Center for Computational Imaging and
Personalized Diagnostics (CCIPD)
2015 Annual Report
Director: Anant Madabhushi, PhD
Professor,
Department of Biomedical Engineering
CCIPD Website: http://ccipd.case.edu
CCIPD in 2015
Wickenden Building
Dept. of Biomedical Engineering
Case Western Reserve University
2071 Martin Luther King Drive
Cleveland, Ohio 44106-7207
Center Space, Room 517
Faculty Offices, Room
523
Center Space, Room 525
Center Members
CCIPD Members
Center Director: Anant Madabhushi, PhD
Research Faculty
Satish Viswanath
George Lee
Pallavi Tiwari
Research Associates
Mahdi Orooji
Andrew Janowczyk
Rakesh Shiradkar
Soumya Ghose
Cheng Lu
Jon Whitney
Jhimli Mitra
Graduate Students
Sahir Ali
Shoshana Ginsburg
Lin Li
Jacob Antunes
Nathan Braman
Prateek Prasanna
Gregory Penzias
Xiangxue Wang
Niha Beig
Scientific Software Engineer
Ahmad Algohary
Yu Zhou
Visiting Scientists
Angel Cruz (Colombia)
Jun Xu (China)
David Romo-Buchelli (Colombia)
Tao Wan (China)
German Corredor Prada (Colombia)
Mehdi Alilou (Iran)
Kan Jia (China)
Gavin Hanson (Case Medical School)
Undergraduate Students
Patrick Leo
Ania Gawlik
Nikita Agrawal
Jay Patel
Andrew Rose
Prathyush Chirra
Nathan Landis
Michael Volkovitsch
Administrative Staff
Ann Tillet
Francisco Aguila
Center Members
Center Director
Anant
Madabhushi,
PhD
Research Faculty
Pallavi Tiwari,
PhD
George Lee,
PhD
Satish Viswanath,
PhD
Center Members
Research Scientists
Mahdi Orooji,
PhD
Cheng Lu, PhD
Jon Whitney,
PhD
Andrew
Janowczyk, PhD
Rakesh
Shrikadkar, PhD
Jhimli Mitra,
PhD
Soumya Ghose,
PhD
Center Members
Graduate Students
Niha Beig, BS
Xiangxue Wang,
BS
Sahir Ali, MS
Lin Li, BS
Prateek
Prasanna, MS
Greg Penzias,
BS
Jacob Antunes,
BS
Shoshana Ginsburg,
MS
Nathan Braman,
BS
Center Members
Visiting Scientists
David RomoBucheli, PhD
Jun Xu, PhD
Mehdi Alilou,
PhD
Gavin Hanson
Tao Wan, PhD
Angel Cruz, PhD
German Corredor
Prada, Phd
Kan Jia, MS
Center Members
Scientific Software Programmers
Yu Zhou, MS
Ahmad
Algohary, MS
Administrative Staff
Ann Tillett, BS
Frankie Aguila, BS
Center Members
Undergraduate Students
Patrick Leo
Ania Gawlik
Jay Patel
Nikita Agrawal
Nathaniel
Landis
Prathyush
Chirra
Michael
Volkovitsch
Andrew Rose
Recent Alumni
George Lee, PhD, Research
Professor, CCIPD
Asha Singanamalli,
PhD, GE Research
Mirabela Rusu, PhD,
GE Research
Haibo Wang, PhD,
Philips Research
Partnerships
CCIPD
Computational Imaging in Health and Precision Medicine
Breast Cancer
Renal Cell Carcinoma
Data
Science
at
CWRU
Colorectal Cancer
Prostate Cancer
Oropharyngeal Cancer
Lung Cancer
Brain Tumor
Awards and Accomplishments
Anant Madabhushi induction into AIMBE
Crain’s 40 under 40 article on Anant Madabhushi
Jacob Antunes, Ania Gawlik, and Michael Volkovitsch winners
of Choose Ohio First Scholarship
Awards and Accomplishments
Prateek Prasanna, Trainee Stipend for ISMRM 2015
Mahdi Orooji, Linda Arena Endowed
Scholarship Award
Shoshana Ginsburg, 1st place Graduate Student Research
Award at ShowCASE
Press Coverage in 2015
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“MRI-based texture analysis predicts survival in glioma patient”,
AuntMinnie.com, November 15th, 2015.
“CWRU researchers building digital pathology tools to predict cancer outcomes:
Receive National Institutes of Health grants to lead image-analysis efforts”,
Press Release (EurekAlerts), November 11th, 2015.
“Find out how translational research impacts health care at Ford Distinguished
Lecture Oct. 26”, Case School of Engineering, October 23rd, 2015.
“Madabhushi and Viswanath awarded patent in multimodal data fusion”
o The Daily, October 23rd, 2015.
“Postdoctoral researcher wins scholarship for training in lung cancer prevention,
treatment”
o The Daily, October 9th, 2015
“Research faculty George Lee receives NIH award for big data”
o The Daily, October 9th, 2015
“Several of CWRU’s own selected to Crain’s Cleveland Business “Forty Under
40” class of 2015”
o http://www.crainscleveland.com/event/crains/3272035/crains-forty-under40
“Biomarker may predict who'll benefit from targeted therapy for HER2-negative
breast cancer”
o Press Release (EurekAlert!), September 17th, 2015
“Case-Coulter Translational Research Partnership awards $1 million in funding
and support for promising biomedical engineering university technologies”,
o Press Release, September 22nd, 2015.
Madabhushi team awarded patent in digital pathology, biomarker quantification”
o Case School of Engineering, August 24th, 2015.
“5 questions with… professor of biomedical engineering Anant Madabhushi”,
o The Daily, August 7th, 2015
“Faculty-student team participating in summer cohort of research
commercialization program”,
o The Daily, August 3rd, 2015.
Press Coverage in 2015
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“Ohio Department of Higher Education Announces Members of I-Corps@Ohio Summer Cohort”,
Press Release, July 8th, 2015.
“Madabhushi awarded NCI grant to study ductal carcinoma in situ”
o Case Comprehensive Cancer Center News Letter, April 27th, 2015.
“BME, CCIPD graduate student wins top honors at Research ShowCASE”, Case School of
Engineering, May 30th, 2015.
“Students honored with 2015 Intersections: SOURCE Symposium & Poster awards”, The Daily, May
1, 2015.
