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Computational Biomedicine Lab: Current Members
• Director– Ioannis A. Kakadiaris
• Research Scientists– Gerd Brunner, Shan Tan
• Ph.D. Students– M. Fang, H. Haberkar, U. Kurkure, D.
Roy, A. Santamaria, G. Toderici, and W. Yang
• M.Sc. Student– R. Yalamanchili and P. Ramesh
• Undergraduate Students– O. Avila Montes and D. Chu
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CBL Mission
• To develop a comprehensive framework that will lead to improved algorithms for analyzing multidimensional data in search of meaningful information. – To allow computers to aid humans in taking full
advantage of the multitude of data sources available through today's technology to extract relevant information in a reliable, accurate, and timely manner.
• To break the barriers of our own specialty and establish solid interdisciplinary teamwork on the basis of “grand challenge problems”.
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New Computational ToolsFor Scientific Discovery
From Algorithmto Bedside / TestBed
Research Teamsof the Future
CBL@UHCS
CBL Roadmap
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Research Teams of The Future: Collaborators• Biologists/Neuroscientists
– Wah Chiu, Baylor College of Medicine– Costa Colbert, UH– Gregory Eichelle, Baylor College of Medicine– Peter Saggau, Baylor College of Medicine
• Computer Scientists– Theoharis Theoharis, Univ. of Athens– Joe Warren, Rice University
• Engineers– Stephane Carlier, CRF– Craig Hartley, BCM– Ralph Metcalfe, UH– K. Ravi-Chandar, Aer. Engineering, UT Austin
• Mathematicians– R. Azencott, E. Papadakis, UH– Ioannis Konstantinidis, U of Maryland
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From Algorithm to Bedside
- Alan B. Lumsden and Neil Kleimann
Morteza NaghaviErling Falk
- Juan Granada
Ippokrateion Hospital-Manolis Vavuranakis
- Matt BudoffJoel Morrisett
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CS@UH research highlights:people’s hearts and mindsCBL@UHCS
People’s hearts and minds
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Areas
• Cardiovascular Informatics• Neuroinformatics• Tissue Modeling & Simulation
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Cell
A Holistic Approach: Multiple scales
Organ
System
Gene
Integrative and personalized biomedicine (prevention, diagnosis, treatment) is multidimensional so that systems approach has to
build models based on data from all scale levels
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Cardiovascular Informatics
To develop the computational tools to aid physicians in scoring the patients vulnerability and the likelihood of a future coronary event.
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Areas
• Cardiovascular Informatics– Left Ventricular Segmentation in MR Images– 4D Analysis of the Coronary Arteries– Automatic Quantification of Abdominal Fat Burden from CT
Data– Intravascular Ultrasound-Based Detection of Vasa Vasorum
• Neuroinformatics• Tissue Modeling & Simulation• Multispectral Biometrics
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Left Ventricular Segmentation in MR Images
Objective: To develop an automated method for computing quantitative indices of ventricular morphology and function from volumetric MR images.
Papillary muscles
Partial voluming
Fuzzy images
Low contrast
Challenges
Methods
LV localization using multiple views, intensity and morphological
information
Myocardial sample region estimation
Hierarchical multi-class multi-feature fuzzy connectedness
Optimal path computation using dynamic programming
Polar transformation
ResultsGoal: To develop a theoretical framework and computational tools to aid physicians in scoring a patient’s vulnerability and the likelihood of a future coronary event.
Segmented end-diastolic myocardium
The ejection fraction computed automatically for 20 subjects has +/-2% of mean bias when compared with manual readings by two experts.
Segmented myocardium (end-diastole to end-
systole)
Segmented end-diastolic myocardium
Impact: Cardiovascular disease (CVD) is the #1 killer in the United States. This work will aid physicians in early diagnosis and treatment planning of CVD.
