Framework for parsing, visualizing and scoring tissue microarray images

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Framework for parsing, visualizing and scoring tissue microarray images. / Rabinovich, Andrew; Krajewski, Stan; Krajewska, Maryla; Shabaik, Ahmed; Hewitt, Stephen M.; Belongie, Serge; Reed, John C.; Price, Jeffrey H.

In: IEEE Transactions on Information Technology in Biomedicine, Vol. 10, No. 2, 04.2006, p. 209-219.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Rabinovich, A, Krajewski, S, Krajewska, M, Shabaik, A, Hewitt, SM, Belongie, S, Reed, JC & Price, JH 2006, 'Framework for parsing, visualizing and scoring tissue microarray images', IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 2, pp. 209-219. https://doi.org/10.1109/TITB.2005.855544

APA

Rabinovich, A., Krajewski, S., Krajewska, M., Shabaik, A., Hewitt, S. M., Belongie, S., Reed, J. C., & Price, J. H. (2006). Framework for parsing, visualizing and scoring tissue microarray images. IEEE Transactions on Information Technology in Biomedicine, 10(2), 209-219. https://doi.org/10.1109/TITB.2005.855544

Vancouver

Rabinovich A, Krajewski S, Krajewska M, Shabaik A, Hewitt SM, Belongie S et al. Framework for parsing, visualizing and scoring tissue microarray images. IEEE Transactions on Information Technology in Biomedicine. 2006 Apr;10(2):209-219. https://doi.org/10.1109/TITB.2005.855544

Author

Rabinovich, Andrew ; Krajewski, Stan ; Krajewska, Maryla ; Shabaik, Ahmed ; Hewitt, Stephen M. ; Belongie, Serge ; Reed, John C. ; Price, Jeffrey H. / Framework for parsing, visualizing and scoring tissue microarray images. In: IEEE Transactions on Information Technology in Biomedicine. 2006 ; Vol. 10, No. 2. pp. 209-219.

Bibtex

@article{a508de07addd45a08123de99f6b3a3ea,
title = "Framework for parsing, visualizing and scoring tissue microarray images",
abstract = "Increasingly automated techniques for arraying, immunostaining, and imaging tissue sections led us to design software for convenient management, display, and scoring. Demand for molecular marker data derived in situ from tissue has driven histology informatics automation to the point where one can envision the computer, rather than the microscope, as the primary viewing platform for histopathological scoring and diagnoses. Tissue microarrays (TMAs), with hundreds or even thousands of patients' tissue sections on each slide, were the first step in this wave of automation. Via TMAs, increasingly rapid identification of the molecular patterns of cancer that define distinct clinical outcome groups among patients has become possible. TMAs have moved the bottleneck of acquiring molecular pattern information away from sampling and processing the tissues to the tasks of scoring and results analyses. The need to read large numbers of new slides, primarily for research purposes, is driving continuing advances in commercially available automated microscopy instruments that already do or soon will automatically image hundreds of slides per day. We reviewed strategies for acquiring, collating, and storing histological images with the goal of streamlining subsequent data analyses. As a result of this work, we report an implementation of software for automated preprocessing, organization, storage, and display of high resolution composite TMA images.",
keywords = "Automated tissue microarray (TMA) scoring, Densitometry/flourometry, Image acquisition, Texture segmentation, Tissue microarrays(TMAs)",
author = "Andrew Rabinovich and Stan Krajewski and Maryla Krajewska and Ahmed Shabaik and Hewitt, {Stephen M.} and Serge Belongie and Reed, {John C.} and Price, {Jeffrey H.}",
note = "Funding Information: Manuscript received July 8, 2004; revised March 23, 2005. This work was supported by the WPC Research and Education Fund, the UCSD Chancellor{\textquoteright}s Scholarship, and the Chris and Warren Hellman Foundation. The high throughput microscopy instrumentation was funded by Whitaker Foundation Biomedical Engineering Research Grants, National Institutes of Health NICHD Grant HD37782, and NSF Major Research Instrumentation (MRI) Grant BES-9871365. Disclosure statement: J. H. Price co-founded two companies: Q3DM Inc., now owned by Beckman Coulter; and Vala Sciences Inc.",
year = "2006",
month = apr,
doi = "10.1109/TITB.2005.855544",
language = "English",
volume = "10",
pages = "209--219",
journal = "IEEE Journal of Biomedical and Health Informatics",
issn = "2168-2194",
publisher = "Institute of Electrical and Electronics Engineers",
number = "2",

}

RIS

TY - JOUR

T1 - Framework for parsing, visualizing and scoring tissue microarray images

AU - Rabinovich, Andrew

AU - Krajewski, Stan

AU - Krajewska, Maryla

AU - Shabaik, Ahmed

AU - Hewitt, Stephen M.

