CCIPD in 2014
Wickenden Building
Dept. of Biomedical Engineering
Case Western Reserve University
2071 Martin Luther King Drive
Cleveland, Ohio 44106-7207
Faculty Offices, Room
523
Center Space, Room 517
Center Space, Room 525

Center Members

Research Faculty
Satish Viswanath
Pallavi Tiwari
CCIPD Members
Research Associates
Mirabela Rusu Mahdi Orooji
Haibo Wang Andrew Janowczyk
Asha Singanamalli George Lee
Jon Whitney Rakesh Shiradkar
Graduate Students
Shoshana Ginsburg
Sahir Ali
Prateek Prasanna
Gregory Penzias
Lin Li
Xiangxue Wang
Jacob Antunes
Scientific Software Engineer
Ahmad Algohary
Yu Zhou
Undergraduate Students
Patrick Leo Ania Gawlik
Nikita Agrawal
Thomas Liao
Ross O’Hagan
Jay Patel
Eaton Guo
Administrative Staff
Ann Tillet
Francisco Aguila
Visiting Scientists
Angel Cruz (Colombia) Jun Xu (China)
Tao Wan (China) Mehdi Alilou (Iran)

Center Director
Center Members
Research Faculty
Anant
Madabhushi,
PhD
Pallavi Tiwari,
PhD
Satish Viswanath,
PhD

Center Members
Research Scientists
Mirabela Rusu,
PhD
Mahdi Orooji,
PhD
Haibo Wang,
PhD
Rakesh
Shrikadkar, PhD
George Lee,
PhD
Asha Singanamalli,
MS
Andrew
Janowczyk,
PhD
Jon Whitney,
PhD

Center Members
Graduate Students
Sahir Ali, MS Shoshana Ginsburg,
MS
Prateek
Prasanna, MS
Xiangxue Wang,
BS
Lin Li, BS Jacob Antunes Greg Penzias,
BS

Center Members
Visiting Scientists
Angel Cruz, MS Tao Wang, PhD
Scientific Software Programmers
Jun Xu, PhD Mehdi Alilou, PhD
Administrative Staff
Ahmad
Algohary, MS
Yu Zhou, MS Ann Tillett, BS Francisco Aguila,
BS

Center Members
Undergraduate Students
Patrick Leo Ania Gawlik Jay Patel
Thomas Liao Nikita Agrawal Eaton Guo Ross O’Hagan

Recent Alumni
George Lee, PhD, Research
Associate, CCIPD
Ajay Basavanhally,
PhD, Diagnostic
Precision, Inc.
Andrew Janowczyk, PhD,
Research Associate, CCIPD
Rachel Sparks, PhD, Post Doc at
University College of London
Rob Toth, PhD, CEO,
Toth Technology

Awards and Accomplishments
Eileen Hwang, Cum Laude Award in SPIE
Medical Imaging, 2014
Jacob Antunes, Reviewers
Choice Award, SPIE Medical
Imaging, 2014
Geert Litjens, 2 nd place SPIE
Medical Imaging Student Paper
Award, 2014
Pallavi Tiwari, Cum Laude Award in SPIE
Medical Imaging, 2014
Prateek Prasanna, Runner Up, Young Scientist Award, MICCAI,
2014

Conference Participation 2014
Left to Right: Larry Clarke, Director of the NCI Cancer Imaging
Program, Navenka Dimitrova, Phillips Research, Dr. Madabhushi,
CCIPD, and Dr. Tiwari, CCIPD: Radiomics Meeting, Houston, TX
Angel Roa-Cruz: SIPIAM in Cartagen,
Colombia
Angel Roa-Cruz: SPIE in San Diego

Conference Participation 2014
Pallavi Tiwari: SPIE in San Diego, CA
Anant Madabhushi: SPIE in San Diego, CA Mirabela Rusu: SPIE in San Diego, CA

Ibris, Inc
CCIPD Startup, showcased on Bioenterprise Wall

Summary of Accomplishments 2014
 Center Members: 32
Faculty: 3
Research Associates: 8
Graduate Students: 6
Undergraduate Students: 7
Scientific Software Engineers: 2
Administrative Assistants: 2
Visiting Scientists: 4
 Theses (1): 1 PhD
 Books: 1
 Book Chapters: 2
 Peer-Reviewed Journal Papers: 21
 Peer-Reviewed Conference Papers: 15
 Peer Reviewed Abstracts: 14
 Issued Patents: 2
 Provisional Patents: 1
 Invention Disclosures: 8
 Technologies Licensed : 6
 Awarded Grants: 6
 Awarded Fellowships: 2
 Ongoing Projects: 30

16
12
8
4
0
Peer Reviewed Publications for 2014
24
20
Books Book Chapters Journal Papers Conference
Papers
Abstracts

Peer Reviewed Publications for 2014
Books
 Gurcan, M, Madabhushi, A, Medical Imaging 2014: Digital Pathology (Proceedings Volume),
Proceedings of SPIE Volume: 904108, ISBN: 9780819498342, doi:10.1117/12.2043683, 2014.
Book Chapters
 Tomaszewski, J, Hipp, J, Tangrea, M, Madabhushi, A, “Machine Vision and Machine Learning in
Digital Pathology. In: Linda M. McManus, Richard N. Mitchell, editors. Pathobiology of Human
Disease: A Dynamic Encyclopedia of Disease Mechanisms,” San Diego: Elsevier, pp. 3711-3722, 2014.
 Veltri, RW, Zhu, G, Lee, G*, Ali, S*, Madabhushi, A, “Histomorphometry of Digital Pathology: Case
Study in Prostate Cancer”, Frontiers in Medical Imaging, 2014.
Journal Papers
 Veta, M, van Diest, PJ, Willems, SM, Wang, H, Madabhushi, A, Cruz-Roa, A, Gonzalez, F, Larsen, ABL,
Vestergaard, JS, Dahl, AB, Cireșan, DC, Schmidhuber, J, Giusti, A, Gambardella, LM, Tek, FB, Walter,
T, Wang, CW, Kondo, S, Matuszewski, BJ, Precioso, F, Snell, V, Kittler, J, de Campos, TE, Khan, AM,
Rajpoot, NM, Arkoumani, E, Lacle, MM, Viergever, MA, Pluim, JPW, “Assessment of algorithms for mitosis detection in breast cancer histopathology images”, Medical Image Analysis, In Press.
 Tiwari, P, Shabbar, D, Madabhushi, A, “Identifying MRI Markers Associated with Early Response following Laser Ablation for Neurological Disorders: Preliminary Findings”, PLOS One, Accepted.
 Ali, S, Veltri, R, Epstein, J, Christudas, C, Madabhushi, A, “Selective Invocation of Shape Priors for
Deformable Segmentation and Morphologic Classification of Prostate Cancer Tissue Microarrays”,
Computerized Medical Imaging and Graphics, Accepted

