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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