detection of prostate cancer from whole-mount histology images using markov random fields
DESCRIPTION
Laboratory for Computational Imaging and Bioinformatics, lcib.rutgers.edu. Detection of Prostate Cancer from Whole-Mount Histology Images Using Markov Random Fields. James P. Monaco 1 , John E. Tomaszewski 2 , Michael D. Feldman 2 , Mehdi Moradi 3 , Parvin Mousavi 3 , - PowerPoint PPT PresentationTRANSCRIPT
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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
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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
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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
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• 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
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MRF Results
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Glands to Regions
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Segmentation: Region Growing
Current Boundary (CB)
Internal Boundary (IB)
Current Region (CR)
boundary_measure = mean(IB)-mean(CB)
Iteration 123451445132126178
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Neighborhood Structure of the Glands
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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
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Qualitative Results
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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