case school of engineering | - laboratory of ......shannon agner, outstanding graduate student...

LABORATORY OF COMPUTATIONAL IMAGING AND BIOINFORMATICS

Director: Dr. Anant MadabhushiAssociate Professor,

Department of Biomedical Engineering

Annual Report: 2011

Laboratory for Computational Imaging and Bioinformatics (LCIB) Annual Report 2011

LCIB Website @ http://lcib.rutgers.edu


LAB MEMBERS

April 2011


LAB MEMBERS

Lab Director: Anant Madabhushi, PhD

Administrative Assistant: Rhonda Breen-Simone

Undergraduate Students • Ronak Chawla• Joe Galero• Abhishek Golugula• Aparma Kannan• Sudha Karthigeyan• Ross Kleiman• Prateeka Koul• Eileen Hwuang• Pratik Patel• Srivathsan Prabu• Elaine Yu

Postdocs: • Mirabela Rusu, PhD• Tao Wan, PhDGraduate Students • Sahir Ali • Ajay Basavanhally• Andrew Janowczyk (IIT, Bombay)• George Lee • Shoshana Rosskamm• Rachel Sparks• Pallavi Tiwari• Rob Toth• SatishViswanath

Research Faculty• James Monaco, PhD


LAB MEMBERS

Lab Director:Anant Madabhushi, PhD

Research FacultyJames Monaco, PhD

Faculty Assistant

Rhonda Breen-Simone

PostDocs

Mirabela Rusu, PhD Tao Wan, PhD

Graduate Students

Sahir Ali Ajay Basavanhally Shoshana RosskammAndrew Janowczyk(IIT, Bombay)

George Lee


LAB MEMBERS

• Ronak Chawla• Aparma Kannan• Sudha Karthigeyan• Ross Kleiman• Prateeka Koul• Eileen Hwuang• Srivathsan Prabu

Graduate Students (Contd)

SatishViswanathPallavi Tiwari Rob Toth

Pratik Patel Abhishek GolugulaJoe Galero Elaine Yu

Undergraduate Students

Rachel Sparks


RECENT ALUMNI

Jun Xu, PhD Professor at Nanjing University

Gaoyu Xiao, PhD

Shannon Agner3rd Year Medical

Student at UMDNJ-RWJMS

Scott DoyleDirector of

Research at Ibris, Inc.

Akshay ShridarAssistant Project

Manager at Integra Life Sciences

Najeeb ChowdhuryHealthcare Market Research Analyst at AlphaDetail Inc.

Jon ChappelowResearch

Scientist at Accuray, Inc.

PostDocs

PhD Students Master Students

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SOME OF OUR COLLABORATORS

Mark Rosen, Michael D. Feldman, Shridar Ganesan

John E .Tomaszewski

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MICCAI: Sahir Ali, Satish Viswanath, Ajay Basavanhally

CONFERENCE PARTICIPATION - 2011

SPIE, Finalist Student Paper Award: Shannon Agner

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SUMMARY OF ACCOMPLISHMENTS 2011

Lab Members: 25 (2 faculty, 2 post-docs, 9 graduate, 1 admin, 11 undergrads)Papers Books: 1 Theses: 3 PhD+2 MS Journal Papers: 24 Peer-Reviewed Conference Papers: 31 Abstracts: 18

5 new PCT Patents Filed5 new grants 1 NIH 1 DOD

On going projects: 4510


Editorial, Committees, Membership - Anant MadabhushiEditor (Guest), Special Issue on IEEE Transactions on Biomedical Engineering Letters: Multi-Scale/Resolution Signal and Image Analysis, 2011.Co-Chair, Session on “Image Analysis in Cancer Detection”, IEEE Engineering in Medicine and Biology Conference 2011, Boston, MA, September 1, 2011.Chair, Workshop on Prostate Cancer Imaging, MICCAI 2011.Chair, Workshop on Histopathology Image Analysis, MICCAI 2011.Co-Organizer, Tutorial on Manifold Learning for Medical Images, MICCAI 2011.Co-Chair, Special Session on Prostate Cancer Imaging, IEEE International Symposium on Biomedical Imaging, March 2011.Program Committee Member, Computer-Aided Diagnosis conference at SPIE Medical Imaging, 2011.Program Committee Member, MICCAI 2011.Panelist and Invited Speaker, “Preparing for Campus Interviews & Negotiating Academic Job Offers”, University of Pennsylvania, January, 2011.Program Committee on IEEE Conference on Bioinformatics and Bioengineering (BIBE), 2011.Technical Advisory Board Member, IEEE EMBS/ISBI, 2010-PresentCo-Chair, Workshop on Trends in Experimental Pathology: Imaging Organisms/Experimental Trends – “Brave New World”, In conjunction with 2011 Annual Meeting at Experimental Biology, American Society for Investigative Pathology, April 9-13, 2011, Washington, DC.

AWARDS AND ACCOMPLISHMENTS


Awards - Anant MadabhushiFirst Prize, SNR Denton Elevator Pitch Competition, Rutgers Entrepreneurship Day, 2011

Media Recognition“Entrepreneurs flock to Rutgers Entrepreneurship Conference”, New Jersey Tech Weekly, November 22nd, 2011.“University Startup receives NIH SBIR Funding”, Daily Targum, October 11, 2011.“Rutgers-Affiliated Start-Up Company Receives Funding for Technology to Help Choose Breast Cancer Treatments”, Rutgers Today, October 6th, 2011. Story featured on the following news sites

“The Race for Another Cure”, Rutgers Magazine, Pg. 31, Winter 2011 Issue.

AAAS EurekAlert! Noodls.comNJBIZ.comwww.njcrea.comwww.biomedicalproducts.com

www.bioprodmag.com www.physorg.comwww.news-medical.netwww.biosciencetechnology.comwww.biosciencetechnology.comwww.bionj.org

AWARDS AND ACCOMPLISHMENTS

http://www.njcrea.com/

http://www.biomedicalproducts.com/

http://www.bioprodmag.com/

http://www.physorg.com/

http://www.news-medical.net/

http://www.biosciencetechnology.com/

http://www.biosciencetechnology.com/

http://www.bionj.org/


NEW GRANTS - 2011Madabhushi, Anant (PI)Date: 07/01/11-12/31/11 Tech Commercialization Fund, ORSP, RutgersImage based risk score predictors for ER+ Breast

CancersRole: PI

Madabhushi, Anant (MPI) Date: 09/03/11-08/31/12NIH R43EB015199-01Decision Support System for Predicting outcome of

ER+ breast cancers Role: PI with Shridar Ganesan (CINJ), David Harding

(IbRiS Inc.)

Madabhushi, Anant (PI) Date: 07/01/11- 06/30/12Pre-doctoral Training FellowshipSociety for Imaging Informatics in MedicineComputerized Decision Support for Prostate Cancer

Detection from Multi-parametric MRIRole: Mentor for Satish Viswanath

Madabhushi, Anant (PI)Date: 01/01/12 -12/31/13Pre-doctoral Training Fellowship in Health DisparityDepartment of DefenseComparing Proteomic, Histological Biomarkers for

Biochemical Failure Among African Americans and Caucasians Following Radical Prostatectomy

Role: Mentor for George Lee

Monaco, James (PI)Date: 06/09/11 - 07/15/11Burroughs Wellcome FundPathology Training and Collaboration between Rutgers and University of Michigan Role: Collaborator


STUDENTS AWARDS AND FELLOWSHIPS

Pallavi Tiwari, Travel Award ($250) from SciWomen, The Office for the promotion of Women in Science, Engineering and Mathematics (STEM), 2011 Sahirzeeshan Ali, Travel Award ($600) from Omnyx to attend Workshop on Histopathology Image Analysis, Toronto, Canada, 2011Ajay Basavanhally, Travel Award ($600) from Omnyx to attend Workshop on Histopathology Image Analysis, Toronto, Canada, 2011Joseph Galero, Elaine Yu, BMEStart Competition, Honorable Mention, NCIAA, 2011.Shannon Agner, “Textural Kinetics: A Novel Dynamic Contrast-Enhanced (DCE)-MRI Feature for Breast Lesion Classification” nominated for best paper of 2010 by the Journal of Digital Imaging editorial board.Joe Galero, Best Poster Award for “Integrated Texton and Bag of Words Classifier for Identifying Anaplastic Medulloblastomas" at BME Senior Design Conference 2011Elaine Yu, Admitted to Pittsburgh Tissue Engineering Initiative Summer Internship program with a research scholarship ($3,850), April 2011.Shannon Agner, Outstanding Graduate Student Award, School of Engineering, April 15, 2011.Pratik Patel, Selected as an intern to (RISE) Research in Science and Engineering Program ($2500), Germany, 2011.