Dr Madabhushi awarded patent for image based risk score”,
o Case Comprehensive Cancer Center News Letter, April 13th, 2015.
“CCIPD, BME Postdoctoral researcher awarded prestigious $125,000 fellowship from in prostate
cancer from DOD”,
o Case Comprehensive Cancer Center News Letter, March 30th, 2015.
“Two Inspirata Strategic Advisory Board Members Inducted into the American Institute for Medical
and Biological Engineering’s College of Fellows”, Press Release 2015.
“Technical conference highlights: Medical Imaging 2015”
o SPIE Newsroom, March 16th, 2015
“Anant Madabhushi named associate member of Quantitative Imaging Network, associate editor of
BMC Medical Imaging”
The Daily, March 13th, 2015
“Several Awards Received by Center for Computational Imaging and Personalized Diagnostics”
o The Daily, April 24th, 2015.
“Awards for Program Sponsored Project Grants from Case CCC Developmental Funds”
o Case Comprehensive Cancer Center News Letter, March 2nd, 2015.
“Medical image analysis technologies from Madabhushi team licensed to Boston-based startup”
o Case School of Engineering, February 16th, 2015.
“Madabhushi Named Associate Member of NCI Quantitative Imaging Network”
o Case Comprehensive Cancer Center News Letter, February 16th, 2015.
“BME’s Anant Madabhushi named AIMBE fellow”
o The Daily, February 9th, 2015
“BME’s Anant Madabhushi to serve on editorial board of Journal of Medical Image Analysis”
Conference Participation 2015
Mehdi Orooji, Mehdi Alilou, and Xiangxue
Wang: I-Corps Workshop in Columbus, OH
Anant Madabhushi: RSNA in
Chicago, IL
Ahmad Algohary: AUA in New Orleans,
LA
Conference Participation 2015
Mirabela Rusu & Greg Penzias: USCAP
in Boston, MA
Jacob Antunes: Research ShowCASE
Anant Madabhushi: USCAP in Boston, MA
Rakesh Shiradkar: Research ShowCASE
Conference Participation 2015
Prateek Prasanna & Greg Penzias: Research
ShowCASE
Anant Madabhushi: SPIE in San Diego, CA
Jay Patel: Research ShowCASE
David Romo-Bucheli & Anant Madabhushi:
SIPIAM in Cuenca, Ecuador
Summary of Research Accomplishments
 Center Members: 34
Faculty: 4
Research Associates: 5
Graduate Students: 8
Undergraduate Students: 8
Scientific Software Engineers: 2
Administrative Assistants: 2
Visiting Scientists: 5
 Theses: 0
Books: 1
 Peer-Reviewed Journal Papers: 13
 Peer-Reviewed Conference Papers: 12
 Peer Reviewed Abstracts: 37
 Awarded Grants: 9
 Awarded Fellowships: 7
 Ongoing Projects: 28
 Issued Patents: 4
Invention Disclosures: 2
 Technologies Licensed: 1
Peer Reviewed Publications for 2015
Summary
40
36
32
28
24
20
16
12
8
4
0
Books
Journal
Papers
Conference
Papers
Abstracts
Patents
Invention
Disclosures
Patents in 2015
Issued Patents
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“Image-Based Risk Score – A Prognostic Predictor of Survival and Outcome from Digital
Histopathology”, Anant Madabhushi, Ajay Basavanhally, Shridar Ganesan, United States
Serial Number (USSN): 9,002,092, April 7th, 2015.
"High Throughput Biomarker Segmentation Utilizing Hierarchical Normalized Cuts”, Anant
Madabhushi, Andrew Janowczyk, Sharat Chandran, United States Serial Number (USSN):
9,111,179, August 19th, 2015.
“Tumor Plus Adjacent Benign Signature (TABS) For Quantitative Histomorphometry” Anant
Madabhushi, George Lee, Sahirzeeshan Ali, Publication number: US20150254493 A1, Sep
10, 2015.
“Enhanced Multi-Protocol Analysis via Intelligent Supervised Embedding (EMPrAvISE) for
Multimodal Data Fusion”, Anant Madabhushi, Satish Viswanath, United States Serial Number
(USSN): 9,159,128, October 13th, 2015.
New Grants Awarded in 2015
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NIH 1U24CA199374-01
Pathology Image Informatics Platform for visualization, analysis and management
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NIH 1R21CA195152-01
Computerized morphologic-molecular predictor of progression in DCIS
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Case Comprehensive Cancer Center Pilot Grant
Radiogenomic approach for predicting pathologic complete response in HER2+ breast cancers
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VeloSano Pilot Grant
Genomic correlation of computer aided radiographic and histomorphometric feature extraction based models
in Lung cancer and novel approach to predicting early resistance to targeted therapy.
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I-Corps (NSF)
Decision support to identify benign infection on lung CT and reduce unnecessary surgical interventions
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Coulter Research Translational Partnership
NeuroRadVisionTM: Image based risk score prediction of recurrent brain tumors (Phase 3)
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CDMRP Prostate Cancer Idea Grant
Department of Defense
Fused Biomarker-Based Prediction of Aggressive from Indolent Prostate Cancer

Development Award in Biomedical Big Data Science (K01)
NIH
Big data convergence of pathology and omics for disease prognosi
Research Portfolio
Treatment Evaluation and Outcome
Prediction
Machine Learning
Multimodal CoRegistration
CCIPD
Imaging
Science
Software
Image Segmentation
and Registration
Multimodal Data Fusion
Object Detection
Computer Aided Diagnosis
and Prognosis
IMAGE REGISTRATION AND
SEGMENTATION
Multi-Attribute Probabilistic Prostate Elastic
Registration (MAPPER)1
28R.
Sparks, B. N. Bloch, E. Feleppa, D. Baratt, D. Mses, L. Ponsky, A. Madabhushi. Multiattribute probabilistic prostate elastic
registration (MAPPER): Application to fusion of ultrasound and magnetic resonance imaging. Medical Physics 42(3): 1153-1163
(2015).
A resolution adaptive deep hierarchical (RADHicaL) learning
scheme applied to nuclear segmentation of pathology images
Algorithm
•
•
•
Training images (blue paths) are resized to train M different
AlexNet networks at different resolutions m.
Test images (red path) are reduced to the smallest magnification,
a threshold is applied to the classifier's probability mask.
Only relevant pixels are mapped into next highest resolution for
further analysis.
