Abstract
Scoring the size/shape variations of cancer nuclei (nuclear pleomorphism) in breast cancer histopathology images is a critical prog-nostic marker in breast cancer grading and has been subject to a con-siderable amount of observer variability and subjectivity issues. In spite of a decade long histopathology image analysis research, automated as-sessment of nuclear pleomorphism remains challenging due to the com-plex visual appearance and huge variability of cancer nuclei.This study proposes a practical application of the deep belief based deep neural net-work (DBN-DNN) model to determine the nuclear pleomorphism score of breast cancer tissue. The DBN-DNN network is trained to classify a breast cancer histology image into one of the three groups: score 1, score 2 and score 3 nuclear pleomorphism by learning the mean and standard deviation of morphological and texture features of the entire nuclei population contained in a breast histology image. The model was trained for features from automatically-segmented nuclei from 80 breast cancer histopathology images selected from publicly available MITOS-ATYPIA dataset. The classification accuracy of the model on the training and testing datasets was found to be 96 % and 90 % respectively.
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References
Cosatto, E., Miller, M., Graf, H.P., Meyer, J.S.: Grading nuclear pleomorphism on histological micrographs. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4 December 2008
Dunne, B., Going, J.J.: Scoring nuclear pleomorphism in breast cancer. Histopathology 39(3), 259–265 (2001)
Elston, C.W., Ellis, I.O.: 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. Histopathology 41(3A), 151–152 (2002). discussion 152–3
Frierson Jr., H.F., Wolber, R.A., Berean, K.W., Franquemont, D.W., Gaffey, M.J., Boyd, J.C., Wilbur, D.C.: Interobserver reproducibility of the nottingham modification of the bloom and richardson histologic grading scheme for infiltrating ductal carcinoma. Am. J. Clin. Pathol. 103(2), 195–198 (1995)
Irshad, H., Veillard, A., Roux, L., Racoceanu, D.: Methods for nuclei detection, segmentation, and classification in digital histopathology: A review of - current status and future potential. Biomed. Eng. IEEE Rev. 7, 97–114 (2014)
Paramanandam, M., Thamburaj, R., Manipadam, M.T., Nagar, A.K.: Boundary extraction for imperfectly segmented nuclei in breast histopathology images – a convex edge grouping approach. In: Barneva, R.P., Brimkov, V.E., Šlapal, J. (eds.) IWCIA 2014. LNCS, vol. 8466, pp. 250–261. Springer, Heidelberg (2014)
Dalle, J.R., Li, H., Huang, C.H., Leow, W.K., Racoceanu, D., Putti, T.C.: Nuclear pleomorphism scoring by selective cell nuclei detection
Tanaka, M., Okutomi, M.: A novel inference of a restricted boltzmann machine. In: Proceedings of the 2014 22Nd International Conference on Pattern Recognition, ICPR 2014, pp. 1526–1531. IEEE Computer Society, Washington, DC, USA (2014)
Tavassoli, F.A., Devilee, P.: International Agency for Research on Cancer, and World Health Organization. Pathology and Genetics of Tumours of the Breast and Female Genital Organs. Who/IARC Classification of Tumours. IARC Press, Lyon (2003)
Wang, H., Cruz-Roa, A., Basavanhally, A., Gilmore, H., Shih, N., Feldman, M., Tomaszewski, J., Gonzalez, F., Madabhushi, A.: Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. J. Med. Imaging 1(3), 034003 (2014)
Watson, S.K., Watson, G.H.: Assessment of nuclear pleomorphism by image analysis, WO Patent App. PCT/GB2004/000, 400, August 26 2004
William, H., Nick, W., Olvi, L., Mangasarian, L.: Importance of nuclear morphology in breast cancer prognosis. Clin. Cancer Res. 5(11), 3542–3548 (1999)
Acknowledgement
The first two authors would like to acknowledge the Department of Science and Technology (DST), Govt. of India for providing computing facility at the institution through the FIST programme. The authors would also like to thank the organizers of the MITOS-ATYPIA 2014 contest for their consent to use images from their dataset.
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Maqlin, P., Thamburaj, R., Mammen, J.J., Manipadam, M.T. (2015). Automated Nuclear Pleomorphism Scoring in Breast Cancer Histopathology Images Using Deep Neural Networks. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_26
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DOI: https://doi.org/10.1007/978-3-319-26832-3_26
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