Reed tompkins depaul medix program 2008 mentor kenji suzuki ph d special thanks to edmund ng
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Reed Tompkins DePaul Medix Program 2008 Mentor: Kenji Suzuki, Ph.D. Special Thanks to Edmund Ng. A 3D Approach for Computer-Aided Liver Lesion Detection. Presentation Outline. Background Information Prior Research Proposed Methodology Liver Segmentation HCC Candidate Detection Results

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Reed tompkins depaul medix program 2008 mentor kenji suzuki ph d special thanks to edmund ng

Reed Tompkins

DePaul Medix Program

2008

Mentor: Kenji Suzuki, Ph.D.

Special Thanks to Edmund Ng

A 3D Approach for Computer-Aided Liver Lesion Detection


Presentation outline
Presentation Outline

  • Background Information

  • Prior Research

  • Proposed Methodology

    • Liver Segmentation

    • HCC Candidate Detection

  • Results

  • Conclusions and Future Work


Hcc background
HCC Background

  • Hepatocellular Carcinoma

  • Primary Liver Cancer

  • Prevalence varies drastically by region

  • Few Symptoms

  • Usually affects people with preexisting liver conditions

Background Information


Hcc background ii
HCC Background II

  • Estimated to cause at least 372,000 deaths annually

  • Other than CT imagery, difficult to detect

  • Difficult / time consuming for radiologists to spot

Background Information


Project background
Project Background

  • 2D Lesion Detector program, “Candidate Finder 1.0,” written and tested in previous summer

  • Written in ITK – open source, C/C++ toolkit

  • CandidateFinder both segments liver and attempts to detect tumor candidates

  • 100% Sensitivity

    • Small Number of Test Cases

Background Information


Project background ii
Project Background II

  • 2D Algorithm resulted in high number of false positives

    • On 2D Data: 24 FPs on average

    • On 3D Data: Hundreds of FPs

  • Program not written using object-oriented techniques

  • No way to view program intermediates

Background Information


Project goals
Project Goals

  • Develop a 3D computerized scheme for detection of hepatocellular carcinoma (HCC) in liver CT images

  • Modify and modularize existing liver lesion detection program

Background Information


Data set
Data Set

  • 15 CT scans, with a total of 17 HCC tumors

  • Contrast-enhanced CT images; arterial phase

  • Resolution: 512 x 512 x (200 – 300)‏

  • Spacing of Pixels = [0.67 mm, 0.67 mm, 0.62 mm]

  • Tumor centers identified by trained radiologist

Background Information


Prior research
Prior Research

  • Gletsos et al (2003)‏

    • Used gray level and texture features to build a classifier for use in a neural network

    • Operated on 2D data, did not focus on HCC specifically

  • Tajima et al (2007)‏

    • Used temporal subtraction and edge processing to detect HCC specifically

    • Required multiple “phases” of CT liver images to work

Prior Research


Prior research ii
Prior Research II

  • Shiraishi et al (2008)‏

    • Used microflow imaging to build an HCC classifier

    • Microflow imaging is not approved by FDA

    • Used ultrasonography, not computer tomography

  • Watershed Algorithm

    • Huang et al (Breast Tumors)‏

    • Marloes et al (Brain Tumors)‏

    • Sheshadri et al (Breast Tumors)‏

Prior Research


Proposed methodology liver segmentation
Proposed Methodology – Liver Segmentation

  • Not a liver segmentation project, but important to do it correctly

  • Not terribly concerned with oversegmentation

  • Method suggested by ITK manual

Liver Lesion

Liver Lesion

Proposed Methodology – Liver Segmentation


Overview of liver segmentation
Overview of Liver Segmentation

Proposed Methodology – Liver Segmentation


Liver pre processing
Liver Pre-Processing

Proposed Methodology – Liver Segmentation


Fast marching segmenter
Fast Marching Segmenter

Proposed Methodology – Liver Segmentation


Geodesic active contours
Geodesic Active Contours

Input Level Set

Edge Image

Proposed Methodology – Liver Segmentation


Binary image
Binary Image

Proposed Methodology – Liver Segmentation


Binary liver mask
Binary Liver Mask

Two Different Binary Liver Masks

Proposed Methodology – Liver Segmentation


Liver segmentation complete
Liver Segmentation Complete

Two Different Segmented Livers

Proposed Methodology – Liver Segmentation


Proposed methodology hcc candidate detection
Proposed Methodology – HCC Candidate Detection

  • Pre-process segmented liver

  • Apply watershed algorithm

  • Eliminate/consolidate watershed regions

  • Check distance from actual tumors

Proposed Methodology – HCC Candidate Detection


Hcc candidates pre processing
HCC Candidates Pre Processing

  • Filter out noise from image

  • Alter pixel intensity

  • Sharpen/define edges

Proposed Methodology – HCC Candidate Detection


Segmented liver with gradient filter applied
Segmented Liver with Gradient Filter Applied

Proposed Methodology – HCC Candidate Detection


Hcc candidates pre processing ii
HCC Candidates Pre Processing II

  • Calculate image statistics (used by watershed algorithm)‏

  • Apply a half-thresholder (try to eliminate uninteresting regions)‏

Proposed Methodology – HCC Candidate Detection


Watershed segmentation conceptual
Watershed Segmentation Conceptual

Proposed Methodology – HCC Candidate Detection


Watershed segmentation
Watershed Segmentation

  • In other words, the watershed algorithm locates the minimum intensity of regions, and keeps growing those enclosed regions until it encounters another growing region, or a boundary.

  • We used the watershed algorithm to find tumor candidates.

Proposed Methodology – HCC Candidate Detection



Quiz time1
QUIZ TIME!

My program attempts to locate HCC within liver CT images.

What does HCC stand for?


Results
Results

  • How do we define “success”?

    • Centroid of 3D watershed region is less than 30 mm away from location of tumor (as marked by radiologist)‏

  • Possible problem with this definition?

Results


Results ii
Results II

  • Average FPs = 14.2 FP, Average Distance = 12.6 mm

Results


Watershed output
Watershed Output

Original Image

Sigmoid

Watershed

Distance =

0.47 mm

Gradient


Watershed output ii
Watershed Output II

Sigmoid

Original Image

Watershed

Gradient


Conclusions
Conclusions

  • We have developed a 3D algorithm for the detection of HCC with 100% sensitivity on 15 test cases with a reasonable number of FPs.

  • We have successfully translated a 2D algorithm to 3D, with fewer false positives.

  • We have successfully modularized the program, allowing intermediates to be output.

Conclusions and Future Work


Future work
Future Work

  • Modify program to help detect cancers other than HCC

    • Possibly integrate project with another student project

  • Add a false positive reducer (MTANN?)

Conclusions and Future Work


Thanks
Thanks!

  • Thanks Again To:

    • Kenji Suzuki, Ph.D.

    • Edmund Ng

    • DePaul Medix Program

    • And, of course…

Contact Information: [email protected]


Any questions
Any Questions?

Thanks To My Momma


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