Results
Output
Comparison of (f) our approach and (c) a naïve DL classifier on (a) original image with
(b) ground truth. The blue channel, indicates the confidence in a pixel belonging to a
nuclei. The red channel indicates the confidence in a particular pixel not belonging to a
nuclei. Lastly, the green channel indicated the confidence that a pixel needs additional
computation at the next level of magnification.
At the (d) lowest resolution of 4x our algorithm is able to approximate the location of
nuclei, obviating the need for the computation of the entire image at the next level as
all of the red pixels can be avoided.
Mapping to (e) 20x shows the blue pixels will be added to the final output, while green
pixels merit additional computation at the next magnification level.
Finally, when presenting (f) which is of the same magnification as Figure 6(c), the
Performance metrics comparing RADHicaL versus the Naïve DL results are nearly identical, yet were produced 6x faster
approach of computing all pixels. Timing, in seconds, shows a 6x
Andrew Janowczyk1, Scott Doyle2, Hannah Gilmore3 , and Anant Madabhushi1
1 Case Western Reserve University, Dept. of Biomedical Engineering, Cleveland, OH,
speed improvement for a typical image.
2 Pathology & Anatomical Sciences, SUNY Buffalo, NY 3 University Hospitals Case Medical Center, Cleveland OH
Sparse Non-negative Matrix Factorization (SNMF)
based Color Unmixing
Xu J, et al. “Sparse Non-negative Matrix Factorization (SNMF) based Color Unmixing for Breast Histopathological
Image Analysis”, Computerized Medical Imaging and Graphics, 2015.
MACHINE LEARNING AND FEATURE ANALYSIS
Classifier Error Rate Extrapolation via the Cross-Validated Inverse
Power Law Method on Limited Training Data from the ADNI Dataset
Abstract Citation: Agrawal, N, Basavanhally, A, Viswanath, S, Madabhushi, A, “Predicting Classifier Performance with Limited Training Data: Validation on the ADNI
Dataset,” Biomedical Engineering Society (BMES), 2015.
Accepted for Poster Presentation at Annual BMES Conference on Oct. 7-10, 2015
Background:
• Clinical trials use machine learning classifiers to predict patient
response to new therapies.
• Need to predict classifier performance for large training datasets
since classifier performance when trained with minimal data does
not guarantee performance when exposed to larger amounts of
training data.
Application:
• An estimated 16 million people will have Alzheimer’s Disease (AD)
by 2050, signifying a need to develop and trial disease-treatment
drugs.
• Creation of Alzheimer's Disease Neuroimaging Initiative (ADNI)
enabled development of classifiers to identify AD patients,
however there is a lack of concordance in classifier methods used
and generalizability of proposed classifiers is unclear.
Objective:
• Need to determine how associated parameters affect crossvalidated inverse power law (CVIPL) approach for extrapolating
classifier error rates: number of subsampling trials, number of
permutation trials, and number of cross-validation iterations
• External validation of CVIPL method would be predicting
performance of various classifiers on publicly available ADNI MRI
data in distinguishing cognitively normal (CN) vs AD studies;
compare extrapolated values against reported values from
literature
Results:
•
Increasing subsampling trials (T1) and permutation trials (T2) from 20
to 40 improved error rate extrapolation for both the Logistic
Regression (LR) and Support Vector Machine (SVM) classifiers, as
evident by decreased mIQR (compare plotted red and blue lines in
Figures 3c).
•
Increasing cross-validation iterations (R) resulted in a decreased
mIQR and hence improved error rate extrapolation for SVM, but an
increased mIQR for the LR classifier. However, for both classifiers,
more cross-validation iterations resulted in more stable mIQR values.
•
CVIPL methodology successfully validated for accurately
extrapolating LR and SVM classifier performance against values
reported in literature.
Radiomics based characterization of early treatment
response in renal cell carcinoma on FLT-PET/MRI
Healthy Tissue
Renal Cell Carcinoma
Goal: Identify radiomic
features and a combination
thereof
that
best
characterize early tumor
response in metastatic
renal cell carcinoma
Results: SUV and ADC
texture features ranked
highest
for
capturing
treatment
response
in
comparison
to
high
variability seen in T2w
MRI.
Antunes, J., Viswanath, S., Rusu, M., Valls L., Sher, A., Hoimes, C., Avril, N., and Madabhushi, A, “An integrated multi-parametric FLT-PET/MRI for evaluating early
treatment response in renal cell carcinoma.” Presented at International Society for Magnetic Resonance in Medicine, 2015.
Correlating Computer Extracted Features from Tubules on ER+
Breast Cancer Images with Oncotype DX Risk Categories
Experimental Setup
• 54 whole slide images (patients)
were used on this study
• The set was split in 39 low
Oncotype DX patients (ODX score
ranging 1 to 17) and 15 high scoring
patients ( ODX score ranging 31 to
69) At most 50 high power fields
per whole slide images were
selected (Those with minimum
fraction of tubule nuclei per
histological sample). All the
magnification fields are sampled
from cancerous regions previously
identified by an expert pathologist.
• Results were correlated with
Oncotype Dx (ODX). A genomic test
commonly used to predict cancer
aggressiveness
Romo-Bucheli, D., Janowczyk, A., Romero E., Gilmore, H. and Madabhushi A.. “Automated Tubule Nuclei Quantification and Correlation with Oncotype DX risk categories in ER+
Breast Cancer Whole Slide Images”. Accepted SPIE Medical Imaging, 2016.
Evaluating Stability of Histomorphometric Features across Scanner and Staining
Variations: Predicting Biochemical Recurrence From Prostate Cancer Whole Slide Images
• Tissue staining, slide preparation and scanning varies across institutions,
resulting in varying feature distributions following quantitative
histomorphometric analysis
Accepted for presentation at SPIE Med Imag and USCAP 2016
Prostate gland lumen feature robustness
Calculated a mean preparation-induced instability (PI) score.
Lumen shape showed the lowest instability
Features dependent on gland lumen architecture showed high volatility via PI
Accepted for presentation at SPIE Med Imag 2016 and USCAP 2016
Quantifying grades of cerebral radiation necrosis
1
60
0.95
50
CoLlAGe entropy
MR Signal Intensity
0.9
0.85
0.8
0.75
0.7
0.
5
40
0.
3
30
20
0.
