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

Authors:
Andrew Janowczyk;Sharat Chandran;Rajendra Singh;Dimitra Sasaroli;George Coukos;Michael D. Feldman;Anant Madabhushi
Affiliations:
Dept of Computer Science & Engineering, Indian Institute of Technology Bombay,;Dept of Computer Science & Engineering, Indian Institute of Technology Bombay,;Quest Diagnostics, Inc, USA;Ovarian Cancer Research Center, University of Pennsylvania, USA;Ovarian Cancer Research Center, University of Pennsylvania, USA;Dept of Pathology and Lab Medicine, University of Pennsylvania, USA;Dept of Biomedical Engineering, Rutgers University, USA
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Year:
2009

Citing 7
Cited 2

Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Fast Gauss Transform and Efficient Kernel Density Estimation

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Weighted Graph Cuts without Eigenvectors A Multilevel Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
A boosting cascade for automated detection of prostate cancer from digitized histology

MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II

High-throughput prostate cancer gland detection, segmentation, and classification from digitized needle core biopsies

MICCAI'10 Proceedings of the 2010 international conference on Prostate cancer imaging: computer-aided diagnosis, prognosis, and intervention
Tumor sensitive matching flow: an approach for ovarian cancer metastasis detection and segmentation

MICCAI'12 Proceedings of the 4th international conference on Abdominal Imaging: computational and clinical applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

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%).