detection of prostate cancer from whole-mount histology images using markov random fields james p....

Detection of Prostate Cancer from Whole-Mount Histology Images

Using Markov Random Fields

James P. Monaco1, John E. Tomaszewski2, Michael D. Feldman2, Mehdi Moradi3, Parvin Mousavi3,

Alexander Boag3, Chris Davidson3, Purang Abolmaesumi3, Anant Madabhushi1

1Rutgers University, USA2University of Pennsylvania, USA

3Queen’s University, Canada

Laboratory for Computational Imaging and Bioinformatics, lcib.rutgers.edu

Prostate Cancer (CaP) Protocol

PSA/Rectal exam TRUS BiopsyPathologist

Diagnosis

ProstatectomyPathologist Diagnosis

Post-surgical Treatment

0

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• Aid doctors with time consuming task– Digitized data about

60,000x40,000 at 0.5 micron

• Can help supply “ground truth” for other modalities

• Quantifiable features facilitate data mining

Computer Aided Detection of CaP in Whole-Mount Histology

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Novel Contributions

• First CAD system for detecting CaP in whole-mount histological images– Tailored to operate at low-resolution (10 micron)

• Novel nonparametric method for modeling Markov Random Fields

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Low-Resolution CaP Detection

• Glands are the prominent visible structures

• Cancerous glands: 1) small, 2) surrounded by cancerous glands

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Overview of Cap Detection AlgorithmGland

Segmentation

Gland Classification

Markov Random Field Iteration

Boundary Aggregation

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Segmentation

S.A. Hojjatoleslami and J. Kittler, “Region growing: a new approach,” IEEE Trans. on Image Processing, vol. 7, no. 7, pp. 1079–1084, July 1998.

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Classification: Glandular Area

Malignant HistogramBenign Histogram

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Markov Random Field Basics

• Goal: Inject knowledge that malignant glands are near malignant glands

• Establish a graph connecting the glands

• Let {a1, a2,…, aN} be the gland areas

• Let {l1, l2,…, lN} be the gland labels with li{m,b}

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Markov Random Field Models

• Prevalent parametric model (Ising)– Generic model used for its simplicity

• Novel nonparametric model– Generated directly from image statistics

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Experiments

• Experiment 1 Evaluate CAD gland classification performance

• Experiment 2 Compare parametric (Ising) and nonparametric models

• Dataset four H&E stained whole-mount histological sections at 10 micron

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Experiment 1: CAD Performance

Area-basedwith MRF

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Experiment 2: Compare Ising and Nonparametric Model

Ising

Nonparametric

Gland Segmentation

GlandClassification

NonparametricMRF

IsingMRF

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Concluding Remarks

• First CAD system for detecting CaP in whole-mount histological images– Sensitivity of 0.8670 and specificity of 0.9524– Requires 4-5 minutes on a 2100×3200 image using

standard desktop PC• Introduced a novel nonparametric model for

Markov Random Fields– Better performance than Ising model– Easily extended to other biological applications

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Acknowledgements

• Wallace H. Coulter Foundation• New Jersey Commission on Cancer Research• National Cancer Institute• Society for Imaging and Informatics on

Medicine• Life Science Commercialization Award

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The End

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Gibbs Formulations

• Generic Ising Model

• Nonparametric Formulation

MRF Basics: Markov Properties• Let G={S,E} define a graph on N glands• Let y = {y1, y2,…, yN} be the gland areas• Let x = {x1, x2,…, xN} be the gland labels with xi{m,b}• Use maximum a posteriori estimation to obtain x• Simplify with Markov Property: p(xs|x-s)=p(xs|xr:rs) • Markov Property implies p(x) is a Gibbs distribution

• Among men in the US prostate cancer (Cap) is second most common cancer and the second leading cause of cancer-related death.

• Histological analysis provides the definite test for CaP.

• Analysis of whole mount histological sections (WMHSs) – Staging and grading of CaP– Ground truth for other modalities

Prostate Cancer

MRF Results

Glands to Regions

Segmentation: Region Growing

Current Boundary (CB)

Internal Boundary (IB)

Current Region (CR)

boundary_measure = mean(IB)-mean(CB)

Iteration 123451445132126178

Neighborhood Structure of the Glands

Quantitative Results

• Review of algorithm– Segmentation– Classification using area

(requires a probability threshold)

– MRF Iteration• Initial conditions affect MRF

results• ROC curve over varying

thresholds

Gland Classification Performance

Qualitative Results

Experiment 1: Gland Classification

• Evaluate the ability to discriminate malignant from benign glands.

• A gland whose centroid lies within the blue truth is considered cancerous, otherwise it is benign.

• Training/test data consists of four slices using a leave-one-out training