1
10
0.65
0.6
Pure Necrosis
Mixed necrosis
Mixed Tumor
Pure Tumor
100% CRN >80% CRN <20% CRN 0% CRN
0
Pure Necrosis
Mixed necrosis
Mixed Tumor
Pure Tumor
100% CRN >80% CRN <20% CRN 0% CRN
--> Distinguishing radiation necrosis from recurrent tumors using both pure and predominant
signatures from cerebral radiation necrosis
--> Dataset: 100% CRN (from head and neck cancer), mixed CRN, 0% CRN (treatment naive GBM
from TCIA)
Prasanna et. al. , SNO 2015
Stacked Sparse Autoencoder for Nuclei Detection
Xu J, et al. “Stacked Sparse Autoencoder (SSAE) based Framework for Nuclei Patch Classification on Breast Cancer Histopathology”，ISBI2014.
Xu J, et al. “Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology”. IEEE Trans. on Medical Imaging, 2015
Zhang X, Dou H, Xu J, Zhang S, “Fusing Heterogeneous Features for the Image-Guided Diagnosis of Intraductal Breast Lesions”, IEEE Journal of Biomedical
and Health Informatics, 2015
Lu C, Xu H, Xu J, Mandal M, and Madabhushi A, “Multiple Passes Adaptive Voting for Nuclei Detection in Histopathlogical Images“, IEEE Journal of Biomedical
and Health Informatics, (Under Preparing)
Automated Discrimination of Epithelial and Stroma
Regions
Jun Xu, et al."A Deep Convolutional Neural Networks based Feature Learning for Detecting and Classifying Epithelial and Stroma
Regions in Histopathological Images" Neurocomputing, (Accepted pending changes)
MULTI-MODAL DATA FUSION
Multi-modal co-registration via multi-scale textural
and spectral embedding representations
Objective: Apply
image-based textural
and spectral
embedding
representations in coregistration of multimodality mages and
compare their
performance against
image intensity alone.
Lin Li, Mirabela Rusu, Satish Viswanath, Anant Madabhushi, accepted for poster
presentation for BMES 2015 and SPIE 2016.
TREATMENT EVALUATION AND
OUTCOME PREDICTION
Biochemical Recurrence Prediction via Tumor plus Adjacent
Benign Signature
• Improvement in classification of BCR following
combination of features extracted from tumor-field and
adjacent benign field (TABS)
• Identify signatures from benign fields of view in
radical prostatectomy samples for predicting
biochemical recurrence
Presented at USCAP and AUA 2015
Prostate Biochemical Recurrence Prediction via H&E
and Feulgen
• Combination shows better prediction
• H&E and Feulgen features show low correlation
• 30% of radical prostatectomy cases
undergo biochemical recurrence
• H&E describes nuclear and gland
architecture and shape
• Feulgen describes DNA ploidy
Presented at BMES 2015 and NIH Big Data to Knowledge All Hands Meeting 2015
Radiomic features on T2w MRI to predict tumor invasiveness
for pre-operative planning in colorectal cancer
Goal: Identify radiomic features predictive of tumor invasiveness on a restaging MRI in colorectal cancer.
Results: Gabor features were found to be most discriminatory, by capturing microarchitectural oriented
gradient differences between patients with positive and negative circumferential margins (CRM)
(f)
(a) Projection of top 3 PCA features into 3D space for 10 patients: blue circles and red squares represent positive and negative CRM patients, respectively, clustered and
separated by ellipses. (b)(d) Original T2w images with annotation for a single patient from each group (top: positive CRM; bottom: negative CRM). (c)(e) heatmaps of topranked Gabor feature overlaid on the original T2w images. Blue indicates low feature expression, whereas red indicates high feature expression. (f) Boxplots of normalized
feature intensities for top 3 features between positive CRM and negative CRM patients. The difference in intensity distributions between the two classes indicates that Gabor
features appear to express different information between invasive and noninvasive colorectal cancer.
Jacob Antunes , Scott Steele, Conor Delaney, Joseph Willis, Justin Brady, Rajmohan Paspulati, Anant Madabhushi, Satish Viswanath. “Radiomic features on T2w MRI to
predict tumor invasiveness for pre-operative planning in colorectal cancer.” Abstract pending submission for International Society for Magnetic Resonance in Medicine, 2016.
Feature Importance in Nonlinear Embeddings (FINE)
Identifies Histo-morphometric Features that Predict
Cancer Outcome Following Treatment
• FINE identifies glandular architecture as important for predicting the risk of
biochemical recurrence of prostate cancer
• FINE identifies nuclear area as an excellent predictor OncotypeDX risk scores for
breast cancer recurrence
S Ginsburg, et al. Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology. IEEE TMI available online.
Predicting Active Surveillance candidates from
Prostate cancer biopsies
• Very low risk (VLR) prostate cancer
- GS 6
- low PSA
- low tumor stage
• Figure 2: A combined classifier consisting of statistically
significant (p < 0.05) features demonstrated 0.78 AUC
• Some in cohort showed
• unfavorable outcomes
- PSA rising
- Upgrade in Gleason score
Presented at USCAP and AUA 2015
Radiomic Markers Across Tumor Habitat on Treatment-Naïve
MRI can Predict Survival in GBM Patients
Distinguishing STS from LTS using Textural
Features
(a)
(d)
(b)
(e)
Necrosis
Top 10
Tumor
Top 10
Edema
Top 10
All Top
10
(c)
(f)
Gd-T1 MRI for STS (a) and LTS (d). Compartmental
segmentations for necrosis, tumor, edema (b, e).
Qualitative per-voxel representation of Haralick Entropy
feature (c, f).
Tiwari et al., RSNA (2015)
Histomorphometric features for predicting
recurrence in early stage lung cancers
AUC = 0.85
(a)
(c)
(e)
Figure 3
Figure 2
(b)
(d)
(f)
Figure 1
Original images of (a) recurrence and (b) non-recurrence patients,
haralick, intensity correlation texture, feature image of (c) recurrence
and (d) non-recurrence patients, cell cluster graph feature images of (e)
recurrence and (f) non-recurrence patients.
