Integration of Radiologists

1
Integration of Radiologists Feedback into
Computer-Aided Diagnosis Systems

• Sarah A. Jabona
• Daniela S. Raicub
• Jacob D. Furstb
• aRose-Hulman Institute of Technology, Terre
  Haute, IN 47803
• bSchool of Computing, CDM, DePaul Universtiy,
  Chicago, IL 60604

2
Overview

• Introduction
• Related Work
• The Data
• Methodology
• Simple Distance Metrics
• Linear Regression
• Principle Component Analysis
• Results
• Simple Distance Metrics
• Linear Regression
• Principle Component Analysis
• Conclusions
• Future Work

3
Introduction

• The 2008 official estimate
• 215,020 cases diagnosed
• 161,840 deaths will occur
• Five-year relative-survival rate (1996 2004)
  15.2
• Computer-aided diagnosis systems can help improve
  early detection

4
Related Work

• El-Naqa et al.
• mammography images
• neural networks and support vector machines
• Muramatsu et al.
• mammography images.
• three-layered artificial neural network to
  predict the semantic similarity rating between
  two nodules
• Park et al.
• linear distance-weighted K-nearest neighbor
  algorithm to identify similar images

5
Related Work

• ASSERT by Purdue University
• Content-based features co-occurrence, shape,
  Fourier Transforms, global gray level statistics
• Radiologists also provide features
• BiasMap by Zhou and Huang
• Relevance feedback, content-based features
• Analysis biased-discriminant analysis (BDA)

6
The Data

• Lung Image Database Consortium
• Reduced 1,989 images down to 149 (one for each
  nodule)
• Summarized the radiologists ratings (up to 4)
  into a single vector
• Each nodule has 7 semantic based characteristics
  and 64 content-based characteristics

7
Overview

• Introduction
• Related Work
• The Data
• Methodology
• Simple Distance Metrics
• Linear Regression
• Principle Component Analysis
• Results
• Simple Distance Metrics
• Linear Regression
• Principle Component Analysis
• Conclusions
• Future Work

8
Methodology
9
Methodology Simple Distance Metrics
Semantic-Based Similarity
Content-Based Similarity
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Simple Distance Metrics

• Content-Based Similarity Values (Euclidean)

• Semantic-Based Similarity Values (1 Cosine)

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Methodology Linear Regression
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Methodology Principle Component Analysis
Lobulation Malignancy Margin Sphericity Spiculation Subtlety Texture
Lobulation 1.000 .199 .085 -.008 .815 .065 .101
Malignancy .199 1.000 .346 .187 .155 .594 .351
Margin .085 .346 1.000 .391 .109 .533 .717
Sphericity -.008 .187 .391 1.000 .078 .300 .230
Spiculation .815 .155 .109 .078 1.000 .156 .146
Subtlety .065 .594 .533 .300 .156 1.000 .523
Texture .101 .351 .717 .230 .146 .523 1.000

• Content-Based Features
• 77 pairs with a correlation gt 0.9
• 136 pairs with a correlation gt 0.8 or lt -0.8

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Scree Plots 5 9 Matches
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Methodology Principle Component Analysis

• PCA on content-based features
• accounts for 99 of the variance
• 23 components
• PCA on semantic-based characteristics
• Method 1
• accounts for 92 of the variance
• 4 components
• Method 2
• accounts for 98 of the variance
• 6 components

15
Overview

• Introduction
• Related Work
• The Data
• Methodology
• Simple Distance Metrics
• Linear Regression
• Principle Component Analysis
• Results
• Simple Distance Metrics
• Linear Regression
• Principle Component Analysis
• Conclusions
• Future Work

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Results Simple Distance Metric
Matches Gabor Markov Co-Occurrence Gabor, Markov, and Co-Occurrence All Features
6 10 24 18 31 36 43
2 5 107 104 94 98 93
0 1 18 27 24 15 13
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Matches Nodule 117
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Simple Distance Metrics
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5 9 Matches PCA and Linear Regression
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Results Linear Regression
Data Set No. of Nodule Pairs ( 2/3 Set) Correlation Euclidean vs. Semantic R2 Adj. R2 Feature Set Distance
6 9 Matches 166 -0.016 0.948 0.871 2 -
6 9 Matches 166 -0.016 0.802 0.679 1 dist3
5 9 Matches 218 -0.006 0.927 0.850 2 -
5 9 Matches 218 -0.006 0.733 0.624 1 dist3
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Results Linear Regression
Data Set No. of Nodule Pairs (1/3 Set) Correlation Euclidean vs. Semantic RMSD Euclidean Correlation Predicted vs. Semantic RMSD Predicted Features
6 9 Matches 85 -0.023 0.2328 0.710 0.0242 128
6 9 Matches 85 -0.023 0.2328 0.748 0.0181 64
5 9 Matches 108 -0.039 0.1985 0.829 0.0136 128
5 9 Matches 108 -0.039 0.1985 0.733 0.0155 64
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Results Linear Regression
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Results Linear Regression
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Results PCA
Data Set No. of Nodule Pairs ( 1/3 Set) Correlation Euclidean vs. Semantic RMSD Euclidean Correlation Predicted vs. Semantic RMSD Predicted Features
6 9 Matches 85 -0.115 0.3043 0.787 0.0061 128
6 9 Matches 85 -0.115 0.3043 0.393 0.0114 64
5 9 Matches 108 -0.094 0.2664 0.570 0.0096 128
5 9 Matches 108 -0.094 0.2664 0.136 0.0112 64
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Results PCA
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Results PCA
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RMSD Percent of Range
Linear Regression No PCA Linear Regression No PCA Linear Regression PCA Linear Regression PCA
Data Set Features Euclidean Predicted Euclidean Predicted
6 9 Matches 128 23.3 17.3 30.4 6.7
6 9 Matches 64 23.3 12.9 30.4 12.5
5 9 Matches 128 19.9 9.7 26.6 10.1
5 9 Matches 64 19.9 11.1 26.6 11.8
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Example Nodule 37 and Nodule 38
Nodule 38 Nodule 37 Euclidean Similarity Value PCA Similarity Value
0.549066 0.004379
Nodule Number Lobulation Malignancy Margin Sphericity Spiculation Subtlety Texture
37 5 3 5 5 5 4 5
38 5 3 5 5 5 5 5
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Future Work

• Perform the analysis only nodules on which all
  three radiologists agree
• In order to address the small size of the data
  set, perform the analysis using a leave one out
  technique (instead of 2/3 training and 1/3
  testing)
• Incorporate relevance feedback into the system

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Questions?