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A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches

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Abstract

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.

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Abbreviations

AUC:

Area under ROC curve

BOF:

Bag-of-features

CAD:

Computer-aided diagnosis

CAE:

Convolutional automatic encoder

CBMIA:

Content-based microscopic image analysis

CCD :

Charged coupled device

CCV:

Color coherence vectors

CDSA:

Cancer digital slide archive

CM:

Co-occurrence Matrix

CNN:

Convolutional neural network

CRF:

Conditional random field

CT:

Computed tomography

DCIS:

Ductal carcinoma In-situ

DCN:

Deep convolutional networks

DNN:

Deep neural network

DT:

Decision tree

FCN:

Fully convolutional network

FESI:

Traditional foreground extraction

FN:

False negatives

FP:

False positives

FRBS:

Fuzzy rule-based model

FROC:

Free receiver operating characteristic curve

GAN:

Generative adversarial network

GBM:

Glioblastoma multiforme

GLCM:

Gray-level co-occurrence matrix

H&E:

Hematoxylin and eosin

HOG:

Histogram of Oriented Gradient

ICIAR:

International conference on image analysis and recognition

IDC:

Invasive ductal carcinoma

IHC:

Immunohistochemistry

ISBI:

International symposium on biomedical imaging

k-NN:

k-nearest neighbor

LBP:

Local binary pattern

LSTM:

Long short term

LYNA:

Lymph node assistant

MFEM:

Multi-scale feature extraction module

MICCAI:

Medical image computing and computer assisted intervention society

MIL:

Multiple instance learning

MIML:

Multi-instance multi-label

MRI:

Magnetic resonance imaging

MSER:

Maximally stable extrernal regions

M-WRSF:

Multi-channel weighted region scalable fitting

OOF:

Out-of-focus

PPV:

Positive predictive value

PSO:

Particle swarm optimization

RAZN:

Reinforced auto-zoom net

RBF:

Radial basis function

ResNet:

Residual network

RF:

Random forest

RMDL:

Recalibrated multi-instance deep learning

ROC:

Receiver operating characteristic

ROI:

Region of interest

RUMC:

Radboud university medical center

SFFS:

Sequential floating forward selection

SIFT:

Scale-invariant feature transform

SLIC:

Simple linear iterative clustering

SVM:

Support vector machine

TCGA:

The cancer genome atlas

TDI:

Time delay and integration

TN:

True negatives

TP:

True positives

TPR:

True positive rate

UMCU:

Utrecht university medical center

VAE:

Variational automatic encoder

WELDON:

Weakly supervised learning of deep convolutional neural networks

WSI:

Whole-slide image

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Acknowledgements

This work is supported by National Natural Science Foundation of China (No. 61806047). We thank Miss Zixian Li and Mr. Guoxian Li for their important discussion. We also thank B.E. Xiaoming Zhou, B.E. Jinghua Zhang and B.E. Jining Li, for their Important technical supports.

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Authors and Affiliations

  1. Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China

    Xintong Li, Chen Li, Md Mamunur Rahaman, Xiaoqi Li & Jian Wu

  2. Shengjing Hospital of China Medical University, Shenyang, 110122, China

    Hongzan Sun

  3. Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA

    Yudong Yao

  4. Institute of Medical Informatics, University of Luebeck, Luebeck, Germany

    Marcin Grzegorzek

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  1. Xintong Li
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  2. Chen Li
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Li, X., Li, C., Rahaman, M.M. et al. A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches. Artif Intell Rev 55, 4809–4878 (2022). https://doi.org/10.1007/s10462-021-10121-0

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