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PHILIPS Philips Medical SystemsHealthcare IT - Advanced Development

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.


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 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!



Normal None 100% (95%) 50 (134)

Low None 56% (95%) 50 (163)

Ultra low None 12% (95%) 44 (312)


Low Bilateral 85% (92%) 38 (109)


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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%



Normal None 100% (95%) 50 (134)

Low None 96% (95%) 50 (163)

Ultra low None 93% (95%) 44 (312)


Low Bilateral 100% (92%) 38 (109)


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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

tu/e philipsphilips medical systems healthcare it - advanced development 1/38 the effects of...

Documents

advanced development

screening slide

days slide

hu slide

gaussian filter slide

bilateral filter slide

bilateral filtering

months ct abdomen