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Hierarchical normalized cuts : unsupervised segmentation of vascular biomarkers from ovarian cancer tissue microarrays

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Title Hierarchical normalized cuts : unsupervised segmentation of vascular biomarkers from ovarian cancer tissue microarrays
 
Creator JANOWCZYK, A
CHANDRAN, S
SINGH, R
SASAROLI, D
COUKOS, G
FELDMAN, MD
MADABHUSHI, A
 
Subject image segmentation
recognition
 
Description Research has shown that turner vascular markers (TVMs) may serve as potential OCa biomarkers for prognosis prediction. One such TVM is ESM-1, which can be visualized by staining ovarian Tissue Microarrays (TMA) with in antibody to ESM-1. The ability to quickly and quantitatively estimate vascular stained regions may yield an image based metric linked to disease survival and outcome. Automated segmentation of the vascular stained regions on the TMAs. however, is hindered by the presence of spuriously stained false positive regions. In this paper, we present a general, robust and efficient unsupervised segmentation algorithm, termed Hierarchical Normalized Cuts (HNCut), and show its application in precisely quantifying the presence and extent of a TVM on OCa TMAs. The strength of HNCut is in the use of a hierarchically represented data structure that bridges the mean shift (MS) and the normalized cuts (NCut,) algorithms. This allows HNCut to efficiently traverse a pyramid of the input image at various color resolutions, efficiently and accurately segmenting the object class of interest (in this case ESM-1. vascular stained regions) by simply annotating half a, dozen pixels belonging to the target; class. Quantitative and qualitative analysis of our results, using 100 pathologist annotated samples across multiple studies, prove the superiority of our method (sensitivity 81%, Positive predictive value (PPV), 80%) versus a popular supervised learning technique, Probabilistic Boosting Trees (sensitivity, PPV of 76% and 66%).
 
Publisher SPRINGER-VERLAG BERLIN
 
Date 2011-10-24T01:45:13Z
2011-12-15T09:11:22Z
2011-10-24T01:45:13Z
2011-12-15T09:11:22Z
2009
 
Type Proceedings Paper
 
Identifier MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2009, PT I, PROCEEDINGS,5761,230-238
978-3-642-04267-6
0302-9743
http://dspace.library.iitb.ac.in/xmlui/handle/10054/15297
http://hdl.handle.net/100/2020
 
Source 12th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2009),London, ENGLAND,SEP 20-24, 2009
 
Language English