Center for Computational Imaging and
Personalized Diagnostics (CCIPD)
2012 Annual Report
Director: Dr. Anant Madabhushi
Associate Professor,
Department of Biomedical Engineering
LCIB-CCIPD Transition
 We have moved! LCIB is now the Center of Computational Imaging and
Personalized Diagnostics (CCIPD) within the Department of Biomedical
Engineering at Case Western Reserve University.
 Dr. Madabhushi and 12 members relocated to start CCIPD at CWRU.
LCIB
Website
CCIPD LG meeting
schedule webpage
2
CCIPD in 2012
525 Wickenden Building
Dept. of Biomedical Engineering
Case Western Reserve University
2071 Martin Luther King Drive
Cleveland, Ohio 44106-7207
Faculty Offices
Center Space
Center Entrance
3
Center Members
Group dinner in Cleveland, Sept. 2012
Farewell dinner in NJ, August 2012
4
CCIPD Members
Center Director: Dr. Anant Madabhushi
Research Faculty
 Satish Viswanath, PhD
 Pallavi Tiwari, PhD
Research Associates
 Mirabela Rusu, PhD
 Tao Wan, PhD
 Haibo Wang, PhD
Undergraduate Students
 Eileen Huang*
 Sudha Karthigeyan*
 Aparna Kannan*
 Srivathsan Prabhu*
 Gabriel Ewing
 Julian Modesto *
 Michael Yim*
Graduate Students
 Andrew Janowczyk (IIT, Bombay)
 Rachel Sparks (Rutgers, NJ)
 Ajay Basavanhally (Rutgers, NJ)
 George Lee (Rutgers, NJ)
 Rob Toth (Rutgers, NJ)
 Shoshana Ginsburg
 Sahir Ali
 Asha Singanamalli
* At Rutgers University
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Center Members
Research Faculty
Pallavi Tiwari, PhD
Satish Viswanath, PhD
Research Associates
Center Director
Dr. Anant Madabhushi, PhD
Mirabela Rusu, PhD
Tao Wan, PhD
Haibo Wang, PhD
6
Center Members
Graduate Students
Sahir Ali
Ajay Basavanhally
Rachel Sparks
Andrew Janowczyk
Rob Toth
George Lee
Shoshana Ginsburg
Asha Singanamalli
7
Recent Alumni
Research Faculty
James Monaco, PhD
Research Scientist at
VuCOMP
PostDocs
Jun Xu, PhD
Professor at
Nanjing
University
Gaoyu Xiao, PhD
PhD Students
Pallavi Tiwari, PhD
Graduated from
LCIB in May, 2012
Research Faculty at
Case Western
Reserve University
Satish Viswanath, PhD
Graduated from LCIB
in May, 2012
Research Faculty at
Case Western Reserve
University
Shannon Agner
4th year medical
student at
UMDNJRWJMS
8
Recent Alumni
PhD Students
Assistant
Jon Chappelow, PhD
Research Scientist at
Accuray Inc.
Scott Doyle, PhD
Research Scientist
at Ibris Inc.
Master Students
Akshay Shridar
Assistant Project
Manager at
Integra
Life Sciences
Najeeb Chowdhury
Healthcare Market
Research Analyst
at AlphaDetail Inc.
Rhonda Breen-Simone
Administrative
Assistant at Dept. of
Nutritional Sciences,
Rutgers University
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Recent Alumni
Undergraduate Students
Joe Galero
Graduate Student
at Johns Hopkins
University
Pratik Patel
Intern at BioLite
Abhishek Golugula
Employee at Accenture
Elaine Yu
Graduate Student
at UC Berkeley
10
Acknowledging just some of our outstanding
clinical collaborators
Dan Sperling (left), Michael D. Feldman (second
from right), B. Nicolas Bloch (right)
John E. Tomaszewski with Bill Clinton
CCIPD also acknowledges the larger number of excellent clinical collaborators across
CINJ, Rutgers, Case Western, MD Anderson, UT Southwestern Dallas, UCSF, UPENN,
SUNY Buffalo, NYU we have been privileged to work with.
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Student Accomplishments
Pallavi Tiwari and Satish Viswanath at the
2012 Graduate Reception having obtained
their PhDs, May 2012.
Group photo with Pallavi Tiwari & Satish
Viswanath immediately after their
successful PhD thesis defense, April 2012
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Conference Participation 2012
SPIE 2012 Best Poster (Cum Laude) in
CAD: Rachel Sparks
RSNA: Satish Viswanath delivering an
oral talk, Nov. 2012.
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Conference Participation 2012
VLPR: Rob Toth (left)
Sahir Ali (right)
VLPR: Sahir Ali
VLPR: Sahir Ali, Rob Toth, Anant Madabhushi, Jun Xu
(alum) with Dr. Xu’s graduate students (standing)
14
CCIPD Workshop 2012
Dr Madabhushi attended and presented at the Radio-omics Workshop organized by Dr. Bob Gillies at
the Moffit Cancer Center in Tampa, FL. Also attendance were folks from the Cancer Imaging Program
at NCI (Drs Larry Clarke and Nordstrom) and members of the Quantitative Imaging Network (QIN).
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CCIPD Workshop 2012
The HIMA (Histopathology Image Analysis) workshop – 4th year running of HIMA at MICCAI 2012
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CCIPD Workshop 2012
The prostate imaging challenge workshop- first prostate segmentation challenge at MICCAI
attended by 13 participating teams.