Ajay Basavanhally, Travel Award ($300) from IEEE Intl Symposium on Biomedical Imaging, 2011Shannon Agner, Departmental Travel Award ($200), 2011Satish Viswanath, Departmental Travel Award ($100), 2011Akshay Shridar, Departmental Travel Award ($100), 2011Ajay Basavanhally, Departmental Travel Award ($100), 2011Scott Doyle, Departmental Travel Award ($100), 2011George Lee, Departmental Travel Award ($100), 2011Satish Viswanath, Interview featured in NJN TV Channel, March 12th, 2011.Satish Viswanath, Interview featured in CP News Story, “Study Finds most Indian graduate students in the US want to use their degrees back home”, 2011.Sudha Karthigeyan, Selected as an intern to (UROP) Undergraduate Research Opportunities Program ($2500), 2011.Shannon Agner, Michael B. Merickel Best Student Paper Award Finalist, SPIE Medical Imaging, 2011.Shannon Agner, Rachel Sparks, BMEIdea Competition Finalist, NCIAA ($500), 2011.

STUDENTS AWARDS AND FELLOWSHIPS (CONTD)


PEER REVIEWED PUBLICATIONS FOR 2011Summary

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35

Theses Journal Papers Conference Papers Abstracts

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BOOKS

Madabhushi, A, Dowling, J, Huisman, H, Barratt, D, Prostate Cancer Imaging: Image analysis and image guided interventions, International Workshop Held in Conjunction with MICCAI 2011, Proceedings Springer VerlagISBN-10: 3642239439, 2011

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THESESPhD

Shannon Agner, A Computerized Image Analysis Framework for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI): Applications to Breast Cancer (04/2011)

Jon Chappelow , Multimodal Image Registration using Multivariate Information Theoretic Similarity Measures: Applications to Prostate Cancer Diagnosis and Targeted Treatment (04/2011)

Scott Doyle, Computerized Detection, Segmentation and Classification of Digital Pathology: Case Study in Prostate Cancer (04/2011)

MSNajeeb Chowdhury, Concurrent segmentation of the prostate on MRI and CT via linked statistical shape models for radiotherapy planning (07/2011)

Akshay Shridar, Content-Based Image Retrieval of Digitized Histopathology via Boosted Spectral Embedding (BoSE) (10/2011)

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JOURNAL PAPERSHipp, J, Monaco, J, Kunju, P Cheng, P, Yagi, Y, Rodriguez-Canales, J, Emmert-Buck, M, Hewitt, S, Feldman, M, Tomaszewski, J, Shih, N, Toner, M, Tompkins, R, Flotte, T, Lucas, D, Gilbertson, Kunju, LP, J, Balis, U, Madabhushi, A, “Integration of architectural and cytologic driven image algorithms for prostate adenocarcinoma identification”, Analytical Cellular Pathology, Accepted.Chowdhury, N, Toth, R, Chappelow, J, Kim, S, Motwani, S, Punekar, S, Lin, H, Both, S, Vapiwala, N, Hahn, S, Madabhushi, A, “Concurrent segmentation of the prostate on MRI and CT via linked statistical shape models for radiotherapy planning”, Medical Physics, Accepted.Golugula, A, Lee, G, Master, S, Feldman, M, Tomaszewski, J, Speicher, D, Madabhushi, A, “Supervised Regularized Canonical Correlation Analysis: Integrating Histologic and Proteomic measurements for predicting Biochemical Failures following Prostate Surgery”, BMC Bioinformatics, Accepted.Viswanath, S, Bloch, B, Chappelow, J, Toth, R, Rofsky, N, Genega, E, Lenkinski, R, Madabhushi, A, “Central Gland and Peripheral Zone Prostate Tumors have Significantly Different Quantitative Imaging Signatures on 3 Tesla Endorectal, In Vivo T2-Weighted Magnetic Resonance Imagery”, Journal of Magnetic Resonance Imaging, Accepted.Janowczyk, A, Chandran, S, Singh, R, Sasaroli, D, Coukos, G, Feldman, M, Madabhushi, A, “High-Throughput Biomarker Segmentation on Ovarian Cancer Tissue Microarrays via Hierarchical Normalized Cuts”, IEEE Trans on Biomedical Engineering, In Press.

PEER REVIEWED PUBLICATIONS


JOURNAL PAPERS (CONTD)Hipp, J, Monaco, J, Kunju, P Cheng, P, Yagi, Y, Rodriguez-Canales, J, Emmert-Buck, M, Hewitt, S, Feldman, M, Tomaszewski, J, Shih, N, Toner, M, Tompkins, R, Flotte, T, Lucas, D, Gilbertson, Madabhushi, A, Kunju, LP, J, Balis, U, “Optimization of complex cancer morphology detection using the SIVQ pattern recognition algorithm”, Analytical Cellular Pathology, 2011 Oct 11 [Epub ahead of print] (PMID: 21988838)Hipp, J, Cheng, J, Pantanowitz, L, Hewitt, S, Yagi, Y, Monaco, J, Madabhushi, A, Rodriguez-canales, J, Hanson, J, Roy-Chowdhuri, S, Filie, A, Feldman, M, Tomaszewski, J, Shih, N, Gilbertson, J, Emmert-Buck, M, Balis, U, “Image Microarrays (IMA) Digital Pathology's Missing Tool”, Journal of Pathology Informatics, AcceptedAli, S, Madabhushi, A, "GPU Implementation of an Integrated Shape Based Active Contour: Application to Digital Pathology", Journal of Pathology Informatics, Accepted.Basavanhally, A, Ganesan, S, Shih, N, Feldman, M, Tomaszewski, J, Madabhushi, A, “Multi-Field-of-View Strategy for Image-Based Outcome Prediction of ER+ Breast Cancer Histopathology Using Spatio-Architectural and Vascular Features”, Journal of Pathology Informatics, Accepted.Ghaznavi, D, Evans, A, Madabhushi, A, Feldman, M, “Digital Imaging in Pathology: Whole-Slide

Imaging and Beyond”, Annual Review of Pathology: Mechanisms of Disease, Accepted. Doyle, S, Monaco, J, Tomaszewski, J, Feldman, M, Madabhushi, A, "An Active Learning Based Classification Strategy for the Minority Class Problem: Application to Histopathology Annotation", BMC Bioinformatics, 12:424, 2011.



JOURNAL PAPERS (CONTD)Hipp, J, Monaco, J, Cheng, J, Lucas, D, Madabhushi, A, Balis, U, Automated Vector Selection of SIVQ and Parallel Computing Integration MATLABTM: Innovations supporting large-scale and high-throughput image analysis studies, Journal of Pathology Informatics, 2:37, 2011 (PMID: 21886893). Tiwari, P, Viswanath, S, Kurhanewicz, J, Shridar, A, Madabhushi, A, “Multimodal Wavelet Embedding

Representation for data Combination (MaWERiC): Integrating Magnetic Resonance Imaging and Spectroscopy for Prostate Cancer Detection”, NMR in Biomedicine, 2011 doi: 10.1002/nbm.1777, (PMID: 21960175).

Hipp, J, Sica, J, McKenna, B, Monaco, J, Madabhushi, A, Cheng, J, Balis, U, The need for the pathology community to sponsor a WSI repository with technical guidance from the pathology informatics community, Journal of Pathology Informatics, 2:31, 2011 (PMID: 21845229). Bulman, J, Toth, R, Patel, AD, Bloch, BN, McMahon, CJ, Ngo, L, Madabhushi, A, Rofsky, N, Automated Computer-Derived Prostate Volumes from MRI Data: Comparison to Radiologist-Derived MRI Volumes and Pathology Specimen Volumes, Radiology, Accepted.Hipp, J, Flotte, T, Monaco, J, Cheng, J, Madabhushi, A, Yagi, Y, Rodriguez-Canales, J, Emmert-Buck, M, Dugan, MC, Hewitt, S, Toner, M, Tompkins, R, Lucas, D, Gilbertson, JR, Balis, U, Computer Aided Diagnostic (CAD) tools aim to empower rather than replace Pathologists: Lessons learned from computational chess, Journal of Pathology Informatics, 2:25, 2011 (PMID: 21773056).



