scholarly journals An image analysis-based approach for automated counting of cancer cell nuclei in tissue sections

Cytometry ◽  
2003 ◽  
Vol 55A (1) ◽  
pp. 30-42 ◽  
Author(s):  
Constantinos G. Loukas ◽  
George D. Wilson ◽  
Borivoj Vojnovic ◽  
Alf Linney
2001 ◽  
Author(s):  
Constantinos G. Loukas ◽  
George D. Wilson ◽  
Borivoj Vojnovic

PLoS ONE ◽  
2017 ◽  
Vol 12 (2) ◽  
pp. e0171417 ◽  
Author(s):  
María Anguiano ◽  
Carlos Castilla ◽  
Martin Maška ◽  
Cristina Ederra ◽  
Rafael Peláez ◽  
...  

2011 ◽  
Vol 55 (1) ◽  
pp. 90-100 ◽  
Author(s):  
Christoph Krafft ◽  
Mehrnaz Alipour Diderhoshan ◽  
Peter Recknagel ◽  
Milos Miljkovic ◽  
Michael Bauer ◽  
...  

2019 ◽  
Author(s):  
Giorgio Cattoretti ◽  
Francesca Maria Bosisio ◽  
Lukas Marcelis ◽  
Maddalena Maria Bolognesi

Abstract Multiplexing, labeling for multiple immunostains the very same cell or tissue section in situ, is of considerable interest. The major obstacles to the diffusion of this technique are high costs in custom antibodies and instruments, low throughput, scarcity of specialized skills or facilities. We have validated and detail here a method based on common primary and secondary antibodies, diffusely available fluorescent image scanners and routinely processed tissue sections \(FFPE). It entails rounds of four-color indirect immunofluorescence, image acquisition and removal \(stripping) of the antibodies, before another stain is applied. The images are digitally registered and the autofluorescence is subtracted. Removal of antibodies is accomplished by disulphide cleavage. In excess of 50 different antibody stains can be applied to one single section from routinely fixed and embedded tissue. This method requires a modest investment in hardware and materials and uses freeware image analysis software.


Author(s):  
Tae-Yun Kim ◽  
Hae-Gil Hwang ◽  
Heung-Kook Choi

We review computerized cancer cell image analysis and visualization research over the past 30 years. Image acquisition, feature extraction, classification, and visualization from two-dimensional to three-dimensional image algorithms are introduced with case studies of bladder, prostate, breast, and renal carcinomas.


1999 ◽  
Vol 5 (S2) ◽  
pp. 1022-1023
Author(s):  
C. Ortiz de Solorzano ◽  
K. Chin ◽  
D. Knowles ◽  
A. Jones ◽  
E. Garcia ◽  
...  

Solid tumors frequently contain cells that are heterogeneous in the copy number of DNA loci. This fact implies the existence of genetic instability, which may be associated with disease aggressiveness. Accurate measurement of this phenomenon requires analysis of intact nuclei within their natural tissue context. We perform these measurements by preparing >30μm thick tissue sections, labeling them with fluorescent labels for total DNA and for specific DNA loci using fluorescence in situ hybridization (FISH) which retain the transparency of the tissue and acquiring 3D images of the tissue using confocal microscopy (figure 1). In this study, we combined automated 3D image analysis (IA) algorithms for segmenting individual nuclei based on the total DNA stain1 and for segmenting the punctuate FISH signals of DNA loci. This enables us to efficiently enumerate the copy number of specific DNA loci in individual cells and as a function of the cell's location in the tissue.


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