Computational methods for automated mitosis detection in histopathology images: A review

2018 Bray, 2018, Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA: Cancer J Clin, 68, 394 Wardle, 2015, Screening for prevention and early diagnosis of cancer, Am Psychol, 70, 119, 10.1037/a0037357 Al-Janabi, 2012, Digital pathology: current status and future perspectives, Histopathology, 61, 1, 10.1111/j.1365-2559.2011.03814.x Higgins, 2015, Applications and challenges of digital pathology and whole slide imaging, Biotechn Histochem, 90, 341, 10.3109/10520295.2015.1044566 Al-Janabi, 2012, Whole slide images for primary diagnostics of gastrointestinal tract pathology: a feasibility study, Human Pathol, 43, 702, 10.1016/j.humpath.2011.06.017 Singletary, 2002, Revision of the american joint committee on cancer staging system for breast cancer, J Clin Oncol, 20, 3628, 10.1200/JCO.2002.02.026 Brierley, 2017 Gleason, 1974, Prediction of prognosis for prostatic adenocarcinoma by combined histological grading and clinical staging, J Urol, 111, 58, 10.1016/S0022-5347(17)59889-4 Epstein, 2005, The 2005 international society of urological pathology (isup) consensus conference on gleason grading of prostatic carcinoma, Am J Surg Pathol, 29, 1228, 10.1097/01.pas.0000173646.99337.b1 Epstein, 2016, The 2014 international society of urological pathology (isup) consensus conference on gleason grading of prostatic carcinoma, Am J Surg Pathol, 40, 244, 10.1097/PAS.0000000000000530 Elston, 2002, Pathological prognostic factors in breast cancer. i. the value of histological grade in breast cancer: experience from a large study with long-term follow-up. CW elston & IO ellis. histopathology 1991; 19; 403-410: Author commentary, Histopathology, 41, 10.1046/j.1365-2559.2002.14691.x Bloom, 1957, Histological grading and prognosis in breast cancer: a study of 1409 cases of which 359 have been followed for 15 years, Br J Cancer, 11, 359, 10.1038/bjc.1957.43 Neumann, 2010, Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes, Nature, 464, 721, 10.1038/nature08869 Nourreddine, 2020, Nf45 and nf90 regulate mitotic gene expression by competing with staufen-mediated mrna decay, Cell Rep, 31, 107660, 10.1016/j.celrep.2020.107660 Mahmoodian, 2016, Using support vector regression in gene selection and fuzzy rule generation for relapse time prediction of breast cancer, Biocybern Biomed Eng, 36, 466, 10.1016/j.bbe.2016.03.003 Chen, 2011, Optical and digital microscopic imaging techniques and applications in pathology, Anal Cell Pathol, 34, 5, 10.1155/2011/150563 Gilad, 2019, Fully unsupervised symmetry-based mitosis detection in time-lapse cell microscopy, Bioinformatics, 35, 2644, 10.1093/bioinformatics/bty1034 Nie, 2018, Mitosis event recognition and detection based on evolution of feature in time domain, Mach Vision Appl, 29, 1249, 10.1007/s00138-018-0913-3 Gupta, 2020, Identification of hep-2 specimen images with mitotic cell patterns, Biocybern Biomed Eng, 40, 1233, 10.1016/j.bbe.2020.07.003 Tonti, 2015, Unsupervised hep-2 mitosis recognition in indirect immunofluorescence imaging, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 8135 Kausar, 2019, Hwdcnn: Multi-class recognition in breast histopathology with Haar wavelet decomposed image based convolution neural network, Biocybern Biomed Eng, 39, 967, 10.1016/j.bbe.2019.09.003 Sun, 2020, Gastric histopathology image segmentation using a hierarchical conditional random field, Biocybern Biomed Eng, 10.1016/j.bbe.2020.09.008 Eismann, 2020, Automated 3d light-sheet screening with high spatiotemporal resolution reveals mitotic phenotypes, J Cell Sci, 133, 10.1242/jcs.245043 Liu, 2020, Breast tumors recognition based on edge feature extraction using support vector machine, Biomed Signal Process Control, 58, 101825, 10.1016/j.bspc.2019.101825 Osman, 2020, Adjusted quick shift phase preserving dynamic