JOURNAL PAPERS (CONTD)Xu, J, Janowcyzk, A., Tomaszewski, J., Feldman, M., and Madabhushi, A, “A High-throughput Active Contour Scheme for Segmentation of Histopathological Imagery”, Medical Image Analysis, vol. 15, pp. 851-62, 2011 (PMID: 21570336). Chappelow, J, Bloch, N., Rofsky, N, Genega, E, Lenkinski, R, DeWolf, W, Madabhushi, A, Elastic Registration of Multimodal Prostate MRI and Histology via Multi-Attribute Combined Mutual Information, Medical Physics, vol. 38[4], pp. 2005-2018, 2011 (PMID: 21626933).Toth, R, Bloch, N, Genega, E, Rofsky, N, Lenkinsky, R, Rosen, M, Kalyanpur, A, Pungavkar, S, Madabhushi, A, Accurate Prostate Volume Estimation Using Multi-Feature Active Shape Models on T2-Weighted MRI, Academic Radiology, vol. 18[6], pp. 745-54, 2011 (PMID: 21549962). Chappelow, J, Feldman, M, Tomaszewski, J, Shih, N, Madabhushi, A, HistoStitcher©: An Interactive Program for Accurate and Rapid Reconstruction of Digitized Whole Histological Sections from Tissue Fragments, Computerized Medical Imaging and Graphics, vol. 35[7-8], pp. 542-56, 2011 (PMID: 21397459). Monaco, J, Madabhushi, A, “Weighted Maximum Posterior Marginals for Random Fields Using an Ensemble of Conditional Densities from Multiple Markov Chain Monte Carlo Simulations”, IEEE Transactions on Medical Imaging, vol. 30[7], pp. 1353-64, 2011 (PMID: 21335309).



JOURNAL PAPERS (CONTD)Madabhushi, A, Agner, S, Doyle, S, Basavanhally, A, Lee, G, Computer-Aided Prognosis: Predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data, Special Issue of Computerized Medical Imaging and Graphics on Whole Slide Microscopic Image Processing, vol. 35[7-8],pp. 506-14, 2011 (PMID: 21333490).Xiao, G, Bloch, N, Chappelow, J, Genega, E, Rofsky, N, Lenkinsky, R, Tomaszewski, J, Feldman, M, Rosen, M, Madabhushi, A, Determining Histology-MRI Slice Correspondences for Defining MRI-based Disease Signatures of Prostate Cancer, Special Issue of Computerized Medical Imaging and Graphics on Whole Slide Microscopic Image Processing, vol. 35[7-8], pp. 568-78, 2011 (PMID: 21255974).Toth, R, Tiwari, P, Rosen, M, Reed, G, Kurhanewicz, J, Kalyanpur, A, Pungavkar, S, Madabhushi, A, A Magnetic Resonance Spectroscopy Driven Initialization Scheme for Active Shape Model Based Prostate Segmentation, Medical Image Analysis, vol. 15(2), pp. 214-225, 2011 (PMID: 21195016).



PEER-REVIEWED CONFERENCE PROCEEDINGSTiwari, P, Viswanath, S, Kurhanewicz, J, Madabhushi, A, Weighted Combination of Multi-parametric MR Imaging markers for Evaluating Radiation Therapy changes in the prostate, Workshop on Prostate Cancer Imaging, In Conjunction with MICCAI 2011, pp. 80-91.Ginsburg, S, Tiwari, P, Kurhanewicz, J, Madabhushi, A, Variable Ranking with PCA: Finding Multiparametric MR Imaging Biomarkers for Prostate Cancer Diagnosis and Grading, Workshop on Prostate Cancer Imaging, In Conjunction with MICCAI 2011, pp. 146-157.Palumbo, D, Yee, B, Leedy, S, O’Dea, P, Viswanath, S, Madabhushi, A, Interplay between Bias Field Correction, Intensity Standardization, and Noise Filtering for T2-weighted MRI, IEEE Engineering in Medicine and Biology Conference, 2011, Accepted.Golugula, A, Lee, G, Master, S, Feldman, M, Tomaszewski, J, Madabhushi, A, Supervised Regularized Canonical Correlation Analysis: Integrating Histologic and Proteomic Data for Predicting Biochemical Failures, IEEE Engineering in Medicine and Biology Conference, 2011, AcceptedGalero, J, Judkins, A, Bacon, J, Ellison, D, Madabhushi, A, An Integrated Texton and Bag of Words Classifier for Identifying Anaplastic Medulloblastomas, IEEE Engineering in Medicine and Biology Conference, 2011, AcceptedPatel, P, Chappelow, J, Tomaszewski, J, Feldman, M, Rosen, M, Madabhushi, A, Spatially Weighted Mutual Information (SWMI) for Registration of Digitally Reconstructed ex vivo Whole Mount Histology and in vivo prostate MRI, IEEE Engineering in Medicine and Biology Conference, 2011, Accepted



PEER-REVIEWED CONFERENCE PROCEEDINGS (CONTD)Yu, E, Monaco, J, Shih, N, Feldman, M, Tomaszewski, J, Madabhushi, A, Detection of Prostate Cancer on Histopathology using Color Fractals and Probabilistic Pairwise Markov Models, IEEE Engineering in Medicine and Biology Conference, 2011, AcceptedGolugula, A, Lee, G, Madabhushi, A, Evaluating Feature Selection Strategies for High Dimensional, Small Sample Size Datasets, IEEE Engineering in Medicine and Biology Conference, 2011, AcceptedAli, S, Veltri, R, Epstein, J, Christudass, C, Madabhushi, A, “Adaptive Energy Selective active contour with Shape Priors For Nuclear Segmentation and Gleason grading of prostate cancer”, In Proc of Medical Image Computing and Computer Assisted Interventions (MICCAI), pp. 661-669, 2011 (PMID: 22003675).Xiao, G, Madabhushi, A, “Aggregated Distance Metric learning (ADM) for image classification inpresence of limited training data”, In Proc of Medical Image Computing and Computer Assisted Interventions (MICCAI), pp. 33-40, 2011 (PMID: 22003681). Tchikindas, L, Sparks, R, Baccon, J, Ellison, D, Judkins, A, Madabhushi, A, “Segmentation of Nodular Medulloblastoma Using Random Walker and Hierarchical Normalized Cuts”, IEEE Northeast Bioengineering Conference (NEBC), pp. 1-2, 2011.Lai, Y, Viswanath, S, Baccon, J, Ellison, D, Judkins, A, Madabhushi, A, “A Texture-based Classifier to Discriminate Anaplastic from Non-Anaplastic Medulloblastoma”, IEEE Northeast Bioengineering Conference (NEBC), pp. 1-2, 2011.



PEER-REVIEWED CONFERENCE PROCEEDINGS (CONTD)Basavanhally, A, Ganesan, S, Shih, Natalie, Mies, C, Feldman, M, Tomaszewski, J, Madabhushi, A, “A Boosted Classifier for Integrating Multiple Fields of View: Breast Cancer Grading in Histopathology”, IEEE International Symposium on Biomedical Imaging, Chicago, IL, pp. 125-128, 2011.Sparks, R, Madabhushi, A, “Out-of-Sample Extrapolation using Semi-Supervised Manifold Learning (OSE-SSL): Content-based Image Retrieval for Prostate Histology Grading”, IEEE International Symposium on Biomedical Imaging, Chicago, IL, pp. 734-737, 2011.Shridar, A, Doyle, S, Madabhushi, A, “Boosted Spectral Embedding (BoSE): Applications to Content-Based Image Retrieval of Histopathology”, IEEE International Symposium on Biomedical Imaging, Chicago, IL, pp. 1897-1900, 2011.Tiwari, P, Viswanath, S, Lee, G, Madabhushi, A, “Multi-modal data fusion schemes for integrated classification of imaging and non-imaging data”, IEEE International Symposium on Biomedical Imaging, Chicago, IL, pp. 165-168, 2011.Toth, R, Sparks, R, Madabhushi, A, “Medial Axis based Statistical Shape Model (MASSM): Applications to 3D Prostate Segmentation on MRI”, IEEE International Symposium on Biomedical Imaging, Chicago, IL, pp. 1463-466, 2011.Doyle, S, Feldman, M, Tomaszewski, J, Shih, N, Madabhushi, A, “Cascaded Multi-class Pair-wise Classifier (CASCAMPA) for normal, cancerous, and cancer confounder classes in prostate histology”, IEEE International Symposium on Biomedical Imaging, Chicago, IL, pp. 715-718, 2011.



