Hierarchical Normalized Cuts: Unsupervised Segmentation of Vascular Biomarkers from Ovarian Cancer Tissue Microarrays

TitleHierarchical Normalized Cuts: Unsupervised Segmentation of Vascular Biomarkers from Ovarian Cancer Tissue Microarrays
Publication TypeConference Paper
Year of Publication2009
AuthorsJanowczyk, A, Chandran S, Singh R, Sasaroli D, Coukos G, Feldman MD, Madabhushi A
Conference NameInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
Accession Number20425992
KeywordsAlgorithms, Angiogenic Proteins/analysis, Artificial Intelligence, Biopsy/methods, Diagnosis, Computer-Assisted/*methods, Female, Gene Expression Profiling/methods, Humans, Image Enhancement/methods, Image Interpretation, Computer-Assisted/methods, Microscopy/*methods, Neoplasm Proteins/*analysis, Ovarian Neoplasms/*diagnosis/metabolism, Pattern Recognition, Automated/*methods, Proteoglycans/*analysis, Reproducibility of Results, Sensitivity and Specificity, Tissue Array Analysis/*methods, Tumor Markers, Biological/*analysis

Research has shown that tumor 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 an 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%).

PDF Link


 *IEEE COPYRIGHT NOTICE: 1997 IEEE. * Personal use of this material is permitted. However, permission to reprint/ republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

*COPYRIGHT NOTICE:* These materials are presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.