liz marai 01/30/09 1 computational modeling and visualization for science liz marai computer science

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Computational Modeling and Visualization for

Science

Liz MaraiComputer Science

http://vis.cs.pitt.edu

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What is Computer Science?

• (… the study of computers?)

• (… the art of programming?)

Edsger Dijkstra: "Computer science is no more about computers than astronomy is about telescopes."

Hint (early ‘computer scientist’ names): turingineer, turologist, flow-charts-man, applied meta-mathematician, comptologist, datalogist, computics specialist, informatik specialist

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Computer Science

• “the study of information and computation, and their implementation and application in computer systems“

[collective wisdom of Wikipedia]

• sub-areas emphasize:

– the computation of specific results (e.g., computer graphics)

– properties of computational problems (e.g., computational complexity theory)

– the challenges in implementing computations (e.g.,

programming language theory, human-computer interaction)

• in a nutshell, the study of computation

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The Study of Computation

“Computer Science is a science of abstraction - creating the right model for a problem and devising the appropriate mechanizable techniques to solve it.”

A. Aho and J. Ullman, 1992

“Computer Scientists are engineers of abstract objects”

H. Zemanek, 1975

“The two A-s of computation:

• abstraction (i.e., modeling)

- e.g., 115 pebbles the natural number 115 -> the string (array) of characters ‘115’ or ‘CXV’

• automation (mechanizing the abstraction)

Computing is the automation of abstractions.”

J. Wing, 2008

Director CISE at NSF

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Examples

• MySpace, You Tube are social networks

• DNA sequences are strings (that can be matched)

• Cells as a self-regulatory system are like electronic circuits

• Astronomy multi-dimensional data are KD-trees

Abstraction: graph

Automation: data structures and algorithms

stack queue tree (upside-down)

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Abstraction and AutomationNote: Neither abstraction nor automation are unique to Computer Science

E.g. abstractions in other fields:

Schroedinger’s equation in physics, chemistry;

natural numbers, sets & tables in math etc.

E.g. automation in other fields:

algorithms for long division or factoring in math

automated processes in engineering

(not surprising! Cca 1960: math + electrical engineering -> CS)

But implementing the “automation of abstractions” process as well as studying the properties of this process are traits of computer science.

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Computer Graphics

• Computer graphics generally means creation, storage and manipulation of geometrical models and their images

• Such models come from diverse, often non-CS fields including physical, mathematical, artistic, biological, and even conceptual (abstract) structures

Frame from animation by William Latham, shown at SIGGRAPH 1992. Latham uses rules that govern patterns of natural forms to create his artwork.

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Keyframing smoke

Adrien Treuille (UW)

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Simulating the air flow around a bat wing

M. Kostandov (Brown)

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• Donald Burke, Pitt Public Health

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Pitt Visualization Research Lab

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Interdisciplinary Visualization

ObserveHypothesize(across disciplines)

Visualize Validate Evaluate Explore (across disciplines)

Measure Model Simulate

Insight

[Laidlaw 2005]

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Example projects

• Motion tracking

• Predictive orthopaedics modeling

• The Chinese Room: collaborative machine translation

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Motion tracking

Joint work with Yinglin Sun, MD. Abedul Haque, Scott Tashman, Bill Anderst

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(not too many sample poses – radiation concerns)

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UPMC: Orthopaedic Biodynamics Laboratory

A consecutive sequence of 2-D radiographs

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Tracking motion: problem imaging artifacts -> limited tracking accuracy -> bone collisions

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1 3 4

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Grey-value matching

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Tracking motion: solution(Marai et al.,TMI'06)

Step 1: extract bone outline from one volume image

Step 2: use tissue-classification (neighborhood) to emphasize the bone boundary

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Tracking motion:solutionStep 3: optimize outline position & orientation until it matches the tissue-classified image

(illustrated here in 2D)

∑ −=

n

iTivtIivjTsI

j1

2

)()(min

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Tracking motion: results

grey-value tissue-classif.vs.

collisionno collision

43% error-decrease compared to grey-value matching

Results on marked cadaver data (motion error relative to ground truth, 0 is good)

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Tracking motion: summary● sub-voxel accurate method for tracking bone-motion

from sequences of CT scans ● 43% error-decrease from state-of-the-art technique● 12 volume images in 1.5 hours on 40 processor cluster● enables the analysis of soft-tissue deformation with

motion● results in a wrist motion database of unprecedented

detail

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Inverse-imaging biological structures

Joint work with David Laidlaw, Trey Crisco

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Computational modeling: joint-spacing and cartilage

Idea: cartilage correlates with bone proximity

parameter: p the proximity threshold

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Cartilage maps: results

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1.05mm1.21mmMax

0.276mm0.275mmMin

0.596mm ± 0.20mm0.601mm ± 0.21mmMean±Std.dev.

Non-invasively (kinem.-generated)

Invasively (µCT-imaged)

Cartilage thickness

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Predictive orthopaedic systems

Joint work with David Laidlaw, Trey Crisco, Douglas Moore

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2images

bone surfaces & motion

anatomy book knowledge

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soft tissuegeometry & behavior

visualization & quantification+…

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The push-up debate (Alexis vs. Crystal)

• on your knuckles or not?

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The push-up debate (Crystal wins)

• previously: computationally intractable• CT volume images of one individual• 7 different poses (knuckle-pose included)• computed cartilage contact & ligament lengthening in

each pose• ~48 hrs, single processor• knuckle pose yields maximum contact

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The push-up debate: knuckle-walkers

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DRUJ malunion

Distal radioulnar joint (DRUJ)

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DRUJ malunion

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The Chinese Room

Joint work with Josh Albrecht & Rebecca Hwa

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What does this say?

Machine translations:

• “He utter eyes and not the slightest attention As leakage.”

• “He Zhengzhao eyes, eyes can no leakage.”

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A collaborative approach

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Chinese Room: results

Example:

• MT: “He utter eyes and not the slightest attention As leakage.”

• Chinese Room MT: “His eyes were placed wide-apart; nothing escaped their attention.”

• MT-quality improved on average from 0.35 to 0.53

• the gap between MT and pro bilingual translations reduced by 36.9%

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CS 2620Interdisciplinary Modeling and Visualization

• Pitt CS Teaching Award ’08

• offered Spring’09

• Mon/Wed 11am

• visualization nuts and bolts

• 2nd half: work in small multidisciplinary groups

Image credits: cs2620 alumni J.Albrecht, M.Grabmair, Yl.Sun, J.D.Park, M.Fagerburg

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Contact

• http://vis.cs.pitt.edu

• marai@cs.pitt.edu

• SENSQ 5423

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