High-throughput biomarker segmentation on ovarian cancer tissue microarrays via hierarchical normalized cuts.

Détails

ID Serval
serval:BIB_A5AD3363CC16
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
High-throughput biomarker segmentation on ovarian cancer tissue microarrays via hierarchical normalized cuts.
Périodique
IEEE Transactions on Bio-medical Engineering
Auteur(s)
Janowczyk A., Chandran S., Singh R., Sasaroli D., Coukos G., Feldman M.D., Madabhushi A.
ISSN
1558-2531 (Electronic)
ISSN-L
0018-9294
Statut éditorial
Publié
Date de publication
2012
Volume
59
Numéro
5
Pages
1240-1252
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov'tPublication Status: ppublish
Résumé
We present a system for accurately quantifying the presence and extent of stain on account of a vascular biomarker on tissue microarrays. We demonstrate our flexible, robust, accurate, and high-throughput minimally supervised segmentation algorithm, termed hierarchical normalized cuts (HNCuts) for the specific problem of quantifying extent of vascular staining on ovarian cancer tissue microarrays. The high-throughput aspect of HNCut is driven by the use of a hierarchically represented data structure that allows us to merge two powerful image segmentation algorithms-a frequency weighted mean shift and the normalized cuts algorithm. HNCuts rapidly traverses a hierarchical pyramid, generated from the input image at various color resolutions, enabling the rapid analysis of large images (e.g., a 1500 × 1500 sized image under 6 s on a standard 2.8-GHz desktop PC). HNCut is easily generalizable to other problem domains and only requires specification of a few representative pixels (swatch) from the object of interest in order to segment the target class. Across ten runs, the HNCut algorithm was found to have average true positive, false positive, and false negative rates (on a per pixel basis) of 82%, 34%, and 18%, in terms of overlap, when evaluated with respect to a pathologist annotated ground truth of the target region of interest. By comparison, a popular supervised classifier (probabilistic boosting trees) was only able to marginally improve on the true positive and false negative rates (84% and 14%) at the expense of a higher false positive rate (73%), with an additional computation time of 62% compared to HNCut. We also compared our scheme against a k-means clustering approach, which both the HNCut and PBT schemes were able to outperform. Our success in accurately quantifying the extent of vascular stain on ovarian cancer TMAs suggests that HNCut could be a very powerful tool in digital pathology and bioinformatics applications where it could be used to facilitate computer-assisted prognostic predictions of disease outcome.
Mots-clé
Algorithms, Female, High-Throughput Screening Assays/methods, Histocytochemistry/methods, Humans, Image Processing, Computer-Assisted/methods, Neovascularization, Pathologic, Ovarian Neoplasms/blood supply, Ovarian Neoplasms/chemistry, Tissue Array Analysis/methods, Tumor Markers, Biological/analysis, Tumor Markers, Biological/chemistry
Pubmed
Web of science
Création de la notice
14/10/2014 11:42
Dernière modification de la notice
20/08/2019 15:10
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