Mitosis detection techniques in H&E stained breast cancer pathological images: A comprehensive review

Computers & Electrical Engineering - Tập 91 - Trang 107038 - 2021
Xipeng Pan1,2, Yinghua Lu3, Rushi Lan1, Zhenbing Liu1, Zujun Qin3, Huadeng Wang1, Zaiyi Liu2
1Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China
2Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
3School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China

Tài liệu tham khảo

Siegel, 2018, Cancer statistics, 2018, CA: A Cancer J Clinicians, 68 Wang, 2019, Interpretation of 2018 global cancer statistical report, J Esophageal Surg (Electron Vers) Veta, 2014, Breast cancer histopathology image analysis: A review, IEEE Trans Bio-Med Eng, 61, 1400, 10.1109/TBME.2014.2303852 López, 2012, Digital image analysis in breast cancer: An example of an automated methodology and the effects of image compression, Stud Health Technol Inform, 179, 155 Lu, 2020, Deep fuzzy hashing network for efficient image retrieval, IEEE Trans Fuzzy Syst, PP, 1 Drrs-bc: Decentralized routing registration system based on blockchain. Serikawa, 2013, Underwater image dehazing using joint trilateral filter, Comput Electr Eng, 40 Lu, 2018, Motor anomaly detection for unmanned aerial vehicles using reinforcement learning, IEEE Internet Things J, 5, 10.1109/JIOT.2017.2737479 Lu, 2020, User-oriented virtual mobile network resource management for vehicle communications, IEEE Trans Intell Transp Syst, PP, 1 Foran, 2011, Imageminer: A software system for comparative analysis of tissue microarrays using content-based image retrieval, high-performance computing, and grid technology, J Amer Med Inform Assoc JAMIA, 18, 403, 10.1136/amiajnl-2011-000170 Kaman, 1984, Image processing for mitoses in sections of breast cancer: A feasibility study, Cytometry, 5, 244, 10.1002/cyto.990050305 Kate, 1993, Method for counting mitoses by image processing in feulgen stained breast cancer sections, Cytometry, 14, 241, 10.1002/cyto.990140302 Roux, 2013, Mitosis detection in breast cancer histological images an icpr 2012 contest, J Pathol Inform, 4, 8, 10.4103/2153-3539.112693 Veta, 2014, Assessment of algorithms for mitosis detection in breast cancer histopathology images, Med Image Anal Veta, 2018 Tellez, 2018, Whole-slide mitosis detection in h e breast histology using phh3 as a reference to train distilled stain-invariant convolutional networks, IEEE Trans Med Imaging, 37, 2126, 10.1109/TMI.2018.2820199 Mercan, 2020, 1770 Su, 2019, Pooled time series representation for mitosis event recognition, Multimedia Syst, 25, 10.1007/s00530-017-0572-7 Dif, 2020, 279 Tashk, 2013, 406 Tashk, 2015, Automatic detection of breast cancer mitotic cells based on the combination of textural, statistical and innovative mathematical features, Appl Math Model, 39, 10.1016/j.apm.2015.01.051 Pourakpour, 2015, 269 Khan, 2013, A gamma-gaussian mixture model for detection of mitotic cells in breast cancer histopathology images, J Pathol Inform, 4, 11, 10.4103/2153-3539.112696 Tek, 2013, Mitosis detection using generic features and an ensemble of cascade adaboosts, J Pathol Inform, 4, 12, 10.4103/2153-3539.112697 Paul, 2015, Mitosis detection for invasive breast cancer grading in histopathological images, IEEE Trans Image Process Publ IEEE Signal Process Soc, 24 Irshad, 2013, Automated mitosis detection using texture, sift features and hmax biologically inspired approach, J Pathol Inform, 4, S12, 10.4103/2153-3539.109870 Irshad, 2013, Automated mitosis detection in histopathology using morphological and multi-channel statistics features, J Pathol Inform, 4, 10, 10.4103/2153-3539.112695 Nateghi, 2017, Maximized inter-class weighted mean for fast and accurate mitosis cells detection in breast cancer histopathology images, J Med Syst, 41, 10.1007/s10916-017-0773-9 Nateghi, 2014, Intelligent cad system for automatic detection of mitotic cells from breast cancer histology slide images based on teaching-learning-based optimization, Comput Biol J, 2014, 1, 10.1155/2014/970898 Irshad, 2013, 6091 Qi, 2019, Mitosis detection of pathological images based on multi-channel feature fusion, Comput Simul, 36, 389 Russakovsky, 2015, Imagenet