range compression method for breast lesions segmentation, Informatics Med Unlocked, 100344, 10.1016/j.imu.2020.100344 Danch-Wierzchowska, 2016, Simplification of breast deformation modelling to support breast cancer treatment planning, Biocybern Biomed Eng, 36, 531, 10.1016/j.bbe.2016.06.001 Paul, 2015, Mitosis detection for invasive breast cancer grading in histopathological images, IEEE Trans Image Process, 24, 4041, 10.1109/TIP.2015.2460455 Fuchs, 2011, Computational pathology: challenges and promises for tissue analysis, Comput Med Imaging Graph, 35, 515, 10.1016/j.compmedimag.2011.02.006 Basavanhally, 2011, Incorporating domain knowledge for tubule detection in breast histopathology using o’callaghan neighborhoods, 796310 Khan, 2015, A global covariance descriptor for nuclear atypia scoring in breast histopathology images, IEEE J Biomed Health Informatics, 19, 1637, 10.1109/JBHI.2015.2447008 Das, 2018, Sparse representation over learned dictionaries on the Riemannian manifold for automated grading of nuclear pleomorphism in breast cancer, IEEE Trans Image Process, 28, 1248, 10.1109/TIP.2018.2877337 Das, 2019, Kernel-based fisher discriminant analysis on the riemannian manifold for nuclear atypia scoring of breast cancer, Biocybern Biomed Eng, 39, 728, 10.1016/j.bbe.2019.06.005 Xu, 2017, Deep learning for histopathological image analysis: towards computerized diagnosis on cancers, Deep Learning and Convolutional Neural Networks for Medical Image Computing, 73, 10.1007/978-3-319-42999-1_6 Das, 2020, Computer-aided histopathological image analysis techniques for automated nuclear atypia scoring of breast cancer: a review, J Digital Imaging, 1 2012 2016 Veta, 2015, Assessment of algorithms for mitosis detection in breast cancer histopathology images, Med Image Anal, 20, 237, 10.1016/j.media.2014.11.010 Roux, 2013, Mitosis detection in breast cancer histological images an Icpr 2012 contest, J Pathol Informatics, 4, 10.4103/2153-3539.112693 Naghibi, 2012, Breast cancer classification based on advanced multi dimensional fuzzy neural network, J Med Syst, 36, 2713, 10.1007/s10916-011-9747-5 Dalwinder, 2020, Simultaneous feature weighting and parameter determination of neural networks using ant lion optimization for the classification of breast cancer, Biocybern Biomed Eng, 40, 337, 10.1016/j.bbe.2019.12.004 Li, 2012, Breast tissue image classification based on semi-supervised locality discriminant projection with kernels, J Med Syst, 36, 2779, 10.1007/s10916-011-9754-6 Huang, 2010, Neural network classifier with entropy based feature selection on breast cancer diagnosis, J Med Syst, 34, 865, 10.1007/s10916-009-9301-x Singh, 2019, Determining relevant biomarkers for prediction of breast cancer using anthropometric and clinical features: a comparative investigation in machine learning paradigm, Biocybern Biomed Eng, 39, 393, 10.1016/j.bbe.2019.03.001 Teot, 2007, The problems and promise of central pathology review: development of a standardized procedure for the children's oncology group, Pediatr Dev Pathol, 10, 199, 10.2350/06-06-0121.1 Kaman, 1984, Image processing for mitoses in sections of breast cancer: a feasibility study, Cytometry: J Int Soc Anal Cytol, 5, 244, 10.1002/cyto.990050305 Malon, 2008, Identifying histological elements with convolutional neural networks, Proceedings of the 5th International Conference on Soft Computing As Transdisciplinary Science and Technology, 450, 10.1145/1456223.1456316 Gallardo, 2004, Mitotic cell recognition with hidden Markov models, 661 Tao, 2007, A support vector machine classifier for recognizing mitotic subphases using high-content screening data, J Biomol Screen, 12, 490, 10.1177/1087057107300707 Liu, 2010, Mitosis sequence detection using hidden conditional random fields, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 580 Ten Kate, 1993, Method for counting mitoses by