A 3D Approach for Computer-Aided Liver Lesion Detection Reed Tompkins DePaul Medix Program 2008 Mentor: Kenji Suzuki, Ph.D. Special Thanks to Edmund Ng

Presentation Outline ➲ Background Information ➲ Prior Research ➲ Proposed Methodology Liver Segmentation HCC Candidate Detection ➲ Results ➲ Conclusions and Future Work

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 ➲ 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 ➲ 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 ➲ 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 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 ➲ 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 ➲ 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 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 ➲ Not a liver segmentation project, but important to do it correctly ➲ Not terribly concerned with oversegmentation ➲ Method suggested by ITK manual Proposed Methodology – Liver Segmentation Liver Lesion

Overview of Liver Segmentation Proposed Methodology – Liver Segmentation

Liver Pre-Processing Proposed Methodology – Liver Segmentation

Fast Marching Segmenter Proposed Methodology – Liver Segmentation

Geodesic Active Contours Edge Image Input Level Set Proposed Methodology – Liver Segmentation

Binary Image Proposed Methodology – Liver Segmentation

Binary Liver Mask Two Different Binary Liver Masks Proposed Methodology – Liver Segmentation

Liver Segmentation Complete Proposed Methodology – Liver Segmentation Two Different Segmented Livers

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 ➲ Filter out noise from image ➲ Alter pixel intensity ➲ Sharpen/define edges Proposed Methodology – HCC Candidate Detection

Segmented Liver with Gradient Filter Applied Proposed Methodology – HCC Candidate Detection

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 Proposed Methodology – HCC Candidate Detection

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 TIME!

My program attempts to locate HCC within liver CT images. What does HCC stand for?

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 ➲ Average FPs = 14.2 FP, Average Distance = 12.6 mm Results

Watershed Output Watershed Original ImageSigmoid Gradient Distance = 0.47 mm

Watershed Output II Original Image Sigmoid Gradient Watershed

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 ➲ 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 Again To: Kenji Suzuki, Ph.D. Edmund Ng DePaul Medix Program And, of course… Contact Information:

Any Questions? Thanks To My Momma