Center for Computational Imaging and
Personalized Diagnostics (CCIPD)
2013 Annual Report
Director: Anant Madabhushi, PhD
Associate Professor,
Department of Biomedical Engineering
CCIPD Website: http://engineering.case.edu/centers/ccipd/
12/31/2013
2
CCIPD in 2013
525 Wickenden Building
Dept. of Biomedical Engineering
Case Western Reserve University
2071 Martin Luther King Drive
Cleveland, Ohio 44106-7207
Faculty Offices
Center Space
3
Center Members
Group dinner in New Jersey, Dec. 2013
Group Dinner in Cleveland
4
CCIPD Members
Center Director: Anant Madabhushi, PhD
Research Faculty
Graduate Students
Satish Viswanath, PhD
Pallavi Tiwari, PhD
Rachel Sparks (Rutgers, NJ)
Ajay Basavanhally (Rutgers, NJ)
George Lee (Rutgers, NJ)
Rob Toth (Rutgers, NJ)
Shoshana Ginsburg
Sahir Ali
Asha Singanamalli
Prateek Prasanna
Bharath Atthe
Research Associates
Mirabela Rusu, PhD
Mahdi Orooji, PhD
Haibo Wang, PhD
Andrew Janowczyk (PhD)
Scientific Programmer
Ahmad Algohary
Visiting Students
Angel Cruz (Colombia)
Geert Litjens (Nijmegen)
Adminstrative Assitant
Ann Tillet
Undergraduate Students
Eileen Huang (Rutgers, NJ)
Aparna Kannan
Michael Yim
Gregory Penzias
Srivathsan Babu Prabu
5
Center Members
Center Director
Anant Madabhushi, PhD
Research Faculty
Pallavi Tiwari, PhD
Mahdi Orooji, PhD
Ann Tillett
Satish Viswanath, PhD
Research Associates
Mirabela Rusu, PhD
Administrative Assistant
Haibo Wang, PhD
Scientific Programmer
Andrew Janowczyk, PhD
Ahmad Algohary
6
Center Members
Graduate Students
Sahir Ali
Rachel Sparks
Ajay Basavanhally
Rob Toth
George Lee
Asha Singanamalli
Prateek Prasanna
Shoshana Ginsburg
Bharath Atthe
7
Recent Alumni
Tao Wang, PhD
Raghav Padmanabhan, PhD
Computational Biologist at
GE Healthcare
8
Student Accomplishments
Group photo with Ajay
Basavanhally and George Lee
immediately after their
successful PhD thesis defenses,
December2013
Group photo with Rachel
Sparks and Robert Toth
immediately after their
successful PhD thesis defenses,
December 2013
9
Conference Participation 2013
MICCAI in Japan:
Angel Cruz
EMBC in Japan:
Eileen Huang
10
Conference Participation 2013
Mirabela Rusu and Rob Toth: “SeguiTM” algorithm wins first
place in prostate zone segmentation challenge, ISBI 2013
George Lee and Mirabela
Rusu: ISBI 2013
Mirabela Rusu and Pallavi Tiwari: Radiogenomics Meeting 2013
11
Summary of Accomplishments 2013
 Center Members: 24
Faculty: 3
Research Associates: 4
Graduate Students: 9
Undergraduate Students: 5
Scientific Programmer: 1
Administrative Assistant: 1
Theses (5): 4 PhD + 1 MS
 Books: 1
 Peer-Reviewed Journal Papers: 15
 Peer-Reviewed Conference Papers: 32
 Peer Reviewed Abstracts: 17
 Issued Patents: 3
 Patents pending: 1
Invention Disclosures: 6
 Awarded Grants: 6
 Awarded Fellowships: 2
 Ongoing Projects: 40
12
Peer Reviewed Publications for 2013
Summary
36
32
28
24
20
16
12
8
4
0
Books
Journal Papers
Conference Papers
Abstracts
13
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




Agner, S, Rosen, M, Englander, S, Thomas, K, Tomaszewski, J, Feldman, M, Zhang, P, Mies, C Schnall,
M, Madabhushi, A, “Computerized Image Analysis for Identifying Triple Negative Breast Cancers and
Distinguishing Triple Negative Breast Cancers from Other Molecular Subtypes of Breast Cancer on
DCE-MRI: A Feasibility Study”, Radiology, Accepted.
Toth, R.; Shih, N.; Tomaszewski, J.; Feldman, M.; Kutter, O.; Yu, D.; Paulus, J.; Paladini,G.; Madabhush,
A.; "Histostitcher™: An Informatics Software Platform for Reconstructing Whole-Mount Prostate
Histology using the Extensible Imaging Platform (XIP™ ) Framework“, Journal of Pathology
Informatics (JPI), Accepted.
Toth, R, Traughber, B, Ellis, R, Kurhanewicz, J, Madabhushi, A, “A Domain Constrained Deformable
(DoCD) Model for Co-registration of Pre- and Post-Radiated Prostate MRI”, Neurocomputing, Special
Issue on Image Guided Interventions, Accepted.
Litjens, G, Toth, R, van de Ven, W, Hoeks, C, Kerkstra, S, van Ginneken, B, Reisaeter, L, Graham, V,
Guillard, G, Birbeck, N, Zhang, J, Strand, R, Malmberg, F, Ou, Y, Davatzikos, C, Kirschner, M, Jung, F,
Yuan, J, Qui, W, Gao, Q, Edwards, P, Maan, B, van der Heijden, F, Ghose, S, Mitra, J, Dowling, J,
Barratt, D, Huisman, H, Madabhushi, A, “Evaluation of prostate segmentation algorithms for MRI: the
PROMISE12 challenge”, Medical Image Analysis, Accepted.
14
Peer Reviewed Publications
Journal Papers (Contd.)







Wan, T, Bloch, BN, Shabbar, D, Madabhushi, A, “A Learning Based Fiducial-driven Registration
Scheme for Evaluating Laser Ablation Changes in Neurological Disorders”, Neurocomputing, Special
Issue on Image Guided Interventions, Accepted.
Lewis, J, Ali, S, Luo, J, Thorstad, W, Madabhushi, A, “A Quantitative Histomorphometric Classifier
(QuHbIC) Identifies Aggressive Versus Indolent p16 Positive Oropharyngeal Squamous Cell
Carcinoma”, American Journal of Surgical Pathology, [Epub ahead of print] (PMID: 24145650).
Sparks, R, Madabhushi, A, “Explicit Shape Descriptors: Novel Morphologic Features for
Histopathology Classification Medical Image Analysis”, Medical Image Analysis, vol. 17(8), pp. 9971009, doi: 10.1016/j.media.2013.06.002. [Epub ahead of print] (PMID: 23850744).
Sparks, R, Madabhushi, A, “Explicit Shape Descriptors: Novel Morphologic Features for
Histopathology Classification Medical Image Analysis”, Medical Image Analysis, vol. 17(8), pp. 9971009, doi: 10.1016/j.media.2013.06.002. [Epub ahead of print] (PMID: 23850744).
Sparks, R, Madabhushi, A, “Statistical Shape Model for Manifold Regularization: Gleason Grading of
prostate histology”, Computer Vision and Image Understanding (Special Issue on Machine Learning
in Medical Imaging), vol. 117(9), pp. 1138-1146, (PMID: 23888106).
Toth, R., Ribault, J., Gentile, J.C., Sperling, D., Madabhushi, A. “Simultaneous Segmentation of
Prostatic Zones Using Active Appearance Models with Multiple Coupled Levelsets,” Computer
Vision and Image Understanding (CVIU) Special Issue on Shape Modeling, 2013, . doi:
10.1016/j.cviu.2012.11.013.
Basavanhally, A, Ganesan, S, Shih, N, Feldman, M, Tomazewski, J, Madabhushi, A, “Multi-Field-ofView Framework for Distinguishing Tumor Grade in ER+ Breast Cancer from Entire Histopathology
Slides”, Institute of Electrical and Electronics Engineers (IEEE) Transactions on Biomedical
Engineering, [Epub ahead of print] (PMID: 23392336).
15
Peer Reviewed Publications
Journal Papers (Contd.)