image processing in feulgen stained breast cancer sections, Cytometry: J Int Soc Anal Cytol, 14, 241, 10.1002/cyto.990140302 2014 Chen, 2016, Mitosis detection in breast cancer histology images via deep cascaded networks, Thirtieth AAAI Conference on Artificial Intelligence, 1166 Khan, 2014, A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution, IEEE Trans Biomed Eng, 61, 1729, 10.1109/TBME.2014.2303294 Li, 2015, A complete color normalization approach to histopathology images using color cues computed from saturation-weighted statistics, IEEE Trans Biomed Eng, 62, 1862, 10.1109/TBME.2015.2405791 Vahadane, 2016, Structure-preserving color normalization and sparse stain separation for histological images, IEEE Trans Med Imaging, 35, 1962, 10.1109/TMI.2016.2529665 Alirezazadeh, 2018, Representation learning-based unsupervised domain adaptation for classification of breast cancer histopathology images, Biocybern Biomed Eng, 38, 671, 10.1016/j.bbe.2018.04.008 Huang, 2012, Automated mitosis detection based on exclusive independent component analysis, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), 1856 Khan, 2012, A gamma-gaussian mixture model for detection of mitotic cells in breast cancer histopathology images, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), 149 Khan, 2012, Ranpec: random projections with ensemble clustering for segmentation of tumor areas in breast histology images, Medical Image Understanding and Analysis, 17 Vapnik, 2013 Malon, 2012, Mitotic figure recognition: agreement among pathologists and computerized detector, Anal Cell Pathol, 35, 97, 10.1155/2012/385271 Sommer, 2012, Learning-based mitotic cell detection in histopathological images, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), 2306 Sommer, 2011, Ilastik: interactive learning and segmentation toolkit, in, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 230 Irshad, 2013, Automated mitosis detection in histopathology using morphological and multi-channel statistics features, J Pathol Informatics, 4, 10.4103/2153-3539.112695 Tek, 2013, Mitosis detection using generic features and an ensemble of cascade adaboosts, J Pathol Informatics, 4, 10.4103/2153-3539.112697 Veta, 2013, Detecting mitotic figures in breast cancer histopathology images, 867607 Irshad, 2014, Multispectral band selection and spatial characterization: application to mitosis detection in breast cancer histopathology, Comput Med Imaging Graph, 38, 390, 10.1016/j.compmedimag.2014.04.003 Irshad, 2014, Spectral band selection for mitosis detection in histopathology, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 1279, 10.1109/ISBI.2014.6868110 Lu, 2013, Toward automatic mitotic cell detection and segmentation in multispectral histopathological images, IEEE J Biomed Health Informatics, 18, 594 Duda, 2012 Lu, 2012, A robust automatic nuclei segmentation technique for quantitative histopathological image analysis, Anal Quant Cytol Histol, 34, 296 Irshad, 2013, Automated mitosis detection using texture, sift features and hmax biologically inspired approach, J Pathol Informatics, 4 Acton, 2000, Scale space classification using area morphology, IEEE Trans Image Process, 9, 623, 10.1109/83.841939 Beevi, 2016, Detection of mitotic nuclei in breast histopathology images using localized acm and random kitchen sink based classifier, 2016 38th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2435 Gandomi, 2012, Krill herd: a new bio-inspired optimization algorithm, Commun Nonlinear Sci Numer Simul, 17, 4831, 10.1016/j.cnsns.2012.05.010 Rahimi, 2009, Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning, Advances in Neural Information Processing Systems, 1313 Nateghi, 2017, Maximized inter-class weighted mean for fast and accurate mitosis cells detection in breast cancer histopathology images, J Med Syst, 41, 146, 10.1007/s10916-017-0773-9 Tashk, 