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4D Analysis of the Coronary Arteries
ED Introduction Results
Methods
1. LAD shape model• Cross sectional plane –
orientation• Parametric curved axis
2. Heart centered coordinate system
3. LAD dynamics: LAD motion is expressed as a composition of three motion primitives:
• LAD longitudinal expansion• LAD radial displacement
(measured from the long axis of the heart)
• LAD twist (w.r.t. the normalized heart’s coordinate system)
Modeling
Objective: To develop the computational tools for shape-motion analysis of the coronary arteries
Experimental Data: All studies were performedusing an Imatron Electron Beam Computed Tomographyscanner on eight asymptomatic volunteers
Background: Coronary heart disease is the leading cause of death in Western nations, claiming approximately 446,000 lives in the United States annually
Challenges
Analysis
1. LAD segmentation
2. Estimation of heart-centered coordinate system
3. Fitting of a deformable model to the LAD
Radial displacement (Subject-3)
Normalized length of the LAD
Base 0.25 .5 0.75 Apex
ED
ES
-5mm -4mm -3mm -2mm -1mm 0mm 1mm 2mm
Longitudinal elongation (Subject-3)
Normalized length of the LAD
Base 0.25 .5 0.75 Apex
ED
ES
-1mm 0mm 1mm 2mm 3mm 4mm 5mm 6mm 7mm 8mm 9mm
Twist (Subject-3)
Normalized length of the LAD
Base 0.25 .5 0.75 Apex
ED
ES
-12 -10 -8 -6 -4 -2 0 2 4 6 8
•Parametric shape-motion model•Global and local deformations
• Registration of coronary artery template• Artery centerline extraction
• Morphology: Coronary arteries are dynamic curvilinear structures with a great degree of variability and tortuosity
• Motion: Complexity of the non-rigid motion of the left ventricle and lack of reference landmarks
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• Training (once):
Feature selection
Construction of an Active Shape Model
template of subcutaneous fat
• Deployment:
Automatic initialization of seed point
using Subcutaneous Fat Template
Compute fuzzy affinity-based object
Threshold the fuzzy affinity object to get
fat burden
(a) Original images (b) Results of FTM (c) Results of our method
Automatic Quantification of Abdominal Fat Burden from CT Data
Goal: To develop the computational tools for automatically estimating total fat burden using Computed Tomography data
ResultsMethods
TP FP FN
Impact: Fat burden is one of the predictors of cardiovascular disease, which is the #1 killer in the United States; this work will aid physicians in its early diagnosis and treatment planning
Objective: To develop an automated method to quantify abdominal fat
Challenges
CT Artifacts Poor contrast Noisy images
Sub
cuta
neo
us fa
t
Visceral fat Retroperitoneal fat
Subject IDA
ccur
acy
(%)
Subject ID
Ove
rlap
Rat
io (
%)
Our Method
Flexible Threshold Method (FTM)
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Intravascular Ultrasound-based Detection of Vasa Vasorum
Challenges
ResultsMethods
Inter-framemotion
Image stabilization &Elastic wall deformation
Vasa vasorum(histology)
Before Microbubble Injection
After Injection
Goal: Early detection of atherosclerotic plaques with a high probability of causing future complications (heart attack or stroke)
Objective: Imaging and quantification of vasa vasorum (microvessels associated with plaque inflammation and vulnerability) through microbubble perfusion analysis
+Video
Multidimensional scaling-basedframe gating
Similarity matrix →Frame similarity space → Stabilized frame ensembles
Rigid/elastic contour tracking
Statistical frame comparison to capture changes due to vasa
vasorum perfusion
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Areas
• Cardiovascular Informatics• Neuroinformatics
– Online Reconstruction and Functional Imaging of Neurons– Statistical Models for Segmentation of Mouse Brain Tissue
Slices Containing Gene Expression Data
• Tissue Modelling & Simulation
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Online Reconstruction and Functional Imaging of Neurons (ORION)
Challenges ResultsMethods
Objective: To produce libraries of neurons that can be used in on-line applications.
