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
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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