Janowczyk, A, Chandran, S, Madabhushi, A, “Quantifying local heterogeneity via morphologic scale:
Distinguishing tumoral regions from stromal regions”, Journal of Pathology Informatics, pp 4-8, 2013.
Tiwari, P, Kurhanewicz, J, Madabhushi, A, “Multi-Kernel Graph Embedding for Detection, Gleason
Grading of Prostate Cancer via MRI/MRS”, Medical Image Analysis, [Epub ahead of print] (PMID:
23294985).
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, 2013, vol. 40(3):032305. doi: 10.1118/1.4790466. (PMID: 23464337) (Cover Article).
Madabhushi, A, Viswanath, S, Lee, G, Tiwari, P, Medical Image Informatics for Personalized
Medicine, Critical Values, pp. 30-32, July 2013 (Invited).
16
Peer Reviewed Publications
Peer-reviewed Conference Papers







Ginsburg, S, Madabhushi, A, “Variable Ranking in Kernel PCA: Applications to Cancer Characterization on
Digitalized Histopathology”, In Proc of Medical Image Computing and Computer Assisted Interventions
(MICCAI), vol. 2, pp. 238-45, 2013.
Ali, S, Lewis, J, Madabhushi, A, “Spatially aware Cell Clusters Graphs: Predicting outcome in HPV associated
oropharyngeal tumors”, In Proc of Medical Image Computing and Computer Assisted Interventions
(MICCAI), vol. 1, pp. 412-419, 2013.
Lee, G, Ali, S, Veltri, R, Epstein, J, Christudass, C, Madabhushi, A, “Cell Orientation Entropy (COrE):
Predicting Biochemical Recurrence from Prostate Cancer Tissue Microarrays”, In Proc of Medical Image
Computing and Computer Assisted Interventions (MICCAI), vol. 3, pp. 396-403, 2013.
Cruz Roa, A, Osorio, FA, Madabhushi, A, Romero, E, “A deep architecture for image representation learning,
visual interpretability and automatic basal-cell carcinoma tumor detection”, In Proc of Medical Image
Computing and Computer Assisted Interventions (MICCAI), vol. 2, pp. 403-410, 2013.
Huang, E, Rusu, M, Sparks, R, Toth, R, Shabbar, D, Madabhushi, A, “Anisotropic Smoothing Regularization
(AnSR) in Thirion's Demons Registration Evaluates Brain MRI Tissue Changes Post-Laser Ablation”, Institute
of Electrical and Electronics Engineers (IEEE) Engineering in Medicine and Biology Conference, pp. 4006-09,
2013 (PMID: 24110610).
Prasanna, P, Jain, S, Bhagat, N, Madabhushi, A, “Decision Support System for Detection of Retinal Diseases
Using Smartphones”, 7th International Conference on Pervasive Computing Technologies for Healthcare, pp.
176-79, 2013.
Wang, H, Madabhushi, A, “Discriminatively weighted multi-scale local binary patterns: Applications in
prostate cancer diagnosis on T2w MRI”, International Symposium on Biomedical Imaging 2013, Accepted.
17
Peer Reviewed Publications
Peer-reviewed Conference Papers (Contd.)







Lee, G, Sparks, R, Ali, S, Feldman, M, Master, S, Shih, N, Tomaszewski, J, Madabhushi, A, “Co-occurring
Gland Tensors in Localized Cluster Graphs: Quantitative Histomorphometry for Predicting Biochemical
Recurrence for Intermediate Grade Prostate Cancer”, International Symposium on Biomedical Imaging 2013,
Accepted.
Wan, T, Bloch, BN, Shabbar, D, Madabhushi, A, “Learning Based Fiducial Driven Registration (LeFiR):
Evaluating Laser Ablation Changes For Neurological Applications”, International Symposium on Biomedical
Imaging 2013, Accepted.
Rusu, M, Kurhanewicz, J, Madabhushi, A, “A prostate MRI atlas of biochemical failure following
radiotherapy“, The International Society for Optics and Photonics (SPIE) Medical Imaging, 2014.
Hwuang, E, Karthigeyan, S, Agner, S, Rusu, M, Sparks, R, Shih, N, Tomaszewski, J, Rosen, M, Feldman, M,
Madabhushi, A, “Spectral Embedding-based Registration (SERg) for Aligning Multimodal Prostate Histology
and MRI“, The International Society for Optics and Photonics (SPIE) Medical Imaging, 2014.
Orooji, M, Madabhushi, A, “Joint image segmentation and feature parameter estimation using expectation
maximization: application to transrectal ultrasound prostate imaging”, The International Society for Optics and
Photonics (SPIE) Medical Imaging, 2014.
Litjens, G, Huisman, H, Elliot, R, Shih, N, Feldman, M, Viswanath, S, Bomers, J, Madabhushi, A,
“Distinguishing prostate cancer from benign confounders via a cascaded classifier on multi-parametric MRI”,
The International Society for Optics and Photonics (SPIE) Medical Imaging, 2014.
Litjens, G, Huisman, H, Elliot, R, Shih, N, Feldman, M, Viswanath, S, Futterer, J, Bomers, J, Madabhushi, A,
“Distinguishing benign confounding treatment changes following laser ablation therapy from residual prostate
cancer on MRI”, The International Society for Optics and Photonics (SPIE) Medical Imaging, 2014.
18
Peer Reviewed Publications
Peer-reviewed Conference Papers (Contd.)







Ginsburg, S, Rusu, M, Kurhanewicz, J, Madabhushi, A, “Computer-extracted texture features on T2w MRI to
predict biochemical recurrence following radiation therapy for prostate cancer“, The International Society for
Optics and Photonics (SPIE) Medical Imaging, 2014.
Singanamalli, A, Wang, H, Lee, G, Shih, N, Ziober, A, Rosen, M, Master, S, Tomaszewski, J, Feldman, M,
Madabhushi, A, “Supervised Multi-View Canonical Correlation Analysis: Fused Multimodal Prediction of
Disease Prognosis“, The International Society for Optics and Photonics (SPIE) Medical Imaging, 2014.
Wang, H, Cruz-Roa, A, Basavanhally, A, Gilmore, H, Shih, N, Feldman, M, Tomaszewski, J, Gonzalez, F,
Madabhushi, A, “Cascaded Ensemble of Convolutional Neural Networks and Handcrafted Features for Mitosis
Detection“, The International Society for Optics and Photonics (SPIE) Medical Imaging, 2014.
Cruz-Roa, A, Basavanhally, A, Gonzalez, F, Gilmore, H, Feldman, M, Ganesan, S, Shih, N, Tomaszewski, J,
Madabhushi, A, “Automatic detection of invasive ductal carcinoma in whole slide images with Convolution
Neural Networks“, The International Society for Optics and Photonics (SPIE) Medical Imaging, 2014.
Tiwari, P, Rogers, L, Wolansky, L, Madabhushi, A, “Differentiating recurrent glioblastoma multiforme from
radiation induced effects via texture analysis on multi-parametric MRI”, The International Society for Optics
and Photonics (SPIE) Medical Imaging, 2014.
Tiwari, P, Shabbar, D, Madabhushi, A, “Identifying MRI markers to evaluate early treatment related changes
post laser ablation for cancer pain management”, The International Society for Optics and Photonics (SPIE)
Medical Imaging, 2014.
Rusu, M, Bloch BN, Jaffe C, Rofsky N, Genega E, Lenkinski R, Madabhushi A, “Statistical 3D Prostate
Imaging Atlas Construction via Anatomically Constrained Registration”, The International Society for Optics
and Photonics (SPIE) Medical Imaging, 2013.
19
Peer Reviewed Publications
Peer-reviewed Conference Papers (Contd.)







