Abstract
This paper presents an analysis of the high resolution histo-pathology images of the prostate with a focus on the evolution of morphological gland features in prostatic adenocarcinoma. Here we propose a novel technique of labeling individual glands as malignant or benign. In the first step, the gland and nuclei objects of the images are automatically segmented. Individual gland units are segmented out by consolidating their lumina with the surrounding layers of epithelium and nuclei. The nuclei objects are segmented by using a marker controlled watershed algorithm. Two new features, Number of Nuclei Layer (N NL ) and Ratio of Epithelial layer area to Lumen area (R EL ) have been extracted from the segmented units. The main advantage of this approach is that it can detect individual malignant gland units, irrespective of neighboring histology and/or the spatial extent of the cancer. The proposed algorithm has been tested on 40 histopathology scenes taken from 10 high resolution whole mount images and achieved a sensitivity of 0.83 and specificity of 0.81 in a leave-75%-out cross-validation.
Chapter PDF
Similar content being viewed by others
References
Bohring, C., Squires, T.: Cancer statistics. CA Cancer J. Clin. 43, 7–26 (1993)
Monaco, J.P., Tomaszewski, J.E., Feldman, M.D., Hagemann, I., Moradi, M., Mousavi, P., Boag, A., Davidson, C., Abolmaesumi, P., Madabhushi, A.: High-throughput detection of prostate cancer in histological sections using probabilistic pairwise markov models. Medical Image Analysis 14(4), 617 (2010)
Nguyen, K., Sarkar, A., Jain, A.K.: Structure and context in prostatic gland segmentation and classification. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 115–123. Springer, Heidelberg (2012)
Clark, M.D., Askin, F.B., Bagnell, C.R.: Nuclear roundness factor: a quantitative approach to grading in prostate carcinoma, reliability of needle biopsy tissue, and the effect of tumor stage fore usefulness. The Prostate 10, 199–206 (1987)
Naik, S., Doyle, S., Feldman, M., Tomaszewski, J., Madabhushi, A.: Gland segmentation and computerized gleason grading of prostate histology by integrating low-, high-level and domain specific information. In: Proc. of 2nd Workshop on Micro. Image Anal. with Applications in Biology (2007)
Epstein, J.I., Netto, G.J.: Biopsy interpretation of the prostate. Lippincott Williams & Wilkins (2007)
Khouzani, J.K., Zadeh, S.H.: Multiwavelet grading of prostate pathological images. In: Proc. SPIE, vol. 4628, pp. 1130–1138 (2002)
Huang, P.W., Lee, C.H.: Automatic classification for pathological prostate images based on fractal analysis. IEEE Transactions on Medical Imaging 28(7), 1037–1050 (2009)
Nguyen, K., Sabata, B., Jain, A.K.: Prostate cancer grading: Gland segmentation and structural features. Pattern Recognition Letters 33, 951–961 (2011)
Krzanowski, W.: Principles of multivariate analysis. Oxford Uni. Press (1996)
Meyer, F.: Topographic distance and watershed lines. Signal Processing 38(1), 113–125 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rashid, S., Fazli, L., Boag, A., Siemens, R., Abolmaesumi, P., Salcudean, S.E. (2013). Separation of Benign and Malignant Glands in Prostatic Adenocarcinoma. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40760-4_58
Download citation
DOI: https://doi.org/10.1007/978-3-642-40760-4_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40759-8
Online ISBN: 978-3-642-40760-4
eBook Packages: Computer ScienceComputer Science (R0)