TY - JOUR
T1 - A comprehensive review of computer-aided whole-slide image analysis
T2 - from datasets to feature extraction, segmentation, classification and detection approaches
AU - Li, Xintong
AU - Li, Chen
AU - Rahaman, Md Mamunur
AU - Sun, Hongzan
AU - Li, Xiaoqi
AU - Wu, Jian
AU - Yao, Yudong
AU - Grzegorzek, Marcin
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature B.V.
PY - 2022/8
Y1 - 2022/8
N2 - With the development of Computer-aided Diagnosis (CAD) and image scanning techniques, Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis. Therefore, WSI analysis has become the key to modern digital histopathology. Since 2004, WSI has been used widely in CAD. Since machine vision methods are usually based on semi-automatic or fully automatic computer algorithms, they are highly efficient and labor-saving. The combination of WSI and CAD technologies for segmentation, classification, and detection helps histopathologists to obtain more stable and quantitative results with minimum labor costs and improved diagnosis objectivity. This paper reviews the methods of WSI analysis based on machine learning. Firstly, the development status of WSI and CAD methods are introduced. Secondly, we discuss publicly available WSI datasets and evaluation metrics for segmentation, classification, and detection tasks. Then, the latest development of machine learning techniques in WSI segmentation, classification, and detection are reviewed. Finally, the existing methods are studied, and the application prospects of the methods in this field are forecasted.
AB - With the development of Computer-aided Diagnosis (CAD) and image scanning techniques, Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis. Therefore, WSI analysis has become the key to modern digital histopathology. Since 2004, WSI has been used widely in CAD. Since machine vision methods are usually based on semi-automatic or fully automatic computer algorithms, they are highly efficient and labor-saving. The combination of WSI and CAD technologies for segmentation, classification, and detection helps histopathologists to obtain more stable and quantitative results with minimum labor costs and improved diagnosis objectivity. This paper reviews the methods of WSI analysis based on machine learning. Firstly, the development status of WSI and CAD methods are introduced. Secondly, we discuss publicly available WSI datasets and evaluation metrics for segmentation, classification, and detection tasks. Then, the latest development of machine learning techniques in WSI segmentation, classification, and detection are reviewed. Finally, the existing methods are studied, and the application prospects of the methods in this field are forecasted.
KW - Computer-aided diagnosis
KW - Feature extraction
KW - Image classification
KW - Image segmentation
KW - Object detection
KW - Whole-slide image analysis
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U2 - 10.1007/s10462-021-10121-0
DO - 10.1007/s10462-021-10121-0
M3 - Article
AN - SCOPUS:85123891837
SN - 0269-2821
VL - 55
SP - 4809
EP - 4878
JO - Artificial Intelligence Review
JF - Artificial Intelligence Review
IS - 6
ER -