PEER-REVIEWED CONFERENCE PROCEEDINGS (CONTD)Ali, S, Madabhushi, A, ”Active Contour for Overlap Resolution using Watershed based Initialization (ACOReW): Applications to Histopathology”, IEEE International Symposium on Biomedical Imaging, Chicago, IL, pp. 614-617, 2011.Viswanath, S, Tiwari, P, Chappelow, J, Toth, R, Kurhanewicz, J, Madabhushi, A, “CadOnc©: An Integrated Toolkit for Evaluating Radiation Therapy Related Changes in the Prostate Using Multi-Parametric MRI”, IEEE International Symposium on Biomedical Imaging, Chicago, IL, pp. 2095-98, 2011.Toth, R, Chappelow, J, Kutter, O, Vetter, C, Russ, C, Feldman, M, Tomaszewski, J, Shih, N, Madabhushi, A, Incorporating the whole-mount prostate histology reconstruction program Histostitcher© into the extensible imaging platform (XIP) framework, SPIE Medical Imaging, 2012, Accepted.Sparks, R, Madabhushi, A, Gleason grading of prostate histology utilizing statistical shape model of manifolds (SSMM), SPIE Medical Imaging, 2012, Accepted.Agner, S, Xu, J, Rosen, M, Karthigeyan, S, Englander, S, Madabhushi, A, Spectral embedding based active contour (SEAC): application to breast lesion segmentation on DCE-MRI. SPIE Medical Imaging, vol. 7963, pp. 796305-1 – 12, 2011.Ali, S, Madabhushi, A, Segmenting multiple overlapping objects via an integrated region and boundary based active contour incorporating shape priors: applications to histopathology, SPIE Medical Imaging, 2011, Accepted.



PEER-REVIEWED CONFERENCE PROCEEDINGS (CONTD)Sparks, R, Madabhushi, A, Content-based image retrieval utilizing shape modeling and manifold learning, SPIE Medical Imaging, 2011, Accepted.Chowdhury, N, Chappelow, J, Toth, R, Madabhushi, A, Novel approach for building linked statistical shape models for multimodal prostate radiotherapy planning, SPIE Medical Imaging, 2011, Accepted.Janowczyk, A, Chandran, S, Feldman, M, Madabhushi, A, Local morphologic scale: application to segmenting tumor infiltrating lymphocytes in ovarian cancer TMAs, SPIE Medical Imaging, 2011, Accepted.Basavanhally, A, Yu, E, Ganesan, S, Feldman, M, Tomaszewski, J, Madabhushi, A, Incorporating domain knowledge for tubule detection in breast histopathology using O'Callaghan neighborhoods, SPIE Medical Imaging, 2011, Accepted.Toth, R, Bulman, J, Patel, A, Bloch, N, Genega, E, Rofsky, N, Lenkinski, R, Madabhushi, A, Integrating an Adaptive Region Based Appearance Model with a Landmark Free Statistical Shape Model: Application to Prostate MRI Segmentation, SPIE Medical Imaging, 2011, vol. 7962, In Press.Viswanath, S, Chappelow, J, Patel, P, A, Bloch, N, Genega, E, Rofsky, N, Lenkinski, R, Madabhushi, A, Enhanced multiprotocol analysis via intelligent supervised embedding (EMPrAvISE) for prostate cancer detection on MRI, SPIE Medical Imaging, 2011, In Press.Viswanath, S, Palumbo, D, Chappelow, J, Patel, P, A, Bloch, N, Genega, E, Rofsky, N, Lenkinski, R, Madabhushi, A, Empirical evaluation of bias field correction algorithms for computer-aided detection of prostate cancer on T2w MRI, SPIE Medical Imaging, 2011, In Press.



P Raess, J Monaco, R Chawla, A Bagg, M Weiss, J Choi, and A Madabhushi “Alpha-Hemoglobin Stabilizing Protein Specifically Identifies Nucleated Erythroid Precursors and Enables Identification of Architectural Distortion in Myelodysplastic Syndromes by Computerized Image Analysis", United States and Canadian Academy of Pathology's 101st Annual Meeting, March 17-23, 2012 Accepted.Hipp, J, Smith, S, Cheng, J, Tomlins, S, Monaco, J, Madabhushi, A, Kunju, P, Balis, U, “Optimization of detection of complex cancer morphology using the SIVQ pattern recognition algorithm”, First Congress of the International Academy of Digital Pathology, pp. 28, 2011 (Best Poster Award). Tiwari, P, Kurhanewicz, J, Madabhushi, A, “Computerized quantitative data integration of multi-protocol MRI for identification of high grade prostate cancer in vivo”, in Proc. ISMRM, pp. 2640, 2011.Sparks, R, Madabhushi, A, “Computerized Classification of Benign and Malignant Breast Lesions on DCE-MRI Utilizing Novel Shape Descriptors“ in Proc. ISMRM, pp. 2626, 2011.Agner, S, Xu, J, Karthigeyan, S, Madabhushi, A, “Computerized Lesion Segmentation on DCE MRI Using Active Contours and Spectral Embedding”, in Proc. ISMRM, pp. 136, 2011. Toth, R., Bloch, B.N., Genega, E.M., Rofsky, N.M., Lenkinski, R.E., Rosen, M, Madabhushi, A, "Accurate Prostate Volume Determination from T2-w MRI using Statistical Shape Models," in Proc. ISMRM, pp. 2643, 2011.Xiao, G, Bloch, B. N., Chappelow, J, Genega, E, Rofsky, N, Lenkinski, R, Tomaszewski, J, Feldman, M, Rosen, M, Kalyanpur, A, and Madabhushi, A, “Determining histology-MRI slice correspondences for mapping prostate cancer extent in vivo”, in Proc. ISMRM, pp. 2645, 2011.

PEER REVIEWED ABSTRACTS


Viswanath, S, Chappelow, J, Tiwari, P, Kurhanewicz, J, Madabhushi, A, "CADOnc: A Computerized Decision Support System for Quantifying Radiation Therapy Changes in the Prostate via Multi-Parametric MRI", in Proc. ISMRM, pp. 2647, 2011.Viswanath, S, Bloch, B. N., Chappelow, J, Patel, P, Rofsky, N, Lenkinski, R, Genega, E, and Madabhushi, A, "EMPrAvISE: A Computerized Decision Support System for Automated Prostate Cancer Detection from Multi-Protocol MRI", in Proc. ISMRM, pp. 2642, 2011.Hipp, J, Cheng, J, Hansen, J, Hewitt, S, Monaco, J, Madabhushi, A, Han, S, Yan, W, Rodriguez-canales, W, Hipp, J, Tangrea, M, Emmert-Buck, M, Balis, U, “Ring Vector Image analysis (SIVQ): A high-throughput discovery tool for surgical pathologists”, United States and Canadian Academy of Pathology's 100th Annual Meeting, San Antonio, TX, February 26 - March 4, 2011, Accepted.Janowczyk, A, Chandran, S, Feldman, M, Madabhushi, A, “Quantifying Tumor Infiltrating Lymphocytes in Ovarian Cancer TMAs”, United States and Canadian Academy of Pathology's 100th Annual Meeting, San Antonio, TX, February 26 - March 4, 2011, Accepted.Xu, J, Sparks, R, Janowczyk, A, Tomaszewski, J, Feldman, M, Madabhushi, A, “High-Throughput Prostate Cancer Gland Segmentation and Classification from Digitized Needle Core Biopsies”, United States and Canadian Academy of Pathology's 100th Annual Meeting, San Antonio, TX, February 26 - March 4, 2011, Accepted.Sparks, R, Madabhushi, A, “Quantifying Gland Morphology for Computerized Prostate Cancer Detection and Gleason Grading”, United States and Canadian Academy of Pathology's 100th Annual Meeting, San Antonio, TX, February 26 - March 4, 2011, Accepted.

