PROJECT 1: Voronoi Probability Maps for Seed Region Detection in Abdominal CT Images PROJECT 2: Kidney Seed Region Detection in Abdominal CT Images

By: Nicholas Cooper, Northern Kentucky University Maureen Kelly, Loyola University Chicago Jacob Furst, DePaul University Daniela Raicu, DePaul University REU Medical Informatics eXpericence (MedIX) 2008 D ePaul University Northwestern University University of Chicago Thursday, August 22, 2008 Voronoi Probability Maps for Seed Region Detection in Abdominal CT Images PROJECT 1

Topics of Discussion 1. Project Goals 2. Why Multiple Organs? 3. Purpose for Radiologists 4. Why Segment the Liver and Spleen Together? 5. Challenging Aspects of Multi-Organ Segmentation 6. Overcoming Challenges 7. Methodology 8. Results

Project Goals To create a robust method that identifies liver and spleen seed regions that can be used for multi-organ segmentation Voronoi Probability Maps (VPM) Largest Probable Connected Components (LPCC)

Why Multiple Organs? Liver Spleen Propose an increased accuracy with multi-organ segmentation using seed regions Two weak individual segmentations could potentially result in an even more exact combination segmentation Allow for radiologists to examine several organs at once instead of just one at a time Better diagnosis of pathologies Treatment planning Anatomical structures study

Purpose for Radiologists Check for diseases Liver Hepatitis (inflammation) Cirrhosis (nodules formation) Cancer Spleen Splenomegaly (enlargement) Asplenia (abnormal function) Spread of a known disease/pathology Is a particular treatment is working for a patient? Show condition after a abdominal injury

Benefits of Segmenting the Spleen and Liver Together Share similarities that would allow for more accurate and repeatable segmentation Texture features Gray-level intensity values Practicality in the technical setting

Challenges Spleen share similar texture features/properties to that of the liver Gray-level similarity of adjacent organs Variations in spleen and liver margins/shape Absence of the spleen Hey, where did the spleen go?! Nick

Overcoming Challenges Create a method that is not based on a common set of parameters organ location patient position Create a method that relies on specific texture or intensity Patient at a 45° angle Typical spleen location

Methodology

Soft Tissue Region Identification Soft tissue is only displayed Fat, bone, and air are removed Regions are created in order to be classified Original ImageSoft Tissue RegionsSoft Tissue

Texture Feature Extraction Co-occurrence matrix Distribution 9 Haralick descriptors Distance and direction Used to help identify soft tissue regions Differentiation between organs: liver vs. spleen Co-occurrence matrixPixel neighborhoodCT image

Created liver and spleen classifiers Manually draw a polygon around the spleen/liver Creates positive (spleen/liver) and negative (non- spleen/non-liver) regions Result: displays the regions in which the classifier declares to be spleen or liver Includes misclassified regions Candidate Seed positive negative Spleen Candidate Seed Detection Detection

Seed Extraction Get specific organ regions Spleen seed points are regions that ONLY contain the spleen, and same for liver. Eliminate the misclassified regions Seeds that are extracted are used as initial points for expanding the spleen/liver regions to achieve the completely segmented organ

Seed Extraction

Calculation of Average Seed Region Location Liver Candidate SeedsSpleen Candidate SeedsAverage Seed Region Location Finding average seed region location for both the liver and spleen

Liver Candidate SeedsSpleen Candidate SeedsVoronoi Probability Map Create Voronoi probability map based on average seed region location Implementation of Voronoi Probability Maps Probability becomes greater as the distance between seed region and bisector increases

Implementation of Voronoi Probability Maps Distance (d) between the bisector and the regions in the Voronoi region of the organ of interest is calculated, such that: d is then used to generate a probability, p, for each region: Once probability, p, is calculated, each connected component, C, is then given the value P such that:

Liver SeedsSpleen SeedsVoronoi Probability Map Finding seeds based on Voronoi probability map using largest connected component and overlap Identification of Seed Regions

The diagram of Voronoi probability map and largest connected component approaches

Largest Connected Component and Overlap Remaining Liver SeedsRemaining Spleen Seeds Overlap between Spleen LPCC and Liver LPCC IMAGES DISCARDED

Results 19 patients images per patient containing liver and spleen TOTAL: 1,125 images Seed region overlap: 176 images No Seed region overlap: 979 images Of the 979, 85% of all the images contained all seed regions within the organ of interest

Conclusion Results show that VPMs and LPCC was successful Succeeded in circumstances in which other methods failed varying organ size texture similarities patient rotation Thanks to Reed’s mother!

By: Nicholas Cooper, Northern Kentucky University Maureen Kelly, Loyola University Chicago Jacob Furst, DePaul University Daniela Raicu, DePaul University REU Medical Informatics eXpericence (MedIX) 2008 DePaul University Northwestern University University of Chicago Thursday, August 22, 2008 Kidney Seed Region Detection in Abdominal CT Images PROJECT 2

Topics of Discussion 1. Project Goals 2. Why Kidneys? 3. Challenge: Why Not Use Previous Method? 4. Overcoming Challenges 5. Methodology 6. Results 7. Conclusion 8. Future Work

Project Goals To create a robust, accurate method that identifies kidney seed regions that can be used for organ segmentation Right Kidney Left Kidney View from behind

Why Kidneys? Detection, prevention, treatment disease One in nine Americans have chronic kidney disease (National Kidney Disease Foundation) Nephritis (inflammation) abdominal injury

Challenge: Why Not Use Previous Method? Liver, spleen and kidneys do not exist within many of the same images Difficulties in distinguishing liver/right kidney and spleen/left kidney 2 of the same organ (right and left kidney) VPMs are based off of distance between regions and bisector Mis-identification Poor kidney candidate seed images Liver and SpleenLiver, Spleen and Kidneys

Overcoming Challenges Use kidney’s high Hounsfield unit (HU) value to our advantage Use spine Kidneys are located on either side of the spine Use revised probability map approach Elliptical-shaped probability map (ESPM)

Methodology

Spine Extraction Located once for each patient using: Many consecutive images Highest intensity values

Probability Map Construction distance (d1) between the center of the spine and outside edge distance (d2) between the center of the spine at x1, y1 and any pixel outside of the spine at x2, y2 d1 and d2 are then used to generate a probability, p, for each pixel p=p=

Probability Map Construction Elliptical-shaped probability map (ESPM) Extended major axis of the spine ellipse separates the right and left kidney ESPM major axis spine

Elimination of Non-kidney Intensity Values Kidney Intensity Ranges

Kidney Seed Extraction Apply elliptical-shaped probability map (ESPM) to each kidney image Check for overlap Right KidneyLeft Kidney

Results 20 patients were tested TOTAL= 2,375 images Seed Region Overlap: 286 images No Seed Region Overlap: 2,089 images Right kidney images: Left kidney images: Of the 2,089 images, 97.75% of the images were correctly identified as kidney Correctly identified kidney images Total kidney images =

Results: Combining Seeds Liver, Right Kidney, Left Kidney, Spleen Seeds (from left to right) Multiple organs each individual organ played a key role in segmenting the other organs Better accuracy Seeds can be used for region growing Complete the segmentation process

Conclusion Results prove that this method is very successful Accurate Reliable Time-efficient Comparable results on other patient data sets?

Future Work Region growing Extend to other organs Liver (blue), Kidneys (green), Spleen (red)

The End Thanks to Reed’s Mom again! Any Questions??