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”, The International Society for Optics and Photonics
(SPIE) Medical Imaging, 2013.
Wan T, Bloch BN, Madabhushi A, “A Novel Point-based Nonrigid Image Registration Scheme Based on
Learning Optimal Landmark Configurations”, The International Society for Optics and Photonics (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”, The International Society for
Optics and Photonics (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”, The International Society for Optics and Photonics (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”, The International
Society for Optics and Photonics (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”, The International Society for Optics and
Photonics (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”, The International Society
for Optics and Photonics (SPIE) Medical Imaging, 2013.
20
Peer Reviewed Publications
Peer-reviewed Conference Papers (Contd.)




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”, The International Society for Optics and
Photonics (SPIE) Medical Imaging, 2013.
Wang H, Rusu M, Golden T, Gow A, Madabhushi A, “Mouse Lung Volume Reconstruction from Efficient
Groupwise Registration of Individual Histological Slices with Natural Gradient”, The International Society for
Optics and Photonics (SPIE) Medical Imaging, 2013.
Basavanhally A, Madabhushi A, “EM-based Segmentation-Driven Color Standardization of Digitized
Histopathology”, The International Society for Optics and Photonics (SPIE) Medical Imaging, 2013.
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, The International
Society for Optics and Photonics (SPIE) Medical Imaging, 2013.
21
Peer Reviewed Abstracts






Danagoulian, J, Keither, JC, Hamilton, J, Hua, N, Pham, T, Madabhushi, A, Wan, T, Phinikaridou, A,
Buckler, A, “Quantitative Assessment of vascular disease severity: Development of a Theranostic
Imaging Biomarker”, Accelerating Development & Advancing Personalized Therapy (ADAPT) 2013,
November 4-6, 2013, Cambridge, MA, Accepted (Poster).
Madabhushi, A, Doyle, S, Basavanhally, A, Gilmore, H, Feldman, M, Mies, C, Tomaszewski, J, Harris,
L, Ganesan, S, “Computer extracted image measurements of nuclear shape and texture from H&E
images appear to stratify low and high risk ER+ breast cancers assessed via Oncotype DX“, 2013 San
Antonio Breast Cancer Symposium, December 10- 14, 2013 in San Antonio, Texas, Accepted (Poster).
Madabhushi, A, Basavanhally, A, Doyle, S, Wan, T, Singanamally, A, Thompson, C, Gilmore, H,
Plecha, D, Harris, L, “Computer extracted image texture features on T2−weighted MRI appear to
correlate with nuclear morphologic descriptors from H&E-stained histopathology in estrogen receptor
positive breast cancers”, 2013 San Antonio Breast Cancer Symposium, December 10- 14, 2013 in San
Antonio, Texas, Accepted (Poster).
Madabhushi, A, Wan, T, Pletcha, D, Bloch, BN, Jaffe, C, Thompson, S, Gilmore, H, Harris, L, ,
“Computer derived image features on DCE-MRI appear to distinguish Estrogen Receptor-positive
breast cancers with low and high Oncotype DX recurrence scores”, 2013 San Antonio Breast Cancer
Symposium, December 10- 14, 2013 in San Antonio, Texas, Accepted (Poster).
Tiwari, P, Danish, S, Madabhushi, A, “How long does the hippocampus take to settle down after MRIguided laser ablation for refractory epilepsy? Proof of concept using a multi-parametric analysis of
MRI markers“, Annual Meeting of the American Epilepsy Society, 2013.
Wan, T, Pletcha, D, Bloch, BN, Jaffe, C, Thompson, S, Gilmore, H, Harris, L, Madabhushi, A,
“Computer Derived Texture Features on DCE-MRI Can Separate ER+ Breast Cancers with Low and
High Oncotype DX Scores”, Proceedings of the Radiologic Society of North America 2013, Accepted.
12/31/2013
22
Peer Reviewed Abstracts (cont.)







Ginsburg, S, Bloch, BN, Rofsky, NM, Genega, E, Lenkinski, RE, Madabhushi, A, “Tumors in the
Peripheral Zone and Central Gland of the Prostate Have Different Perfusion Characteristics on
Dynamic Contrast-enhanced MRI”, Proceedings of the Radiologic Society of North America 2013,
Accepted.
Tiwari, P, Rogers, L, Wolansky, L, Madabhushi, A, “Computerized image analysis of texture
descriptors in multi-parametric MRI to distinguish recurrent glioblastoma multiforme from radiation
necrosis”, 4th Quadrennial Meeting of the World Federation of Neuro-Oncology held in conjunction
with the 2013 Scientific Meeting and Education Day of the Society for Neuro-Oncology, San
Francisco, CA, November 2013.
Viswanath, S, Madabhushi, A, “Bias field correction and intensity standardization improve the
diagnostic accuracy of multi-parametric MRI in computerized detection of prostate cancer in vivo”,
Society for Imaging Informatics in Medicine (SIIM), Dallas, TX, 2013
Buckler, A, Keith, J, Madabhushi, A, Hamilton, J, “Non-invasive theranostic to predict and assess
response to atherosclerotic drugs”, Experimental Biology, 2013.
Toth, R, Kurhanewicz, J, Madabhushi, A, “Registration of Pre and Post Intensity Modulated Radiation
Therapy Prostate MRI for Quantification of MR Imaging Marker Changes and Precise Local Prostate
Deformations”, The International Society for Magnetic Resonance in Medicine (ISMRM), 2013.
Singanamally, A, Sparks, R, Rusu, M, Shih, N, Ziober, A, Tomaszewski, J, Rosen, M, Feldman, M,
Madabhushi, A, “Radiomics Driven Image Analysis and Co-registration Scheme to Identify DCE MRI
Markers for Microvascular Density“, The International Society for Magnetic Resonance in Medicine
(ISMRM), 2013.
Tiwari, P, Shabbar, D, Wong, S, Madabhushi, A, “Quantitative Study of changes in multi-parametric
MRI markers post-laser interstitial ablation therapy (LITT) for epilespy“, The International Society for
Magnetic Resonance in Medicine (ISMRM), 2013.
12/31/2013
23
Peer Reviewed Abstracts (cont.)




