Hostname: page-component-5c6d5d7d68-7tdvq Total loading time: 0 Render date: 2024-08-19T05:50:11.912Z Has data issue: false hasContentIssue false
  • English
  • Français

Automatic Hough Transform-Based 3D Segmentation of Cell Nuclei in Thick Tissue Sections

Published online by Cambridge University Press:  02 July 2020

S.J. Lockett ,
E.G. Rodriguez ,
C. Ortiz de Solorzano ,
D. Sudar ,
D. Pinkel  and
J.W. Gray
S.J. Lockett
Affiliation:
Life Sciences Division, Ernest Orlando Lawrence Berkeley National Laboratory, CA94720
E.G. Rodriguez
Affiliation:
Life Sciences Division, Ernest Orlando Lawrence Berkeley National Laboratory, CA94720
C. Ortiz de Solorzano
Affiliation:
Life Sciences Division, Ernest Orlando Lawrence Berkeley National Laboratory, CA94720
D. Sudar
Affiliation:
Life Sciences Division, Ernest Orlando Lawrence Berkeley National Laboratory, CA94720
D. Pinkel
Affiliation:
Life Sciences Division, Ernest Orlando Lawrence Berkeley National Laboratory, CA94720
J.W. Gray
Affiliation:
Life Sciences Division, Ernest Orlando Lawrence Berkeley National Laboratory, CA94720
Get access

Extract

Combining pathology, which reports the structural features of individual cells and their spatial organi-zation in a tissue specimen, with molecular biology techniques (immunocytochemistry and fluores-cence in situ hybridization, FISH) for detecting the distribution of specific molecular species in individual cells is a powerful approach for gaining insight into the underlying disease mechanisms of carcinogenesis. This approach requires analysis of thick (>20 ¼m) tissue sections in which cells are preserved intact within the context of their environment. 3D (confocal) microscope image acquisition followed by 3D image analysis (IA) for extracting quantitative information are then used for quantita-tive analysis of tissue features such as nuclear and/or cell volume, shape, total fluorescence or FISH signal number. An essential component of the IA is detection of the individual cells, or their nuclei since many molecular species of interest are localized within the nucleus (e.g. genetic aberrations). We present here a completely automatic, 3D algorithm for this task, which based on the Hough transform (HT).

Type
Computational Advances and Enabling Technologies for 3D Microscopies in Biology
Information
Microscopy and Microanalysis , Volume 3 , Issue S2: Proceedings: Microscopy & Microanalysis '97, Microscopy Society of America 55th Annual Meeting, Microbeam Analysis Society 31st Annual Meeting, Histochemical Society 48th Annual Meeting, Cleveland, Ohio, August 10-14, 1997 , August 1997 , pp. 1121 - 1122
Copyright
Copyright © Microscopy Society of America 1997

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Ancin, H.et al., Cytometry, 25(1996)221.10.1002/(SICI)1097-0320(19961101)25:3<221::AID-CYTO3>3.0.CO;2-I3.0.CO;2-I>CrossRef3.0.CO;2-I>Google Scholar
2.Ballard, D.H., Pattern Recognition, 12(1981)111.10.1016/0031-3203(81)90009-1CrossRefGoogle Scholar
3.Lockett, S.J.et al., Analyt. Quant. Cytol Histol, 13(1991)27.Google Scholar
4.Malladi, R.et al., Proc. MMBIA., (1996) 244.Google Scholar
5.Strasters, K.C.et al., Pattern Recognition Letters, 12(1991)307.10.1016/0167-8655(91)90414-HCrossRefGoogle Scholar
6. Supported by the Director, Office of Energy Research, Office of Health and Environmental Research of the U.S. Department of Energy under contract NO. DE-AC03-76SF00098, NIH grant CA-67412 and a contract with Carl Zeiss Inc.Google Scholar