Training
Validation
Total Number
Positive
44
51
95
Negative
40
66
106
Table1
Total Number
84
117
201
Characteristic
Gender
Male
Female
T Pathological
T1/T2
T3/T4
N Pathological
N0/N1
N2/N3
Major clinical and pathological features by classif result
Entire Vali Set =
Classi Non-Recur =
Classi Recur =
117,
45,
72,
N(%)
N(%)
N(%)
P
60(51.3)
57(48.7)
27(60.0)
18(40.0)
33(45.8)
39(54.2)
0.1832
71(60.7)
46(39.3)
31(68.9)
14(31.1)
40(55.6)
32(44.4)
0.176
77(65.8)
40(34.2)
31(68.9)
14(31.1)
46(63.9)
26(36.1)
0.689
Table2
Table 1. Dataset summary; Table 2. Clinical and pathological features on entire validation set and by classification of 12 hitomorphometric
features. No Features show significant correlation with classification result. Figure 2. ROC plot on Validation set Figure 3. Kaplan-Meier curve by
classification results show good separation and log-rank test show statistically different
Xiangxue Wang, Andrew Janowczyk, Yu Zhou, Sagar Rakshit, Vamsidhar Velcheti, Anant Madabhushi*, “Computer extracted features of nuclear morphology
from digital H&E images are predictive of recurrence in stage I and stage II non-small cell lung cancer” accepted by USCAP 2016 Annual Meeting
COMPUTER AIDED DIAGNOSIS AND PROGNOSIS
Breast Cancer Recurrence Risk Categories and Automatic
Mitotic Detection Algorithms Based on Deep Learning
Experimental Setup
- 54 whole slide images (patients) were used on this study
- The set was split in 39 low Oncotype DX patients (ODX score
ranging 1 to 17) and 15 high scoring patients ( ODX score
ranging 31 to 69)
- Results were correlated with Oncotype Dx (ODX). A genomic
test commonly used to predict cancer aggressiveness
Results
The mitotic information of each case is organized on a histogram
of mitotic counts. Then, each histogram is treated as a feature
vector to train a SVM classifier. There are 54 feature vectors.
The data points are centered at their mean, and scaled to have
unit standard deviation, before training.Using a leave one out
scheme, a 87% accuracy on the HH-LL dataset was obtained
Low ODX and mBR score
High ODX and mBR score
Classified as Low Risk
36
4
Classified as High Risk
3
11
Romo-Bucheli, D., Janowczyk, A., Romero E., Gilmore, H. and Madabhushi A.. “Prediction of Breast Cancer Recurrence Risk Categories Using Automatic Mitotic Detection Algorithms
Based on Deep Learning”. Accepted USCAP meeting, 2016.
A Novel Computer-Assisted Approach for Prostate
Cancer Diagnosis on T2W MRI
• The approach well solves the
crucial problem of extracting
computerized image features at
multiple scales from T2w MRI for
prostate cancer diagnosis.
• It suggests a higher accuracy and
lower
computational
time
compared to using T2w MRI
intensity and exhaustive multiscale features.
Haibo Wang, Satish Viswanath, Asha Singanamalli, and Anant Madabhushi, A Novel Computer-Assisted Approach for
Prostate Cancer Diagnosis on T2w MRI, ISMRM 2015, Toronto, Ontario, Canada.
Identifying in vivo DCE MRI Markers of Prostate
Cancer Risk
•
•
Examined correlations between DCE MRI kinetic features and pathological prognostic markers, Gleason
scores and microvessel architecture.
Washout gradient and enhancement ratio were found to be correlated with microvessel architectural features
(max rho = -0.62, -0.52), and were predictive of low Gleason scores (AUC = 0.77, 0.78)
Singanamalli, A., Rusu, M., Sparks, RE., Shih, NC., Ziober, A, Wang L, Tomaszewski, J., Rosen, M., Feldman, M., and Madabhushi, A., “Identifying in vivo
DCE MRI Markers Associated with Microvessel Architecture and Gleason Grades of Prostate Cancer.” Journal of Magnetic Resonance Imaging 2015
Computer-extracted features can distinguish noncancerous confounding
disease from prostatic adenocarcinoma at multiparametric MR imaging.
Pathologic-MR imaging mapping procedure in two patients. (a) MR image (left),
pathologic image (center), and MR image overlaid with pathologic image (right)
show prostate cancer (yellow line). The large lesion has a Gleason score of 3 + 4,
and the other two lesions have a Gleason score of 3 + 3. Inflammation (green
line), PIN (blue line), and atrophy (orange line) are also seen.
Geert Litjens, Robin Elliott, Natalie Shih, Michael Feldman, Thiele
Kobus, Christina Hulsbergen – van de Kaa, Jelle Barentsz, Henkjan
Huisman and Anant Madabhushi. Radiology, 2015
Feature maps of the top three selected features for atrophy, BPH, PIN, and
inflammation (cf Table 6) show cancer, with low, intermediate, and high grades
grouped together (red line), and the specific benign class (yellow line). The axial
T2-weighted image is provided as a reference (left-most column). The selected
features provide a good contrast between cancer and the specific benign class.
Computer Extracted Texture Features on Multi-parametric MRI Differentiate Risk
Categories of Prostate Cancer Patients on Active Surveillance
PCa-positive region annotated by expert
radiologist
Cases identified by
Exp
QDA heatmap for PIRADS-positive, biopsy-positive case indicating correctly detected
tumor (red outline)
• 10 Radiomic features used to create probability
maps; evaluated for cancer presence
• Experiment 1: Radiomic features on par with
PIRADS in identifying clinically significant
disease.
• Experiment 2: Radiomic features better than
PIRADS in identifying absence of disease
1
2
Type
Total
PIRADS
QDA
maps
1,2
4,5
-
+
Biopsy +
(≥ 3+4)
19
4
15
0
19
Biopsy +
(≤ 3+3)
13
12
1
9
4
Biopsy -
10
0
10
9
1
Comparing PIRADS assessment to featurebased QDA map assessment
• Radiomic features PPV = 0.76, NPV = 0.93;
52% higher than PIRADS.
Ahmad Algohary, Satish Viswanath, Prateek Prasanna, Shivani Pahwa, Vikas Gulani, Daniel Moses, Ronald Shnier, Maret Böhm, Anne-Maree Haynes, Phillip Brenner, Warick Delprado, James
Thompson, Marley Pulbrock, Phillip Stricker, Lee Ponsky, Anant Madabhushi. AUA 2015
SOFTWARE
AutoStitcherTM: An automated program for accurate reconstruction
of digitized whole histological sections from tissue fragments
• Stitch using domain constraints
a.
b.
c.
