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 DePaul UniversityNorthwestern UniversityUniversity of Chicago

Thursday, August 22, 2008

Voronoi Probability Maps for Seed Region Detection in Abdominal CT Images

PROJECT 1

Topics of Discussion1. 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 Image Soft 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

positivenegative

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 Seeds Spleen Candidate SeedsAverage Seed Region Location

Finding average seed region location for both the liver and spleen

Liver Candidate Seeds Spleen 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

22

||),(

ba

cbyaxyxd

),(max

),(),(

yxd

yxdyxp

xy

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:

Cyx

yxpP,

),(

Liver Seeds Spleen SeedsVoronoi Probability Map

Finding seeds based on Voronoi probability map using largest connected component and overlap

Identification of Seed Regions

Identification of Seed Regions

The diagram of Voronoi probability map and largest connected component approaches

Largest Connected Component and Overlap

Remaining Liver Seeds Remaining Spleen Seeds

Overlap between

Spleen LPCC and Liver LPCC

IMAGES DISCARDEDIMAGES DISCARDED

Results 19 patients

10-125 images per patient containing liver and spleen

TOTAL: 1,125 imagesSeed region overlap: 176 imagesNo 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) 2008DePaul UniversityNorthwestern UniversityUniversity of Chicago

Thursday, August 22, 2008

Kidney Seed Region Detection in Abdominal CT

ImagesPROJECT 2

Topics of Discussion1. 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 KidneyLeft 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

2 21 ( cos ) ( sin )d A B

212

2122 )()( yyxxd

21

1

dd 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 Kidney Left 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

%83.971059

1036

%67.971030

1006 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??