AU - Belongie, Serge

AU - Reed, John C.

AU - Price, Jeffrey H.

N1 - Funding Information: Manuscript received July 8, 2004; revised March 23, 2005. This work was supported by the WPC Research and Education Fund, the UCSD Chancellor’s Scholarship, and the Chris and Warren Hellman Foundation. The high throughput microscopy instrumentation was funded by Whitaker Foundation Biomedical Engineering Research Grants, National Institutes of Health NICHD Grant HD37782, and NSF Major Research Instrumentation (MRI) Grant BES-9871365. Disclosure statement: J. H. Price co-founded two companies: Q3DM Inc., now owned by Beckman Coulter; and Vala Sciences Inc.

PY - 2006/4

Y1 - 2006/4

N2 - Increasingly automated techniques for arraying, immunostaining, and imaging tissue sections led us to design software for convenient management, display, and scoring. Demand for molecular marker data derived in situ from tissue has driven histology informatics automation to the point where one can envision the computer, rather than the microscope, as the primary viewing platform for histopathological scoring and diagnoses. Tissue microarrays (TMAs), with hundreds or even thousands of patients' tissue sections on each slide, were the first step in this wave of automation. Via TMAs, increasingly rapid identification of the molecular patterns of cancer that define distinct clinical outcome groups among patients has become possible. TMAs have moved the bottleneck of acquiring molecular pattern information away from sampling and processing the tissues to the tasks of scoring and results analyses. The need to read large numbers of new slides, primarily for research purposes, is driving continuing advances in commercially available automated microscopy instruments that already do or soon will automatically image hundreds of slides per day. We reviewed strategies for acquiring, collating, and storing histological images with the goal of streamlining subsequent data analyses. As a result of this work, we report an implementation of software for automated preprocessing, organization, storage, and display of high resolution composite TMA images.

AB - Increasingly automated techniques for arraying, immunostaining, and imaging tissue sections led us to design software for convenient management, display, and scoring. Demand for molecular marker data derived in situ from tissue has driven histology informatics automation to the point where one can envision the computer, rather than the microscope, as the primary viewing platform for histopathological scoring and diagnoses. Tissue microarrays (TMAs), with hundreds or even thousands of patients' tissue sections on each slide, were the first step in this wave of automation. Via TMAs, increasingly rapid identification of the molecular patterns of cancer that define distinct clinical outcome groups among patients has become possible. TMAs have moved the bottleneck of acquiring molecular pattern information away from sampling and processing the tissues to the tasks of scoring and results analyses. The need to read large numbers of new slides, primarily for research purposes, is driving continuing advances in commercially available automated microscopy instruments that already do or soon will automatically image hundreds of slides per day. We reviewed strategies for acquiring, collating, and storing histological images with the goal of streamlining subsequent data analyses. As a result of this work, we report an implementation of software for automated preprocessing, organization, storage, and display of high resolution composite TMA images.

KW - Automated tissue microarray (TMA) scoring

KW - Densitometry/flourometry

KW - Image acquisition

KW - Texture segmentation

KW - Tissue microarrays(TMAs)

UR - http://www.scopus.com/inward/record.url?scp=33645719375&partnerID=8YFLogxK

U2 - 10.1109/TITB.2005.855544

DO - 10.1109/TITB.2005.855544

M3 - Journal article

C2 - 16617609

AN - SCOPUS:33645719375

VL - 10

SP - 209

EP - 219

JO - IEEE Journal of Biomedical and Health Informatics

JF - IEEE Journal of Biomedical and Health Informatics

SN - 2168-2194

IS - 2

ER -

ID: 302054249