Non-Overlap
Region-Matching
Non-Overhang
Stitched High Resolution Result
Low Resolution Initialization
Oral Presentation, USCAP 2015, Boston MA
Gregory Penzias, Andrew Janowczyk, Asha Singanamalli, Mirabela Rusu, Natalie Shih, Michael Feldman, Satish Viswanath and
Anant Madabhushi
HistoView
A robust, user-friendly application for pathology image analysis and computer-aided
annotation.
• HistoView supports whole-slide
images and enables high speed
calculation on pyramid image.
• HistoView provides a variety of
automatic segmentation methods
based on existing famous
algorithms: color deconvolution
and CCIPD’s literature: HNCuts,
Deep Learning segmentation, etc.
• HistoView allows the pathologist
to make accurate and rapid
annotation based on the
segmentation result from different
algorithms with classical painting
tools.
Windows Caffe platform
• Windows Caffe platform enables
the user, in an all-in-one solution,
to train DL classifiers and
generating segmentation result
using Caffe framework.
• Training includes four steps: patch
extraction, cross-validation
creation, database creation and
training for DL classifier.
• Testing provides three pre-trained
classifiers: nuclear segmentation,
mitosis detection and epitheliumstroma segmentation.
• Windows Caffe platform is
continuously maintained with new
pre-trained models being added
regularly to the mdoel zoo.
RadCAD Ver. 3
 Emerged from the inhouse developed software
ProstaCAD
 ProstaCAD is a Tumor
Detection Software for T2
Images developed at
CCIPD.
 New MRI protocols are
now supported in addition
to T2w
 Now 4D volume images
from Dynamic Contrast
Enhancement is
supported.
 New Tumor classification
Radiomic features were
added (e.g. Coliage)
Peer Reviewed Publications for 2015
Books

Gurcan, M, Madabhushi, A, Medical Imaging 2015: Digital Pathology (Proceedings Volume),
Proceedings of International Society of Optics and Photonics (SPIE) Volume: 942001;
doi:10.1117/12.2193831
Journal Papers
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Bhargava, R and Madabhushi, A, “Emerging Themes in Image Informatics and Molecular Analysis for
Digital Pathology”, Annual Reviews of Biomedical Engineering 2016, Accepted.
Tiwari, P, Danish, S, Madabhushi, A, “Computerized texture features on MRI capture early treatment
response following laser ablation for neuropathic cancer pain: Preliminary findings”, Journal of Medical
Imaging, In Press.
Varadan, V, Kamalakaran, S, Gilmore, H, Banerjee, N, Janevski, A, Miskimen, K, Williams, N,
Basavanhally, A, Madabhushi, A, Lezon-Geyda, K, Bossuyt, V, Lannin, D, Abu-Khalaf, M, Sikov, W,
Dimitrova, N, Harris, L, “Brief-exposure to preoperative bevacizumab reveals a TGF-β signature
predictive of response in HER2-negative breast cancers”, Int J Cancer. 2015 Aug 18. doi:
10.1002/ijc.29808. [Epub ahead of print] (PMID: 26284485).
Xu, J, Xiang, L, Liu, Q, Gilmore, H, Wu, J, Tang, J, Madabhushi, A, “Stacked Sparse Autoencoder
(SSAE) for Nuclei Detection on Breast Cancer Histopathology images”, IEEE Transactions on Medical
Imaging, 2015 Jul 20. [Epub ahead of print] (PMID: 26208307).
Rusu, M, Wang, H, Golden, Thea, Gow, A, Madabhushi, A, “Framework for 3D histologic
reconstruction and fusion with in vivo MRI: Preliminary results of characterizing pulmonary
inflammation in a mouse model”, Medical Physics, 42(8):4822. doi: 10.1118/1.4923161., (PMID:
26233209).
Peer Reviewed Publications for 2015
Journal Papers (Contd.)

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
Ginsburg, S, Lee, G, Ali, S, Madabhushi, A, Feature Importance in Nonlinear Embeddings (FINE):
Applications in Digital Pathology, IEEE Transactions on Medical Imaging, 2015 Jul 14. [Epub ahead of
print] PMID: 26186772).
Litjens, G, Huisman, H, Elliot, R, Shih, N, Feldman, M, Viswanath, S, Bomers, J, Madabhushi, A,
“Computer-extracted features can distinguish benign confounding disease from prostatic
adenocarcinoma on multi-parametric MRI”, Radiology, 2015 Jul 17:142856. [Epub ahead of print]
(PMID: 26192734).
Shridar, A, Doyle, S, Madabhushi, A, “Content-Based Image Retrieval of Digitized Histopathology in
Boosted Spectrally Embedded Spaces”, Journal of Pathology Informatics, Jun 29;6:41. doi:
10.4103/2153-3539.159441. eCollection 2015 (PMID: 26167385).
Singanamalli, A, Sparks, R, Rusu, M, Shih, N, Ziober, A, Tomaszewski, J, Rosen, M, Feldman, M,
Madabhushi, A, “Identifying in vivo DCE MRI markers associated with Microvessel Architecture and
Gleason Grades of Prostate Cancer—Preliminary Findings”, Journal of Magnetic Resonance Imaging,
doi: 10.1002/jmri.24975. [Epub ahead of print] (PMID:26110513).
Basavanhally, A, Viswanath, S, Madabhushi, A, “Predicting Classifier Performance with Limited
Training Data: Applications to Computer-Aided Diagnosis in Breast and Prostate Cancer”, PLOS One,
2015 May 18;10(5):e0117900. doi: 10.1371/journal.pone.0117900. eCollection 2015.
(PMID:25993029).
Xu, J, Xiang, L, Wang, G, Ganesan, S, Feldman, M, Shih, N, Gilmore, H, Madabhushi, A, “Sparse
Non-negative Matrix Factorization (SNMF) based Color Unmixing for Breast Histopathological Image
Analysis”, Computerized Medical Imaging and Graphics (CMIG), pii: S0895-6111(15)00077-4. doi:
10.1016/j.compmedimag.2015.04.002. [Epub ahead of print] (PMID: 25958195).
Peer Reviewed Publications for 2015
Journal Papers (Contd.)


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, Mar;42(3):1153-63. doi: 10.1118/1.4905104.