PEER REVIEWED ABSTRACTS (CONTD)


Monaco, J, Tomaszewski, J, Madabhushi, A, “Automated Tumor Volume Estimation Using Digitized Prostatectomy Specimens”, United States and Canadian Academy of Pathology's 100th Annual Meeting, San Antonio, TX, February 26 - March 4, 2011, Accepted.Lee, G, Doyle, S, Monaco, J, Feldman, M, Tomaszewski, J, Master, S, Madabhushi, A, “Fusion of proteomic and histologic image features for predicting prostate cancer recurrence after radical prostatectomy”, United States and Canadian Academy of Pathology's 100th Annual Meeting, San Antonio, TX, February 26 - March 4, 2011, Accepted.Basavanhally, A, Ganesan, S, Feldman, M, Mies, C, Tomaszewski, J, Madabhushi, A, “Histologic Image-based Classifier for Predicting Outcome of ER+ Breast Cancers”, Laboratory Investigation, vol. 91[1], pp. 27A-27A, 2011.Sridhar, A, Doyle, S, Tomaszewski, J, Feldman, M, Madabhushi, A, “A Content-Based Image Retrieval System for Digitized Prostate Histopathology”, United States and Canadian Academy of Pathology's 100th Annual Meeting, San Antonio, TX, February 26 - March 4, 2011, Accepted.Tiwari, P, Kurhanewicz, J, Madabhushi, A, “Multimodal Integration of Magnetic Resonance Imaging and Spectroscopy for Detection of Aggressive Prostate Cancer”, Innovative Minds in Prostate Cancer Today (IMPaCT), 2011, Accepted.

PEER REVIEWED ABSTRACTS (CONTD)


LCIB WORKSHOPS 2011

3. Tutorial on Manifold Learning With Medical Images, MICCAI 2011http://campar.in.tum.de/personal/mateus/2011MiccaiManifoldTutorial/html

2. Prostate Cancer Imaging, Computer Aided Diagnosis, Prognosis, and Intervention, MICCAI 2011http://lcib.rutgers.edu/pci2011/

1. Histopathology Image Analysis, Clinical Challenges and Quantitative Image Analysis Solutions, MICCAI 2011http://lcib.rutgers.edu/hima2011/

32

http://campar.in.tum.de/personal/mateus/2011MiccaiManifoldTutorial/html

http://lcib.rutgers.edu/pci2011/

http://lcib.rutgers.edu/hima2011/


SPECIAL ISSUE JOURNAL OF PATHOLOGY INFORMATICS

In conjunction with the Histopathology Image Analysis (HIMA) workshop at MICCAI 2011Editors: Anant Madabhushi, NasirRajpoot, Metin Gurcan, Michael D. Feldman

http://www.jpathinformatics.org/

33

http://www.jpathinformatics.org/


ACTIVE RESEARCH

PROSTATE CANCEROVARIAN CANCERBREAST CANCERMEDULLOBLASTOMAMYELODYSPLASTIC SYNDROME

IMAGE SEGMENTATIONMACHINE LEARNINGIMAGE REGISTRATION

RADIOLOGY HISTOPATHOLOGYBIOINFORMATICS

MULTIMODAL DATA INTEGRATION

COMPUTER AIDED DIAGNOSIS

METHODS

34


METHODS

IMAGE SEGMENTATION

MULTI-MODAL REGISTRATION

ACTIVE LEARNING & CONTENT BASED

IMAGE RETRIEVAL

NON-LINEAR DIMENSIONALITY

REDUCTION

KNOWLEDGE REPRESENTATION

AND MULTI-MODAL DATA FUSION

CLASSIFICATIONFEATURE SELECTION


IMAGE REGISTRATION

36


Incorporating Histostitcher© into the Extensible Imaging Platform (XIP) Framework

Toth, R., Chappelow, J., Kutter, O., Vetter, C., Russ, C., Feldman, M., Tomaszewski, J., Shih, N., Madabhushi, A., “Incorporating the Whole-Mount Prostate Histology Reconstruction Program Histostitcher© into the Extensible Imaging Platform (XIP) Framework,” SPIE Medical Imaging, 2012, Accepted.

• Histostitcher algorithm incorporated into professional XIP software framework.• GPU rendering, “Google Maps” – like zooming and scrolling

Previous version of Histostitcher© graphical user interface usable prototype developed using Matlab.

New version of Histostitcher© graphical user interface developed using XIP.

37


MULTIMODAL IMAGE REGISTRATION

38


Histology-MRI Fusion

Step 2: Stitching sections using Histostitcher ©

Step 3: Histology + MRI Fusion

Step 1: Multiprotocol MRI Registration

T2 DCE T2+DCE

1st Stitch 2nd Stitch 3rd Stitch

f

f

f

• CaP ground truth exists on high resolution histology fragments

• Extent of CaP is difficult to find on multi protocol MRI

Patel, P, Chappelow J, Tomaszewski JE, Feldman MD, Rosen MA, Shih N, Madabhushi A. 2011. Spatially weighted mutual information (SWMI) for registration of digitally reconstructed ex vivo whole mount histology and in vivo prostate MRI. IEEE International Conference of Engineering in Medicine and Biology Society (EMBS). :6269-627

39


Determination of histology-MRI slice correspondences

group-wisecomparison

2D affine histology-MRI registration

Final histology-MRI slice correspondences

itted to Computerized Medical Imaging and

40


KNOWLEDGE REPRESENTATION AND DATA FUSION


Weighted Multi-Kernel Learning (WMKL)

A multi-channel framework for merging imaging and non-imaging modalities for stratifying at-risk prostate cancer patients

35 45 55 65 75 85 95

Cla

ssific

atio

n Ac

cura

cy

WMKL (Fusion)ProteomicsArchitecturalMorphological

Lee, G, Feldman MD, Master SR, Tomaszewski, JE, Madabhushi, A. Weighted Multi-Kernel Learning (WMKL): Predicting Biochemical Recurrence by Combining Histologic Imaging and Proteomic Signatures (Submitted to Bioinformatics 2011)

42


T-testWRSTWLT

Score 1Score 2:Score n

Optimal Score

Class 1

Class 2

Supervised Variation

Histology Images Proteomic data

Supervised Regularized Canonical Correlation AnalysisObjective: Integrate Histology and Proteomic data to make prognostic decisions

Novel Variation: Uses a Feature Selection method to determine optimal embedding

SRCCA produces higher classification accuracies Optimal SRCCA EmbeddingGolugula, A, Lee G, Master SR, Feldman MD, Tomaszewski JE, Madabhushi A. 2011. Supervised Regularized Canonical Correlation Analysis: Integrating Histologic and Proteomic Measurements for Predicting Biochemical Recurrence Following Prostate Surgery. Accepted for publication in BMC Bioiformatics 2011

43


IMAGE SEGMENTATION


MRS signals inside the prostate are distinct from signals outside the prostate

The cluster with the most signals, in all experiments, shown to be background spectra (green below)

Therefore, largest cluster is removed

ASM Initialization

Toth, R., Tiwari, P., Rosen, M., Reed, G., Kurhanewicz, J., Kalyanpur, A., Pungavkar, S., Madabhushi, A. “A Magnetic Resonance Spectroscopy Driven Initialization Scheme for Active Shape Model Based Prostate Segmentation.” Medical Image Analysis 15 (2), Apr 2011. pp. 214-225.

45


Prostate Volume Estimation

Each voxel:= 0.22cm x 0.027cm x 0.027cm= 1.6 x 10-4 mL

Red = Ground TruthGreen = ASM Result

R2 = .82

R2 = .70

Robert Toth, B. Nicolas Bloch, Elizabeth M. Genega, Neil M. Rofsky, Robert E. Lenkinski, Mark A. Rosen, Arjun Kalyanpur, Sona Pungavkar, Anant Madabhushi., “Accurate Prostate Volume Estimation Using Multi-Feature Active Shape Models on T2-Weighted MRI,” Academic Radiology, 2011, 18 (2), Jun 2011, pp. 745-754.