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Summary of Accomplishments 2012
 Lab Members: 20
 Faculty: 3
 Research Associates: 3
 Graduate Students: 8
 Undergraduate Students: 7
Theses (3): 2 PhD + 1 MS
 Books: 1
 Journal Papers: 19
 Peer-Reviewed Conference Papers: 19
 Peer reviewed Abstracts: 7
Issued Patents: 3
Grants: 4
Ongoing Projects: 31
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Peer Reviewed Publications for 2012
Summary
20
18
16
14
12
10
8
6
4
2
0
Theses
Journal Papers
Conference Papers
Abstracts
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Books
Gurcan, M, Madabhushi, A, Medical Imaging 2013: Digital Pathology (Proceedings Volume), Proceedings of
SPIE Volume: 8676, ISBN: 9780819494504, 2013.
Peer Reviewed Publications
Journal Papers
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Shridar, A, Doyle, S, Madabhushi, A, “Content-Based Image Retrieval of Digitized Histopathology in
Boosted Spectrally Embedded Spaces”, IEEE Transactions on Information Technology in Biomedicine,
Minor Changes.
Toth, R, Gentile, J, Sperling, D, Madabhushi, A, “Simultaneous Segmentation of Prostatic Zones
Using Active Appearance Models with Multiple Coupled Levelsets”, Computer Vision and Image
Understanding, Minor changes.
Janowczyk, A, Chandran, S, Madabhushi, A, “Quantifying local heterogeneity via morphologic scale:
Distinguishing tumoral regions from stromal regions”, Journal of Pathology Informatics, Accepted.
Tiwari, P, Kurhanewicz, J, Madabhushi, A, “Multi-Kernel Graph Embedding for Detection, Gleason
Grading of Prostate Cancer via MRI/MRS”, Medical Image Analysis, Accepted.
Doyle, S, Tomaszewski, J, Feldman, M, Shih, N, Madabhushi, A, “Cascaded Multi-Class Pairwise
Approach to Automated Classification of Normal, Cancerous, and Confounder Classes of Prostate
Tissue”, BMC Bioinformatics, 2012 Oct 30;13(1):282. [Epub ahead of print] PMID: 23110677.
Agner, S, Xu, J, Madabhushi, A, “Spectral Embedding based Active Contour (SEAC) for Lesion
Segmentation on Breast Dynamic Contrast Enhanced Magnetic Resonance Imaging”, Medical Physics,
Accepted.
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Peer Reviewed Publications
Journal Papers (Contd.)
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Monaco, J, Madabhushi, A, “Class-Specific Weighting for Markov Random Field Estimation:
Application to Medical Image Segmentation”, Medical Image Analysis, [Epub ahead of print]
(PMID: 22986078).
Ali, S, Madabhushi, A, “An Integrated Region, Boundary, Shape based Active Contour for multiple
object overlap resolution in histological imagery”, IEEE Transactions on Medical Imaging, vol. 31[7],
pp. 1448-60, 2012 (PMID: 22498689).
Toth, R, Madabhushi, A, “Multi-Feature Landmark-Free Active Appearance Models: Application to
Prostate MRI Segmentation”, IEEE Transactions on Medical Imaging, vol. 31[8], pp. 1638-50, 2012,
(PMID: 22665505).
Viswanath, S, Madabhushi, A, “Consensus Embedding: Theory, Algorithms and Application to
Segmentation and Classification of Biomedical Data”, BMC Bioinformatics, 13(1): 26, 2012 (PMID:
22316103) (Highly accessed).
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, Kunju,
LP, J, Balis, U, Madabhushi, A, “Integration of architectural and cytologic driven image algorithms
for prostate adenocarcinoma identification”, Analytical Cellular Pathology, (PMID: 22425661).
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, vol. 39(4), pp. 2214-28, 2012
(PMID: 22482643) (Editors Pick).
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Peer Reviewed Publications
Journal Papers (Contd.)
 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, 2012 [Epub ahead of print] (PMID: 22337003).
 Ghaznavi, D, Evans, A, Madabhushi, A, Feldman, M, “Digital Imaging in Pathology: Whole-Slide
Imaging and Beyond”, Annual Review of Pathology: Mechanisms of Disease, vol. 19[3], pp. 331-359,
2012 (PMID: 23157334).
 Ali, S, Madabhushi, A, "GPU Implementation of an Integrated Shape Based Active Contour:
Application to Digital Pathology", Journal of Pathology Informatics, 2:13, 2012, (PMID: 22811957).
 Basavanhally, A, Ganesan, S, Shih, N, Feldman, M, Tomaszewski, J, Madabhushi, A, “Multi-Field-ofView Strategy for Image-Based Outcome Prediction of ER+ Breast Cancer Histopathology Using
Spatio-Architectural and Vascular Features”, Journal of Pathology Informatics, 2:1, 2012, (PMID:
22665505).
 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, vol. 262[1], pp. 144-151, 2012 (PMID:
22190657).
 Doyle, S, Tomaszewski, J, Feldman, M, Madabhushi, A, A Boosted Bayesian Multi-Resolution
Classifier for Prostate Cancer Detection from Digitized Needle Biopsies, IEEE Transactions on
Biomedical Engineering, vol. 59(5), pp. 1205-18, 2012 (PMID: 20570758).
 Janowczyk, A, Chandran, S, Singh, R, Sasaroli, D, Coukos, G, Feldman, M, Madabhushi, A, “HighThroughput Biomarker Segmentation on Ovarian Cancer Tissue Microarrays via Hierarchical
Normalized Cuts”, IEEE Trans on Biomedical Engineering, vol. 59(5), pp. 1240-1252, 2012 (PMID:
22180503).
22
Peer Reviewed Publications
Peer-reviewed Conference Papers
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Janowczyk, A, Chandran, A, Madabhushi, A, “Quantifying local heterogeneity via morphologic scale:
Distinguishing tumor from stroma”, Workshop on Histopathologic Image Analysis, In Conjunction with
Medical Image Computing and Computer Assisted Interventions (MICCAI),
(http://www2.warwick.ac.uk/fac/sci/dcs/events/hima2012/hima2012_programdraft.pdf), 2012.