Tiwari, P, Kurhanewicz, J, Madabhushi, A, “A quantitative framework to study MRI related treatment
changes in the prostate post-IMRT“, The International Society for Magnetic Resonance in Medicine
(ISMRM), 2013.
Viswanath, S, Futterer, J, Lepor, H, Sperling, D, Madabhushi, A, “Quantitative Evaluation of Treatment
Related Changes on Multi-Parametric MRI after Laser Interstitial Thermal Therapy of Prostate Cancer
“, The International Society for Magnetic Resonance in Medicine (ISMRM), 2013.
Wan, T, Wong, S, Shabbar, D, Madabhushi, A, “Co-registration of MRI via a Learning Based Fiducialdriven Registration (LeFiR) Scheme: Evaluating Laser Irradiation Changes for Glioblastomas and
Epilepsy“, The International Society for Magnetic Resonance in Medicine (ISMRM), 2013.
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.
12/31/2013
24
Invited Lectures










“Image Analytics in Digital Pathology: Prognosis prediction and data enrichment”, Department of
Ophthalmology, Case Western Reserve University, Cleveland, OH, November 8th, 2013.
“Image Analytics in Digital Pathology: Prognosis prediction and data enrichment”, Plenary Lecture,
First Nordic Digital Pathology Conference, Linkoping, Sweden, October 31, 2013.
“Computer aided diagnosis: Nuts and Bolts”, Department of Radiology, Case Western Reserve
University, Cleveland, OH, October 22, 2013.
“Convergence of multi-scale, multi-modal data: How to get more out of radiologic imaging via
pathology, omics, and image analytics”, Grand Rounds, Department of Radiology, Case Western
Reserve University, Cleveland, OH, October 22, 2013.
“Computerized Image Based Predictor of Aggressiveness in ER+ Breast Cancers”, Imaging Center
Seminar Series, Case Western Reserve University, September 16th, 2013.
“Image Analytics and Radiomics”, Workshop on Imaging and Genomics, National Cancer Institute,
Bethesda, MD, June 27th, 2013.
“Image Analytics and Personalized Medicine in Prostate Cancer: Who to treat? How to Treat? Where
to treat? Did it work?”, Department of Urology, Weil Cornell Medical Center, New York, NY, June
17th, 2013.
“Fusion of multi-scale, multi-modal data”, Ultrasonic Imaging and Tissue Characterization
Symposium, Arlington, VA, June 12, 2013.
“Image Analytics in Personalized Medicine: Who to treat? How to treat? Where to treat? Did it work?”,
Grand Rounds in Radiation Oncology, Case Western Reserve University, May 21, 2013.
“Quantitative data convergence: Correlating multi-scale, multi-modal data for disease diagnosis and
prognosis”, Invited Seminar, Institute of Information Sciences in Imaging (ISIS), Stanford University,
Palo Alto, March 20, 2013.
12/31/2013
25
Invited Lectures





“Quantitative Image Biomarkers for MRI and histopathology: Applications in Breast Cancer
Prognosis”, Breast Cancer Retreat, Case Comprehensive Cancer Center, Cleveland, OH, March 11,
2013.
“Image based histologic risk score (IbRiS): Predicting Disease Aggressiveness and Patient outcome”,
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, March 8,
2013.
“Quantitative Histologic based Image Classifier (QuHbIC): Predicting Disease Aggressiveness and
Patient outcome”, Grand Rounds in Pathology, Case Western Reserve University, February 27, 2013.
“Quantitative Data Convergence: Use case in Prostate Cancer Imaging”, Invited Lecture, International
Society for Magnetic Resonance in Medicine (ISMRM) workshop on MRI for Cancer Gone
Multimodal, Valencia, Spain, February 19th-22nd, 2013.
“Quantitative Histomorphometry and Radiogenomics: Applications to breast cancer prognosis”, Breast
Cancer Research Incubator Meeting, Case Western Reserve University, January 17th, 2013.
12/31/2013
26
Patents
Issued Patents



“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.
"Combined feature ensemble mutual information image registration", Anant Madabhushi, Jonathan
Chappelow, Mark Rosen, John Tomaszewski, Michael Feldman, United States Serial Number
(USSN): 8,442,285.
Patents Pending

“Classification of Biological Tissue by Multi-mode data registration, segmentation, and
characterization”, Andrew Buckler, Anant Madabhushi, James Hamilton, Shannon Agner, Mark
Rosen, US 2013/0202173 A1
27
Patents
Invention Disclosures (cont.)






“A Texture Based Finite Element Model Registration Scheme For Registering Pre-Treatment 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.
“Co-occurring Nuclear Tensors in Localized Cluster Graphs for Quantitative Histomorphometry”,
Anant Madabhushi, George Lee, Sahir Ali, Rachel Sparks, Case No. 2013-2452.
“Spatially aware Cell Cluster (SpACCl) Graphs for Quantitative Histomorphometry”, Anant
Madabhushi, Sahirzeeshan Ali, Case No. 2013-2471.
“Cascaded Ensemble of Convolutional Neural Networks and Handcrafted Features For Breast Cancer
Diagnosis”, Anant Madabhushi, Haibo Wang, Angel Cruz, Case No. 2014-2572.
28
Awards and Accomplishments in 2013
Honors, Awards and Recognition



First place Scientific award for “Bias field correction and intensity standardization improve the diagnostic
accuracy of multi-parametric MRI in computerized detection of prostate cancer in vivo” at the Society for
Imaging Informatics in Medicine (SIIM) meeting, Dallas, TX, June 7th, 2013
First Place in “National Cancer Institute International Society for Biomedical Imaging (NCI-ISBI) 2013
Challenge - Automated Segmentation of Prostate Structures” held in conjunction with Institute of Electrical
and Electronics Engineers (IEEE) International Symposium on Biomedical Imaging, San Francisco, CA,
April 7th, 2013.
Paper “Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast
enhanced magnetic resonance imaging” featured on cover of Medical Physics, April 2013.
Media Recognition






“Teaching Computers to Tell Cancer Cells Apart”, Prostate Cancer Discovery, A Publication of the Patrick C.
Walsh Prostate Cancer Research Fund, vol. 10, Winter 2014.
“Digital Pathology can solve crisis”, Linkoping University, News, Faculty of Health Sciences, Nov. 2013.
“New Center Makes Diagnoses more personal”, Case School of Engineering, Annual Report 2012-2013, pp.
11, Nov. 2013.
“Biomedical engineering’s Anant Madabhushi to serve on editorial board for new Journal on Medical
Imaging”, The Daily, September 27th, 2013.
“Battling Brain Radiation Necrosis”, Innovations in Neurosciences, pp. 3, Fall 2013.
“Team from the Center for Computational Imaging and Personalized Diagnostics Receives Coulter
Translational Award”, Featured in Case Comprehensive Cancer Center Newsletter, August 5, 2013.
29
Awards and Accomplishments in 2013
Media Recognition (cont.)






“Dr. Madabhushi awarded breast cancer grant from Ohio Third Frontier”, Featured on:
• The Daily, July 8th, 2013.
• Case Comprehensive Cancer Center Newsletter, June 24th, 2013.
“Biomedical Engineering’s faculty members receive scientific award”, The Daily, June 14th, 2013.
“Anant Madabhushi’s biomedical engineering team awarded multiple patents; Madabhushi invited to
conference”, The Daily, June 13th, 2013.
“Using big data to identify prostate cancers and vest treatments”, Press Release, May 23rd, 2013. Featured on:
• Phys.org
• Eurekalert.org
• Freenewspos.com
• News-medical.net
• www.healthcanal.com
• Bio-medicine.org
• Mdxlinx.com/urology
• Topnews.us
• Textradar.com
• primeurmagazine.com
• freenewspos.com
“Biomedical engineering research lab aims to improve cancer treatments”, Feature Story, The Daily, May
22nd, 2013.
“Biomedical engineering’s Anant Madabhushi awarded clinical trial grant”, The Daily, May 17th, 2013.
30
Awards and Accomplishments in 2013
Media Recognition (cont.)