(PMID:25735270)
Hoimes, C, Madabhushi A, “Editorial Comment on Biparametric Prostate MRI in Prostate Cancer
Detection”, Journal of Urology, In Press
Peer Reviewed Publications for 2015
Peer-reviewed Conference Papers
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Cruz-Roa, A, Arevalo, J, Judkins, A, Madabhushi, A, Gonzalez, F, “A method for Medulloblastoma
Tumor Differentiation based on Convolutional Neural Networks and Transfer Learning, Proc. SPIE,
11th International Symposium on Medical Information Processing and Analysis, Accepted.
Janowczyk, A, Doyle, S, Gilmore, H, Madabhushi, A, “A resolution adaptive hierarchical deep learning
scheme applied to nuclear segmentation in histology images”, Workshop on Deep Learning, Medical
Image Computing and Computer-Assisted Intervention (MICCAI) 2015, Accepted.
Otálora, S, Cruz-Roa, A, Arevalo, J, Atzori, M, Madabhushi, A, Judkins, AR, González, F, Müller, H,
Depeursinge, A. “Anaplastic Medulloblastoma tumor differentiation by combining Unsupervised
Feature Learning and Riesz Wavelets for histopathology image representation”, Medical Image
Computing and Computer-Assisted Intervention (MICCAI) 2015, pp. 581-588.
Cruz Roa, A, Xu, J, Madabhushi, A,“ A note on the stability and discriminability of graph-based
features for classification problems in digital pathology”, Proc. SPIE 9287, 10th International
Symposium on Medical Information Processing and Analysis, 928703 (January 28, 2015);
doi:10.1117/12.2085141.
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.
Li, L, Rusu, M, Viswanath, S, Madabhushi, A, Multi-modal co-registration via multi-scale textural and
spectral embedding representations, The International Society for Optics and Photonics (SPIE)
Medical Imaging, 2015.
Romo-Bucheli, DE, Janowczyk, A, Romero, E, Madabhushi, A, Automated tubule nuclei quantification
on ER+ breast cancer images: Comparison with Oncotype DX risk categories, The International
Society for Optics and Photonics (SPIE) Medical Imaging, 2015.
Peer Reviewed Publications for 2015
Peer-reviewed Conference Papers (Contd.)

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


Leo, P, Lee, G, Madabhushi, A, Evaluating stability of histomorphometric features across scanner and
staining variations: predicting biochemical recurrence from prostate cancer whole slide images, The
International Society for Optics and Photonics (SPIE) Medical Imaging, 2015.
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.
Romo-Bucheli, DE, Janowczyk, A, Romero, E, Madabhushi, A, Automated tubule nuclei quantification
on ER+ breast cancer images: Comparison with Oncotype DX risk categories, The International
Society for Optics and Photonics (SPIE) Medical Imaging, 2015.
Leo, P, Lee, G, Madabhushi, A, Evaluating stability of histomorphometric features across scanner and
staining variations: predicting biochemical recurrence from prostate cancer whole slide images, The
International Society for Optics and Photonics (SPIE) Medical Imaging, 2015.
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.
Peer Reviewed Publications for 2015
Peer-reviewed Abstracts

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



Wang, X, Janowczyk, A, Sagar, R, Velcheti, V, Madabhushi, A, “Computer Extracted Features of
Nuclear Morphology from Digital H&E Images Are Predictive of Recurrence in Stage I and II NonSmall Cell Lung Cancer”, United States and Canadian Academy of Pathology's 105th Annual Meeting,
Salt Lake City, UT, March 12-18, 2016.
Romo, D, Janowczyk, A, Gilmore, H, Romero, E, Madabhushi, A, “Prediction of Breast Cancer
Recurrence Risk Categories Using Automatic Mitotic Detection Algorithms Based on Deep Learning”,
United States and Canadian Academy of Pathology's 105th Annual Meeting, Salt Lake City, UT, March
12-18, 2016.
Janowczyk, A, Basavanhally, A, Madabhushi, A, “Stain Normalization in Digital Pathology Images
Using Deep Learning”, United States and Canadian Academy of Pathology's 105th Annual Meeting,
Salt Lake City, UT, March 12-18, 2016.
Janowczyk, A, Doyle, S, Gilmore, H, Madabhushi, A, “Fully Automated, Accurate, and Efficient
Segmentation of Cancer Nuclei in Breast Pathology Images”, United States and Canadian Academy of
Pathology's 105th Annual Meeting, Salt Lake City, UT, March 12-18, 2016.
Leo, P, Lee, G, Madabhushi, A, Evaluating Reproducibility of Computer Extracted Histologic Image
Features for Predicting Biochemical Recurrence in Prostate Cancer: A Multi-Site, Multi-Scanner Study,
United States and Canadian Academy of Pathology's 105th Annual Meeting, Salt Lake City, UT, March
12-18, 2016.
Ali, S, Rimm, D, Ganesan, D, Madabhushi, A, “Local nuclear architecture features from H&E images
predict early versus distant recurrence in lymph node negative, ER+ breast cancers”, 2015 San
Antonio Breast Cancer Symposium, December 8-12, 2015 in San Antonio, Texas, (Poster).
Tiwari, P, Prasanna, P, Patel, J, Madabhushi, A, “Computer extracted texture descriptors from different
tissue compartments within the tumor habitat on treatment-naïve MRI predict clinical survival in
glioblastoma patients”, Society of Neurooncology, 2015, Accepted.
Peer Reviewed Publications for 2015
Peer-reviewed Abstracts (Contd.)
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Prasanna, P, Siddalingappa, A, Wolansky, L, Rogers, L, Tai-Chung Lam, V, Madabhushi, A, Tiwari, P,
“Morphologic heterogeneity at a pixel-level captured via entropy of gradient orientations on T1-post
contrast MRI enables discrimination of tumor recurrence from cerebral radiation necrosis”, Society of
Neurooncology, 2015, Accepted.
Tiwari, P, Patel, J, Partovi, S, Prasanna, P, Madabhushi, A, “Computer extracted texture descriptors from
different tissue compartments within the tumor habitat on treatment-naïve MRI predict clinical survival in
glioblastoma patients”, Proceedings of the Radiologic Society of North America, 2015, Accepted.
Rusu, M, Orooji, M, Gilkeson, R, Prabhakar, R, Yang, M, Jacono, F, Linden, P, Madabhushi, A,
“Accurate co-registration of ex vivo histology and in vivo CT in ground glass nodules enables the
identification of computer extracted textural features to predict extent of invasion”, Proceedings of the
Radiologic Society of North America, 2015, Accepted.
