Download Course Introduction

#170.03: Medical Imaging Informatics
Introductory Comments

Goals:
1. Describe modern tools for processing and analyzing
large amounts of imaging data.
2. Describe strategies to utilize effectively information
from these large sets of imaging and metadata.
3. Provide examples of the use of these tools in a
current research setting
4. Use easily obtainable and extensible open source
tools (e.g. R and Weka)
5. Point to important literature in this field.
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Medical Imaging Informatics
Introductory Comments

Disclaimers:
– Course aimed at broad audience so:
 Technical types may expect more rigor
 Non-technical types may occasionally feel
overwhelmed
– Course is experimental
 Intent is to provide flavor of current research so
there is no textbook (but based on how successful
course is there may eventually be)
 Direction can vary based on student input
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Medical Imaging Informatics
Introductory Comments

Disclaimers:
– Course is team taught:
 Satisfies individual teaching requirements !
 May experience some discontinuity but:
– All lecturers are from same lab and have regular
meetings re. course content
– Individual lecturers bring particular expertise
– Course will focus on MRI data
 Data mining and statistical techniques introduced
in course are data neutral but want to stress
importance of knowing ones data so we will use
data we know best for illustration
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Medical Imaging Informatics
Introductory Comments

Disclaimers:
– Course is team taught:
 Satisfies individual teaching requirements !
 May experience some discontinuity but:
– All lecturers are from same lab and have regular
meetings re. course content
– Individual lecturers bring particular expertise
– Course will focus on MRI data
 Data mining and statistical techniques introduced
in course are data neutral but want to stress
importance of knowing ones data so we will use
data we know best for illustration
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Medical Imaging Informatics
A Brief Example

MRI data and metadata on PTSD patients
– Imaging:
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Hippocampal volume from structural MRI
Intracranial volume from structural MRI
Metabolite concentrations from Spectroscopic Imaging (SI)
Tissue composition of SI voxels
– Metadata:
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Age
Gender
Education
CAPS score (estimated PTSD severity based on psychological
exam)
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Medical Imaging Informatics
A Brief Example

Image Reconstruction and Processing
– Reconstruction:
 Formation from truncated sampling
 Restoration for distortions and noise
– Processing:
 Segmentation and Classification
 Registration, i. e. between different image modalities
 Spatial normalization, i.e. for group analysis
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Medical Imaging Informatics
A Brief Example

Image data Analysis
–
–
–
–
–
Data Visualization
Unbiased Quality Assessment
Hypothesis driven and exploratory analyses
Design of statistical models
Data Mining
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Medical Imaging Informatics
A Brief Example

Decision Tree:
<= 13 Education
<=3.01.
Rt Hipp Vol >3.01
Age
PTSD-
<=2.66
> 13
Lt Hipp Vol >2.66
PTSD+
Rt Hipp Vol
<=43
PTSD-
<=2.43
>43
PTSD+
>2.43
PTSD-
Education
<= 15
PTSD-
> 15
Intracr Vol
<= 1403
PTSD-
> 1403
Age
<= 33
PTSD-
> 33
PTSD+
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Medical Imaging Informatics
A Brief Example

Analysis using WEKA (10 times, 10 fold cross
validation) – prediction accuracy:
Naieve
Bayes
One
Rule
Bayesian
NN
C4.5
PART Support Random
Network Instance Decision
Vector Forests
Learner
Tree
Machine
CAPS/2 64.7(20.4) 82.1(17.6) 47.3(15.7) 84.8(16.6) 81.0(18.9) 72.8(19.4) 68.7(20.4) 94.3(13.6)
CAPS/3 40.0(18.7) 60.5(16.6) 54.5( 8.7) 89.9(14.8) 78.4(18.0) 77.5(15.5) 55.1( 8.4) 92.4(13.2)
CAPS/4 53.6(20.4) 41.8(14.6) 42.3(14.0) 80.3(14.4) 66.9(18.5) 66.2(17.9) 34.4(18.9) 94.3(10.9)
CAPS/5 50.2(19.8) 43.7(15.6) |47.1( 9.4) 88.7(14.4) 50.1(16.3) 59.0(18.7) 46.7( 9.7) 93.1(11.4)
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Tentative Syllabus
Dates
1/02
1/08
1/15
1/22
1/29
2/05
2/12
2/19
2/26
3/04
3/11
3/18
3/25
Room
Presenters
Lectures
No Lecture
N-423
HWS-303
HWS-303
N-423
U-506
U-506
U-506
U-506
U-506
U-506
U-506
U-506
Wang/Norbert
Ashish
Medical Imaging
1. Acquisition (uni/multimodal data)
2. Signal Estimation and Detection
Colin
Colin
Ashish
Wang/Ashish
Image Processing
3. Rigid Image Registration
4. Non-rigid Registration/Atlas building
5. Segmentation
6. Spatial Connectivity
Norb
Xiaoping
John
Statistical Analysis
7. Data Visualization
8. Feature Extraction
9. Linear Modeling in Medical Imaging
Karl
Karl
Karl/Norbert
Data Mining
10. Principles of Data Mining
11. Multimodal Image Mining
12. Applications and Examples
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Instructors
Karl Young, PhD.
[email protected]
415.221.4810 x3114
Norbert Schuff, PhD
[email protected]
415.221.4810 x4904
John Kornak, PhD
[email protected]
Ashish Raj, PhD
[email protected]
Colin Studholme, PhD
[email protected]
Zhan Wang, PhD
[email protected]
Xiaoping Zhu MD, PhD
[email protected]
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Teaching Material
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
Course material will be available on the web or by handouts
http://www.cind.research.va.gov/teaching/medical_imag
ing.asp
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