Abstract
Recurrent miscarriage can be caused by an abnormally high number of Uterine Natural Killer (UNK) cells in human female uterus lining. Recently a diagnosis protocol has been developed based on the ratio of UNK cells to stromal cells in endometrial biopsy slides immunohistochemically stained with Haematoxylin for all cells and CD56 as a marker for the UNK cells. The counting of UNK cells and stromal cells is an essential process in the protocol. However, the cell counts must not include epithelial cells from glandular structures and UNK cells from epithelium. In this paper, we propose a novel superpixel based epithelium segmentation algorithm based on the observation that neighbouring epithelial cells packed at the boundary of glandular structures or background tend to have similar local orientations. Our main contribution is a novel cell orientation congruence descriptor in a machine learning framework to differentiate between epithelial and non-epithelial cells.
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References
Quenby, S., Nik, H., Innes, B., Lash, G., Turner, M., Drury, J., Bulmer, J.: Uterine natural killer cells and angiogenesis in recurrent reproductive failure. Hum. Reprod. 24(1), 45–54 (2009)
Li, G., Sanchez, V., Patel, G., Quenby, S., Rajpoot, N.: Localisation of luminal epithelium edge in digital histopathology images of ihc stained slides of endometrial biopsies. Comput. Med. Imaging Graph. 42, 56–63 (2014)
Farjam, R., Soltanian-Zadeh, H., Jafari-Khouzani, K., Zoroofi, R.A.: An image analysis approach for automatic malignancy determination of prostate pathological images. Cytometry Part B Clin. Cytometry 72(4), 227–240 (2007)
Naik, S., Doyle, S., Agner, S., Madabhushi, A., Feldman, M., Tomaszewski, J.: Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. In: 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2008, pp. 284–287. IEEE (2008)
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)
Gunduz-Demir, C., Kandemir, M., Tosun, A.B., Sokmensuer, C.: Automatic segmentation of colon glands using object-graphs. Med. Image Anal. 14(1), 1–12 (2010)
Sirinukunwattana, K., Snead, D., Rajpoot, N.: A stochastic polygons model for glandular structures in colon histology images. IEEE Trans. Med. Imaging (2015)
Khan, A.M., Rajpoot, N., Treanor, D., Magee, D.: A non-linear mapping approach to stain normalisation in digital histopathology images using image-specific colour deconvolution. IEEE Trans. Biomed. Eng. 61, 1729–1738 (2014)
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Otsu, N.: A threshold selection method from gray-level histograms. Automatica 11(285–296), 23–27 (1975)
Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 777–789 (1998)
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Li, G., Raza, S.E.A., Rajpoot, N. (2015). A Novel Cell Orientation Congruence Descriptor for Superpixel Based Epithelium Segmentation in Endometrial Histology Images. In: Wu, G., Coupé, P., Zhan, Y., Munsell, B., Rueckert, D. (eds) Patch-Based Techniques in Medical Imaging. Patch-MI 2015. Lecture Notes in Computer Science(), vol 9467. Springer, Cham. https://doi.org/10.1007/978-3-319-28194-0_21
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DOI: https://doi.org/10.1007/978-3-319-28194-0_21
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