tu/e philipsphilips medical systems healthcare it - advanced development 1/38 the effects of...
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The Effects of Filtering on Visualization and Detection of Colonic Polyps in Ultra Low Dose Multi-Detector CT Data
Gert SchoonenbergBiomedical Engineering student, TU/e
Final presentation
Supervisors:
Roel Truyen, Anna Vilanova and Frans Gerritsen
Project period:
13-9-2004 – 12-10-2005
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Overview
• Motivation: colorectal cancer• Screening methods• Research questions• Dose in Computed Tomography (CT)• Filtering• Computer-aided polyp detection• Visualization• Conclusion• Time for questions
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Colorectal cancer
For industrialized countries:• Second leading cause of cancer-related death• Accounts for 10% of all cancer mortality
For the Netherlands:• Causes each year over 9,100 new cases• Causes each year over 4,400 deaths• Accounts for 3% of all deaths
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Screening for colorectal cancer
Colorectal cancer:• High prevalence• Long asymptomatic premalignant phase• Well treatable when detected early
Suitable disease
for screening
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Outcomes of screening
Target condition
present absent
Diagnostic result
positive TP (true positive) FP (false positive)
negative FN (false negative) TN (true negative)
FNTP
TPysensitivit
FPTN
TNyspecificit
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Screening methods
Imaging technique Other technique
Scanner X-ray(DCBE)
Endoscope Proteomics Fecal tests
CT MR Sigmoidoscopy Colonoscopy Blood(FOBT)
DNA
Screening methods
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Research questions
• Is computer-aided polyp detection still possible if the dose is reduced?
• Can the artifacts caused by noise in endoluminal visualizations be reduced?
Investigate the use of noise reduction techniques.
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Data & CT effective dose
Description Effective dose (mSv)
Comparable to natural background radiation for
CT abdomen (2 scans, 70 mAs
8.4 2.8 years
CT abdomen (2 scans, 6.25 mAs
0.75 3 months
CT abdomen (2 scans, 1.39 mAs
0.17 < 1 month
Mammography 0.7 3 months
Chest X-ray 0.1 10 days
Coast-to-coast round trip in the US
0.03 4 days
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Filtering
Gaussian filtering
Bilateral filtering
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Gaussian filter
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Bilateral filter
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Results filter methods
Gaussian filteringScale = 2.0 mm
Bilateral filteringScale = 2.95 mmScale = 250 HU
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Computer-aided polyp detection
Algorithm developed by Simona Grigorescu and Joost Peters,
Advanced Development, Healthcare IT, Philips Medical Systems Best
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Polyp detection overview
Three steps:• Colon segmentation• Polyp detection: identification and detection• Polyp classification
– Bounding box– Linear classifier
Colon segmentation
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PD: identification and detection
• Detection step: shape based– All regions with high curvature are selected– Features are calculated for shell volume– Features are calculated for core volume
airtissue
polyphigh curvature shell
core
colonwall
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PD: Classification
• Feature selection for bounding box– Purpose: discarding outliers.– Select only those features for which the polyp class
is compact.– Select only those features that really discard FP.
Feature 1
Fea
ture
3
Feature 2
Fea
ture
3
Not a polypPolyp
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PD: Classification
• Feature selection for linear classifiers (outliers unwanted)– Rank features according to their Gaussianity
(minimal overlapping).– Forward selection of features which increase the
cluster separability with a minimal value.
bad good
Not a polypPolyp
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Training
• Detection step– detect all candidates and calculate shell features
and core features.
• Bounding box– find a minimal cube in the feature space that
contains all polyp examples to get rid of outliers.
• Linear classifier– find a linear boundary between two classes based
on the examples (without outliers).
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Testing
• Detection step– Detect all candidates and calculate shell features
and core features.
• Bounding box– Bounding Box: Select only candidates inside the
hypercube.
• Linear classifier– Classification: Classify candidates.
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Experiment 1
Test performance of detection step using:
• Bilateral filtered data• Unfiltered data
All parameters in the CAD algorithm are kept constant.
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Results: detection step
Bilateral filtering 33% FP reduction 2.2% decrease in sensitivity (polyps > 6 mm)
Dose level Filtering Sensitivity FP
Normal None 95% 134
Low None 95% 163
Ultra low None 95% 312
Normal Bilateral 93% 97
Low Bilateral 92% 109
Ultra low Bilateral 93% 201
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Feature selection for classification
Experiment 2a:• Optimal feature set for each dose level
Experiment 2b:• Optimal feature set for normal dose trained on
normal dose and tested on all dose levels
Experiment 2c:• Robust feature set trained on normal dose and
tested on all dose levels
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Results 2a: optimal features
Bounding Box
Average false positive reduction is 67%
No loss in sensitivity for polyps 6 mm and larger
In parenthesis () the results of the detection step are given.
Dose level Filtering Sensitivity FP
Normal None 100% (95%) 50 (134)
Low None 100% (95%) 60 (163)
Ultra low None 100% (95%) 71 (312)
Normal Bilateral 100% (93%) 40 (97)
Low Bilateral 100% (92%) 28 (109)
Ultra low Bilateral 100% (93%) 66 (201)
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Results: linear classifier
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Results: linear classifier
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Results 2b: Normal dose features
Bounding boxOptimal feature set for normal dose trained on
normal dose and tested on all dose levels.Decrease of sensitivity on lower dose levels!
In parenthesis () the results of the detection step are given.
Dose level Filtering Sensitivity FP
Normal None 100% (95%) 50 (134)
Low None 56% (95%) 50 (163)
Ultra low None 12% (95%) 44 (312)
Normal Bilateral 100% (93%) 40 (97)
Low Bilateral 85% (92%) 38 (109)
Ultra low Bilateral 9% (93%) 28 (201)
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Result: linear classifier
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Results 2c: Robust features
Bounding boxNot filtered data: Sensitivity: 96%, average FP reduction: 30%
Bilateral filtered data: Sensitivity: 98%, average FP reduction: 40%
In parenthesis () the results of the detection step are given.
Dose level Filtering Sensitivity FP
Normal None 100% (95%) 50 (134)
Low None 96% (95%) 50 (163)
Ultra low None 93% (95%) 44 (312)
Normal Bilateral 100% (93%) 40 (97)
Low Bilateral 100% (92%) 38 (109)
Ultra low Bilateral 95% (93%) 28 (201)
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Results: linear classifier
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Visualization
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Perspective ray-casting
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Current visualization
Normal doseSmooth surface
Low doseBlobs appear
Normal doseRough surface
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Proposed solutions
• Bilateral filtering blobs• Gradient smoothing rough surface
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Results: normal dose
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Results: all dose levels
1.6 mAs 6.25 mAs 64 mAs
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Conclusions
Very low dose: filtering before rendering is required!
V The new rendering (gradient smoothing) gives similar renderings for low and high dose.
V With the new rendering the wall appears relatively smooth when it in fact should be smooth.
X No smoothing of important structures.The noise level changes within a dataset. In the really noisy regions strong filtering is needed and smoothing occurs. [New scanners: dose modulation]
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Overall conclusion
• Visualization and computer-aided detection of colorectal polyps is feasible on ultra low dose CT colonography data.
• It is also possible to create one visualization algorithm and one computer-aided detection algorithm that can cope with various dose levels.
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Time for questions
Questions?
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Current visualization
Problem:Bumpy colonsurface.
Cause:Not theisosurfacelocation, butsurface normals.
Data:64 mAs datapelvic region
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Simulating low dose
01
1
1 1
nqn poidev
q
q: ratio of actual and desired mAs level
poidev: Poisson distribution
n0: detected photons
n1: simulated amount of photons