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Visualization in Medical Education

Mai H. El-Shehaly

CS 6603: Reinventing CS education through the eTextbookSpring 2012

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1) Computer-based education relies on CS research:

◦ Image processing◦ Database systems◦ Massive Model Processing◦ Programming GPUs◦ 3D visualization

2) Experience from another discipline 3) Research in medical data visualization

serves other massive models

Why should we care ?

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For Physicians, education is a lifelong process

Medical practice changes rapidly

Many specialties require recertification every few years

Motivation

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The primary purpose of medical education is to teach: ◦ Problem solving (diagnosis)◦ Problem management (therapy)

The goal of visualization is to make learning more efficient and engaging

Goals

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Bassett Collection of Stereoscopic Images of Human Anatomy (1948 – 1965)

More than 1,550 images of human dissections Atlas of Human Anatomy, 1962 (24- volumes)

A bit of History

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The “Visible Human Project”: (1989-1995)◦ A dataset of cross-sectional photographs of

human body◦ Purpose: anatomy visualization ◦ Male cadaver 1,871 slices ( 1mm - 65 GB)◦ Female cadaver 0.33 mm – 40 GB

A bit of History

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Visible Human Project

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Ethical concerns University of Vienna demanded that the

images be withdrawn: medical profession should have no association with executions, and that the donor's informed consent could be scrutinised

Inaccuracies with the dataset◦Freezing, formalin injection, and slicing

damaged: brain, blood vessels, missing organs, etc

What’s wrong with the Visible Human ?

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Healthcare students have difficulties:◦ Achieving conceptual understanding of 3D

anatomy◦ Hard to address misconceptions about

physiological phenomena

Cadaver vs. live body

Problem

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Need to study tissue undergoing certain natural processes

Complex physiological processes

Concepts of functional anatomy are poorly conveyed via textbooks and figures

Are still images enough ?

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Medline search (Medical literature analysis and retrieval system) for publications discussing e-learning in radiology

A review of 38 human studies

Different visualization and interaction techniques

E-learning and education in radiology [2011]

[1] Antonio Pinto, Luca Brunese, Fabio Pinto, Ciro Acampora, Luigia Romano, E-learning and education in radiology, European Journal of Radiology, Volume 78, Issue 3, June 2011, Pages 368-371, ISSN 0720-048X,

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1. Impact of the Internet on education: ◦ From personal collections to hospital libraries

◦ Web sites that show series of radiographs or CT images with every anatomic or pathologic finding labeled are NOT of great educational value.

◦ Web site design to emulate “hot seat” session

◦ Wiki sites encourage collaboration

E-Learning in Radiology

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3. Computer-based simulator:◦ Case-based education improves students problem

solving ability

◦ In an ideal simulator:

Functionality of a real picture archiving and communication system

Immediate and long-term feedback

E-Learning in Radiology

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2. Software technologies to implement e-learning:

◦ Educator-centric LMS acts as a superset for electronic material

◦ Teleconferencing + image databases

◦ Hypermedia documents

E-Learning in Radiology

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Small-group learning: PBL vs. CBL

PBL

•Open Inquiry Approach

•Focus on discovery by learner

• Start with Clinical case

•Minimal educator role

•Stimulate post-session reading and exploration

CBL

•Guided Inquiry Approach

•Focus on problem solving

• Start with Clinical case + advance preparation

•Facilitator asks guiding questions

•Minimal post-session work

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Which is better ?PBL CBL

Pros Encourage lifelong learning Simulate clinical practice Encourage curiosity Broader understanding of complexity of medicine

Encourage debate, discussion, and exploration of ambiguity Provide structure Efficient Goal-directed

Cons Time-inefficient Frustrating Leads to errorneous conclusions

Faculty are more encouraged to lecture than facilitate Spoon-feeding mentality

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CBLPBL

89 %

11%

Which one do students favor ?

Comparing problem-based learning with case-based learning: effects of a major curricular shift at two institutions. [2007]

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Case Studies

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10 radiology cases + a voluntary weekly lecture 225 students randomly assigned to 4 groups:

• Group A: Computer-based cases + Interactive elements• Group B: Computer-based cases + no interaction• Group C: Paper-based cases + interactive elements• Group D: Control group no cases

Do Computers Teach Better? A Media comparison study for case-based teaching in radiology

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Do Computers Teach Better ?

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Do Computers Teach Better ?

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Do Computers Teach Better ?

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A project at Linkoping university, Sweden Aims:

◦ Develop 3D visualizations & integrate them in different learning situations

◦ Enhance our knowledge about educational value of 3D visualizations in education

Advanced 3-D Visualization in Student-centered Medical Education [2008]

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Based on: cognitive psychology, social constructivism

Students work with scenarios in small groups (6 – 9 students & a tutor)

Identify problems and learning objectives Resource sessions, seminars, self-studies,

practice in professional domain, etc..

Student – Centered Education

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Scenarios based on authentic situations serve as a meaningful context for learning

Learner’s processing of information:

◦ Posing questions◦ Looking for answers◦ Analyzing and reflecting

Problem-Based Learning

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In Fall 2005:Students were introduced to images and films in

an Internet-based scenario:1. Rotating CT image of the heart2. MRI movie of the pumping heart

Pedagogical aim: To challenge the students’ interpretation of the

visualizationsTrigger their formulation of learning needs

Learning Situations in the medical program

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In Spring 2006:A lecture given by a radiologist and physiologist,

using an advanced rendering workstation to show planar and 3D images of the heart:Clinically important anatomical relationsVariations of normal behavior

A demo in the VR theatre: 4D MRI images of pumping heart

Self-study of volume-rendering images stored in QTVR format

Learning Situations in the medical program

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Aim of the lecture and demo was to present difficult phenomena using advanced technology not possible for students to handle alone

Interactive QTVR images of CT datasets were run as self-study on students’ PCs

Learning Situations in the medical program

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Results

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Results

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Results

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Challenges &

Limitations

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1- Size

Stored data

Stored data

Stored data

Stored model of size O(M ∞ )

Displayed subset of size O(N < M)

Interactive rates 10-100 Hz

Limited Bandwidth

Access Times

Output Sensitive

Techniques

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Rasterization:

2- Rendering Techniques

Ray Tracing:

Problem: Both are brute-force methods with linear

complexity

AccelerationStructures

SolutionsAccelerationStructures View-

dependent Rendering

Memory Managemen

t

• Spatial Indexing• Multiresolution structures • Visibility Culling

• LOD

• Cache Coherence• Speculative Pre-fetching

GPU Parallelism

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E-learning will become an important source of education in medicine

Case-based learning is favored by students Future medical eTextbooks should:

1. Emulate “hot seat” sessions2. Play the role of a facilitator3. Simulate clinical practice4. Provide structured, goal-directed questions

Visualization of massive data requires GPU utilization

Conclusion

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Research @ VT

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Thank you