Peer Reviewed Publications for 2014
Journal Papers (Contd.)
 Basanvally, A, Viswanath, S, Madabhushi, A, “Predicting Classifier Performance with Limited Training
Data: Applications to Computer-Aided Diagnosis in Breast and Prostate Cancer”, PLOS One,
Accepted.
 Colen, R, Foster, I, Gatenby, R, Giger, M, Gillies, R, Gutman, D, Heller, M, Jain, R, Madabhushi, A,
Madhavan, S, Napel, S, Rao, A, Saltz, J, Tatum, J, Verhaak, R, Whitman, G, “NCI Workshop Report:
Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics
Signatures,” Translational Oncology, vol. 7[5], pp. 556-569, 2014. (PMID: 25389451).
 Sparks, R, Bloch, N, Moses, D, Ponsky, L, Barratt, D, Feleppa, E, Madabhushi, A, “Multi-Attribute
Probabilistic Prostate Elastic Registration (MAPPER): Application to Fusion of Ultrasound and
Magnetic Resonance Imaging”, Medical Physics, Accepted pending minor changes.
 Litjens, G, Huisman, H, Elliot, R, Shih, N, Feldman, M, Viswanath, S, Futterer, J, Bomers, J,
Madabhushi, A, “Quantitative identification of MRI features of prostate cancer response following laser ablation and radical prostatectomy”, Journal of Medical Imaging, vol. 1[3], 2014.
 Wang, H, Cruz, A, Basavanhally, A, Gilmore, H, Shih, N, Feldman, M, Tomaszewski, J, Gonzalez, F,
Madabhushi, A, “Mitosis Detection in Breast Cancer Pathology Images by Combining Handcrafted and
Convolutional Neural Network Features”, J. Med. Imag., vol. 1[3], pg. 034003, 2014.
 Lee, G, Singanamalli, A, Wang, H, Feldman, M, Master, SR, Shih, N, Spangle, E, Rebbeck, T,
Tomaszewski, J, Madabhushi, A, “Supervised Multi-View Canonical Correlation Analysis (sMVCCA):
Integrating histologic and proteomic features for predicting recurrent prostate cancer”, IEEE
Transactions on Medical Imaging, IEEE Trans Med Imaging, 2014, (PMID: 25203987).
 Ginsburg, S, Bloch, BN, Genega, E, Lenkinsky, R, Feleppa, E, Rofsky, N, Madabhushi, A, “A Novel
PCA-VIP Scheme for Ranking MRI Protocols and Identifying Computer Extracted MRI Measurements
Associated with Central Gland and Peripheral Zone Prostate Tumors”, Journal of Magnetic
Resonance Imaging, 2014. doi: 10.1002/jmri.24676. (PMID: 24943647).

Peer Reviewed Publications for 2014
Journal Papers (Contd.)
 Rusu, M, Bloch, BN, Jaffe, C, Genega, E, Lenkinsky, R, Feleppa, E, Rofsky, N, Madabhushi, A,
“Prostatome: A combined anatomical and disease based MRI atlas of the prostate”, Medical Physics , vol. 41[7], pg. 072301, 2014 (PMID: 24989400).
 Lee, G, Sparks, R, Ali, S, Feldman, M, Master, S, Shih, N, Tomaszewski, J, Madabhushi, A, “Cooccurring Gland Tensors in Localized Subgraphs: Predicting Biochemical Recurrence in Intermediaterisk Prostate Cancer Patients”, PLOS ONE , vol. 9[5], e97954, 2014 (PMID: 24875018).
 Viswanath S, Sperling D, Lepor H, Futterer J, Madabhushi A “Identifying Quantitative In Vivo Multi-
Parametric MRI Features For Treatment Related Changes after Laser Interstitial Thermal Therapy of
Prostate Cancer” Neurocomputing, Special Issue on Image Guided Interventions , vol. 144, pp. 13-23,
2014 (PMID: 25346574).
 Shridar, A, Doyle, S, Madabhushi, A, “Content-Based Image Retrieval of Digitized Histopathology in
Boosted Spectrally Embedded Spaces”, Journal of Pathology Informatics , Accepted.
 Wan, T, Madabhushi, A, Phinikaridou, A, Hamilton, J, Hua, N, Pham, T, Danagoulian, J, Buckler, A,
“Spatio-temporal texture (SpTeT) for distinguishing vulnerable from stable atherosclerotic plaque on
DCE-MRI in a rabbit model”, Medical Physics, vol. 41[4], pg. 042303, 2014 (PMID: 24694153).
 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 , vol. 272[1], pp. 91-9, 2014 (PMID: 24620909).
 Toth, R, Feldman, M, Yu, D, Tomaszewski, J, Madabhushi, A, “Histostitcher™: An Informatics Software
Platform for Reconstructing Whole-Mount Prostate Histology using the Extensible Imaging Platform
(XIP™) Framework,” Journal of Pathology Informatics, vol. 5, pg. 8, 2014 (PMID: 24843820, PMCID:
PMC4023035).

Peer Reviewed Publications for 2014
Journal Papers (Contd.)
 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 , vol. 144, pp. 3-12, 2014 (PMID: 25267873, PMCID:
PMC4175430).
 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 , pp. 359-73, vol. 18[2], 2014 (PMID: 24418598).
 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 , vol. 144, pp. 24-37, 2014 (PMID:25225455).
 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, pp. 128-37, vol. 38[1], 2014 (PMID:
24145650).

Peer Reviewed Publications for 2014
Peer-reviewed Conference Papers
 Cruz Roa, A, Osorio, FA, Madabhushi, A, Gonzalez, F, “A comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation”,
In 10th International Symposium on Medical Information Processing and Analysis, Accepted.
 Wang, H, Singanamalli, A, Ginsberg, S, Madabhushi, A, “Selecting Features with Group-sparse
Nonnegative supervised CCA (GNCCA): Multimodal Prostate Cancer Prognosis,” In Proc of Medical
Image Computing and Computer Assisted Interventions (MICCAI), vol. 17[3], pp. 385-92, 2014 (PMID:
25320823).
 Prasanna, P, Tiwari, P, Madabhushi, A, “Co-occurrence of Local Anisotropic Gradient Orientations
(CoLlAGe): Distinguishing tumor confounders and molecular subtypes on MRI,” In Proc of Medical
Image Computing and Computer Assisted Interventions (MICCAI), vol. 17[3], pp. 73-80, 2014 (PMID:
25320784).
 Singanamalli, A, Pisipati, S., Ali, A., Wang, V., Tang, C.T., Taouli, B., Tewari, A., and Madabhushi, A,
"Correlating gland orientation patterns on ex vivo 7 Tesla MRI with corresponding histology for prostate cancer diagnosis: Preliminary results," The International Society for Optics and Photonics
(SPIE) Medical Imaging, 2015.
 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.