“BME Student Wins Second Place in Imaging Conference”, Featured in:
• Case Comprehensive Cancer Center Newsletter, May 6th, 2013.
• The Daily, May 9th, 2013.
“CWRU team takes first at National Cancer Institute Competition”, Featured in:
• Case Comprehensive Cancer Center Newsletter, April 15th, 2013.
• The Daily, April 15th, 2013.
“Biomedical engineering faculty member’s article featured on cover of “Medical Physics”, Featured in The
Daily, April 5, 2013.
“Technologies to aid stroke and Prostate Cancer Patients win grants from Philadelphia Program”,
TechLifeSciNews, pp. 23, March 2013.
“Strong Cancer related funding week for Anant Madabhushi and Team”, Case Comprehensive Cancer Center
Newsletter, Jan 14th, 2013.
“vascuVis awarded NSF SBIR Phase I funding”, Spin-Off and Start-up News, January, 2013.
31
Professional/Editorial Activities in 2013
Chairing, Membership Program Committees of Conferences, Workshops, Special issues










IAPR TC20 technical committee member for Pattern Recognition for Bioinformatics, 2014.
Session Chair, Conference 9401: Digital Pathology, Keynote Session, International Society for Optics and
Photonics (SPIE) Medical Imaging, Feb 18th, 2014, San Diego, CA.
Co-organizer and Co-Chair, Workshop: What do pathologists see on a slide? Implications for digital pathology,
International Society for Optics and Photonics (SPIE) Medical Imaging, Feb 18th, 2014, San Diego, CA.
Scientific Selection Committee Member, Special issue of Computerized Medical Imaging and Graphics,
Elsevier on "Breakthrough technologies in digital histopathology", 2013.
Chair, Oral Session “Microscopy, histology, and computer aided diagnosis”, Medical Image Computing and
Computer Assisted Intervention Society (MICCAI) 2013, September 24th, 2012, Nagoya, Japan.
IAPR TC20 technical committee member for Pattern Recognition for Bioinformatics, July 2013.
Participant and Panelist, National Cancer Institute’s Cancer Imaging Program (CIP) sponsored workshop,
“Correlating Imaging Phenotypes with Genomic Signatures”, NIH Campus-Natcher, June 26-27th, 2013.
Co-organizer and Co-Chair, Fourth workshop on Content-based retrieval for clinical decision support, Medical
Image Computing and Computer Assisted Intervention Society (MICCAI) 2013, Nagoya, Japan.
Co-Organizer and Co-Chair, Grand Challenge on Segmentation of Prostatic Structures from MRI, Medical
Image Computing and Computer Assisted Intervention Society (MICCAI) 2013, Nagoya, Japan.
NIH Clinical Micro-dissection Working Group, 2013-Present
32
New Grants Awarded in 2013

Madabhushi, Anant (PI)
09/01/13-08/31/16
DOD Prostate Cancer Synergistic Idea Development Award (PC120857) $1,178,117/Award = $570,618
Active surveillance biomarkers for reclassification of Small Low Grade, Low Stage T1c Indolent Prostate
Cancer Tumors
Role: Partnering PI with Robert Veltri the initiating PI

Gulani, Vikas (PI)
06/01/13 - 05/30/18
NIH, R01DK098503-02
Comprehensive Quantitative Ultrafast 3D Liver MRI
Role: Co-I (Madabhushi)

Buckler, Andrew (PI),
01/01/13 - 06/01/13
NSF IIP-1248316,
Computer assisted prognosis of debilitating disease
Role: Co-I (Madabhushi)

Madabhushi, Anant (PI)
09/01/13-08/31/14
Ohio Third Frontier Program
Histologic Image based predictors for treatment prediction in ER+ breast cancers
Role: PI

Tiwari, Pallavi (PI)
09/01/13 - 08/31/14
Coulter Research Translational Partnership
NeuroRadVisionTM: Image based risk score prediction of recurrent brain tumors
33
Grants and Research Supports in 2013
Competitive Fellowship-Sponsored Postdoctoral Advisees – Anant Madabhushi