Bulman, J.C., Toth, R., Patel, A.D., Bloch, N.B., MacMahon C.J., Ngo L., Madabhushi, A., Rofsky, N.M., "Automated Computer-Derived Prostate Volumes from MRI Data: Comparison to Radiologist-Derived MRI Volumes and Pathology Specimen Volumes," Radiology 2011. Accepted pending revisions.

46



Toth, R., Bloch, B.N., Genega, E.M., Rofsky, N.M., Lenkinski, R.E., Rosen, M.A.,Madabhushi, A., "Accurate Prostate Volume Determination from T2-w MRI using Statistical Shape Models," ISMRM 2011.

Ellipsoid (yellow) may be significantly different from ground truth volume (green).

(b) Ellipsoid (yellow) is generated from the axes, and volume of ellipsoid is calculated.

Aggregate of slice segmentations (pink) are used to estimate the prostate volume (green).

47



Bulman, J.C., Toth, R., Patel, A.D., Bloch, N.B., MacMahon C.J., Ngo L., Madabhushi, A., Rofsky, N.M., "Automated Computer-Derived Prostate Volumes from MRI Data: Comparison to Radiologist-Derived MRI Volumes and Pathology Specimen Volumes," Radiology 2011. Accepted pending revisions.

y = 0.888x + 12.554R² = 0.865

0

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160

0 50 100 150

Path

Vol

ume

(cc)

MFA Volume (cc)

• Comparison of automated segmentation prostate volume (“MFA”) to clinical pathology volume.

• R2 = 0.865

48


Medial Axis Statistical Shape Model

• Markers (“atoms”) along the medial axis of an object can capture underlying shape variations.

• Much less reconstruction error (dark red on the right) than traditional landmark based shape models.

• Accurate shape variations capture in application to prostate segmentation (bottom right)

Robert Toth, Rachel Sparks, Anant Madabhushi., “Medial Axis Based Statistical Shape Model (Massm): Applications To 3d Prostate Segmentation On MRI,” ISBI 2011, Under Review.

49


Landmark Free Statistical Shape Models and Region Based Statistical Appearance Models

Robert Toth, Julie Bulman, Amish Patel, B. Nicholas Bloch, Elizabeth M. Genega, Neil M. Rofsky, Robert E. Lenkinski, Anant Madabhushi. “Integrating an Adaptive Region Based Appearance Model with a Landmark Free Statistical Shape Model: Application to Prostate MRI Segmentation.” SPIE Medical Imaging 2011, Accepted.

• Levelset function used to create statistical shape model.

• Smooth and accurate

• Bayesian based appearance model adapts to current image, and is intelligently combined with statistical shape model.

• Highly accurate segmentation results for prostate MRI.

50


Figure PCA representation of nuclear morphologic features reveals separation of (a) primary grade 3 and grade 4 and (b) Gleason patterns 6, 7, 8 and 9. How many studies used here – specify clearly.

- Able to predict Gleason grade 3 vs 4 with 84.1% accuracy- Using 7 nuclear features extracted from individual nuclei- Developed an efficient segmentation scheme that selectively invoked shape prior in to active contour.

Initial Scene Segmentation

from Watershed

Transformation

Detect Overlap –Concavity Detection

Selectively invoke

Shape Prior

Evolve

appropriate level sets

Adaptive Energy Selective Active Contour with Shape Priors for Nuclear Segmentation and Gleason Grading of Prostate Cancer

Ali, S, Veltri R, Epstein JI, Christudass C, Madabhushi A. 2011. Adaptive Energy Selective Active Contour with Shape Priors for Nuclear Segmentation and Gleason Grading of Prostate Cancer. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) . 6891:661–669

51


A GPU accelerated framework, implemented using the NVIDIAs (CUDA), aimed for the segmentation of nuclei in (H&E) images based on an integrated active contour model.Parallelized implementation of the Ali et al. scheme with multiple level sets operating in parallel.Exploit parallelism and efficient memory management in the GPU to achieve massive speed up of 19x compared to CPU.

GPU Implementation of Integrated Shape Based Active Contour: Application to Digital Pathology

Ali, S, Madabhushi, A. ”Segmenting multiple overlapping objects via an integrated region and boundary based active contour incorporating shape priors: applications to histopathology”, SPIE Medical Imaging, 79622W (2011).


Segmentation of Nodular Medulloblastoma

A novel fusion of Hierarchical Normalized Cuts (HN-Cuts) and Random Walker (RW) to segment medulloblastoma nodules

(a) Original Image (b) HN-Cuts Segmentation

(c) RW Probability Map

(d) Final Segmentation

Tchikindas, L, Sparks R, Baccon J, Ellison D, Judkins AR, Madabhushi A. 2011. Segmentation of nodular medulloblastoma using Random Walker and Hierarchical Normalized Cuts. IEEE North-East Bioengineering Conference (NEBEC). :1-2

53


High-throughput Gland Segmentation and Classification as Benign and Malignant from Digitized Needle Core Biopsies

Detection: a hierarchical frequency weighted mean shift normalized cut (HNCut) for initial detection of glands

Segmentation: Color gradient based geodesic active contour model (CGAC) for gland segmentation

Classification: a diffeomorphic based similarity (DBS) feature extraction for classification of glands as benign or cancerous.

Xu, J, Janowczyk A, Chandran S, Madabhushi A. 2011. A high-throughput active contour scheme for segmentation of histopathological imagery. Medical Image Analysis

HNCut-CGAC model for gland segmentation

Morphological feature extraction

SVM for gland classification

54


NON-LINEAR DIMENSIONALITY REDUCTION AND MANIFOLD

LEARNING


Out-of-Sample Extrapolation Utilizing Semi-Supervised Learning (OSE-SSL)

Sparks, RE, Madabhushi, A. Out-of-Sample Extrapolation for Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval of Prostate Histology. IEEE International Society of Biomedical Imaging (ISBI) 2011, pp 734-738.

Semi-supervised learning Out of Sample ExtrapolationLearn embedding with class labels known for some samples

Estimate embeddings for new samples

OSE-SSLIntegrating the use of known label information (SSL) and the ability to estimate new embeddings (OSE). Resulting in an manifold learning technique that is able to extrapolate new cases and take semantic information into account.

56


Statistical Shape Model of Manifolds (SSMM)Changes in dataset affect low dimensional embedding obtained from manifold learning

Sparks, RE, Madabhushi, A. Gleason grading of prostate histology utilizing statistical shape model of manifolds (SSMM.) In Proc. SPIE 2012, in press.

Figure 1. Two manifolds for a dataset of 888 prostate histopathology glands, where each manifold is trained with only 800 glands.

(1) From a set of manifolds, we construct a SSMM.

(2) SSMM constrains the manifold for the full dataset, giving a robust representation

(a) (a)

(b) (b)

(c)(c)

(a)

(b)

(a)

(c)

(b)

μ

μ+3σ

μ-3σ

(c)

57


Semi-Supervised Graph Embedding with Active LearningNew methodology leverages Active Learning with Semi-Supervised Dimensionality Reduction (DR) for improved data representations

Original Data Representation New Data Representation

New Active Learning

CandidatesActive Learning

Candidates improve data representation

F

Class 1Class 2QueryUnlabeled

SVM Decision BoundaryInitial Training

Labels

Lee, G., Madabhushi A., "Semi-Supervised Graph Embedding Scheme with Active Learning (SSGEAL): Classifying High Dimensional Biomedical Data", in Pattern Recognition in Bioinformatics (PRIB), LNCS 6282, pp. 207-218, 2010.


CLASSIFICATION


Training cohort of

different classes:

( : image, : label)

Distance of test image

to each class

based on all learned

t

…

BDM

BDM

…

Final classification via NN search

Training image sub-sets

… …

Aggregated distance

BDM on eachto get distancemetric

t

t

Xiao, G., Madabhushi, A.: Aggregated distance metric learning (ADM) for image classification in presence of limited training data, MICCAI, Part III, LNCS 6893, 33-40 (2011)

Aggregated Distance Metric Learning (ADM) for Image Classification in Presence of Limited Training Data

60


ACTIVE LEARNING AND CONTENT BASED IMAGE

RETRIEVAL


Boosted Spectral Embedding (BoSE): Applications to Content-Based Image Retrieval

Sridhar, A,. Doyle S., Madabhushi, A. Content-Based Image Retrieval of Digitized Histopathology in Boosted Spectrally Embedded Spaces . IEEE Transactions on Information Technology in Biomedicine, November 2011. (submitted)

…

Query, Annotated Database

…

…

…

Feature Weighting

BoSE

Feature Extraction

PR Curves

Reduce dimensionality of data using BoSE: it selectively weights the features that are best able to discriminate between two classes.