Peer Reviewed Publications for 2014
Peer-reviewed Conference Papers (Contd.)
 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 multiparametric 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.
 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.

Peer Reviewed Publications for 2014
Peer-reviewed Conference Papers (Contd.)
 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. (PMID: 25075271, PMCID: PMC4112118)

Peer Reviewed Publications for 2014
Peer-reviewed Abstracts
 Ali S, Basavanhally, A, Madabhushi, A, “Histogram of Hosoya Indices for Assessing Similarity Across
Subgraph Populations: Breast Cancer Prognosis Prediction From Digital Pathology” United States and
Canadian Academy of Pathology's 104th Annual Meeting, Boston, MA, March 21-27, 2015.
 Ali S, Lewis J, and Madabhushi, A “A Quantitative Histomorphometric Classifier Identifies Role of
Stromal and Epithelial Features in Prediction of Disease Recurrence in p16+ Oropharyngeal
Squamous Cell Carcinoma” United States and Canadian Academy of Pathology's 104th Annual
Meeting, Boston, MA, March 21-27, 2015.
 Cruz-Roa A, Basavanhally A, Gonzalez F, Feldman M, Ganesan S, Shih N, Tomaszewsky J, Gilmore H,
Madabhushi, A “A Feature Learning Framework for Reproducible Invasive Tumor Detection of Breast
Cancer in Whole-Slide Images” United States and Canadian Academy of Pathology's 104th Annual
Meeting, Boston, MA, March 21-27, 2015.
 Lee G, Veltri, Zhu G, Epstein J, and Madabhushi, A “Computerized Nuclear Shape Analysis of
Prostate Biopsy Images Predict Favorable Outcome in Active Surveillance Patients” United States and Canadian Academy of Pathology's 104th Annual Meeting, Boston, MA, March 21-27, 2015.
 Lee G, Veltri R, Ali S, Epstein J, Christudass C, and Madabhushi, A “Prostate cancer recurrence can be predicted by measuring cell graph and nuclear shape parameters in the benign cancer-adjacent field of surgical specimens” United States and Canadian Academy of Pathology's 104th Annual
Meeting, Boston, MA, March 21-27, 2015.
 Penzias G, Janowczyk A, Singanamalli A, Rusu M, Shih N, Feldman M, Viswanath S and Madabhushi, A
“AutoStitcher©: An Automated Program for Accurate Reconstruction of Digitized Whole Histological
Sections From Tissue Fragments” United States and Canadian Academy of Pathology's 104th Annual
Meeting, Boston, MA, March 21-27, 2015.

Peer Reviewed Publications for 2014
Peer-reviewed Abstracts (Contd.)
 Rusu M, Yang M, Rajiah P, Jacono F, Gilkeson R, Linden P, and Madabhushi, A “Histology – CT Fusion
Facilitates the Characterization of Suspicious Lung Lesions with No, Minimal, and Significant Invasion on CT” United States and Canadian Academy of Pathology's 104th Annual Meeting, Boston, MA, March
21-27, 2015.
 Viswanath S, Paspulati R, Delaney C, Willis J, and Madabhushi, A “A Novel Pathology-Radiology Fusion
Workflow for Predicting Treatment Response and Patient Outcome in Rectal Cancers” United States and Canadian Academy of Pathology's 104th Annual Meeting, Boston, MA, March 21-27, 2015.
 Tiwari, P, Prasanna, P, Barholtz-Sloan, J, Sloan, A, Ostrom, Q, Jiang, B, Madabhushi, A, “Quantitative texture descriptors on baseline-MRI can predict patient survival in newly diagnosed glioblastoma multiforme patients,” Annual Society for Neuro-oncology Scientific Meeting, 2014, Accepted.
 Tiwari, P, Prasanna, P, Rogers, L, Wolansky, Leo, Madabhushi, A, “Computer extracted oriented texture features on T1-Gadolinium MRI for distinguishing radiation necrosis from recurrent brain tumors,” Annual Society for Neuro-oncology Scientific Meeting, 2014, Accepted.
 Orooji, M, Linden, P, Gilkeson, R, Prabhakar, R, Yang, M, Jacono, F, Rusu, M, Madabhushi, A,
“Computer Extracted Texture Features on CT Predict Level of Invasion in Ground Glass Non-Small Cell
Lung Nodules,” Proceedings of the Radiologic Society of North America, 2014, Accepted (Oral).
 Patel, J, Prasanna, P, Tiwari, P, Madabhushi, A, “Identifying MRI Markers On Newly Diagnosed
Glioblastoma Multiforme To Distinguish Patients With Long And Short Term Survival,” Biomedical
Engineering Society (BMES), 2014.
 Antunes, J, Viswanath, S, Sher, A, Avril, N, Madabhushi, A, “Identifying PET/MRI Parameters for Early
Treatment Response in Renal Cell Carcinoma,” Biomedical Engineering Society (BMES), 2014.
 Odgers, T, Massa, C, Rusu, M, Wang, H, Madabhushi, A, and Gow, A, “Structural And Functional
Modeling Of Pulmonary Function In Heterogenous Lung Pathology,” Novel and Traditional Lung
Function Assessment. May 1, A3572-A3572, 2014.

Invited Lectures
Dr. Madabhushi just after his talk at
Teleradiology
Solutions in
Bangalore, India
Dr. Madabhushi giving lecture at
University of
British Colombia,
Vancouver, BC,
Canada. (youtube link under picture) https://www.youtube.com/watch?v=gH-mXQ7jt7E
 “Computational Knowledge Fusion and sub-visual image features for personalizing medicine”,
Department of Electrical and Computer Engineering, University of British Colombia, Vancouver, BC,
Canada, December 1, 2014. (Anant Madabhushi)
 “Computer Extracted Texture Features on CT Predict Level of Invasion in Ground Glass Non-Small Cell
Lung Nodules,” Radiology Society Of North America, Chicago, IL, December 1 st , 2014. (Mirabela Rusu)
 “Computational MRI analysis for treatment evaluation and survival prediction in brain tumors,”
Imaging Seminar Series, Department of Biomedical Engineering, CWRU, November 25th, 2014. (Pallavi
Tiwari)
 “Computational data convergence: Applications in diagnosis and prognosis of prostate cancer”,
Department of Urology, Cleveland Clinic, Cleveland, OH, October 22 nd , 2014. (Anant Madabhushi)
 “Careers in Computational Imaging: Interface of computer science and biomedical engineering”,
Invited Talk, Department of Electrical Engineering, Nacionale Universidad de Colombia, Bogota,
Colombia, Oct. 17 th , 2014. (Anant Madabhushi)
 “‘Radiomics’ risk score: Image based risk assessment for presence of recurrent tumor or radiation effects on MRI,” Grand rounds in Oncology, University Hospitals, Cleveland, October 15th, 2014.
(Pallavi Tiwari)