large scale visual recognition challenge, Int J Comput Vis, 115, 1, 10.1007/s11263-015-0816-y He, 2016, 770 Ren, 2015, Faster r-cnn: Towards real-time object detection with region proposal networks, IEEE Trans Pattern Anal Mach Intell, 39 Li, 2017, Neural features for pedestrian detection, Neurocomputing, 238, 10.1016/j.neucom.2017.01.084 Liu, 2018, 1 Ronneberger, 2015, 234 Janowczyk, 2016, Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases, J Pathol Inform, 7, 29, 10.4103/2153-3539.186902 Cireşan, 2013, 411 Zerhouni, 2017, 924 Wu, 2017, 249 Kausar, 2018, 47 Wahab, 2017, Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection, Comput Biol Med, 85, 10.1016/j.compbiomed.2017.04.012 Paeng, 2017, 231 Wahab, 2019, Transfer learning based deep cnn for segmentation and detection of mitoses in breast cancer histopathological images, Microscopy (Oxford, England), 68, 216 Chen, 2016, Mitosis detection in breast cancer histology images via deep cascaded networks, 1160 Xu, 2018 Li, 2019, Weakly supervised mitosis detection in breast histopathology images using concentric loss, Med Image Anal, 53, 10.1016/j.media.2019.01.013 K, 2017, A multi-classifier system for automatic mitosis detection in breast histopathology images using deep belief networks, IEEE J Transl Eng Health Med, PP, 1 Albayrak, 2016, 000335 Albarqouni, 2016, Aggnet: Deep learning from crowds for mitosis detection in breast cancer histology images, IEEE Trans Med Imaging, 35, 1313, 10.1109/TMI.2016.2528120 López-Tapia, 2019, 135 He, 2017 Sohail, 2020 Ma, 2018, 3892 Li, 2018, Deepmitosis: Mitosis detection via deep detection, verication and segmentation networks, Med Image Anal, 45, 10.1016/j.media.2017.12.002 Lei, 2019, 130 Cai, 2019, 919 Mahmood, 2020, Artificial intelligence-based mitosis detection in breast cancer histopathology images using faster r-cnn and deep cnns, J Clin Med, 9, 749, 10.3390/jcm9030749 Sebai, 2020, Partmitosis: A partially supervised deep learning framework for mitosis detection in breast cancer histopathology images, IEEE Access, PP, 1 Sebai, 2020, Maskmitosis: a deep learning framework for fully supervised, weakly supervised, and unsupervised mitosis detection in histopathology images, Med Biol Eng Comput, 58, 10.1007/s11517-020-02175-z Alom, 2020, Mitosisnet: End-to-end mitotic cell detection by multi-task learning, IEEE Access, PP, 1 Yancey, 2020 Ma, 2018, Chs-net: A cascaded neural network with semi-focal loss for mitosis detection, vol. 95, 161 Malon, 2013, Classification of mitotic figures with convolutional neural networks and seeded blob features, J Pathol Inform, 4, 9, 10.4103/2153-3539.112694 Wang, 2014 Huifang, 2019, Mitosis detection in breast tissue pathological images by combining additional features into the densenet, J Wuhan Univ(Nat Ence Ed) Saha, 2017, Efficient deep learning model for mitosis detection using breast histopathology images, Comput Med Imaging Graph, 64 Bai, 2018, 52 Dodballapur, 2019, 1855 Wang, 2014, Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features, J Med Imaging, 1, 10.1117/1.JMI.1.3.034003 Cheng, 2019, 453 Liu, 2014 Hwee-Kuan, 2012 Tang, 2014, Mitosis detection in breast cancer histopathology with cascaded classifier algorithm, Comput Appl Res, 33 Sommer, 2012, 2306 Nateghi, 2015, Automatic detection of mitosis cell in breast cancer histopathology images using genetic algorithm, 1 Ismail Sayed, 2017, Moth-flame swarm optimization with neutrosophic sets for automatic mitosis detection in breast cancer histology images, Appl Intell, 47 Mirjalili, 2015, Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm, Knowl-Based Syst, 89, 10.1016/j.knosys.2015.07.006 Beevi, 2016, 2435 Sirinukunwattana, 2014, Cell words: Modelling the visual appearance of cells in histopathology images, Comput Med Imaging Graph Off J Comput Med Imaging Soc, 16 Wan, 2017, Automated mitosis detection in histopathology based on non-gaussian modeling of complex wavelet coefficients, Neurocomputing, 237, 10.1016/j.neucom.2017.01.008 Nie, 2018, Mitosis event recognition and detection based on evolution of feature in time domain, Mach Vis Appl, 29, 10.1007/s00138-018-0913-3 Ortiz, 2017, 137 Balkenhol, 2018, 34 2012 2013 2014 2016