Toth, R, Madabhushi, A, “Deformable Landmark-Free Active Appearance Models: Application to
Segmentation of Multi-institutional Prostate MRI Data”, (http://promise12.grandchallenge.org/Results/Overview), MICCAI Grand Challenge: Prostate MR Image Segmentation (PROMISE)
2012.
Cruz Roa, A, Galero, J, Osorio, FA, Madabhushi, A, Romero, E, “A Visual Latent Semantic Approach for
Automatic Analysis and Interpretation of Anaplastic Medulloblastoma Virtual Slides”, In Proc of Medical
Image Computing and Computer Assisted Interventions (MICCAI), vol. 1, pp. 157-164, 2012.
Monaco, J, Madabhushi, A, “Image Segmentation with Implicit Color Standardization Using Spatially
Constrained Expectation Maximization: Detection of Nuclei”, In Proc of Medical Image Computing and
Computer Assisted Interventions (MICCAI), vol. 1, pp. 365-372, 2012.
Monaco, J, Raess, P, Chawla, R, Bagg, A, Weiss, M, Choi, J, Madabhushi, A, “Image Segmentation with
Implicit Color Standardization Using Cascaded EM: Detection of Myelodysplastic Syndromes”, IEEE
International Symposium on Biomedical Imaging, pp. 740-743, 2012.
Rusu, M, Bloch BN, Jaffe C, Rofsky N, Genega E, Lenkinski R, Madabhushi A, “Statistical 3D Prostate
Imaging Atlas Construction via Anatomically Constrained Registration”, SPIE Medical Imaging, 2013.
Rusu M, Wang H, Golden T, Gow A, Madabhushi A, “Multi-Scale, Multi-Modal Fusion of Histological and
MRI Lung Volumes for Characterization of Airways”, SPIE Medical Imaging, 2013.
23
Peer Reviewed Publications
Peer-reviewed Conference Papers (Contd.)
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Wan T, Bloch BN, Madabhushi A, “A Novel Point-based Nonrigid Image Registration Scheme Based on
Learning Optimal Landmark Configurations”, SPIE Medical Imaging, 2013.
Tiwari, P, Danish, S, Wong, S, Madabhushi A, “Quantitative Evaluation of Multi-parametric MR Imaging
Marker Changes Post-laser Interstitial Ablation Therapy (LITT) for Epilepsy”, SPIE Medical Imaging, 2013.
Singanamalli, A, Sparks, R, Rusu, M, Shih, N, Ziober, A, Tomaszewski, JE, Rosen, M, Feldman, M,
Madabhushi, A “Correlating in vivo Imaging and ex vivo Vascular Markers to Identify Aggressive Prostate
Cancer: Preliminary Results”, SPIE Medical Imaging, 2013.
Prabu SB, Toth R, Madabhushi A, “A Statistical Deformation Model (SDM) based Regularizer for Non-rigid
Image Registration: Application to registration of multimodal prostate MRI and histology”, SPIE Medical
Imaging, 2013.
Sparks R, Feleppa E, Barratt D, Bloch BN, Madabhushi A, “Fully Automated Prostate MRI and Transrectal
Ultrasound(TRUS) Fusion via a Probabilistic Registration Metric”, SPIE Medical Imaging, 2013.
Ginsburg SB, Bloch BN, Rofsky NM, Genega E, Lenkinski RN, Madabhushi A, “Iterative Multiple
Reference Tissue Method for Estimating Pharmacokinetic Parameters on Prostate DCE MRI”, SPIE Medical
Imaging, 2013.
Ali S, Veltri R, Epstein J, Christudass C, Madabhushi A, “Cell Cluster Graph for Prediction of Biochemical
Recurrence in Prostate Cancer Patients from Tissue Microarrays”, SPIE Medical Imaging, 2013.
Wang H, Rusu M, Golden T, Gow A, Madabhushi A, “Mouse Lung Volume Reconstruction from Efcient
Groupwise Registration of Individual Histological Slices with Natural Gradient”, SPIE Medical Imaging,
2013.
Basavanhally A, Madabhushi A, “EM-based Segmentation-Driven Color Standardization of Digitized
Histopathology”, SPIE Medical Imaging, 2013.
24
Peer Reviewed Publications
Peer-reviewed Conference Papers (Contd.)
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Viswanath S, Sperling D, Lepor H, Futterer J, Madabhushi A, “Quantitative evaluation of treatment
related changes on multi-parametric MRI after laser interstitial thermal therapy of prostate cancer,
SPIE Medical Imaging, 2013.
Incorporating the whole-mount prostate histology reconstruction program 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, SPIE Medical Imaging, 2012, Accepted.
Gleason grading of prostate histology utilizing statistical shape model of manifolds (SSMM), Sparks,
R, Madabhushi, A, SPIE Medical Imaging, 2012, Accepted (Cum Laude, Best Poster in CAD
Conference at SPIE Medical Imaging 2012).
2/11/2015
25
Peer Reviewed Abstracts
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Lewis, J, Ali, S, Thorstad, W, Madabhushi, A, “A Quantitative Histomorphometric Classifier
Identifies Aggressive Versus Indolent p16 Positive Oropharyngeal Squamous Cell Carcinoma”,
United States and Canadian Academy of Pathology's 102nd Annual Meeting, 2013, Accepted.
Viswanath, S, Madabhushi, A, “Evaluating the need for intensity artifact correction in multiparametric MRI for computerized detection of prostate cancer in vivo”, Society for Imaging
Informatics in Medicine, 2013.
