Abstract
Cancer is a group of diseases involving abnormal cell growth with varying malignancy and extent across different patients. Cytological features like prominent nucleoli, nuclear enlargement, and hyperchromasia are important to the tumor pathologist in assessment of cancer malignancy from tissue biopsies. In a recent study, Yap et al. [21] proposed effective prominent nucleoli detectors in histopathological images and developed different feature generation methods. These methods were based on polar gradients and were used along with support vector machine (SVM) and AdaBoost for detection purposes. In this study, we benchmark the performance of these methods along with convolutional and fully connected networks for the task of distinguishing between nuclei with and without prominent nucleolus.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
American Cancer Society: Cancer Facts & Figures 2017. American Cancer Society, Atlanta (2017)
Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013, pp. 411–418. Springer (2013)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Cruz-Roa, A.A., Ovalle, J.E.A., Madabhushi, A., Osorio, F.A.G.: A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 403–410. Springer (2013)
Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)
Greenspan, H., van Ginneken, B., Summers, R.M.: Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans. Med. Imaging 35(5), 1153–1159 (2016)
Gulshan, V., Peng, L., Coram, M., Stumpe, M.C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016)
Irshad, H., Veillard, A., Roux, L., Racoceanu, D.: Methods for nuclei detection, segmentation, and classification in digital histopathology: a review-current status and future potential. IEEE Rev. Biomed. Eng. 7, 97 (2014)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Madabhushi, A., Lee, G.: Image analysis and machine learning in digital pathology: challenges and opportunities. Med. Image Anal. 33(6), 170–175 (2016)
Masci, J., Meier, U., Cireşan, D., Schmidhuber, J.: Stacked convolutional auto-encoders for hierarchical feature extraction. In: International Conference on Artificial Neural Networks, pp. 52–59. Springer (2011)
Schneider, C.A., Rasband, W.S., Eliceiri, K.W.: NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9(7), 671 (2012)
Shin, H.C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., Summers, R.M.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)
Sirinukunwattana, K., Raza, S.E.A., Tsang, Y.W., Snead, D.R., Cree, I.A., Rajpoot, N.M.: Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35(5), 1196–1206 (2016)
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)
Wang, H., Cruz-Roa, A., Basavanhally, A., Gilmore, H., Shih, N., Feldman, M., Tomaszewski, J., Gonzalez, F., Madabhushi, A.: Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection. In: SPIE Medical Imaging, p. 90410B. International Society for Optics and Photonics (2014)
Xie, Y., Kong, X., Xing, F., Liu, F., Su, H., Yang, L.: Deep voting: a robust approach toward nucleus localization in microscopy images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 374–382. Springer (2015)
Xu, J., Xiang, L., Liu, Q., Gilmore, H., Wu, J., Tang, J., Madabhushi, A.: Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans. Med. Imaging 35(1), 119–130 (2016)
Yap, C.K., Kalaw, E.M., Singh, M., Chong, K.T., Giron, D.M., Huang, C.H., Cheng, L., Law, Y.N., Lee, H.K.: Automated image based prominent nucleoli detection. J. Pathol. Inform. 6, 39 (2015)
Acknowledgments
This work was supported in part by the Biomedical Research Council of A*STAR (Agency for Science, Technology and Research), Singapore; the National University of Singapore, Singapore, the Departments of Urology and Pathology at Tan Tock Seng Hospital, Singapore and Singapore-China NRF-NSFC Grant (No. NRF2016NRF-NSFC001-111). Part of the computational work for this article was done on resources of the National Supercomputing Computer Singapore (https://www.nscc.sg).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Singh, M., Zeng, Z., Kalaw, E.M., Giron, D.M., Chong, KT., Lee, H.K. (2018). A Study of Nuclei Classification Methods in Histopathological Images. In: Chen, YW., Tanaka, S., Howlett, R., Jain, L. (eds) Innovation in Medicine and Healthcare 2017. KES-InMed 2018 2017. Smart Innovation, Systems and Technologies, vol 71. Springer, Cham. https://doi.org/10.1007/978-3-319-59397-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-59397-5_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59396-8
Online ISBN: 978-3-319-59397-5
eBook Packages: EngineeringEngineering (R0)