Rusu, Mirabela (PI)
04/01/13 - 03/30/13
Post-doctoral Training Fellowship
$125,500
Department of Defense
“Identifying MRI markers for viable, post-radiation prostate tumors in men undergoing radiation therapy
followed by prostatectomy”
Role: Mentor
34
Student, Post-doctoral Awards and Fellowships
 Eileen Hwuang, SPIE Medical Imaging Student Grant ($200), 2013
 Asha Singanamally, SPIE Medical Imaging Student Grant ($200), 2013
 Angel Alfonso Cruz Roa, Medical Image Computing and Computer Assisted Intervention
Society (MICCAI) Travel Award ($500), 2013.
 Mirabela Rusu, Postdoc representative for Case Western Reserve University Convocation,
2013.
 Mirabela Rusu, First Place in Postdoctoral Poster Presentation Award, Research ShowCASE
($550), 2013.
 George Lee, Travel award to attend Intl Symposium on Biomedical Imaging ($350), 2013.
 Haibo Wang, Tutorial award to attend Intl Symposium on Biomedical Imaging ($150), 2013.
 Eileen Hwang, Scholarship to BME REU Program at Case Western Reserve University
($4900), 2013.
 Srivatsan Babu Prabhu, Scholarship to BME REU Program at California Institute of
Technology ($4900), 2013.
 Shannon Agner, Outstanding MD/PhD Student Award, UMDNJ/Rutgers, March 15,
2013.Eileen Hwuang, Research grant ($626), Institute of Electrical and Electronics Engineers
(IEEE) Princeton/Central Jersey Chapter, 2013Asha Singanamally, Michael B. Merickel Best
Student Paper Award, Runner Up ($500), SPIE Medical Imaging, 2013
 Rachel Sparks, Graduate School of New Brunswick Travel Award ($200), 2013
35
ACTIVE RESEARCH
METHOD
S
MULTIMODAL
DATA
INTEGRATION
COMPUTER
AIDED
DIAGNOSIS
IMAGE SEGMENTATION
MACHINE LEARNING
IMAGE REGISTRATION
RADIOLOGY
HISTOPATHOLOGY
BIOINFORMATICS
PROSTATE CANCER
BREAST CANCER
MEDULLOBLASTOMA
OROPHARYNGEAL CANCER
LUNG CANCER
36
DIMENSIONALITY
REDUCTION
MULTI-MODAL
REGISTRATION
FEATURE
SELECTION
CLASSIFICATION
METHODS
FEATURE
EXTRACTION
IMAGE
SEGMENTATION
ACTIVE LEARNING &
CONTENT BASED
IMAGE RETRIEVAL
KNOWLEDGE
REPRESENTATION
AND MULTI-MODAL
DATA FUSION
IMAGE REGISTRATION AND
SEGMENTATION
38
Multi-Attribute Probabilistic Prostate Elastic
Registration (MAPPER)
R. Sparks, B. N. Bloch, E. Feleppa, D. Baratt, A. Madabhushi. Fully automated prostate magnetic resonance imaging and transrectal ultrasound fusion via a probabilistic
registration metric. In Proc. SPIE 86710A, 2013.
39
Spectral Embedding-Based Registration (SERg) Aligning
Multimodal Prostate Histology and MRI
Performing registration on spectral embedding representations of multimodal images, i.e. parametric eigenvector
images (PrEIms), results in alignment superior to that of direct registration of grayscale images when using intensity
difference-based similarity metrics.
Demonstration
SERg on synthetic
brain images
Intensity MRI and histology images
MRI and histology PrEIms
Demons
Demons registration
registration performed on PrEIms
E. Hwuang, S. Karthigeyan, S. C. Agner, M. Rusu, R. Sparks, N. Shih, J. E. Tomaszewski, M. Rosen, M. Feldman, and A. Madabhushi. “Spectral Embedding-Based
Registration (SERg) Aligning Multimodal Prostate Histology and MRI.” Accepted SPIE Medical Imaging 2014.
40
Prostatome: A Combined Anatomical and Disease Based
MRI Atlas of the Prostate
Top: Statistical shape
atlas of the central gland,
peripheral zone and
prostate
Middle: spatial
distribution of cancer
relative to the prostate
statistical shape
Bottom: spatial
distribution of cancer
relative to the central
gland and peripheral
zone shape
Rusu et. al. Prostatome: A combined anatomical and disease based MRI atlas of the prostate, Medical Physics (In Review)
41
Simultaneous Segmentation of Prostatic Zones via Active
Appearance Models with Multiple Coupled Level-sets
T2-weighted MRI
Prostate (yellow) is comprised of central zone (red) and peripheral zone (purple)
Toth, R., Ribault, J., Gentile, J.C., Sperling, D., Madabhushi, A. “Simultaneous Segmentation of Prostatic Zones Using Active Appearance Models with
Multiple Coupled Levelsets,” CVIU Special Issue on Shape Modeling 117(9), Sep 2013. pp. 1051-1060. doi: 10.1016/j.cviu.2012.11.013.
42
Domain-Constrained Deformable (DoCD) Model
 Biomechanical finite element
model (FEM) for spatially
registering pre-, post-treatment
prostate MRI
FEM
 Explicit shrinking due to radiation
treatment modeled
 Necessary for determining voxellevel treatment changes
 RMS error of manually identified
internal prostate fiducials <3mm
R. Toth, B. Traughber, R. Ellis, J. Kurhanewicz, and A. Madabhushi. Registering pre-, post-radiation treatment prostate imagery using domain-specific
biomechanical model. Neurocomputing, Special Issue on Image Guided Interventions: Accepted, 2013.
43
43
Fully Automatic Unsupervised Transrectal Ultrasound
Image Segmentation and Parameter Estimation
Results of Segmentation
Results of Parameter Estimation
ˆ  argmax
Zˆ , 
P (D | Z, )
Z ,
0.04
0.035
0.025
0.03
Probability
Probability
0.02
0.025
0.02
0.015
0.015
0.01
0.01
0.005
0.005
0
0
0.5
1
1.5
2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Cramer Rao Lower Band
Orooji, M, Madabhushi, A, “Joint image segmentation and feature parameter estimation using expectation maximization: application to transrectal ultrasound
prostate imaging”, The International Society for Optics and Photonics (SPIE) Medical Imaging, 2014.
44
FEATURE EXTRACTION
45
Explicit Shape Descriptors for Cancer Aggressiveness
1.Shape Model
Construction
3. Shape
Dissimilarity
2. Shape Model Registration
Gleason pattern 3 from Gleason pattern 4
Sensitivity
Sensitivity
Benign from Malignant
4. Dimensionality
Reduction
1-Specificity
1-Specificity
R. Sparks and A. Madabhushi. Explicit shape descriptors: novel morphologic features for histopathology classification. Medial Image Analysis 17: 997-1009, 2013.
46
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 and ve 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
Ginsburg S., et al. Tumors in the peripheral zone and central gland of the prostate have different perfusion characteristics on dynamic contrast-enhanced MRI.
RSNA 2013.
47
Cell Orientation Entropy (COrE) Features Stratify More
and Less Aggressive Prostate Cancer on Tissue Microarrays
Aggressive cancer (left) shows more disorder in orientation
of the nuclei compared to less aggressive cancer (right)
Lee, G, Ali, S, et al., “Cell Orientation Entropy (COrE): Predicting Biochemical Recurrence from Prostate Cancer Tissue
Microarrays”, In Proc of Medical Image Computing and Computer Assisted Interventions (MICCAI), vol. 3, pp. 396-403, 2013.
48
Co-Occurring Gland Tensors for Differentiating Between
Benign and Malignant Glands in Prostate Cancer
•
•
Co-occurrence Matrices reveal the difference between malignant and benign glands in CaP tissue
Brighter pixels in the off-diagonal components signify frequent co-occurrence of glands of
different directionality
AUC = 0.7959
Malignant prostate glands
Brighter off-diagonal
components
Dimmer off-diagonal
Normal benign
components
prostate glands
Lee, G, Sparks, R, Ali, S, Feldman, M, Master, S, Shih, N, Tomaszewski, J, Madabhushi, A, “Co-occurring Gland Tensors in Localized
Cluster Graphs: Quantitative Histomorphometry for Predicting Biochemical Recurrence for Intermediate Grade Prostate Cancer”, International
Symposium on Biomedical Imaging 2013
49
Spatially Aware Cell Cluster (SpACCl) Graphs:
Predicting Outcome in Oropharyngeal p16+ Tumors
Classify location of
nuclei as stroma or
epithelium via Superpixel
Build separate graph
for each tissue type
Extract separate
features from
each tissue class
Representative TMA spot images of H&E stained histology for
(a) non-progressing and (d) progressing p16+ OSCC with (b),
(e) associated cell cluster identification with epithelium nodes in
yellow, and stromal nuclei in green and (c), (f) cell cluster
graphs. Note differences in CCGs for the two classes of OSC
(top versus bottom row).
S Ali, J Lewis, A Madabhushi, Spatially Aware Cell Cluster (SpACCl) Graphs: Predicting Outcome in Oropharyngeal p16+ Tumors - Medical Image Computing and
Computer-Assisted Interventions, 2013
50
MACHINE LEARNING
51
Variable Importance in Nonlinear Kernels for Feature
Reduction in High-Dimensional Classification Problems
Step 1: Feature Extraction
Step 2: Dimensionality Reduction
Prostate cancer:
biochemical
recurrence
Features selected by VINK, in conjunction with logistic
regression classifiers, lead to similar areas under the ROC
curves to the entire high-dimensional feature set:
applications in (a) prostate cancer, (b) breast cancer, and (c)
oropharyngeal cancer.
Step 3: Assess Variable Importance
1
0.95
Prostate cancer:
no biochemical
recurrence
0.9
0.85
AUC
0.8
0.75
0.7
Breast cancer:
low Oncotype
DX score
0.6
0.55
Laplacian
eigenmap
Gaussian
KPCA
Isomap
0.5
1.1
All features
PCA
Gaussian KPCA
Top 5 features
Isomap
Laplacian Eigenmap
1
0.9
Step 3: Compute VINK
AUC
Breast cancer:
high Oncotype
DX score
0.65
0.8
0.7
Oropharyngeal
cancer:
progressor
Assess variable contributions to
classification on the embedding
0.6
0.5
All features
Top 2 features
All features
Top 5 features
160
150
140
130
120
MSE
Oropharyngeal
cancer:
Non-progressor
110
100
90
80
Ginsburg S, “Variable importance in nonlinear kernels (VINK): Classification of digitized histopathology,”
In Proc. MICCAI 2013.
70
60
52
Supervised Multiview CCA: Data Fusion Strategy
Goal: Identification of ex vivo markers of 5 year prostate cancer recurrence
Representations of Histology
Proteomic Expression
Singanamalli et al. “Supervised Multi-View Canonical Correlation Analysis: Fused Multimodal Predictors of Disease Prognosis”. To appear in proc SPIE 2014.
53
Manifold Regularization via Statistical Shape
Model of Manifolds
ℳ
SSMM
Classification results for
ℳ and SSMM on
prostate histopathology.
Identifies noisy samples on ℳ which cause classification errors.
Removal of noisy samples results in a manifold SSMM, with better defined
decision boundaries
R. Sparks and A. Madabhushi. Statistical shape model for manifold regularization: Gleason grading of prostate histology. Computer Vision and Image
Understanding 117: 1138-1146, 2013.
54
Predicting Error Rates for Large Datasets
From Smaller Cohorts
Goal: Capture the variability of small dataset lost during random repeated sampling
Solution: Rotate training and testing pools using K-fold cross-validation sampling
First, a dataset is partitioned into training and testing pools using a K-fold sampling strategy. Each of the K training pools undergoes repeated
random sampling (RRS), in which error rates are calculated at different training set sizes via a subsampling procedure. A permutation test is used to