Viswanath, S, Bloch, N, Lenkinski, R, Rofsky, N, Madabhushi, A, “Computer-aided Detection of
Peripheral Zone and Central Gland Prostate Tumors on T2-weighted MRI”, Proceedings of the
Radiologic Society of North America 2012, Accepted.
Ali, S, Lewis, J, Madabhushi, A, “Use of quantitative histomorphometrics to classify disease
progression in HPV-positive squamous cell carcinoma “, J Clin Oncol 30: 2012 (suppl 30; abstr 73).
Veltri, R, Christudass, C, Epstein, J, Ali, S, Yoon, H-J, Li, C-C, Madabhushi, A, Computer-assisted
Gleason grading of Prostate Cancer: Two novel approaches using nuclear shape and texture feature to
classify pathologic Gleason grade patterns 3 and 4, American Association for Cancer Research, 2012.
Reiss, P, Monaco, J, Bagg, A, Weiss, M, Madabhushi, A, Choi, J, “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, Vancouver,
BC, Canada.
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, Kunju,
LP, J, Balis, U, Madabhushi, A, “Integration of architectural and cytologic driven image algorithms
for prostate adenocarcinoma identification”, Modern Pathology, vol. 25[2], pp. 213A-214A, 2012.
2/11/2015
26
Patents
Issued Patents
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“Systems and Methods for Classification of Biological Datasets", Anant Madabhushi, Michael D.
Feldman, Jianbo Shi, Mark Rosen, John Tomaszewski, United States Serial Number (USSN):
8,204,315.
“Malignancy Diagnosis Using Content-Based Image Retrieval of Tissue Histopathology”, Anant
Madabhushi, Michael D. Feldman, John Tomaszewski, Scott Doyle, United States Serial Number
(USSN): 8,280,132.
“Computer Assisted Diagnosis (CAD) of cancer using Multi-Functional Multi-Modal in vivo
Magnetic Resonance Spectroscopy (MRS) and Imaging (MRI)", Anant Madabhushi, Satish Viswanath,
Pallavi Tiwari, Robert Toth, Mark Rosen, John Tomaszewski, Michael D. Feldman, United States
Serial Number (USSN): 8,295,575.
Patents Pending
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“Method and Apparatus for Shape based deformable segmentation of multiple overlapping objects”,
Anant Madabhushi, Sahirzeeshan Ali, PCT/US12/20816, Jan. 2012.
“Local Morphologic Scale with applications to Multi-protocol Registration and Tissue
classification”, Anant Madabhushi, Andrew Janowczyk, Sharat Chandran, PCT/US12/21133, Jan.
2012.
“Enhanced Multi-Protocol Analysis via Intelligent Supervised Embedding (EMPrAvISE) for
Multimodal Data Fusion”, Anant Madabhushi, Satish Viswanath, PCT/US12/21373.
27
Patents
Provisional Patents Applications
 Method and Apparatus for an Integrated, Multivariate Histologic Image-based risk
predictor for outcome of ER+ breast cancer”, Anant Madabhushi, Ajay Basavanhally, RU
2011-160.
 “Method and Apparatus for Registering Image Data Between Different Types of Image
Data”, Anant Madabhushi, Rachel Sparks, P05561US0.
 “Color Standardization of Digital Histological Images”, Anant Madabhushi, Ajay
Basavanhally, US Provisional Application No: 61/715,645.
Invention Disclosures
 “A Texture Based Finite Element Model Registration Scheme For Registering PreTreatment Intensity Modulated Radiation Therapy MR Imagery To Post-Treatment MR
Imagery”, Anant Madabhushi, Robert Toth, Case 2013-2375.
 “Discriminatively weighted multi-scale local binary patterns for object detection”, Anant
Madabhushi, Haibo Wang, Case 2013-2391.
 “Co-occurring Gland Tensors in Localized Cluster Graphs for Quantitative
Histomorphometry”, Anant Madabhushi, George Lee, Sahir Ali, Rachel Sparks, Case
2013-2369.
28
Awards and Accomplishments in 2012
Professional, Editorial, Activities, Institutional Service – Anant Madabhushi
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Chair, Poster Session Analysis of Microscopic and Optical Images II, MICCAI 2012, October 4th, 2012, Nice, France
Member of the MICCAI 2012 Young Scientist Award Committee.
Area Chair, Eight Indian Conference on Vision, Graphics, and Image Processing (ICVGIP) 2012.
Chair, Prostate Segmentation Challenge, MICCAI 2012.
Chair, Workshop on Histopathology Image Analysis, MICCAI 2012.
Advisory Committee, Workshop: Bio-computing, Genomics and Epigenomics, DIMAC, Rutgers University,
September 2012.
Panelist and Invited Speaker, “Careers in Biomedical Engineering”, Engineering Governing Council, Rutgers
University, March, 2012.
Program Committee Member, Imaging Track of the 2nd annual IEEE International Conference on Healthcare
Informatics, Imaging and Systems Biology(HISB), 2012.
Associate Editor, IEEE Transactions in Biomedical Engineering 2009-present.
Associate Editor, IEEE Transactions in Biomedical Engineering (Letters) 2008-present.
Program Committee Member, Asian Conference on Computer Vision (ACCV 2012), 2012.
Program Committee Member, ASE/IEEE International Conference on BioMedical Computing, 2012.
Program Committee Member, MICCAI 2012.
Chair, Conference 8315: Computer Aided Diagnosis, Session on Abdomen Imaging, SPIE Medical Imaging, Feb 2012.
Chair, Conference 8315: Computer Aided Diagnosis, Session on Digital Pathology I, SPIE Medical Imaging, Feb 2012.