Impact: To understand computational principles and cellular mechanisms that underlie brain function, in both normal and diseased states.
Goal: Realtime mapping of functional imaging data (e.g., spatio-temporal patterns of dendritic voltages or intracellular ions) from neuronal structure during the critically limited duration of an acute experiment
100 µm
Original Volume
Morphological Representation
Intensity Decay
IrregularShape
Noise and Image artifacts
Frame BasedDenoising
Action potential simulation from reconstructionSpatial error:
Max: 6.325 voxels Mean: 0.4 voxels
Skeletonization and morphological description
Segmentation
Volume Registration and Frame-Based Denoising
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Statistical Atlas-based Segmentation of Mouse Brain Tissue Slices Containing Gene Expression Data
Objective: Automatically and accurately annotate anatomical regions in mouse brain tissue sections revealing gene expression patterns
Methods
Anatomical landmarks…
…and region boundary information.
Results
Challenges
Distorted topography
Before fitting
After fitting
…hybrid atlas at multiple resolutions, including shape…
Goal: Mapping of gene expression patterns at different developmental stages in the context of mouse brain anatomy
Comparison with manual annotation
Impact: Studying gene expression patterns in the mouse brain will greatly enhance our understanding of the function and diseases of the human brain
Appearancevariation
Shape variation
Missing parts
Distortedtopography
Overlap Ratio
0
20
40
60
80
100
< 0.5 0.5-0.6 0.6-0.7 0.7-0.8 0.8-0.9 0.9-1.0
Ranges of Overlap Ratio
% o
f Im
ages
Seg
men
ted
……
Midbrain
Medulla
Pons
Cortex
Probability estimate for landmarks
Atlas fitted to image
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Areas
• Cardiovascular Informatics• Neuroinformatics• Tissue Modelling & Simulation
– Computer-Assisted Post Mastectomy Breast Reconstructive Surgery
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Computer-Assisted Post Mastectomy Breast Reconstructive Surgery
Background Methods Results
GoalDevelop a system that will enable
• a surgeon to plan a breast reconstructive surgery using patient-specific data
• a tissue engineer to obtain design parameters (surface area, volume, cell number, 3D Scaffold shape).
• a patient to visualize possible outcomes
Current Practice
Trial and error process• Depends heavily on the
-experience- training-artistic and surgical skills of the practitioner
• The patient does not know the final result
Shape Modeling
• Parametric Deformable Breast Model with global deformations
Horizontal Deviation
Upper Pole Medial
Lower Pole
AxillaryTail
Shape Prediction
• 2D Analytical Model
• Finite Element Model
TRAMImplant
Shape Modeling
Automatic fitting of the parametric model
Implant 5kpaTRAM 15kpa
T(s2)
q(s)
T(s1)
Deformation Parameters
Upper Pole (-1.543, -6.915, 1.915, -2.128)Lower Pole (0.213, -0.160)Horizontal Deviation (0.160, 0.000)Medial (0.319, -1.489)Axillary Tail (0.160, -0.372)
Shape Prediction
• 2D Analytical Model
• Finite Element Model
1cm
17kP
a
0.25c
m
24kPa
87cc
400P
a
175cc
800Pa15 kpa
Overview
Biomedical Sciences & EngineeringOverview of CBL• Role of UH
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Data Availability
TodayNear Future
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Analysis
What we need now
What we will need in the future
Current technology
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New Computational ToolsFor Scientific Discovery
From Algorithmto Bedside / TestBed
Research Teamsof the Future
CBL@UHCS
Roadmap
Ask UH
• Email: Ioannis Kakadiaris (ioannisk@uh.edu)
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Contact Us
Computational Biomedicine Labhttp://www.cbl.uh.edu/
Prof. Ioannis A. Kakadiarishttp://www.cbl.uh.edu/~ioannisk
ioannisk@uh.edu
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