Computational Biomedicine Lab: Current Members, pumpsandpipesmdhc

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

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”.

CBL Roadmap New Computational Tools For Scientific Discovery From Algorithm to Bedside / TestBed Research Teams of the Future [email_address]

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

From Algorithm to Bedside

- Alan B. Lumsden and Neil Kleimann

Morteza Naghavi Erling Falk

- Juan Granada

Ippokrateion Hospital

-Manolis Vavuranakis

- Matt Budoff

Joel Morrisett

CS@UH research highlights: people’s hearts and minds [email_address] People’s hearts and minds

Areas

Cardiovascular Informatics

Neuroinformatics

Tissue Modeling & Simulation

A Holistic Approach: Multiple scales Organ System Integrative and personalized biomedicine (prevention, diagnosis, treatment) is multidimensional so that systems approach has to build models based on data from all scale levels Cell Gene

To develop the computational tools to aid physicians in scoring the patients vulnerability and the likelihood of a future coronary event.

Areas

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

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 Results Goal: 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.

4D Analysis of the Coronary Arteries ED Introduction Results Methods

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 performed using an Imatron Electron Beam Computed Tomography scanner 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

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

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

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 Results Methods 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 TP FP FN Challenges CT Artifacts Poor contrast Noisy images Subcutaneous fat Visceral fat Retroperitoneal fat Subject ID Accuracy (%) Subject ID Overlap Ratio (%) Our Method Flexible Threshold Method (FTM)

Intravascular Ultrasound-based Detection of Vasa Vasorum Challenges Results Methods Inter-frame motion Image stabilization & Elastic wall deformation Vasa vasorum (histology) 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-based frame gating Rigid/elastic contour tracking Statistical frame comparison to capture changes due to vasa vasorum perfusion Before Microbubble Injection + Similarity matrix -> Frame similarity space -> Stabilized frame ensembles

Areas

Neuroinformatics

Online Reconstruction and Functional Imaging of Neurons

Statistical Models for Segmentation of Mouse Brain Tissue Slices Containing Gene Expression Data

Tissue Modelling & Simulation

Online Reconstruction and Functional Imaging of Neurons (ORION) Challenges Results Methods 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 Original Volume Morphological Representation Intensity Decay Irregular Shape Noise and Image artifacts Frame Based Denoising Action potential simulation from reconstruction Spatial error: Max: 6.325 voxels Mean: 0.4 voxels Skeletonization and morphological description Segmentation Volume Registration and Frame-Based Denoising 100 µ m

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 Appearance variation Shape variation Missing parts Distorted topography Probability estimate for landmarks Atlas fitted to image

Areas

Neuroinformatics

Tissue Modelling & Simulation

Computer-Assisted Post Mastectomy Breast Reconstructive Surgery

Computer-Assisted Post Mastectomy Breast Reconstructive Surgery

Goal

Develop 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

Background Methods Results

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

Shape Prediction

2D Analytical Model

Finite Element Model

TRAM Implant Shape Modeling Automatic fitting of the parametric model T( s 1 ) 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 17kPa 0.25cm 24kPa 87cc 400Pa 175cc 800Pa 15 kpa Horizontal Deviation Upper Pole Medial Lower Pole Axillary Tail Implant 5kpa TRAM 15kpa T(s 2 ) q( s )

Overview