Chair, Conference 8315: Computer Aided Diagnosis, Session on Digital Pathology II, SPIE Medical Imaging, Feb
2012.
29
Awards and Accomplishments
Professional, Editorial, Activities, Institutional Service – Anant Madabhushi (Contd.)
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Program Committee Member, Workshop on Machine Learning in Medical Imaging, MLMI'12.
Program Committee Member, International Conference on Image and Signal Processing, ICISP'12.
Co-Chair, Workshop on Non-invasive imaging/imaging phenome, American Society for Investigative
Pathology, San Diego, 2012.
Awards
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RSNA Abstract “Computer-aided Detection of Peripheral Zone and Central Gland Prostate Tumors on T2weighted MRI”, Featured on AuntMinnie.com in the Advanced Visualization preview, 2012.
Runner Up for Investors Choice Award, New Jersey Entrepreneurial Network (NJEN), 2012.
Media Recognition
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“ECE Student awarded travel grant to attend VLPR 2012”, Featured on front page of Rutgers Department of
Electrical Engineering Homepage, July 23, 2012, http://www.ece.rutgers.edu/node/947
“Translational Research: Great Promise and Major Challenges”, LifeSci Trends, March 2012, vol. 11[1], pp.
17.
“Building a better predictor”, Page 6, CINJ Oncolyte, Winter 2012 Issue, February 2012.
“Startups Present Posters at Packed NJEN Event”, New Jersey Tech Weekly, February 7th, 2012.
30
New Grants-2012
Madabhushi, Anant (PI)
Date: 9/12/12-8/31/14
NIH R21CA167811-01
Decision support with MRI for targeting, evaluating
laser ablation for prostate cancers
Role: PI
Madabhushi, Anant (PI)
Date: 01/01/13-12/31/13
QED Grant, University City Science Center
ProstaCAD™: Computer based detection of Prostate
Cancer from MRI
Role: PI
Madabhushi, Anant (PI)
Date: 07/01/12-06/31/13
Proof of Concept Award, Rutgers
Computerized Decision Support for Prostate Cancer
Detection and Treatment via Multi-parametric MRI
Role: PI
Buckler, Andrew (PI)
Date: 01/01/13 - 06/01/13
NSF SBIR Phase 1
Computer assisted prognosis of debilitating disease
Role: Co-PI (Madabhushi)
31
Student, Post-doctoral Awards and Fellowships
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Andrew Janowcyz, Travel award ($500) to attend HIMA Workshop in conjunction with MICCAI
2012.
Sahirzeeshan Ali, Travel award ($2000) to attend USA-Sino Summer School in Vision,
Learning, Pattern Recognition: VLPR 2012 (Theme: Computer Vision in Biomedical Image
Applications: From Micro to Macro), 2012
Robert Toth, Travel award ($2000) to attend USA-Sino Summer School in Vision,
Learning, Pattern Recognition: VLPR 2012 (Theme: Computer Vision in Biomedical Image
Applications: From Micro to Macro), 2012
Angel Alfonso Cruz Roa, MICCAI Travel Award ($600), 2012.
Srivatsan Babu, Aresty 2012-2013 RA Award, “Characterizing Elastic Properties of the Prostate”,
May 2012.
Eileen Hwuang, First Place (Overall) for Best Student Research Presentation ($250), SOE Research
Symposium, Rutgers University, 2012
Joseph Galero, Fulbright Fellowship, 2012
Najeeb Chowdhury, “Concurrent segmentation of the prostate on MRI and CT via linked statistical
shape models for radiotherapy planning”, Highlighted in Medical Physics Editors Pick Column.
Satish Viswanath, “Consensus Embedding: Theory, Algorithms and Applications to Biomedical
Segmentation and Classification”, BMC Bioinformatics Highly accessed paper.
Elaine Yu, NSF Graduate Research Fellowship, 2012
Joseph Galero, Honorable Mention, NSF Graduate Research Fellowship, 2012
Rachel Sparks, Cum laude for Best Poster Presentation at the Computer Aided Diagnosis (CAD)
Conference, SPIE Medical Imaging, 2012
32
Active Research (32 projects)
Image Segmentation
Image Registration
Methods
Machine Learning
Image Reconstruction
Multimodal Data
Integration and
Evaluating Therapy
Radiology
Histopathology
Bioinformatics
Computer Aided
Diagnosis and Prognosis
Prostate Cancer
Medulloblastoma
Breast Cancer
Oropharyangeal
Brain Glioma
Epilepsy
Carotid Plaque
Myelodysplasia
33
IMAGE SEGMENTATION
34
Spectral Embedding based Active Contour (SEAC) for
Lesion Segmentation on Breast MRI
(a) Original grayscale post-contrast
image and image representations
derived from (b) FCM and (c) SE.
Note that the colormaps displayed
for both methods only reflect the
voxel similarities and determined
by the 2 schemes, voxels with
similar time-intensity curves being
assigned similar colors. The second
row of images shows the ground
truth segmentation (e) in red and the
hybrid AC segmentation driven by
(f) the FCM+AC segmentation
overlaid (yellow line) on ground
truth, (g) PCA+AC segmentation,
and (h) SEAC segmentation
overlaid on ground truth.
Quantitative results obtained
from the SEAC segmetnation
compared to the FCM+AC, and
PCA+AC methods.
Agner S., Madabhushi, A., Xu J., “Spectral Embedding based Active Contour (SEAC) for Lesion Segmentation on Breast Dynamic Contrast Enhanced Magnetic Resonance
Imaging,” Proc. SPIE 7963, Medical Imaging 2011: Computer Aided Diagnosis, 796305.