2013, An automatic mitosis detection method for breast cancer histopathology slide images based on objective and pixel-wise textural features classification, The 5th Conference on Information and Knowledge Technology, 406, 10.1109/IKT.2013.6620101 Tashk, 2015, Automatic detection of breast cancer mitotic cells based on the combination of textural, statistical and innovative mathematical features, Appl Math Modell, 39, 6165, 10.1016/j.apm.2015.01.051 Giovannelli, 2014, Element stiffness matrix integration in image-based cartesian grid finite element method, International Symposium Computational Modeling of Objects Represented in Images, 304 Roullier, 2011, Multi-resolution graph-based analysis of histopathological whole slide images: application to mitotic cell extraction and visualization, Comput Med Imaging Graph, 35, 603, 10.1016/j.compmedimag.2011.02.005 Nateghi, 2014, Automatic detection of mitosis cell in breast cancer histopathology images using genetic algorithm, 2014 21th Iranian Conference on Biomedical Engineering (ICBME), 1 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, 10.1155/2014/970898 Litjens, 2017 Litjens, 2017, A survey on deep learning in medical image analysis, Med Image Anal, 42, 60, 10.1016/j.media.2017.07.005 Anwar, 2018, Medical image analysis using convolutional neural networks: a review, J Med Syst, 42, 226, 10.1007/s10916-018-1088-1 Amin, 2020, Brain tumor detection by using stacked autoencoders in deep learning, J Med Syst, 44, 32, 10.1007/s10916-019-1483-2 Zhao, 2019, The application of deep learning in the risk grading of skin tumors for patients using clinical images, J Med Syst, 43, 283, 10.1007/s10916-019-1414-2 Kadam, 2019, Breast cancer diagnosis using feature ensemble learning based on stacked sparse autoencoders and softmax regression, J Med Syst, 43, 263, 10.1007/s10916-019-1397-z Wang, 2019, Recognition and clinical diagnosis of cervical cancer cells based on our improved lightweight deep network for pathological image, J Med Syst, 43, 301, 10.1007/s10916-019-1426-y Ramzan, 2020, A deep learning approach for automated diagnosis and multi-class classification of alzheimer's disease stages using resting-state fmri and residual neural networks, J Med Syst, 44, 37, 10.1007/s10916-019-1475-2 Gurovich, 2019, Identifying facial phenotypes of genetic disorders using deep learning, Nat Med, 25, 60, 10.1038/s41591-018-0279-0 Wu, 2017, 23 Cireşan, 2013, Mitosis detection in breast cancer histology images with deep neural networks, International Conference on Medical Image Computing and Computer-Assisted Intervention, 411 Giusti, 2014, A comparison of algorithms and humans for mitosis detection, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 1360, 10.1109/ISBI.2014.6868130 Yu, 2013, Crowdsourcing participatory evaluation of medical pictograms using amazon mechanical turk, J Med Internet Res, 15, e108, 10.2196/jmir.2513 Celi, 2014, Crowdsourcing knowledge discovery and innovations in medicine, J Med Internet Res, 16, e216, 10.2196/jmir.3761 Gurari, 2015, How to collect segmentations for biomedical images? a benchmark evaluating the performance of experts, crowdsourced non-experts, and algorithms, 2015 IEEE Winter Conference on Applications of Computer Vision, 1169, 10.1109/WACV.2015.160 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 Chen, 2016, Automated mitosis detection with deep regression networks, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 1204, 10.1109/ISBI.2016.7493482 Chen, 2014 Romo-Bucheli, 2017, A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers, Cytometry Part A, 91, 566, 10.1002/cyto.a.23065 Wollmann, 2017, Deep residual Hough voting for mitotic cell detection in histopathology images, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 341, 10.1109/ISBI.2017.7950533 Li, 2018, Deepmitosis: mitosis detection via deep detection, verification