Invited Lectures (Contd.)
 “Computational Imaging and mining sub-visual image features for personalized medicine: Use cases in breast, prostate, oropharyngeal and lung cancers”, Plenary Talk, 10 th International Symposium on
Medical Information Processing and Analysis, Cartagena, Colombia, Oct. 14 th , 2014. (Anant
Madabhushi)
 “Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A domain-inspired descriptor to distinguish ‘similar appearing’ pathologies on imaging”, Workshop on Radiomics, MD Anderson, Texas,
October 1st, 2014.
 “Computational convergence of imaging, pathology, and omics data: Use case in prostate cancer”,
Workshop on Radiogenomics, MD Anderson Cancer Center, Houston, TX, September 29 th , 2014. (Anant
Madabhushi)
 “Multi-scale, sub-visual features for personalizing medicine”, Biomedical and Health Informatics
Workshop, Case Western Reserve University, Cleveland, OH, September 16 th , 2014. (Anant Madabhushi)
 “Radiology-pathology correlation: enriching imaging to enable disease signature discovery,” Imaging
Hour, Case Western Reserve University, Cleveland, OH, September 9 th , 2014 (Mirabela Rusu)
 “Computational Imaging and mining sub-visual image features for personalized medicine: Use cases in breast, prostate, oropharyngeal and lung cancers”, Case Comprehensive Cancer Center, Cleveland,
OH, September 5 th , 2014. (Anant Madabhushi)
 “Multi-scale Data Enrichment in Prostate Cancer: Diagnosis, Prognosis and Population Based
Analytics,” International MRI Summer School, Iasi, Romania, August 5 th , 2014. (Mirabela Rusu)
 “Computational Breast Imaging and mining sub-visual image features for personalized medicine”,
Tata Memorial Hospital, Mumbai, India, July 24 th , 2014. (Anant Madabhushi)
 “Computational Imaging: Blending computer science and biomedical engineering”, Indian Institute of
Technology Bombay, Mumbai, India, July 21 st , 2014. (Anant Madabhushi)

Invited Lectures (Contd.)
 “Computational Imaging and mining sub-visual image features for personalized medicine: Use cases in breast, prostate, oropharyngeal and lung cancers”, Tata Memorial Hospital, Mumbai, India, July 17 th ,
2014. (Anant Madabhushi)
 “Computational Imaging and mining sub-visual image features for personalized medicine”, General
Electric, Bangalore, India, July 16 th , 2014. (Anant Madabhushi)
 “Computational Imaging and mining sub-visual image features for personalized medicine”,
Teleradiology Solutions, Bangalore, India, July 16 th , 2014. (Anant Madabhushi)
 “Computational Imaging and the Personalized Image based Risk Score”, Case Comprehensive Cancer
Center Retreat, Cleveland, OH, July 11 th , 2014. (Anant Madabhushi)
 “Multi-scale and sub-visual features: Applications in Personalized Medicine”, Workshop on Image
Ontologies, SUNY Buffalo, Buffalo, NY, June 25 th , 2014. (Anant Madabhushi)
 “3D Printing To Facilitate Prostate Cancer Diagnosis and Prognosis” Rapid, Detroit, MI, June 10 th , 2014.
(Mirabela Rusu)
 “Computer assisted diagnosis, prognosis, and treatment evaluation of prostate cancer from MRI and digital pathology”, Prostate Cancer Seminar Series, Cleveland Clinic, Cleveland, OH, May 15 th , 2014.
(Anant Madabhushi)
 “Preparing yourself for a career in non-academic environments?”, Graduate Student Senate
Professional Development Conference, Case Western Reserve University, Cleveland, OH, May 2 nd , 2014.
(Anant Madabhushi)
 “Computational convergence of radiology and pathology data”, Joint workshop on Radiology-Pathology
Fusion by Radiological Society of North America (RSNA) and American Society of Clinical Pathology
(ASCP), Chicago, IL, April 22 nd , 2014. (Anant Madabhushi)

Invited Lectures (Contd.)
 “Radiology-Pathology Convergence: Application to Biological Quantitation and Disease
Characterization”, R25T Seminar Series, Memorial Sloan Kettering Institute Dept of Radiochemistry,
New York, NY, April 18 th , 2014. (Satish Viswanath
 “Computational Imaging and Personalized Medicine”, Department of Thoracic Oncology, Cleveland
Clinic, Cleveland, OH, April 16 th , 2014. (Anant Madabhushi)
 “Computational pathology: Personalized Medicine and Enriching Radiology and molecular data”,
Department of Rheumatology, Cleveland Clinic, Cleveland, OH, April 15 th , 2014. (Anant Madabhushi)
 “Computational pathology: Squeezing the most out your pathology images”, Imaging Hour, Case
Western Reserve University, Cleveland, OH, April 15 th , 2014. (Anant Madabhushi)
 “Computational pathology: Squeezing the most out your pathology images”, University of Uppsala,
Center for Medical Image Analysis, Uppsala, Sweden, April 10 th , 2014. (Anant Madabhushi)
 “Computational Imaging and Big Data Convergence in Personalized Medicine”, Grand Rounds in
Urology, University of Cincinnati, Cincinnati, OH, April 7 th , 2014. (Anant Madabhushi)
 “ Computer-extracted texture features on T2w MRI to predict biochemical recurrence following radiation therapy for prostate cancer” SPIE Medical Imaging, San Diego, CA, March 24 th , 2014. (Mirabela Rusu)
 “Computational pathology: Image analysis for Big Pathology Data”, American Society for Clinical
Pathology, Miami, FL, March 20 th , 2014. (Anant Madabhushi)
 “ A prostate MRI atlas of biochemical failures following radiotherapy,” SPIE Medical Imaging, San Diego,
CA, March 18 th , 2014. (Mirabela Rusu)
 “Computational Imaging and Big Data Convergence in Personalized Medicine of Prostate Cancers”,
Grand Rounds in Department of Urology, Mount Sinai Medical Center, New York City, NY, March 5 th ,
2014. (Anant Madabhushi)
 “Computational Imaging and Big Data Convergence in Personalized Medicine”, Executive Council
Meeting, Case Comprehensive Cancer Center, Cleveland, OH, February 27 th , 2014. (Anant Madabhushi)

