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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