and segmentation networks, Med Image Anal, 45, 121, 10.1016/j.media.2017.12.002 Wahab, 2017, Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection, Comput Biol Med, 85, 86, 10.1016/j.compbiomed.2017.04.012 Cai, 2019, Efficient mitosis detection in breast cancer histology images by rcnn, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 919, 10.1109/ISBI.2019.8759461 Ren, 2015, Faster r-cnn: Towards real-time object detection with region proposal networks, Advances in Neural Information Processing Systems, 91 Shaban, 2019, Staingan: stain style transfer for digital histological images, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 953, 10.1109/ISBI.2019.8759152 Li, 2017 He, 2016, Deep residual learning for image recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770 Jiménez, 2019, Deep learning for semantic segmentation versus classification in computational pathology: application to mitosis analysis in breast cancer grading, Front Bioeng Biotechnol, 7, 145, 10.3389/fbioe.2019.00145 Krizhevsky, 2017, Imagenet classification with deep convolutional neural networks, Commun ACM, 60, 84, 10.1145/3065386 Ronneberger, 2015, U-net: convolutional networks for biomedical image segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention, 234 Malon, 2013, Classification of mitotic figures with convolutional neural networks and seeded blob features, J Pathol Informatics, 4, 10.4103/2153-3539.112694 Wang, 2014, Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features, J Med Imaging, 1, 034003, 10.1117/1.JMI.1.3.034003 Saha, 2018, Efficient deep learning model for mitosis detection using breast histopathology images, Comput Med Imaging Graph, 64, 29, 10.1016/j.compmedimag.2017.12.001 Maas, 2013, Rectifier nonlinearities improve neural network acoustic models, Proc ICML, vol. 30, 3 Beevi, 2019, Automatic mitosis detection in breast histopathology images using convolutional neural network based deep transfer learning, Biocybern Biomed Eng, 39, 214, 10.1016/j.bbe.2018.10.007 Simonyan, 2014 Khan, 2014, A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution, IEEE Trans Biomed Eng, 61, 1729, 10.1109/TBME.2014.2303294 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 Beevi, 2017, A multi-classifier system for automatic mitosis detection in breast histopathology images using deep belief networks, IEEE J Transl Eng Health Med, 5, 1, 10.1109/JTEHM.2017.2694004 Das, 2019, Efficient automated detection of mitotic cells from breast histological images using deep convolution neutral network with wavelet decomposed patches, Comput Biol Med, 104, 29, 10.1016/j.compbiomed.2018.11.001 Dalle, 2008, Automatic breast cancer grading of histopathological images, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 3052, 10.1109/IEMBS.2008.4649847 2012 Bertram, 2019, A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor, Sci Data, 6, 1, 10.1038/s41597-019-0290-4 Kermani, 2019, Ki-67 status in patients with primary breast cancer and its relationship with other prognostic factors, Biomed Res Ther, 6, 2986, 10.15419/bmrat.v6i2.520 Shokouh, 2015, Interrelationships between ki67, her2/neu, p53, er, and pr status and their associations with tumor grade and lymph node involvement in breast carcinoma subtypes: retrospective-observational analytical study, Medicine, 94, 10.1097/MD.0000000000001359 Alheejawi, 2019, Automated melanoma staging in lymph node biopsy image using deep learning, 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), 1 Senaras, 2018, Optimized generation of high-resolution phantom images using cgan: application to quantification of ki67 breast cancer images, PLOS ONE, 13, 10.1371/journal.pone.0196846 Saha, 2017, An advanced deep learning approach for ki-67 stained hotspot detection and proliferation rate scoring for prognostic evaluation of breast cancer, Sci Rep, 7, 1, 10.1038/s41598-017-03405-5