Invited Lectures (Contd.)
 “Quantitative Data Convergence: Applications to Personalized Medicine”, Department of Mathematics,
Case Western Reserve University, Cleveland, OH, February 26 th , 2014. (Anant Madabhushi)
 “Computational pathology: Personalized Medicine and Enriching Imaging”, Translational Hematology and Oncology Research (THOR) Seminar Series, Cleveland Clinic, Cleveland, OH, February 25 th , 2014.
(Anant Madabhushi)
 “Computational pathology: Personalized Medicine and Enriching Imaging”, Department of
Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, February 20 th , 2014.
(Anant Madabhushi)
 “Image based risk score: Application to ER+ breast cancers”, Department of Biomedical Engineering
Seminar Series, Case Western Reserve University, Cleveland, OH, February 10 th , 2014. (Anant
Madabhushi)
 “Computational pathology: Personalized Medicine and Enriching Imaging”, Department of Pathology and Anatomic Medicine, University of Pennsylvania, Philadelphia, PA, January 21 st , 2014. (Anant
Madabhushi)

Patents
Issued Patents
 “System and Method for Accurate and Rapid Identification of Diseased Regions on
Biological Images with Applications to Disease Diagnosis and Prognosis”, Anant Madabhushi,
James Monaco, John E Tomaszewski, Michael D. Feldman, Ajay Basavanhally, United States
Serial Number (USSN): 8,718,340.
 "System and Method for Automated Segmentation, Characterization, and Classification of possibly malignant Lesions and Stratification of Malignant tumors”, Anant Madabhushi,
Shannon Agner, Mark Rosen, United States Serial Number (USSN): 8,774,479.
Provisional Patent Applications
 “Methodology for textural analysis of nodules on imaging to determine extent of invasion”
Invention Disclosures
 “Cascaded Ensemble of Convolutional Neural Networks and Handcrafted Features For Breast Cancer
Diagnosis”, Case No. 2014-2572.
 “Histogram of Hosoya Index (HoH) features for Quantitative Histomorphometry ”, Case No. 2014-2657
 “A Group-Sparse Feature Selection Method for Multi-Modal Disease Prognosis ” , Case No. 2014-2656
 “Co-Occurrence of Local Anisotropic Gradient Orientations (CoLIAGe) ” , Case No. 2014-2655
 “Tumor+Adjacent Benign Signature (TABS) For Quantitative Histomorphometry ” , Case No. 2014-2654
 “Computational Scalpel: Treatment Planning for Rectal Cancer via Image Analytics”, Case No. 2014-
2684.
 “Methodology for creation of a differential atlas”, Case No. 2015-2775
 “Methodology for fusion of pathology and radiology data for disease characterization”, Case No.
2015-277

Awards and Accomplishments in 2014
Innovation
Award, Case
School of
Engineering,
2014
Media Recognition
 “Undergraduate wins Research Choice Award at biomedical engineering conference”, The Daily,
November 13 th , 2014.
 “Texture analysis shows level of invasion of ground-glass lung nodules”, AuntMinnie.com, November
10 th , 2014.

Awards and Accomplishments in 2014
Media Recognition (cont.)
 “CCIPD/BME Graduate Student Wins Young Scientist Runners up at MICCAI
2014”, Case Comprehensive Cancer Center News Letter, November 10 th , 2014.
 “ Featured Faculty Member” , case.edu/faculty, October 30 th , 2014.
 “Madabhushi Awarded Two-year NIH Grant on Predicting Aggressive Head &
Neck Cancers from Digital Pathology” , Case Comprehensive Cancer Center
News Letter, September 2 nd , 2014.
 “Bioengineering’s Anant Madabhushi, team awarded patent relating to radiologic imaging of tumors” , Case School of Engineering, July 28 th , 2014.
 “The Prostatome – a Novel Prostate Atlas Combining Anatomic and Disease
Pathology Data Founded” , Labmedica.com, July 24 th , 2014.

Awards and Accomplishments in 2014
Media Recognition (cont.)
 “Prostate Cancer: Crunching the Numbers”, Biomedical Computation
Review, July 11 th , 2014.
 “Dr. Anant Madabhushi Awarded Phase II Coulter Grant on Brain Tumor”,
The Daily, July 7 th , 2014
 “Precision Medicine depends on big data”, Tech Page One, June 25 th , 2014.
 “Biomedical engineering’s Anant Madabhushi and team awarded V
Foundation Translational Research Grant”, The Daily, June 20 th , 2014.
 “CTSC/Coulter grant awarded to biomedical engineering, medicine faculty”, The Daily, May 30 th , 2014.

Awards and Accomplishments in 2014
Media Recognition (cont.)
 “Anant Madabhushi to Serve on Editorial Board for New IEEE "Journal of Translational Engineering in Health and Medicine", Case Comprehensive Cancer Center Newsletter, May 26 th , 2014.
 “Madabhushi team awarded patent in digital pathology, cancer detection”, The Daily, May 16 th ,
2014.
 “Biomedical engineering’s Anant Madabhushi and team receive innovation research grant”, The
Daily, April 25 th , 2014.
 “vascuVis Inc., a wholly owned subsidiary of Elucid Bioimaging, has been awarded a two-year,
$696,200 Small Business Innovation Research (SBIR) Phase II Grant from the National Science
Foundation”, Press Release, March 31, 2014.
 “Using big data to identify triple-negative breast, oropharyngeal, and lung cancers”, Press Release,
Eurekalert.org
, March 18 th , 2014.

Awards and Accomplishments in 2014
Media Recognition (cont.)
 “HPV: Computerized Image Analysis May Distinguish Potentially Progressive Disease”,
DermatologistsBlog.com
 “Computational imaging/Madabhushi team takes home honors at SPIE Medical Imaging 2014” , The
Daily, February 28 th , 2014.
 “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.

Professional/Editorial Activities in 2014
Chairing, Membership Program Committees of Conferences, Workshops, Special issues
 Session Chair, Cancer Imaging Track, 10 th International Symposium on Medical Information Processing and Analysis, Cartagena, Colombia, Oct. 14 th , 2014 (Anant Madabhushi).
 Program Committee Member, Ontology and Imaging Informatics, SUNY Buffalo, June 23 rd , 2014
(Anant Madabhushi).
 Program Committee Member, 10 th International Symposium on Medical Information Processing and
Analysis, Cartagena, Colombia, Oct. 14-16, 2014 (Anant Madabhushi).
 Session Chair, Conference 9401: Digital Pathology, Keynote Session, International Society for Optics and Photonics (SPIE) Medical Imaging, Feb 18 th , 2014, San Diego, CA (Anant Madabhushi).
 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 18 th , 2014, San
Diego, CA (Anant Madabhushi).
Editorial Boards
 Associate Editor, IEEE International Symposium on Biomedical Imaging (ISBI) 2015 (Anant
Madabhushi).
 Associate Editor, IEEE Journal of Translational Engineering in Health and Medicine, May 2014-Present
(Anant Madabhushi).
 Editorial Board, IEEE Journal of Translational Engineering in Health and Medicine, May 2014-Present
(Anant Madabhushi).