Li, L, Rusu, M, Madabhushi, A, “Texture based similarity measure for multi-modal co-registration”,
Biomedical Engineering Society (BMES), 2015.
Agrawal, N, Basavanhally, A, Viswanath, S, Madabhushi, A, “Predicting Classifier Performance with
Limited Training Data: Validation on the ADNI Dataset”, Biomedical Engineering Society (BMES), 2015.
Gawlik, A, Lee, G, Whitney, J, Epstein, J, Veltri, R, Madabhushi, A, “Computer Extracted Nuclear
Features from Feulgen and H&E Images Predict Prostate Cancer Outcomes”, Biomedical Engineering
Society (BMES), 2015.
Golden, T, Massa CB, Rusu M, Wang H, Madabhushi, A, Gow AJ, Structural and Functional Modeling Of
Chronic Lung Inflammation: Loss of Function Mechanisms, Experimental Biology 2015
Viswanath S, Crawshaw, B, Willis J, Delaney C, Paspulati R and Madabhushi, A “Computational
Radiology-Pathology Fusion for Improved Treatment Planning and Patient Selection in Rectal Cancer”
3rd World Rectal Conference on Organ preserving Perspectives, Montreal, Canada, 2015.
Peer Reviewed Publications for 2015
Peer-reviewed Abstracts (Contd.)

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



Wang, H, Viswanath, S, Singanamalli, A, Madabhushi, A, A Novel Computer-Assisted Approach for
Prostate Cancer Diagnosis on T2w MRI, Annual Meeting of the International Society for Magnetic
Resonance in Medicine (ISMRM), Toronto, Canada, 2015.
Prasanna, P, Tiwari, P, Siddalingappa, A, Wolansky, L, Rogers, L, Lam, TC, To, V, and Madabhushi, A,
Study of contrast-enhanced T1-w MRI markers of cerebral radiation necrosis manifested in head-andneck cancers, primary, and metastatic brain tumors: Preliminary findings, Annual Meeting of the
International Society for Magnetic Resonance in Medicine (ISMRM), Toronto, Canada, 2015.
Antunes, J, Viswanath, S, Rusu, M, Vallis, L, Avril, N, Hoimes, C, Madabhushi, A, Quantitative Analysis
of Multi-parametric FLT-PET/MRI in Evaluating Early Treatment Response in Renal Cell Carcinoma,
Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM), Toronto,
Canada, 2015.
Viswanath, S, Yim, CC, Bloch, NB, Rosen, M, Kurhanewicz, J, Madabhushi, A, A multi-site study to
develop a new pseudo-quantitative T2w MRI map for prostate cancer characterization: Preliminary
findings, Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM),
Toronto, Canada, 2015.
Algohary, A, Viswanath, S, Prasanna, P, Pahwa, S, Gulani, V, Ponsky, L, Stricker, P, Moses, D, Shnier,
R, Madabhushi, A, Quantitative assessment of T2-weighted MRI to better identify patients with prostate
cancer in a screening population, American Urologic Association (AUA), 2015.
Lee, G, Veltri, R, Ali, S, Epstein, J, Christudass, C, Madabhushi, A, Prostate cancer recurrence can be
predicted by measuring nuclear organization and shape parameters in adjacent benign regions on
radical prostatectomy specimens, American Urologic Association (AUA), 2015.
Lee, G, Veltri, R, Zhu, G, Carter, B, Landis, P, Epstein, J, Madabhushi, A, Quantitative
Histomorphometric Analysis of Prostate Biopsy Images Predict Favorable Outcome in Active
Surveillance Patients, American Urologic Association (AUA), 2015.
Peer Reviewed Publications for 2015
Peer-reviewed Abstracts (Contd.)

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



Zhu, G, Lee, G, Davis, C, Kagohara, LT, Epstein, JI, Landis, P, Carter, BH, Madabhushi, A, Veltri, R.
Prediction of favorable and unfavorable biopsy pathology results of active surveillance patients using
nuclear morphometry and molecular biomarkers, American Association for Cancer Research, 2015.
Veltri, R, Ali, S, Lin, W-C, Zhu, G, Epstein, JI, Li, C-C, Madabhushi, A, Cancer Histologic and Cell
Nucleus Architecture differentiate Prostate Cancer Gleason Patterns, American Association for Cancer
Research, 2015.
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, R, 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.
Peer Reviewed Publications for 2015
Peer-reviewed Abstracts (Contd.)



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.
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.
Non-Peer Reviewed Publications for 2015
Non-Peer Reviewed Abstracts





Prasanna P, Tiwari P, Madabhushi A. Co-occurrence of Local Anisotropic Gradient Orientations
(CoLIAGe): Novel radiomic feature descriptors for brain and breast cancer characterization on MRI,
Data and Life Science Symposium, Case Western Reserve University, 2015
Romo-Bucheli, D., Janowczyk, A., Romero, E., Gilmore, H., Madabhushi, A. Correlating Computer
Extracted Features from Tubules on ER+ Breast Cancer Images with Oncotype DX Risk Categories,
Data and Life Science Symposium, Case Western Reserve University, 2015
Orooji M, Alilou M, Rajiah P, Velcheti V, Yang M, Jacono F, Gilkeson R, Linden P, Madabhushi, A. A
Combination of Shape and Texture Features Enables Discrimination of Benign Fungal Infection from
Non Small Cell Lung Cancer on Chest CT, Data and Life Science Symposium, Case Western Reserve
University, 2015
Wang X, Janowczyk A., Velcheti V., Madabhushi, A. Computer extracted Nuclear features predict
recurrence in stage I, stage II non-small cell lung cancer, Data and Life Science Symposium, Case
Western Reserve University, 2015
Madabhushi, A, Viswanath, S, Tiwari, P, Rusu, M, Singanamalli, A, Orooji, M, Algohary, A, Lee, G,
Janowyzk, A, Ali, S, Ginsberg, S, Prasanna, P, Penzias, G, "Center for Computational Imaging and
Personalized Diagnostics", Research ShowCASE, April, 2015.
INTERESTED IN JOINING CCIPD?
We are always looking for enthusiastic and
motivated graduate, undergraduate students, postdoctoral and research scientists.
If you think you would be a good fit for CCIPD, send
over your complete CV and 3 representative
publications to “anantm” @ “case.edu”
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