Advertisement

Gland Ring Morphometry for Prostate Cancer Prognosis in Multispectral Immunofluorescence Images

  • Richard Scott
  • Faisal M. Khan
  • Jack Zeineh
  • Michael Donovan
  • Gerardo Fernandez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

Morphometric features characterizing the fusion and fragmentation of the glandular architecture of advanced prostate cancer have not previously been based upon the automated segmentation of discrete gland rings, due in part to the difficulty of extracting these structures from the H&E stained tissues. We present a novel approach for segmenting gland rings in multi-spectral immunofluorescence (IF) images and demonstrate the utility of the resultant features in predicting cancer recurrence in a cohort of 1956 images of prostate biopsies and prostatectomies from 679 patients. The proposed approach is evaluated for prediction of actual clinical outcomes of interest to physicians in comparison with previously published gland-unit features, yielding a concordance index (CI) of 0.67. This compares favorably to the CI of 0.66 obtained using a semi-automated segmentation of the corresponding H&E images from the same patients. This work presents the first algorithms for segmentation of gland rings lacking a central lumen, and for separation of touching epithelial units, and introduces new gland adjacency features for predicting prostate cancer clinical progression across both biopsy and prostatectomy images.

Keywords

Gleason prostate segmentation watershed Voronoi gland 

References

  1. 1.
    Doyle, S., Hwang, M., Shah, K., Madabhushi, A., Feldman, M., Tomaszeweski, J.: Automated grading of prostate cancer using architectural and textural image features. In: Proc. IEEE Int. Symp. Biomed. Imaging, pp. 1284–1287 (2007)Google Scholar
  2. 2.
    Fogarasi, S., Khan, F., Pang, H., Mesa-Tejada, R., Donovan, M., Fernandez, G.: Glandular Object Based Tumor Morphometry in H&E Biopsy Samples for Prostate Cancer Prognosis. In: Proc. SPIE (2011)Google Scholar
  3. 3.
    Ajemba, P., Scott, R., Ramachandran, J., Liu, Q., Khan, F., Zeineh, J., Fernandez, G.: Iterative approach to joint segmentation of cellular structures. In: Proc. SPIE (2011)Google Scholar
  4. 4.
    Sethian, J.: Level Set Methods and Fast Marching Methods. Cambridge University Press (1996)Google Scholar
  5. 5.
    Tabesh, A., Vengrenyuk, Y., Teverovskiy, M., Khan, F., Sapir, M., Powell, D., Mesa-Tejada, R., Donovan, M., Fernandez, G.: Robust Tumor Morphometry in Multispectral Fluorescence Microscopy. In: Proc. SPIE (2009)Google Scholar
  6. 6.
    Naik, S., Madabhushi, A., Tomaszeweski, J., Feldman, M.: A Quantitative Exploration of Efficacy of Gland Morphology in Prostate Cancer Grading. In: IEEE 33rd North East Bioengineering Conference, pp. 58–59 (2007)Google Scholar
  7. 7.
    Naik, S., Doyle, S., Madabhushi, A., Tomaszewski, J., Feldman, M.: Gland Segmentation and Gleason Grading of Prostate Histology by Integrating Low-, High-level and Domain Specific Information. In: Workshop on Microscopic Image Analysis with Applications in Biology (2007)Google Scholar
  8. 8.
    Sparks, R., Madabhushi, A.: Statistical shape model for manifold regularization: Gleason grading of prostate histology. Computer Vision and Image Understanding 117(9), 1138–1146 (2013)CrossRefGoogle Scholar
  9. 9.
    Nguyen, K., Sabata, B., Jain, A.K.: Prostate cancer grading: Gland segmentation and structural features. Pattern Recognition Letters 33(7), 951–961 (2012)CrossRefGoogle Scholar
  10. 10.
    Lopez, C.M., Agaian, S., Sanchez, I., Almuntashri, A., Zinalabdin, O., Rikabi, A.A., Thompson, I.: Exploration of efficacy of gland morphology and architectural features in prostate cancer gleason grading. In: IEEE Int. Conf. on SMC, pp. 2849–2854 (2012)Google Scholar
  11. 11.
    Rashid, S., Fazli, L., Boag, A., Siemens, R., Abolmaesumi, P., Salcudean, S.E.: Separation of Benign and Malignant Glands in Prostatic Adenocarcinoma. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part III. LNCS, vol. 8151, pp. 461–468. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Richard Scott
    • 1
  • Faisal M. Khan
    • 1
  • Jack Zeineh
    • 1
  • Michael Donovan
    • 1
  • Gerardo Fernandez
    • 1
  1. 1.Icahn School of Medicine at Mount SinaiNew YorkUSA

Personalised recommendations