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, Alexander Boag 3, Chris Davidson 3, Purang Abolmaesumi 3, Anant Madabhushi 1 1 Rutgers University, USA 2 University of Pennsylvania, USA 3 Queen’s University, Canada Laboratory for Computational Imaging and Bioinformatics, lcib.rutgers.edu

Prostate Cancer (CaP) Protocol PSA/Rectal exam TRUS Biopsy Pathologist Diagnosis Prostatectomy Pathologist Diagnosis Post-surgical Treatment 0 lcib.rutgers.edu

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 lcib.rutgers.edu

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 lcib.rutgers.edu

Low-Resolution CaP Detection Glands are the prominent visible structures Cancerous glands: 1) small, 2) surrounded by cancerous glands lcib.rutgers.edu

Overview of Cap Detection Algorithm Gland Segmentation Gland Classification Markov Random Field Iteration Boundary Aggregation lcib.rutgers.edu

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 lcib.rutgers.edu

Classification: Glandular Area Malignant Histogram Benign Histogram Remove legend Remove legend lcib.rutgers.edu

Markov Random Field Basics Goal: Inject knowledge that malignant glands are near malignant glands Establish a graph connecting the glands Let {a 1, a 2,…, a N } be the gland areas Let {l 1, l 2,…, l N } be the gland labels with l i  {m,b} lcib.rutgers.edu

Markov Random Field Models Prevalent parametric model (Ising) – Generic model used for its simplicity Novel nonparametric model – Generated directly from image statistics lcib.rutgers.edu

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 lcib.rutgers.edu

Experiment 1: CAD Performance Area-based with MRF lcib.rutgers.edu

Experiment 2: Compare Ising and Nonparametric Model Ising Nonparametric Gland Segmentation Gland Classification Nonparametric MRF Ising MRF lcib.rutgers.edu

Concluding Remarks First CAD system for detecting CaP in whole- mount histological images – Sensitivity of and specificity of – 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 lcib.rutgers.edu

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 lcib.rutgers.edu

The End lcib.rutgers.edu

Gibbs Formulations Generic Ising Model Nonparametric Formulation

MRF Basics: Markov Properties Let G={S,E} define a graph on N glands Let y = {y 1, y 2,…, y N } be the gland areas Let x = {x 1, x 2,…, x N } be the gland labels with x i  {m,b} Use maximum a posteriori estimation to obtain x Simplify with Markov Property: p(x s |x -s )=p(x s |x r :r  s ) 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

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