New Grants Awarded in 2014
 Madabhushi, Anant (Co-I)
V Foundation
01/01/14 - 10/31/14
Use of PET and MR Imaging Biomarkers to Predict Response of Renal Cell Carcinoma to Tyrosine
Kinase Inhibitor Therapy
 Madabhushi, Anant (Co-I)
NSF
Computer assisted prognosis of debilitating disease
01/01/14 - 12/31/15
 Madabhushi, Anant (PI)
CTSC Coulter Annual Pilot Grant
06/01/14-05/30/15
Computerized Histologic Image-based predictor of recurrence in breast cancers following treatment
 Madabhushi, Anant (PI) 09/01/14-08/31/16
DOD CDMRP Lung Cancer Research Idea Development Award New Investigator (LC130463)
Computer extracted CT features for distinguishing suspicious lung lesions with no, minimal, and significant invasion
 Tiwari, Pallavi (PI)
Coulter Research Translational Partnership
09/01/14 - 08/31/15
NeuroRadVision TM : Image based risk score prediction of recurrent brain tumors (Phase 2)
 Madabhushi, Anant (PI) 9/01/14 - 8/30/16
NIH 1R21CA179327-01A1
Histologic image-based aggressiveness prediction in p16+ oropharyngeal carcinoma

Student Fellowships in 2014
 Patel, Jay (PI)
SOURCE CAA 2014 Summer Research Scholar
06/01/14-09/01/14
Case Western Reserve University
Segmentation and Shape Based Feature Modeling for Treatment Evaluation of Glioblastoma Multiforme
 Penzias, Gregory (PI)
SOURCE CAA 2014 Summer Research Scholar
06/01/14-09/01/14
Case Western Reserve University
Automatic Fusion of Prostate Histology, Multi-Parametric MRI, and PET for Improved Characterization of
Prostate Cancer

 Jacob Antunes, Reviewers Choice Award, Biomedical Engineering Society Department of Imaging and Optics Chair (5% of all submissions receive this commendation), 2014
 Jacob Antunes, Biomedical Engineering Society Scholarship for involvement in a professional integrity workshop focused on ethics of authorship, 2014
 Jacob Antunes, Case Western Reserve University Biomedical Engineering Society Executive Board
Travel Award, 2014
 Jay Patel, SOURCE CAA Travel Award, Case Western Reserve University, 2014
 Prateek Prasanna, Runner Up, Young Scientist Award, Medical Image Computing and Computer
Assisted Intervention Society (MICCAI), 2014
 Andrew Janowczyk, Excellence in PhD Thesis Award, Indian Institute of Technology Bombay, 2014
 Prateek Prasanna, Medical Image Computing and Computer Assisted Intervention Society (MICCAI)
Travel Award, 2014
 Gregory Penzias, SOURCE CAA Summer Research Scholar, Case Western Reserve University, 2014
 Prateek Prasanna, Semi-finalist Launchtown Competition, 2014
 Jay Patel, SOURCE CAA Summer Research Scholar, Case Western Reserve University, 2014
 Eileen Hwuang, NSF Graduate Research Fellowship, 2014
 Eileen Hwuang, Cum Laude for Best Poster Presentation at the Image Processing Conference,
International Society for optics and Photonics (SPIE) Medical Imaging, 2014
 Geert Litjens, Robert F. Wagner Best Student Paper Award, Runner up, International Society for optics and Photonics (SPIE) Medical Imaging, 2014
 Eileen Hwuang, SPIE Medical Imaging Student Grant, 2014

RESEARCH PORTFOLIO

• Model of prostate tissue properties (e.g. elasticity, compressibility)
• Physically-real deformations applied to prostate & internal zones
• Spatial alignment of pre-, posttreatment prostate volumes
• RMS error of alignment: 2.99 mm
• Traditional biomechanical model
(not considering internal zones)
RMS error: 5.07 mm Pre-Treatment MRI Aligned Post-Treatment MRI
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 144(20) Nov 2014. pp. 3-12, doi: 10.1016/j.neucom.2014.01.058.

 Orbital Boarder Model: New Statistical Shape Model for Prostate Segmentation in
Transrectal Ultrasound Imagery
Spatially Aware Laplasian Shape
EM Prior Prob
Orbital Boarder
Model
Calculate Prob. of each ray
50
100
150
200
250
300
350
50 100 150 200 250 300 350 400 450
50
100
150
200
250
300
350
100 150 200 250 300 350 400 450
50
100
150
200
250
300
350
50 100 150 200 250 300 350 400 450
Transrectal Ultrasound Imagery”, SPIE 2014

1. Segment
Prostate on MRI
2. Construct
Model on TRUS
3. Align MRI Mask to TRUS Model
6
T a
T e
4
2
0
Intensity
Multifeature
Feature
Rayleigh
R. Sparks, B. N. Bloch, E. Feleppa, D. Barratt, L. Ponsky, A. Madabhushi. Multi-attribute Probabilistic Prostate Elastic Registration
(MAPPER): Application to Fusion of Ultrasound and Magnetic Resonance Imaging. Medical Physics, in press .

(a)
(b) (c)
(d) (e) (f)
Ground glass nodule histology-CT fusion; (a) 3D view of nodule with axial (blue) and oblique (red) cutting plane; (b) CT intensities (oblique cut); (c) CT intensities (axial cut); (d) H&E section corresponding to the oblique cut (b); invasion
(black) and adenocarcinoma in situ + invasion (yellow); (e) the interactive alignment of histology and CT allows to map extent of invasion from histology onto CT; (f) CT-based textures will be included in a predictor
Rusu et. al. (Accepted Annual Meeting of the United States and Canadian Association of Pathology)

• Active surveillance (AS), an accepted monitoring program, may be offered for men with very low risk (VLR)
CaP in lieu of immediate intervention to reduce unnecessary treatment and improve quality of life.
• Our objective is to identify computationally derived features from digitized biopsy core images which can predict favorable and unfavorable outcomes for VLR AS patients.
65 H&E stained biopsy core images (30 Favorable, 35
Unfavorable) obtained from 51 AS
CaP patients
Nuclear shape features (AUC =
0.78)
Gleason Score (AUC = 0.60).
Lee et al. Accepted for presentation at United States and Canadian Academy of Pathology (USCAP) 2015