Perform image retrieval in lower-dimensional space created by BoSEand compare it to image retrieval in lower-dimensional space created by spectral embedding (SE).

The greater the area under the Precision-Recall curve (AUC), the better the performance of the CBIR system. 62


Content Based Image Retrieval (CBIR) with Explicit Shape Descriptors

Sparks, RE, Madabhushi, A.. Content-based image retrieval utilizing Explicit Shape Descriptors: applications to breast MRI and prostate histopathology. Proc. SPIE 2011, Vol. 7692 pp. 79621I--13.

a. Database Image Feature Extraction OSE-SSL

c. Query Image Feature Extraction

b. Learn Embeddings

d. Extrapolate Query

e. Image Retrieval

….

Results

Precision –Recall Curves. OSE-SSL has better performance rates compared to manifold learning or OSE. Evaluated on a prostate histopathology dataset containing 888 glands.

63


Consensus Active LearningActive Learning:

Intermediate Samples EligibleRandom Learning: All Samples Eligible

High Certainty

High Certainty

Intermediate Certainty

0.90.80.70.60.50.40.30.20.1 1.0

Active Learning Example

Consensus Learning:Intermediate Samples from Multiple Algorithms

+ =

Active Learning (left) identifies samples whose class is uncertain, and these samples are assigned to an expert for labeling. Compared with Random Learning (top right), Active Learning reduces the number of eligible samples for labeling. Multiple Active Learning algorithms can be combined (bottom right) to generate a consensus learning set, which yields an accurate classifier with fewer samples eligible for labeling.

Uninformative Points

Consensus Learning Points

Eligible for Labeling

Doyle, S, Monaco JP, Feldman MD, Tomaszewski JE, Madabhushi A. 2011. An Active Learning Based Classification Strategy for the Minority Class Problem: Application to Histopathology Annotation. BMC bioinformatics. 12(1):424

64


BIOINFORMATICS APPLICATIONSFEATURE SELECTION/EVALUATION


Variable Ranking with PCA

A method for finding biomarkers applied to identify multiparametricMR imaging markers for prostate cancer diagnosis and grading

Ginsburg, S, Tiwari, P, Kurhanewicz, J, Madabhushi, A. 2011. Variable ranking with PCA: Finding multiparametric MR imaging markers for prostate cancer diagnosis and grading. Workshop on Prostate Cancer Imaging: Computer-Aided Diagnosis, Prognosis, and Intervention (in conjunction with MICCAI). 6963:146-157.

Perform PCA Compute PCA-VIP

Identify imaging markers

66


Golugula, A, Lee, G and Madabhushi, A, Evaluating Feature Selection Strategies for High Dimensional, Small Sample Size Datasets, IEEE Engineering in Medicine and Biology Conference, 2011

Problem: Classification Accuracy, de facto measure to evaluate FS, is not always sufficientObjective: To determine a new measure to evaluate FS in the context of small sample size data

Robustness: higher is more optimal

With subsampling to create a small sample size, few features appear more than 10 times and results are not consistent

High Dimensional Proteomic Feature vector

Robustness of a Feature Selection (FS) Scheme

67


COMPUTER AIDED DIAGNOSIS (CAD)


CAD FOR PROSTATE MRI


Evaluation of bias field correction (BFC) schemes for prostate cancer detection on T2w MRI

Compared 3 different algorithms, N3 found to offer best BFC.Choice of BFC scheme should be based on overarching application, current evaluation measures not dependent on application.

Viswanath, SE, Palumbo D, Chappelow J, Patel P, Bloch BN, Rofsky NM, Lenkinski RE, Genega EM, Madabhushi A. 2011. Empirical evaluation of bias field correction algorithms for computer-aided detection of prostate cancer on T2w MRI. SPIE Medical Imaging. 7963:79630V.

70


Interplay between bias field correction, intensity standardization, and noise filtering for T2w MRI

Quantitatively evaluated 7 different combinations of 3 pre-processing operations, determined best for optimal prostate cancer detection

Original Bias field correction

Noise correction

Intensity standardization

Palumbo, D, Yee B, O'Dea P, Leedy S, Viswanath SE, Madabhushi A. 2011. Interplay between bias field correction, intensity standardization, and noise filtering for T2-weighted MRI. IEEE International Conference of Engineering in Medicine and Biology Society (EMBS). :5080-5083.

Histograms from 4 different studies showing the effect on the intensity distributions after each operation. Last set show relatively noise-free, well-aligned distributions, shown to be most optimal for classification.

After After After

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Determining Quantitative Imaging Signatures (QIS) for Central Gland and Peripheral Zone Prostate Tumors on T2-w MRI

Determined that unique combinations of texture features characterize each of CG and PZ CaPClassification via QDA yielded 0.86 AUC for CG CaP, 0.74 AUC for PZ CaP

Central Gland (CG) Prostate Cancer

Original MRI + CaP extent

Top texture feature in QIS

CaP detection resultbased off QIS

Peripheral Zone (PZ) Prostate Cancer

S. Viswanath, BN Bloch, JC Chappelow, R Toth, NM Rofsky, EM Genega, RE Lenkinski, A. Madabhushi, Central Gland and Peripheral Zone Prostate Tumors have Significantly Different Quantitative Imaging Signatures on 3 Tesla Endorectal, In Vivo T2-Weighted Magnetic Resonance Imagery, JMRI, (Provisionally accepted), 2011

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Multi-modal Weighted Embedding Representation for data Combination (MaWERiC) for prostate cancer diagnosis

Tiwari, P, et al.. Multimodal wavelet embedding representation for data combination (MaWERiC): integrating magnetic resonance imaging and spectroscopy for prostate cancerdetection. NMR in Biomedicine, 2011.

Wavelet Feature Extraction- MRI

Wavelet Feature Extraction- MRS

DR on high D Gabor features

DR on high D wavelet MRS features

Data Integration(MRI+MRS)

Data classification

Feature Extraction

Data Representation

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Weighted Combination of Multi-parametric MR markers for evaluating radiation therapy related changes in the prostate

Heat map for treatment evaluation

Low treatment

change

High treatment

change

CaP pre-RT New CaP foci

CaP recurrence

Expert delineated ground truth

T2w MRI

ADC map

Metabolic map

MP-MRI map

Tiwari, P, et al. . Weighted Combination of Multi-Parametric MR Imaging Markers for Evaluating Radiation Therapy Related Changes in the Prostate. Workshop on Prostate Cancer Imaging: Computer-Aided Diagnosis, Prognosis, and Intervention (in conjunction with MICCAI). 6963:80-91

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Concurrent segmentation of the prostate on MRI and CT via linked statistical shape models for radiotherapy planning

Chowdhury, N, ,Toth R, Chappelow J, Kim S, Motwani, S, Punekar, S, Hahn S, Vapiwala N, Lin H, Both S, Madabhushi A. Concurrent segmentation of the prostate on MRI and CT via linked statistical shape models for radiotherapy planning (accepted, pending changes to Medical Physics)

75


Multimodal integration of magnetic resonance imaging and spectroscopy for detection of high grade prostate cancer

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

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T2-w MRIMRST2-w MRI + MRS

Clinical Goal: To quantitatively combine imaging (T2-w MRI) and nonimaging (MRS) data to distinguish high (aggressive) vs. low-grade (indolent) prostate cancer (CaP)

Step 1. Integrated low dimensionalrepresentation of T2w MRI/MRS

Receiver operating characteristic (ROC) curve for high vs. low grade CaP classified on a per voxel basis via a RF classifier.

Tiwari, P, Kurhanewicz J, Rosen MA, Madabhushi A. 2010. Semi Supervised Multi Kernel (SeSMiK) Graph Embedding: Identifying Aggressive Prostate Cancer via Magnetic Resonance Imaging and Spectroscopy. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). 6363:666-673.