identify statistically significant error rates, which are then used to extrapolate learning curves and predict error rates for larger datasets.
55
TREATMENT EVALUATION
56
A Learning Based Fiducial-driven Registration Scheme for
Evaluating Laser Ablation Changes in Neurological Disorders
(A)
(A) Shows the identification of important landmarks using the deformed
synthetic images. (B) illustrate the prediction of optimal configuration of
landmarks for new images. The estimated landmark set can be applied to real
clinical deformation to recover the focal deformation.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(B)
The first and second rows show two different registration experiments
performed on an Epilepsy case and a glioblastoma multiforme (GBM) case.
(1),(2) and (5),(6) are pre- and post- laser induced interstitial thermal
therapy (LITT) images. (3),(4) and (7),(8) are the different maps between
the registered and pre-LITT images using the presented LeFiR method and a
uniform landmark spacing method.
T.Wan, B.N.Bloch, S.Danish, A.Madabhushi, A Learning Based Fiducial-driven Registration Scheme for Evaluating Laser Ablation Changes in Neurological Disorders,
accepted by Neurocomputing, 2013.
57
A Preliminary Quantitative Study of Changes in MultiParametric MRI Markers Post Ablation for Epilepsy
Tiwari et al., ISMRM 2013, SPIE 2013.
58
Identifying MRI Markers for Treatment Changes Post-Laser
Ablation for Cancer Pain Management
Tiwari et al., SPIE 2014.
59
Anisotropic Smoothing Regularization (AnSR) in Thirion’s Demons
Registration Evaluates Brain MRI Tissue Changes Post-Laser Ablation
Normalized sum of squared difference alignment
error:
Anisotropic smoothing regularizer
(AnSR) utilizes edge-detection
and denoising within the Demons
registration framework to
regularize the deformation field
more aggressively in regions of
homogeneously oriented
displacements while
simultaneously regularizing less
aggressively in areas containing
heterogeneous local deformation
and tissue interfaces. This method
contributes to assessment of shortterm treatment via laser-induced
interstitial thermal therapy (LITT)
by evaluating changes in posttreatment MRI as a measure of
response.
Hwuang, E, Danish S, Rusu M, Sparks R, Toth R, Madabhushi A. 2013. “Anisotropic smoothing regularization (AnSR) in Thirion's Demons registration evaluates brain
MRI tissue changes post-laser ablation.” Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013:4006-9.
60
Identifying Quantitative in Vivo Multi-Parametric MRI Features
for Treatment Related Changes After LITT of Prostate Cancer
Identifying most effective quantitative features from in vivo MP-MRI that can depict
changes within the ablation zone as well as LITT-related effects.
Red outline – CaP, green outline – normal
LITT-related benign change
Viswanath S, etal. “Identifying Quantitative In Vivo Multi-Parametric MRI Features For Treatment Related Changes after LITT of Prostate Cancer”,
Neurocomputing, Under revision.
61
A Prostate MRI Atlas of Biochemical Failures
Following Radiotherapy
Per voxel difference between
Biochemical and non
biochemical recurrence
MRI atlases are built for the patients with Biochemical Recurrence
(1a) and population without biochemical recurrence (1b); The two
atlases are compared on a per voxel basis as well as (3)
morphologically.
Morphological differences
Rusu et. al. SPIE Medical Imaging 2014 (accepted).
62
COMPUTER AIDED
DIAGNOSIS AND PROGNOSIS
63
Decision Support System for Distinguishing Recurrent
Brain Tumors from Radiation Necrosis
Computer-extracted texture descriptors distinguish recurrent brain tumors from treatmentinduced effects
Recurrent
Brain Tumor
Radiation
Necrosis
Tiwari P, Prasanna P, Rogers L, etal., “Computerized image analysis of texture descriptors in multi-parametric MRI to distinguish recurrent brain tumor from radiation
necrosis,” 18th Annual Society of Neuro-Oncology Scientififc Meeting, 2013.
64
Discriminatively Weighted Multi-Scale Binary Patterns for
Prostate Cancer Detection on T2w MRI
Multi-scale Local
binary patterns
Weight learning for
each scale
Classification via
feature matching
Figure. First row: groundtruth; Middle row: DWLBP; Bottom row: LBP
Wang H, et al. Discriminatively weighted multi-scale local binary patterns: Applications in prostate cancer diagnosis on T2w MRI, ISBI 2013.
65
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 MP-MRI
protocols (T2w, DCE, DWI) prior to combining them within a classification scheme will yield
improved CaP detection accuracy in vivo (won best scientific poster award)
“Evaluating the need for intensity artifact correction in MP-MRI for computerized detection of prostate cancer in vivo”, SIIM, 2013
66
Radiohistomorphometry: Identification of Quantitative
Radiological Imaging Biomarkers of Prostate Cancer
Quantitative representation of tumor
microvessel arrangement
Correlation heatmap of DCE MRI and
microvessel features
Quantitative representation of tumor on
corresponding in vivo DCE MRI
Singanamalli et al. “Identifying in vivo DCE MRI parameters correlated with ex vivo quantitative microvessel architecture: A Radiohistomorphometric Approach”. In
Proc SPIE 2013.
67
Correlation between Quantitative Imaging Biomarkers on T2wMRI
and H&E Stained Histopathology in ER+ Breast Cancer
Goal: Provide histological context to MRI-based QIBs by identifying highly correlated
pairs of QIBs between the two modalities
Histology: More than 2000 QIBs describing nuclear morphology & texture
MRI: More than 650 QIBs describing lesion texture
Madabhushi, A, Basavanhally, A, Doyle, S, Wan, T, Singanamally, A, Thompson, C, Gilmore, H, Plecha, D, Harris, L, “Computer extracted
image texture features on T2−weighted MRI appear to correlate with nuclear morphologic descriptors from H&E-stained histopathology in
estrogen receptor positive breast cancers”, 2013 San Antonio Breast Cancer Symposium, December 10- 14, 2013 in San Antonio, Texas
68
Identifying Molecular Subtypes of Breast Cancers on MRI
via Texture Kinetics
Agner, S, Rosen, M, Englander, S, Thomas, K, Tomaszewski, J, Feldman, M, Zhang, P, Mies, C Schnall, M, Madabhushi, A, “Computerized Image
Analysis for Identifying Triple Negative Breast Cancers and Distinguishing Triple Negative Breast Cancers from Other Molecular Subtypes of Breast
Cancer on DCE-MRI: A Feasibility Study”, Radiology, Accepted
69
Predicting Biochemical Recurrence Following Radiation
Therapy for Prostate Cancer on Pre-Treatment T2w MRI
Three texture features extracted from cancerous regions on
pre-treatment T2w MRI can be used to discriminate
between patients who will develop biochemical recurrence
and those who will remain recurrence-free (below). KaplanMeier curves show improved prediction of recurrence-free
survival for texture features (above, right) than for the
Kattan nomogram (below, right).
Ginsburg S, et al. Computer extracted texture features on T2w MRI to predict biochemical recurrence following radiation therapy for prostate cancer. SPIE 2014.
70
A Deep Learning Architecture for Image Representation, Visual
Interpretability and Automated Basal-Cell Carcinoma Detection
Goal: Basal Cell Carcinoma
(BCC) detection (cancerous
or healthy tissue) with good
classification performance
and visual interpretability
(a) An unified deep learning method for image
representation learning, classification and
prediction interpretability was proposed for
BCC detection. A database of squared
histopathology image are used (1) to train a
Unsupervised Feature Learning method and
learn a set of visual features. (2) Then a
convolutional auto-encoder and pooling layer is
used to represent the histopathology images and
(3) classify them between cancerours or healthy
tissue with a soft-max classifier. Finally, (4) a
novel strategy exploits learned representation to
produce visual interpretable predictions like a
digital stain. (b) Evaluation results show the
good classification performance of method, its
prediction and salient discriminatn maps
(red:cancer, blue: healthy).
a)
b)
Angel Cruz-Roa, John Arevalo, Anant Madabhushi, Fabio González. A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated BasalCell Carcinoma Cancer Detection. MICCAI 2013, the 16th International Conference on Medical Image Computing and Computer Assisted Intervention. Nagoya (Japan),
September 22-26 2013. Lectures Notes in Computer Sciences. Vol. 8150. pp 403-410.
12/06/13
71
Automatic Detection of Invasive Ductal Carcinoma in
Whole Slide Images with Convolutional Neural Networks
Goal: Invasive ductal carcinoma detection automatically in whole-slide image tissues of BCa.
(1) Square image patches are sampled from tissue region of whole-slide images (WSI) (2) to train a 3-layer Convolutional Neural Network model (CNN).
(3) The trained CNN model is posteriorly used to predict the probability of each square image patch sampled belongs to Invasive Ductal Carcinoma from
a new WSI of Breast Cancer (BCa) to build a IDC probability map.
Manual annotation
Automatic annotation
Manual annotation
Automatic annotation
Angel Cruz-Roa, Ajay Basavanhally, Fabio Gonzalez, Hannah Gilmore, Michael Feldman, Shridar Ganesan, Natalie Shih, John Tomaszewski and Anant Madabhushi.
Automatic detection of invasive ductal carcinoma in whole slide images with Convolutional Neural Networks. Digital Pathology Conference. SPIE Medical Imaging 2014,
15 - 20 February 2014. Town & Country Resort and Convention Center, San Diego, California, USA. (Accepted)
72
Quantitative Histomorphometry to Identify Aggressive vs.
Indolent p16+ Oropharyngeal Squamous Cell Carcinoma
Image analysis of the tissue
microarrays. H&E images of
tumors from patients who either
did not develop recurrent
disease (A) or who went on to
develop recurrent disease (D)
(each 10X magnification).
Nuclei were identified by
computerized image analysis (B
and E) and cell cluster graphs
(C and F), shown by blue nodes
for the nuclei with
interconnecting red lines,
generated. Insets of each panel
show magnified areas of
analysis.
Preliminary results show
Kaplan-Meier curves for
160 p16+ OSCC for (a) Tstage (T), (b) nodal status,
and (c) QuHbIC. QuHbIC
clearly outperforms T- and
N-stage.
Lewis, J, Ali, S, Luo, J, Thorstad, W, Madabhushi, A. A Quantitative Histomorphometric Classifier (QuHbIC) Identifies Aggressive Versus Indolent p16-Positive
Oropharyngeal Squamous Cell Carcinoma. American Journal of Surgical Pathology: January 2014 - Volume 38 - Issue 1 - p 128–137
73
Cascaded Combination of CNN and Hand-crafted
Features for Mitosis Detection