Goal: To detect mitosis figures in high power fields of breast cancer tissues.
a) b)
(a) Our mitosis detection framework detect nuclei candidates from a high-power field (HPF) using blueration color transformation as segmentation method and then each candidate is used to train and classify whether is a mitotic figure or not by a handcrafted based classifier and a feature learning classifier using a
Convolutional Neural Network. When both classifiers disagree about the classification, other classifier combine both features, handcrafted and learned, to the final classification of these confounding cases. (b)
Evaluation results show that our combined feature strategy outperform the performance of each feature independently and most of the previous baseline. (c) An example of mitosis detection in a HPF is presented with
TP (green), FN(yellow) and FP(red)..
c)
Haibo Wang, Angel Cruz-Roa, Ajay Basavanhally, Hannah Gilmore, Natalie Shih, Mike Feldman, John Tomaszewski, Fabio Gonzalez, and Anant Madabhushi.
Mitosis Detection in Breast Cancer Pathology Images by Combining Handcrafted and Convolutional Neural Network Features. Journal of Medical Imaging.
1(3):034003 (2014). ISSN: 2329-4302. doi:10.1117/1.JMI.1.3.034003

Goal: To evaluate and compare different computational methods for mitosis detection.
(a) By taking a set of high-power fields (HPF), different methods for mitosis detection were evaluated in the AMIDA challenge 2013
(http://amida13.isi.uu.nl/). (b) Evaluation results show the best results for IDSIA and DTU algorithms outperforming the performance measures (Precision, Recall and F-measure) of others approaches. (c) Sumarize the performance measure of each approach in the final evaluation of the challenges where our approach (CCIPD/MINDLAB) occupied the 6 th place but without significant statistical difference with the third one.
a) b) c)
Mitko Veta, Paul J. van Diest, Stefan M. Willems, Haibo Wang, Anant Madabhushi, Angel Cruz-Roa, Fabio Gonzalez, Anders B. L. Larsen, Jacob S.
Vestergaard, Anders B. Dahl, Dan C. Cireșan, Jürgen Schmidhuber, Alessandro Giusti, Luca M. Gambardella, F. Boray Tek, Thomas Walter, Ching-Wei Wang,
Satoshi Kondo, Bogdan J. Matuszewski, Frederic Precioso, Violet Snell, Josef Kittler, Teofilo E. de Campos, Adnan M. Khan, Nasir M. Rajpoot, Evdokia
Arkoumani, Miangela M. Lacle, Max A. Viergever, Josien P.W. Pluim. Assessment of algorithms for mitosis detection in breast cancer histopathology images.
Journal of Medical Image Analysis. 2014. (In press)

Frank Invasion Minimal Invasion
 Quantitative characterization of spatial heterogeneity using computerized methods
 Textural analysis could identify subtle cues of invasion on
CT that might not be visible
 25% of positive nodules on baseline CT are Ground Glass or Semi-solid nodules
 The extent of invasion is correlated with prognosis
Disease free survival at 5 years when resected:
– 100 % : Minimally invasive
• Adenocarcinoma in situ
• Minimally Invasive Adenocarcinoma (≤ 5 mm invasion)
– 67-90 %: Frank invasion
• Invasive Adenocarcinoma (> 5 mm invasion)
 Currently radiologists are unable to distinguish the level of invasion from in situ on CT
Orooji, M.; Rusu, M.; Rajiah, P.; Yang, M.; Jacono, F.; Gilkeson, R.; Linden, P.;
Madabhushi, A.; “Computer Extracted Texture Features on CT Predict Level of
Invasion in Ground Glass Non-Small Cell Lung Nodules”, Radiological Society of
North America (RSNA), Chicago, IL.

Recurrent GBM Higher Density of high entropy regions
Radiation Necrosis
Prasanna, Tiwari et al. SNO (2014)
Lower density of high entropy regions

ER+
(c)
HER2+
(f)
Fibroadenoma
(i)
Prasanna, Tiwari, Madabhushi, Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): Distinguishing Tumor Confounders and
Molecular Subtypes on MRI, Conference: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014

A B C D
E F
G H
Example of both local and global graphs built into a TMA image of OCSCC which was previously segmented by automatic thresholding into blue ratio colour space for nuclei detection. Cell Cluster Graphs (A and E), Voronoi
Diagram (B and F), Delaunay Triangulation (C and G), and Minimum Spanning Tree (D and H). First row shows the graphs over TMA image (A-D) and second row details only the graphs (E-H). Notice that all graphs were built using the same nuclei segmentation method.
Angel Cruz-Roa, Jun Xu, Anant Madabhushi, "A note on the stability and discriminability of graph based features for classification problems in digital pathology",
SIPAIM 2014

Goal: To evaluate and compare different methods of representation and deep learning for anaplastic medulloblastoma tumor differentiation.
(a) A representation learning framework is proposed to train and classify square tissue regions from medulloblastoma tumors. In order to evaluate which kind of representation learning, unsupervised or supervised, we evaluate the representation learning module by using different methods. Unsupervised feature learning methods used were: Sparse Autoencoders (sAE), Topographic Independent Component
Analysis Autoencoders (TICA), and Supervised feature learning method was: Convolutional Neural Network
(CNN). All methods were trained and evaluated using the same experimental setup to classify between anaplastic and non-anaplastic tumor. (b) Evaluation results show how unsupervised feature learning method TICA obtained the best results for different configuration followed by a large supervised feature learning method of CNN. TICA has the advantage that introduce invariant properties of visual features that can be useful for this task. Interestingly, all representation learning methods, unsupervised and supervised, outperform the data-driven baseline methods based on bag of features (BOF).
a) b)
Angel Cruz-Roa, John Arévalo, Ajay Basavanhally, Anant Madabhushi, Fabio González. (2014, October 14-16). A comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation. Tenth International Symposium on Medical Information
Processing and Analysis (SIPAIM 2014), Cartagena, Colombia.

• Our new data integration methodology, supervised Multi-view Canonical Correlation Analysis
(sMVCCA), aims to integrate infinite views of highdimensional data to provide more amenable data representations for disease classification.
• Additionally, we demonstrate sMVCCA using Spearman’s rank correlation which, unlike
Pearson’s correlation, can account for non-linear correlations and outliers.
Lee et al. IEEE Trans Med Imaging (2014)

T2w MRI 112-D features classification
DCE MRI
56-D features
Wang et al., Selecting Features with Group-sparse Nonnegative Supervised Canonical Correlation Analysis: Multi-modal Prostate Cancer Prognosis, MICCAI 2014.

