical Image Segmentation [J].
Ronneberger, Olaf ;
Fischer, Philipp ;
Brox, Thomas .
MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION, PT III, 2015, 9351 :234-241
[66]   Deep learning [J].
Rusk, Nicole .
NATURE METHODS, 2016, 13 (01) :35-35
[67]  
Sharma H., 2017 IEEE 30 INT S C
[68]  
Sharma H, IEEE 15 INT C BIOINF
[69]  
Sharma H., P 10 INT C COMP VIS
[70]   Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology [J].
Sharma, Harshita ;
Zerbe, Norman ;
Klempert, Iris ;
Hellwich, Olaf ;
Hufnagl, Peter .
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2017, 61 :2-13