Automatic mitosis detection in breast histopathology images using Convolutional Neural Network based deep transfer learning

• Autorzy: Sabeena Beevi K. , Nair Madhu S. , Bindu G. R.

Identyfikatory

DOI

10.1016/j.bbe.2018.10.007

• Języki publikacji: EN

Abstrakty

EN

The exact measure of mitotic count is one of the crucial parameters in breast cancer grading and prognosis. Detection of mitosis in standard H & E stained histopathology images is challenging due to diffused intensities along object boundaries and shape variation in different stages of mitosis. This paper explores the feasibility of transfer learning for mitosis detection. A pre-trained Convolutional Neural Network is transformed by coupling random forest classifier with the initial fully connected layers to extract discriminant features from nuclei patches and to precisely prognosticate the class label of cell nuclei. The modified Convolutional Neural Network accurately classify the detected cell nuclei with limited training data. The designed framework accomplishes higher classification accuracy by carefully fine tuning the pre-trained model and pre-processing the extracted features. Moreover, proposed method is evaluated on MITOS dataset provided for the MITOS-ATYPIA contest 2014 and clinical data set from Regional Cancer Centre, Thiruvananthapuram, India. Significance of Convolutional Neural Network based method is justified by comparing with recently reported works including a Multi Classifier System based on Deep Belief Network. Experiments show that the pre-trained Convolutional Neural Network model outperforms conventionally used detection systems and provides at least 15% improvement in F-score on other state-of-the-art techniques.

Słowa kluczowe

EN

histopathology image; mitosis; Krill Herd optimization; convolutional neural network; transfer learning; random forest

PL

obraz histopatologiczny; mitoza; sieć neuronowa konwolucyjna; las losowy

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2019
• Tom: Vol. 39, no. 1
• Strony: 214--223
• Opis fizyczny: Bibliogr. 40 poz., rys., tab., wykr.

Twórcy

autor

Sabeena Beevi K.

• Electrical & Electronics Department, T. K. M College of Engineering, Kollam, Kerala, India

autor

Nair Madhu S.

• Department of Computer Science, Cochin University of Science and Technology, Kochi, Kerala, India

autor

Bindu G. R.

• Electrical Engineering Department, College of Engineering Wayanad, Wayanad, Kerala, India