35
Simultaneous Segmentation of Prostatic Zones Using Active
Appearance Models with Multiple Coupled Levelsets
•
•
Use of Active Appearance Models (AAM’s) to couple
multiple levelsets using PCA
Simultaneous segmentation of prostate, central gland
(CG), peripheral zone (PZ).
Figure 2.
Segmentation
result for Study
#1.
Figure 3.
Segmentation
result for Study
#2.
Figure 1. AAM coupling multiple shapes
Toth, R., Ribault, J., Gentile, J., Sperling, D., Madabhushi, A., “Simultaneous Segmentation of Prostatic Zones Using Active Appearance Models with Multiple
Coupled Levelsets,” CVIU, 2013. Pending Minor Revisions.
36
Image Segmentation with Implicit Color Standardization
Using Spatially Constrained
Most EM-based algorithms
ignore spatial constraints. Thus,
we presented spatiallyconstrained EM (SCEM), a novel
approach for incorporating
Markov priors into the EM
framework. We validated SCEM
by integrating it into our
computerized system to segment
nuclei in H&E stained
histopathology.
J Monaco, J Hipp, David Lucas, U Balis, S Smith, A Madabhushi, “Image Segmentation with Implicit Color Standardization Using Spatially Constrained
Expectation Maximization: Detection of Nuclei,” MICCAI 2012.
37
Image Segmentation with Implicit Color Standardization
Using Cascaded EM Detection of Myelodysplastic
Syndromes
Color nonstandardness − the propensity
for similar objects to exhibit different
color properties across images − poses a
significant problem in digital
histopathology. We developed a unique
instantiation of the expectation
maximization (called cascaded EM) to
help create a novel color segmentation
algorithm which is robust to
nonstandardness. We validated our
algorithm by detecting myelodysplastic
syndrome on bone marrow specimens.
J Monaco, P Raess, R Chawla, A Bagg, M Weiss, J Choi, A Madabhushi, “Image Segmentation with Implicit Color Standardization Using Cascaded EM:
Detection of Myelodysplastic Syndromes,” ISBI 2012.
38
A Region-boundary and Shape based Active Contour
Hybrid Active Contour scheme that can simultaneously segment
overlapping and non-overlapping objects.
Ali, Sahirzeeshan and Madabhushi, Anant “An Integrated Region, Boundary, Shape based Active Contour for multiple object overlap resolution in histological
imagery”, IEEE Trans on Medical Imaging, 2012.
39
EM-Based Segmentation-Driven Color Standardization of
Digitized Histopathology
Goal: Improve color constancy across histology images by realigning color distributions to
match a pre-defined template.
0
50
100
150
200
250
(a) A new histopathology image is
standardized to (b) a template image
using the (c) Expectation Maximization
algorithm to decompose the image into
tissue classes. (d) Color histograms are
aligned separately for each class and
subsequently recombined to form a
standardized image.
Green outlines
illustrate how standardization yields
more consistent segmentation of nuclei.
In addition, histograms demonstrating
alignment of multiple images (a) before
and (d) after standardization.
(a)
(c)
(d)
(b)
0
50
100
150
200
40
250
Multi-Feature, Landmark-Free Active Appearance
Models (MFLAAM)
Error
• Levelset function used to create
statistical shape model in an
AAM framework.
• Generalized AAM’s to
incorporate image derived
attributes such as gradient or
texture information – only
considered intensities previously
• “Registration”-like template
matching scheme to locate
prostate
Toth, R., Madabhushi, A., “Multi-Feature Landmark-Free Active Appearance Models: Application to Prostate MRI Segmentation,” IEEE Transactions on Medical
Imaging 31(8), pp. 1638-1650. June 2012.
41
IMAGE REGISTRATION
42
Prostatome™: Prostate Imaging Atlas via Anatomic
Constrained Registration
Statistical Models for the
Anatomic Structures:
- Prostate capsule
- Central Gland
- Peripheral Zone
Distribution of cancer:
- High frequency
- Medium
- Low
M. Rusu, B. N. Bloch, C. C. Jaffe, N. M. Rofsky, E. M. Genega, R. E. Lenkinski, A. Madabhushi , Statistical 3D prostate imaging atlas construction via anatomically
constrained registration, Accepted SPIE 2013
43
• Regularizer needed in registration to allow for a
physically meaning transformation.
• Here, we leverage knowledge of known, valid
deformations to train a statistical deformation
model (SDM)
• Harness this knowledge alongside FFD to
provide an accurate overlay of histology on
MRI.
Template Image
Moving Image
Mean Deformation Field
from Training Set
A Statistical Deformation Model (SDM) based Regularizer for Non-rigid Image
Registration: Application to registration of multimodal prostate MRI and histology
Moved Image (FFD)
Moved Image (FFD+SDM)
S. Babu Prabu,, R. Toth, A. Madabhushi, “A Statistical Deformation Model (SDM) based Regularizer for Non-rigid Image Registration: Application to registration of
multimodal prostate MRI and histology,” Proc. SPIE, Medical Imaging: Digital Pathology, 2013.
44
Incorporating the Whole-Mount Prostate Histology
Reconstruction Program Histostitcher© into the Extensible
Imaging Platform (XIP) Framework
• Histostitcher algorithm incorporated into
professional XIP software framework.
• GPU rendering, “Google Maps” – like
zooming, scrolling
Previous version of Histostitcher©
graphical user interface usable
prototype developed using Matlab.
New version of
Histostitcher©
graphical user
interface
developed
using XIP.
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 8315, 2012.