76


CAD FOR PROSTATE WHOLE MOUNT HISTOLOGY


Detection of Prostate Cancer on Histopathology using Color Fractals and Probabilistic Pairwise Markov Models

Novel per pixel color fractal dimension algorithm for whole-mount prostate histopathologyIncorporated novel Markov Prior (PPMM) for spatial dependenciesProvide means for tumor volume estimation; facilitate initial step of Gleason grading; reduce inter-expert variability

Something

Ground Truth CFD Alone CFD + PPMM

Yu E, Monaco JP, Tomaszewski JE, Shih N, Feldman MD, Madabhushi A. 2011. Detection of prostate cancer on histopathology using color fractals and Probabilistic PairwiseMarkov models. IEEE International Conference of Engineering in Medicine and Biology Society (EMBS): 3427-3430.

78


Two Novel Means for Varying the Performance of MRF-based Classification Systems

J Monaco and A Madabhushi, Class-Specific Weighting for Markov Random Field Estimation: Application to Medical Image Segmentation, submitted to Medical Image Analysis.

First method allows variation of performance of MRFs classified using MAP estimation

Second method allows variation of performance of MRFs classified using MPM estimation

Prostate histology images

79


Weighted Maximum Posterior Marginals for Random Fields

(a) H&E stained whole-mount prostate histology section; black ink markindicates “ground-truth” of CaP extent as delineated by a pathologist. (b) Resultof automated gland segmentation. (c) Magnified view of white box in (b). (d)Green dots indicate the centroids of those glands labeled as malignant.

Monaco, JP, Madabhushi A. 2011. Weighted Maximum Posterior Marginals for Random Fields using an Ensemble of Conditional Densities from Multiple Markov Chain Monte Carlo Simulations.. IEEE Transactions on Medical Imaging

80


Cascaded Multi-Category Classification

Cancer

Non-Cancer

Grade 3+4

Grade 4

Dataset

Grade 5

Grade 3

• Distinguish classes sequentially, determined via domain knowledge• Advantages: Binary decision boundaries, simple classifiers, optimized feature sets, high class separability, minimal class heterogeneity

Doyle, S, Feldman MD, Tomaszewski JE, Shih N, Madabhushi A. 2011. Cascaded Multi-Class Pairwise Classifier (CascaMPa) For Normal, Cancerous, And Cancer Confounder Classes In Prostate Histology. IEEE International Symposium on Biomedical Imaging (ISBI). :715-718


CAD FOR BREAST DCE MRI


Spectral Embedding based Active Contour (SEAC)

SpectralEmbedding

Active contour segmentation

Agner, S, Xu J, Rosen MA, Karthigeyan S, Englander S, Madabhushi A. 2011. Spectral embedding based active contour (SEAC): application to breast lesion segmentation on DCE-MRI. SPIE Medical Imaging. 7963


Spectral Embedding based Registration Registration of Breast Dynamic Contrast Enhanced Magnetic Resonance Imaging

Unregistered

SI Registered

SERg

Karthigeyan S, Agner, S, et al. Submitted for Aresty

84


Explicit Shape Descriptors Quantifying Breast DCE-MRI Morphology

Sparks, RE, Madabhushi, A. Computerized Classification of Benign and Malignant Breast Lesions on DCE-MRI Utilizing Novel Shape Descriptors. ISMRM

DissimilarHigh

Value

Similar Low

Value

85


CAD ON BREAST CANCER HISTOPATHOLOGY


Tubule Detection in Breast Cancer Histopathology

Identifying tubules by quantifying the spatial arrangement of nuclei and lumen objects

Basavanhally, A, Yu E, Xu J, Ganesan S, Feldman MD, Tomaszewski JE, Madabhushi A. 2011. Incorporating domain knowledge for tubule detection in breast histopathology using O'Callaghan neighborhoods. SPIE Medical Imaging. 7963:796310.

87


Boosted Classifier For Integrating Multiple Fields Of View: Breast Cancer Grading In Histopathology

Combining FOVs at various sizes to yield improved grading for ER+ breast cancer patients

Basavanhally, A, Ganesan S, Shih N, Mies C, Feldman MD, Tomaszewski JE, Madabhushi A. 2011. A Boosted Classifier For Integrating Multiple Fields Of View: Breast Cancer Grading In Histopathology. IEEE International Symposium on Biomedical Imaging (ISBI). :125-128

FOV size AUC4000 x 4000 0.5732000 x 2000 0.7021000 x 1000 0.787500 x 500 0.819250 x 250 0.605multi-FOV 0.816

Multi-FOV Framework Results


Multi-Parametric, Multi-FOV ER+ Breast Cancer Prognosis: Combination of H & E and IHC Stained HistopathologyIndependent multi-FOV classifiers for tissue architecture (H & E) and CD34 expression (IHC) are combined to yield improved prognostic prediction

Prediction of patient outcome

Patient

Basavanhally, A, Feldman MD, Shih N, Mies C, Tomaszewski JE, Ganesan S, Madabhushi A., Multi-field-of-view strategy for image-based outcome prediction of multi-parametric estrogen receptor-positive breast cancer histopathology: Comparison to Oncotype DX, Journal of Pathology Informatics, 2011 (in press).

CD34 expression

Tissue architecture


CAD ON OVARIAN CANCER TISSUE MICROARRAYS

90


TILs

Non-TILs

Local Morphologic Scale (LMS): Classifying Tumor Infiltrating Lymphocytes in Ovarian Cancer Tissue MicroArrays

A. Janowczyk, S. Chandran, M. Feldman, A. Madabhushi Local Morphologic Scale: Application to Segmenting Tumor Infiltrating Lymphocytes in Ovarian Cancer Tissue MicroArrays SPIE Medical Imaging 2011.

1. Identify lymphocytes using HNcut2. Quantify local morphology using LMS3. Generate features from LMS signature

4. Use supervised classifier to identify Tumor Infiltrating Lymphocytes (TILs), a prognostic indicator for Ovarian cancer


CAD FOR THE DETECTION OF MYELODYSPLASTIC SYNDROME

92


Automated Detection of Myelodysplastic SyndromeGoal: differentiate MDS from normal tissue via the analysis of islands erythroid precursorsProcedure: Step1) Segment islands, Step2) Extract features, Step 3) Classify images

Map of adipose (black) and everything else (white)MDS tissue specimen (erythroid precursors in brown)

Segmentations or precursor islands

85th Percentile of Area (cyan)Adipose Area (green)Both (black)

Classification Results: Receiver Operator Characteristic CurveP Raess, J Monaco, R Chawla, A Bagg, M Weiss, J Choi, and A Madabhushi, Image Segmentation with Implicit Color Standardization Using Cascaded EM: Detection of Myelodysplastic Syndromes, United States and Canadian Academy of Pathology's 101st Annual Meeting, March 17-23, 2012, Accepted

93


CLASSIFICATION OF ANAPLASTIC

MEDULLOBLASTOMA

94


An Integrated Texton and Bag of Words Classifier for Identifying Anaplastic Medulloblastomas

Determine the aggressiveness of medulloblastoma to help construct personalized treatment plans for patients, a novel application of the Texton and Bag of Words classification schemeCurrently achieving an average of .76 classification accuracy, a .17 increase over initial parameters

Feature Generation Texton Map Texton Model

for ClassifyingOriginal Image

Bag of Words Application

TextonApplication

Galaro, J, Judkins AR, Ellison D, Baccon J, Madabhushi A. 2011. An integrated texton and bag of words classifier for identifying anaplastic medulloblastomas. IEEE International Conference of Engineering in Medicine and Biology Society (EMBS). :3443-3446.


A Texture-based Classifier to Discriminate Anaplastic from Non-Anaplastic Medulloblastoma

Texture features in conjunction with ensemble classification found to allow differentiation between anaplastic and non-anaplastic medulloblastoma

Haar Haralick Laws

Lai, Y, Viswanath SE, Baccon J, Ellison D, Judkins AR, Madabhushi A. 2011. A texture-based classifier to discriminate anaplastic from non-anaplastic medulloblastoma. IEEE North-East Bioengineering Conference (NEBEC). :1-2.

96


Funding Agencies (Acknowledgements)

• National Cancer Institute (NIH)• Department of Defense (DOD)• New Jersey Commission on Cancer Research (NJCCR)• Society for Imaging Informatics in Medicine (SIIM)• Aresty Foundation• Rutgers University• Cancer Institute of New Jersey• Burroughs Wellcome Fund

case school of engineering | - laboratory of ......shannon agner, outstanding graduate student...

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