Bibliografia

• [1] Malon CD, Cosatto E, et al. Classification of mitotic figures with convolutional neural networks and seeded blob features. J Pathol Inform 2013;4(1):9.
• [2] Elston E, Ellis I. Method for grading breast cancer. J Clin Pathol 1993;46(2):189.
• [3] Barlow P. Changes in chromatin structure during the mitotic cycle. Protoplasma 1977;91(2):207–11.
• [4] Dundar MM, Badve S, Bilgin G, Raykar V, Jain R, Sertel O, et al. Computerized classification of intraductal breast lesions using histopathological images. IEEE Trans Biomed Eng 2011;58(7):1977–84.
• [5] Chen H, Dou Q, Wang X, Qin J, Heng PA. Mitosis detection in breast cancer histology images via deep cascaded networks. Thirtieth AAAI Conference on Artificial Intelligence; 2016.
• [6] Cireşan DC, Giusti A, Gambardella LM, Schmidhuber J. Mitosis detection in breast cancer histology images with deep neural networks. International Conference on Medical Image Computing and Computer-assisted Intervention. Springer; 2013. p. 411–8.
• [7] Yosinski J, Clune J, Bengio Y, Lipson H. How transferable are features in deep neural networks?Advances in neural information processing systems. 2014;3320–8.
• [8] Xu Y, Mo T, Feng Q, Zhong P, Lai M, Eric I, et al. Deep learning of feature representation with multiple instance learning for medical image analysis. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2014. pp. 1626–30.
• [9] Gandomi AH, Alavi AH. Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 2012;17(12):4831–45.
• [10] K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556.
• [11] Jia Y, et al. Caffe: an open source convolutional architecture for fast feature embedding; 2013.
• [12] MITOS, ICPR 2014 Contest. IPAL UMI CNRS Lab Std; 2014, http://ipal.cnrs.fr/ICPR.
• [13] Angshuman P, Mukherjee DP. Mitosis detection for invasive breast cancer grading in histopathological images. IEEE Trans Image Process 2015;24(11):4041–54.
• [14] Beevi KS, Nair MS, Bindu G. A multi-classifier system for automatic mitosis detection in breast histopathology images using deep belief networks. IEEE J Transl Eng Health Med 2017;5:1–11.
• [15] Belien J, Baak J, van Diest PJ, Van Ginkel A. Counting mitoses by image processing in Feulgen stained breast cancer sections: the influence of resolution. Cytometry Part A 1997;28(2):135–40.
• [16] Sertel HSO, Catalyurek UV, Guican MN. Computeraided prognosis of neuroblastoma: detection of mitosis and karyorrhexis cells in digitized histological images. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC); 2009. pp. 1433–6.
• [17] Irshad H, Roux L, Racoceanu D. Multi-channels statistical and morphological features based mitosis detection in breast cancer histopathology. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2013. pp. 6091–4.
• [18] Khan AM, El-Daly H, Rajpoot NM. A gamma-gaussian mixture model for detection of mitotic cells in breast cancer histopathology images. 2012 21st International Conference on Pattern Recognition (ICPR); 2012. pp. 149–52.
• [19] Roullier V, Lézoray O, Ta V-T, Elmoataz A. Multi-resolution graph-based analysis of histopathological whole slide images: application to mitotic cell extraction and visualization. Comput Med Imaging Graphics 2011;35(7): 603–15.
• [20] Anari V, Mahzouni P, Amirfattahi R. Computer-aided detection of proliferative cells and mitosis index in immunohistichemically images of meningioma. 2010 6th Iranian Conference on Machine Vision and Image Processing; 2010. pp. 1–5.
• [21] Beevi S, Nair MS, Bindu G. Automatic segmentation and classification of mitotic cell nuclei in histopathology images based on active contour model. 2014 International Conference on Contemporary Computing and Informatics (IC3I); 2014. pp. 740–4.
• [22] Irshad H, Jalali S, Roux L, Racoceanu D, Hwee LJ, Le Naour G, et al. Automated mitosis detection using texture, sift features and hmax biologically inspired approach. J Pathol Inform 2013;4(2):12.
• [23] Tek FB, et al. Mitosis detection using generic features and an ensemble of cascade adaboosts. J Pathol Inform 2013;4 (1):12.
• [24] Beevi KS, Nair MS, Bindu G. Detection of mitotic nuclei in breast histopathology images using localized acm and random kitchen sink based classifier. 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC); 2016. pp. 2435–9.
• [25] Doyle S, Agner S, Madabhushi A, Feldman M, Tomaszewski J. Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features. 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008; 2008. pp. 496–9.
• [26] Lu C, Mandal M. Toward automatic mitotic cell detection and segmentation in multispectral histopathological images. IEEE J Biomed Health Inform 2014;18(2):594–605.
• [27] Wang H, Cruz-Roa A, Basavanhally A, Gilmore H, Shih N, Feldman M, et al. Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection. SPIE medical imaging. International Society for Optics and Photonics; 2014. p. 90410B.
• [28] Gopakumar G, Babu KH, Mishra D, Gorthi SS, Subrahmanyam GRS. Cytopathological image analysis using deep-learning networks in microfluidic microscopy. JOSA A 2017;34(1):111–21.
• [29] Albayrak A, Bilgin G. Mitosis detection using convolutional neural network based features. 2016 IEEE 17th International Symposium on Computational Intelligence and Informatics (CINTI); 2016. pp. 000335–40.
• [30] Li Y, Mercan E, Knezevitch S, Elmore JG, Shapiro LG. Efficient and accurate mitosis detection-a lightweight rcnn approach. ICPRAM. 2018. pp. 69–77.
• [31] Malon CD, Cosatto E, et al. Classification of mitotic figures with convolutional neural networks and seeded blob features. J Pathol Inform 2013;4(1):9.
• [32] Sirinukunwattana K, Raza SEA, Tsang Y-W, Snead DR, Cree IA, Rajpoot NM. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging 2016;35 (5):1196–206.
• [33] Wan T, Cao J, Chen J, Qin Z. Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features. Neurocomputing 2017;229:34–44.
• [34] Ciresan DC, Meier U, Masci J, Maria Gambardella L, Schmidhuber J. Flexible, high performance convolutional neural networks for image classification.IJCAI Proceedings- International Joint Conference on Artificial Intelligence; vol. 22. 2011. p. 1237.
• [35] Khan AM, Rajpoot N, Treanor D, Magee D. A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans Biomed Eng 2014;61(6):1729–38.
• [36] Beevi S, Nair MS, Bindu G. Automatic segmentation of cell nuclei using krill herd optimization based multi-thresholding and localized active contour model. Biocybern Biomed Eng 2016;36(4):584–96.
• [37] Vedaldi A, Lenc K. Matconvnet-convolutional neural networks for matlab. corr abs/1412.4564; 2014.
• [38] Sharif Razavian SC. Azizpour, Cnn features off-the-shelf: an astounding baseline for recognition. Computer Vision and Pattern Recognition Workshops, 2014; 2014. pp. 806–13.
• [39] Jolliffe I. Principal component analysis for special types of data. Principal Component Analysis. 2002;338–72.
• [40] Blum AL, Langley P. Selection of relevant features and examples in machine learning. Artif Intell 1997;97(1): 245–71.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).