45
Fully-Automated T2 W MRI-TRUS Fusion
Objective: Registration of MRI-TRUS with no manual intervention
Module 1. MRI
Prostate
Segmentation
Module 2. TRUS Probabilistic Model of
Prostate Location
(a) Spatial Prior
Module 3. Register Model
to Segmentation
(a) Affine Registration
(b) Estimation of texture-base pixel-wise probability
(b) Elastic Registration
R. Sparks, E. Feleppa, N. Bloch, D. Barratt and A. Madabhushi. Fully Automated Prostate Magnetic Resonance Imaging and Transrectal Ultrasound Fusion via a
Probabilistic Registration Metric. In Proc. SPIE 2012, in press.
46
Learning-based Landmark Driven Image Registration
The objective of this work is to develop an effective approach to estimate an optimal landmark
configuration in order to driving point-based non-rigid image registration schemes.
The difference map between: (a) the original
and deformed images, and (b), (c) the original
and registered images using ICP-based, and
FFD methods, respectively.
(a)
(b)
(c)
Tao Wan, B.Nicolas Bloch, and Anant Madabhushi, A Novel Point-based Nonrigid Image Registration Scheme Based on Learning Optimal Landmark Configurations, to
appear in Proc. SPIE 2013.
47
Co-registration of MRI via a Learning Based Fiducialdriven registration (LeFiR) Scheme
A
G
B
H
C
I
D
J
E
K
F
L
(A)(B), (G)(H) pre- and posttreatment brain images, (C)(I)
identified landmarks, (D)(J)
uniformly spaced landmarks, and
difference images between preand registered images using (E)(K)
identified landmarks, (F)(L)
uniformly spaced landmarks.
T.Wan, B.N.Bloch, A.Madabhushi, Learning based fiducial driven registration (LeFiR): Evaluating laser ablation changes for neurological applications, submitted to ISBI 2013.
48
Mouse Lung Reconstruction from Groupwise Registration
of Individual Histological Slices with Natural Gradient
Perform groupwise registration of individual
histological slices.

Present a fast incremental Lucas-Kanade
computation framework.

Integrate natural gradient for faster and more
reliable convergence.

Fig. 1 Reconstructed volumes
Fig. 2 Convergence curves of registration approaches
Haibo Wang, etc., Mouse Lung Volume Reconstruction from Efficient Groupwise Registration of Individual Histological Slices with Natural Gradient, SPIE 2013.
49
Fusion of histological and MRI lung volumes
Multiscale, multimodal fusion of histological and MRI lung volumes for characterization of airways, M. Rusu, H. Wang, T. Golden, A. Gow, A. Madabhushi, Accepted
SPIE 2013
50
MACHINE LEARNING
51
Class-Specific Weighting for Markov Random Field
Estimation
Methods for varying the performance of MRFs is conspicuously absent from the literature.
Thus, we introduced multiplicative weighted MAP estimation for MRFs.
Class-specific weighting for
Markov random field estimation:
Application to medical image
segmentation, Medical Image
Analysis. 2012 Dec;16(8):1477-89.
52
Comparing Classifier Schemes for CaP Detection from
T2w MRI
Empirically compared 12 different classifier schemes for detecting prostate cancer on a
per-voxel basis using textural representations of T2w MRI. Simple QDA classifier
performed comparably to popular complex SVM classifier.
“Computer-aided Detection of Peripheral Zone and Central Gland Prostate Tumors on
T2-weighted MRI”, RSNA, 2012
53
Variable Ranking for PCA
• Quantify the contributions of individual, high
dimensional features to a PCA embedding
• Multi-parametric MR imaging markers for prostate
cancer:
Perform PCA
Compute PCA-VIP
– Importance of ADC intensity, in contrast to T2w
intensity
– Importance of Gabor texture features extracted from
ADC maps and T2w MRI
– Importance of choline, polyamine, and citrate
metabolites measured on magnetic resonance
spectroscopy
• Facilitates the use and interpretation of high
dimensional features while avoiding the curse of
dimensionality
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.
54
Statistical Shape Model of Manifolds
 Objective: Accurately model manifold structure
a. Divide dataset
into K folds
b. Generate manifold for
each fold
c. Learn Model
R. Sparks and A. Madabhushi. Gleason grading of prostate histology utilizing statistical shape model of manifolds (SSMM.) In Proc. SPIE 2012, 8315: 83151J-83151J13.
55
MULTIMODAL DATA
INTEGRATION AND TREATMENT
EVALUATION
56
Radiohistomorphometry™: Correlating DCE-MRI and
Microvascular Parameters to Identify in vivo Markers for
Prostate Cancer Aggressiveness
Vascular Feature
Extraction
Correlation
CD31 stained
Stitched section with CaP
histology quadrants
annotations
Radio-histomorphometric
Analysis to screen for
candidate Imaging Markers
DCE MRI
Histo-MR registration
DCE-MRI Kinetic
Feature Extraction
Singanamalli et al. To appear in SPIE Medial Imaging 2013
57
Quantitative Evaluation of Treatment Related Changes
on Multi-Parametric MRI after Laser Interstitial
Thermal Therapy of Prostate Cancer
Preliminary results of developing a quantitative framework to evaluate treatmentrelated changes post-LITT on a per-voxel basis (high resolution), via construction of an
integrated MP-MRI signature
T2/DCE
T2/DWI
Ablation zone
T2 difference map
Pre/Post
Above, checkerboard registration results showing
alignment of multi-protocol MRI, pre-/post-LITT
MRI
Right, difference maps showing significant changes
in MR parameters within LITT ablation zone
ADC difference map
MP-MRI difference
“Quantitative Evaluation of Treatment Related Changes on Multi-Parametric MRI after Laser Interstitial Thermal Therapy of Prostate Cancer”, SPIE, 2013
58
MP-MRI for Evaluating Treatment Related Changes
Post-LITT in Epilepsy
Objective: To quantitatively evaluate changes in MP-MRI imaging markers (T1-w, T2-w,
T2-GRE, T2-FLAIR, and ADC) over the epileptogenic foci, pre- and post- laser interstitial
thermal therapy (LITT).