–

DWLBP
CNN
Convolutional
neural networks
Handcrafted features
–
Haralick
–
HOG
–
LBP
–
Run-length
–
Color histogram
–
Morphological
false positives

Cascaded ensemble
01/08/13
true positives
false negatives
Wang H, et al. “Cascaded ensemble of CNN and hand-crafted features for mitosis detection”, SPIE Medical imaging, 2014.
74
Spatio-temporal Texture (SpTeT) for Distinguishing Vulnerable from
StableAtherosclerotic Plaque on Dynamic Contrast Enhanced MRI in a Rabbit Model
Module 1: Data preprocessing
Step 1: 3D registration
Lumen
Vessel outer wall
T1wBB
DCE-MRI
DCE-MRI
T1wBB
Before Registration
Step 2: Segmentation
After Registration
T1wBB
T1wBB
Vessel wall 3D visualization
Two examples of 3D plots for (a): first-order textural feature (mean); (b) Haralick
(intensity entropy); The figures showed that the SpTeT features are effective
imaging based descriptors for differentiating vulnerable and stable plaques.
DCE-MRI
DCE-MRI
1
Module 2: Feature extraction
Vessel wall
Polynomial fitting
Module 3: Classification
Feature selection
Random forests classifier
0
(a)
Texture images
3D plotting
(b)
(c)
(d)
1
ROC curve
0
Main components: Module 1 shows the preprocessing procedure to register
and segment DCE-MRI and T1wBB. Module 2 extracts the SpTeT features,
including Gabor, Kirsch, Sobel, Haralick, and first-order textural. In Module
3, quantitative evaluation of the features in distinguishing vulnerable from
stable plaque on MRI was done via a random forests classifier.
(e)
(f)
(g)
(h)
Examples of the contrast enhancement patterns associated with stable plaque (1st
row) and vulnerable plaque (2nd row). (a), (e) and (b), (f): pre-contrast and postcontrast images at peak enhancement. (c) and (g) are the vessel and lumen
segmentation. (d) and (h) are texture images using first-order texture feature.
T.Wan, A. Phinikaridou, J.A.Hamilton, N.Hua, T. Pham, J.Danagoulian, R. Kleiman, A.J.Buckler, and A.Madabhushi, Spatio-temporal texture (SpTeT) for distinguishing vulnerable from stable
atherosclerotic plaque on dynamic contrast enhancement (DCE) MRI in a rabbit model, submitted to Medical Physics, 2013.
75
Imaging Biomarkers for Stratifying ER+ Breast Cancer Risk
We developed an image-based risk score (Ibris) using imaging biomarkers on
biopsy samples to predict ER+ BCa risk (as determined by ODX).
Nuclear Boundaries
Median Value
Sobel Filtered Image
Morphological features from
nuclear boundaries quantify
pleomorphism (21 features)
Texture features from inside
nuclei quantify mitotic index
(2,322 features)
Supervised Classifier Performance
Low Risk Patient (RS < 18)
Metric
Average (σ)
AUC
0.87 (0.018)
PPV
0.81 (0.039)
NPV
0.88 (0.017)
High Risk Patient (RS > 30)
Madabhushi, A, Doyle, S, Basavanhally, A, Gilmore, H, Feldman, M, Mies, C, Tomaszewski, J, Harris, L, Ganesan, S, “Computer extracted image
measurements of nuclear shape and texture from H&E images appear to stratify low and high risk ER+ breast cancers assessed via Oncotype DX“, 2013
San Antonio Breast Cancer Symposium, December 10- 14, 2013 in San Antonio, Texas
76
SOFTWARE
77
ProstaCAD Ver. 1
 ProstaCAD is a
Tumor Detection
Software for T2
Images developed at
CCIPD.
 Gives a simple means
of visualization,
manipulation and
processing for 3D
volume images.
 Extracts pre-specified
set of Texture features
from the given
volume images.
 Classifies tumorous
regions using
discriminant analysis
(LDA and QDA)
78
ProstaCAD Ver. 2
 Supports Extended set
of Texture Features
with numerous
customization settings.
 A lot of preprocessing
and features have been
added
 Supports ADC and
DCE MRI
 Training module has
been extended
 Validation module
implementation is under
progress.
79
CaPView Ver. 1
 User-Friendly Graphical
User interface for Prostate
Cancer Probability
Visualization
 Supports T2/ADC MRI.
 Supports 3D Visualization
of the prostate surface as
well as cancer probability
heatmap in a fully
customizable, yet simple,
easy to understand
environment.
80
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
81