(a) (b) (c)
(d) (e) (f)
Multi-modal fusion to characterize the appearance of lung inflammation in a mouse model: (a) 3D reconstructed histology shows the extent of inflammation in 3D; (b) fusion of 3D histology and in vivo MRI; (c) 3D inflammation is mapped from histology onto in vivo MRI;
Gabor feature in (d) wild type control mouse, (e) SPDKO inflammation caring mouse; (f) within the inflammation region
Rusu et. al. (submitted), Medical Physics

Goal: Fusion of multi-modal data to improve prediction of disease diagnosis and prognosis
Fused and Individual Predictors of Alzheimers Disease
4 0.9
3
0.85
*
Early Diagnosis of Alzheimer’s
Disease
2
1
0
-1
0.8
0.75
0.7
0.65
*
0.6
T1w MRI Plasma Proteomics 0.55
sMVCCA T1w MRI Proteomics
Fused and Individual Predictors of Prostate Cancer Grade
In vivo prediction of prostate cancer risk
0.8
0.75
0.7
0.65
0.6
0.55
0.5
*
*
T2w MRI DCE MRI sMVCCA T2w MRI DCE MRI
Fusion vs. Individual Modalities
Prediction of
Prostate Cancer
5-year
Biochemical
Recurrence
0.85
0.8
0.75
0.7
0.65
0.6
0.55
0.5
*
*
Histology
Proteomics sMVCCA Histology Proteomics
Singanamalli, A., Lee, G., Wang, H., et al. “Supervised multi-view canonical correlation analysis: fused multimodal prediction of disease diagnosis and prognosis”, SPIE 2014

Pre-treatment
MRI Follow-up (t
1
) Follow-up (t
2
) Follow-up (t
3
) Follow-up (t
4
)
Tiwari et al, PlosOne (in press)

Correlate rectal surgical specimens with pre-operative MRI, identify imaging signatures for chemoradiation response and different treatment effects
Viswanath et al, “A Novel Pathology-Radiology Fusion Workflow for Predicting Treatment
Response and Patient Outcome in Rectal Cancers”, USCAP 2015 (accepted)

Normal tissue:
No expected change
PrePost-
RCC:
Expected change
PrePost-
 Goal: Identify quantitative PET/MRI parameters that reflect early response in metastatic RCC to tyrosine kinase inhibitor treatment
 Quantified SUV, ADC, and T2W sum average parameters appear to be reflective of early changes due to cytostatic drug treatment response
Antunes, J., Viswanath, S., Rusu, M., Sher, A., Hoimes, C., Avril, N., and Madabhushi, A, “Identifying PET/MRI Parameters for
Early Treatment Response in Renal Cell Carcinoma,” Annual National Biomedical Engineering Society Conference, 2014

Co-registration of post-LITT MRI and histopathology
Co-registration of post- and pre-LITT
MRI MRI feature extraction
Co-registration of radiological and histopathological data can help determine differentially expressing features in preand post therapy MRI to assess laser-interstitial thermotherapy success in prostate cancer ablation
Clustering to determine residual disease extent using differentially expressing features obtained in previous steps
Determining changes in features from pre- to post-treatment MRI in ablated areas and residual disease
Geert JS Litjens, Henkjan J Huisman, Robin M Elliott, Natalie Nc Shih, Michael D Feldman, Satish Viswanath, Jurgen J Fütterer, Joyce GR Bomers, Anant
Madabhushi. Journal of Medical Imaging 1 (3), 035001-035001

Radiomic Markers on Treatment-Naïve MRI can
Predict Survival in GBM Patients
Kaplan Meier (KM) survival
FLAIR-MRI Intensity Sum Entropy Correlation curves for long and short-term survival GBM patients
Signal Intensity
Shortterm survival
1
0.8
Short Term
Long Term
0.6
p-value: 0.77773
0.4
Longterm survival
Tiwari et al, SNO (2014)
0.4
0.2
0
0
0.2
0.8
1
0
0 10 50 20 30 40
Time (months)
Radiomic Features
Short Term
Long Term p-value: 5.716e-07
0.6
10 20 30 40
Time (months)
50

• The 'field effect' describes the micro-environment around the site of the tumor which may lead to a progression of disease.
• Combined features extracted from images corresponding to tumor regions with that of images corresponding to benign adjacent regions to create a Tumor + Adjacent Benign Signature
140 H&E stained biopsy core images from 70 patients (22 progressors, 48 nonprogressors)
Lee et al. Accepted for presentation at United States and Canadian Academy of Pathology (USCAP) 2015

Overview: Disorder in orientation of visible glands on ex vivo 7T prostate MRI can predict cancer presence and are correlated with disorder in gland orientations on pathology
5
2
1
4
3
(a) (b) (c) Benign (d) Tumor
3.5
3
2.5
2
1.5
(e) (f) (g)
1
Benign (h) Tumor
(a) Co-registration of histology and (b) ex vivo 7T MRI; Co-occurring gland tensors of (b, f) benign and (c, g) tumor tissues on (b, c) pathology and (f, g) 7T MRI ; Entropy of gland orientations as computed from (d) pathology and from (h)
7T MRI distinguish between benign and tumor tissues
Singanamalli, A., Pisipati, S., Ali, A., Wang, V., Tang, C.Y., ,Taouli, B., Tewari, A., Madabhushi, A., “Correlating gland orientation patterns on ex vivo 7
Tesla MRI with corresponding histology for prostate cancer diagnosis: Preliminary analysis”, To appear in proc SPIE 2015

Purpose: To identify computer-extracted features from multiparametric MRI that are useful for detecting and localizing prostate tumors in the central gland or peripheral zone of the prostate.
T2w
MRI
DCE
MRI
ADC
Map
T2w + DCE
+ ADC
Texture
S Ginsburg, et al. Novel PCA-VIP scheme for ranking MRI protocols and
Features identifying computer-extracted MRI measurements associated with central gland and peripheral zone prostate tumors. JMRI available online.

 Identifying similar subgraph structures that are recurring across the population and their effect on overall tumor morphology remains unexplored.
 Hosoya index (HI) (originally introduced for analysis of chemical bonds) is a measure of a bond (in this context nuclei connections in a graph)
 In this work, we have leverage HI to measure structural similarities of graphs across the populations that are indicative of recurrence in breast cancer tissue images
Illustration of Hosoya Index calculation
Figure 1. Original BCa TMA representing tumor with (a) recurrent tumor (d) nonrecurrent tumor. (b) and (e) represent the corresponding cell graphs and resulting hosoya signature in (c) and (f) respectively.
Ali et al, USCAP 2015

TM :
Preliminary Algorithm
Accepted for
Presentation at USCAP
2015, Boston MA

 Updated
 GUI Redesigned
 Based Image
Processing
Redesigned
 New Features
Added
 A Generic
Image Analysis
Framework
Ver. 2

 HistoView is a graphical user interface for pathology image analysis and visualization.
 Separates the image into different channels, corresponding to the actual colors of the stain used.
 Binarizes the channel image by thresholding and visualizes thresholded result.
 Supports whole-slide images.