Tiwari, P, et al. . Quantitative evaluation of multi-parametric MRI marker changes post laset-interstitial ablation therapy (LITT) for epilepsy, SPIE 2013.
59
COMPUTER AIDED
DIAGNOSIS AND PROGNOSIS
60
Pharmacokinetic Modeling for Prostate Cancer Localization
• Iterative multiple reference
tissue method (IMRTM) for
pharmacokinetic (PK) feature
extraction from DCE MRI
– Patient-specific values
for Ktrans (volume transfer
constant) and ve
(extravascularextracellular volume
fraction) are iteratively
estimated for voxels in
tissues A and B.
– No need for arterial input
function
– Does not rely on
population-based PK
parameters
• Preliminary results shown on
the right for a CG tumor
Ground truth
Ktrans
ve
61
Hierarchical Prostate Cancer Grade Classification
Ground Truth – Study 1
Ground Truth – Study 2
Cancer classification – Study 1
Cancer classification – Study 2
Grade classification – Study 1
Grade classification – Study 2
P. Tiwari, A. Madabhushi et al. Medical Image Analysis (MeDiA) – In press.
62
Quantifying Local Heterogeneity via Morphologic Scale (MS)
Distinguishing Tumoral Regions from Stromal Regions
Algorithm
Output
AUC
Results
The MS signature overlaid on a tumor regions in an (a) ovarian,(b) prostate H, (c)
breast HE, and (d) prostate HE image, to be compared with the benign signatures in
((i)-(l)), respectively. We can see that in the presented non-tumor regions ((i)-(l))
the MS signature has fewer and smaller objects to obstruct its path, and thus the
rays are less tortuous, unlike in the respective tumoral regions ((a)-(d)). This allows
a supervised classifier to be able to separate the two classes.
Data Type
Breast
Prostate HE
Prostate H
Ovarian
Matlab/C++/CPU
AUC +/- Range
.80 +/- .01
.88 +/- .01
.87 +/- .02
.88 +/- .01
0.0058 sec/sample
Andrew Janowczyk, Sharat Chandran, and Anant Madabhushi. Quantifying local heterogeneity via morphologic scale: Distinguishing tumor from stroma. HIMA
Workshop MICCAI, 2012.
63
Evaluating the need for intensity artifact correction in MPMRI for computerized detection of prostate cancer in vivo
Demonstrated that explicit correction of intensity inhomogeneity and drift in all 3 MPMRI protocols (T2w, DCE, DWI) prior to combining them within a classification
scheme will yield improved CaP detection accuracy in vivo.
“Evaluating the need for intensity artifact correction in MP-MRI for computerized detection of prostate cancer in vivo”, SIIM, 2013
64
A Visual Latent Semantic Approach for Automatic Analysis and
Interpretation of Anaplastic Medulloblastoma Virtual Slices
Goal: Medulloblastoma Tumor Classification (Anaplastic vs Non-anaplastic) with good
classification performance and visual interpretability.
c)
(a) New histopathology images are represented as (b) a histogram of Bag of Features (BOF)
according to (c) a dictionary of BOF learning using Haar-based descriptors of patches. (d) A
probabilistic latent semantic analysis (pLSA) model is trained with image representation and their
classes membership of training dataset in order to (e) predict the probability that a given image
belongs to Anaplastic or Non-anaplastic type of Medulloblastoma tumor. Finally, (f) a visual
probabilistic map is provided according to regions in the image associated with each subtype.
a)
d)
b)
e)
f)
Angel et al., “A Visual Latent Semantic Approach for Automatic Analysis and Interpretation of Anaplastic Medulloblastoma Virtual Slices”, MICCAI, 2012.
65
Co-occurring Gland Tensors (CGT) in Localized Cluster
Graphs for CaP Histology
CGTs describe the ability for glands to orient themselves with respect to prostate stroma
Calculate Gland Direction
Establish Locality
CGT
CaP Recurrence
No Recurrence
Lee et al. ISBI 2013 Under Review
66
Cell Cluster Graph for Prediction of Biochemical Recurrence
in Prostate Cancer Patients from Tissue Microarrays
 Novel Cell Cluster graph (CCG)
that can quantify tumor
morphology
 Extracted features from CCG
can predict Biochemical
recurrence in Prostate Cancer in
80 patients.
Ali, Sahirzeeshan, Veltri, Robert, Epstein, Jonathan, and Madabhushi, Anant “Cell Cluster Graph for Prediction of Biochemical Recurrence in Prostate Cancer Patients from
Tissue Microarrays”, SPIE Medical Imaging, 2013.(In Press)
67
Use of Quantitative Histomorphometry to Classify Disease
Progression in HPV-positive Squamous Cell Carcinoma
Using Cell Cluster graphs to differentiate Progressors vs Non-Progressors in p16+
Oropharyangeal tumors
Ali, Sahirzeeshan, Lewis, James, Madabhushi, Anant, J Use of quantitative histomorphometrics to classify disease progression in HPV-positive squamous cell carcinoma.
J. Clin Oncol 30: 2012 (suppl 30; abstr 73)
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INTERESTED IN JOINING CCIPD?
We are always looking for enthusiastic and motivated
graduate, undergraduate students and research scientists.
If you think you would be a good fit for CCIPD, send over
your complete CV and 3 representative publications to
“anant.madabhushi” @ “case.edu”
Follow us on Twitter: @CCIPD_Case
69