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Path Planning in Virtual Bronchoscopy

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Path Planning in Virtual Bronchoscopy

Mohamadreza Negahdar

Supervisor : Dr. Ahmadian

Co-supervisor : Prof. Navab

Tehran University of Medical Sciences

January 2006

- Progress Report

- Lung cancer is the most common cause of cancer related death*
- 164,000 new cases and 156,000 deaths estimated in 2003 in the US The average 5 yr. survival rate is only 12%
- Diagnosis of disease at early stage with subsequent treatment may dramatically increase cure and survival rate
- Since its introduction in 1990 spiral CT has helped physicians visualize pulmonary nodules with a better diagnostic confidence compared to chest X-ray

*American Cancer Assc. Update 2003

- High-resolution 3D CT pulmonary images permit evaluation of thin tubular structures (e.g., airways) and provide 3D position/shape information (e.g., for cancers)
However, 3D images are hard to assess manually.

- Virtual Bronchoscopic (VB) system enable 3D image probing and treatment planning
- Both for ease of use and for quantitative assessment, Virtual Bronchoscopic systems need airway paths for effective use

- VB is a computer-based approach for navigating virtually through airways captured in a 3-D MDCT image
- VB 3-D image analysis:
- Guidance of bronchoscopy
- Human lung-cancer assessment
- Planning and guiding bronchoscopic biopsies
- Quantitative airway analysis â€“noninvasively-
- Smooth virtual navigation

- A suitable method must:
- Provide a detailed, smooth structure of the airway treeâ€™s central axes
- Require little human interaction
- Function over a wide range of conditions as observed in typical lung-cancer patients

- A major component of path planning system for VB is a method for computing the central airway paths (centerlines) from a 3-D MDCT chest image
- Two general approaches for path definition:
- Manual-Path definition: time-consuming, error-prone, cannot readily get many paths.
- Recent automated techniques: donâ€™t use gray-scale information,

- Quicksee-Basic operation:
- Load Data
- 3D radiologic image

- Do Automatic Analysis
- Compute
- Paths (axes) through airways
- Extract regions (airways)

- Save results for interactive navigation

- Compute
- Perform Interactive navigation/assessment
- View, Edit, create paths through 3D image
- View structure; get quantitative data
- Many visual aids and viewers available

- Load Data

- Goals:
- The aim of our work is to build trajectories for virtual endoscopy inside 3D medical images, using the most automatic way.
- Virtual endoscopy results are shown in various anatomical regions (bronchi, colon, brain vessels, arteries) with different 3D imaging protocols (CT, MR).
- In my thesis, an automatic centerline determination algorithm for three dimensional virtual bronchoscopy CT image will be presented.
- We try that our method:
- Be faster
- Needs less interaction
- Be more robust and reproducible

ïƒ¼

- Path through a tubular structure defines a trajectory along tubeâ€™s central axis
- A Path denoted as:
- Medial (central) axes of branches
- Preserve homotopy of structure
- Continuous for smooth visualization

Path is spine of cylinder

- Automated approaches:
- Segmentation followed by 3D skeletonization
- Active contour models
- Morphological operations
- Estimation of principal eigenvectors
- Vector fields

- Shortcomings: Some lead to imprecise/missing paths and require long processing time

- 2D
- Morphological Operations (Algorithm I)
- Distance Transformation (Algorithm II)

- 3D
- A Combination of Methods with Novelty
- Phantom
- Human airways

- A Combination of Methods with Novelty

- Load an Object

- Distance from Boundary

- Gradient of DT from Boundary

Gradient < 0

- Thinning

- Skeletonization

False Branches

- Length-based Elimination

Branch Points

End Points

Distance Transformation (Chamfer Distance)

Start Point

Start Point

- City block
dist4(p,q) = | px â€“ qx | + | py â€“ qy |

- Chess board
dist8(p,q) = max { | px â€“ qx |,| py â€“ qy | }

- Chamfer
distcha<A,B>(p,q) = A. max { | px â€“ qx |,| py â€“ qy | } +

(B-A). min { | px â€“ qx |,| py â€“ qy | }

- Euclidean
diste(p,q) = âˆš( ( px â€“ qx )2 + ( py â€“ qy )2 )

- Squared Euclidean
distE(p,q) = ( px â€“ qx )2 + ( py â€“ qy )2

End Point Detection & Shortest Path

Shortest Path

End Point

Start Point

Steepest Descent

Local Maxima

End Points

- Our Procedure
- Prepare the Data
- Start Point Detection
- Boundary Extraction
- End Points Detection
- Path Initialization
- Centering
- Refinements

- Segmentation & Create the 3D Image
- Slicing the Segmented Image
- Feed the Slice Images
- Refine slices & Create 3D Image Matrices
- Binarize the Object
- Optimize the dataset

- Morphological Operations
Boundary = Dilated Image â€“ Original Image

Boundary = Original Image â€“ Eroded Image

- Distance Transformation from boundary to middle
ïƒ¼ Boundary = ( DT == 1 )

- Distance Transformation
- Assigns larger number to voxels with region growing in comparison to exact Euclidean metric
- More accurate approximation of true Euclidean distance metric
- Allocate integer values to voxels which speeds up the next computations

ïƒ¼

EDT

< 1 , 2 , 3 >

< 3 , 4 , 5 >

- distcha<A,B,C>(p,q) = A. max { | px â€“ qx |,| py â€“ qy |,|pz â€“ qz | } +
(B-A). max{ min{ | px â€“ qx |,| py â€“ qy | },

min{ | px â€“ qx |,| pz â€“ qz | },

min{ | py â€“ qy |,| pz â€“ qz | } } +

(C-B-A). min{ | px â€“ qx |,| py â€“ qy |,|pz â€“ qz | }

- distcha<A,B,C>(p,Origin) =
A. px + (B-A). py + (E-B-A). pz if px >= py+pz

(E-C). px + (C+B-E). py + (C-B). pz if px <= py+pz

(E >= A+B) & (E >= B/2+C)

ïƒ¼

- Neighboring Window

ïƒ¼

- Neighboring

ïƒ¼

ïƒ¼

Start Point

End Points

Farest End Point

- What is a snake?
An energy minimizing spline guided by external constraint forces and pulled by image forces toward features:

- Edge detection
- Subjective contours
- Motion tracking
- Stereo matching
- â€¦
Basically, snakes are trying to match a deformable model to an

image by means of energy minimization.

G

D

- Energy & Gradient of Image
D = EDT from Boundary to middle

G (i,j,k) = Gradient ( D(i,j,k) )

Gx = 0.5 ( D(i+1,j,k) â€“ D(i-1,j,k) )

Gy = 0.5 ( D(i,j+1,k) â€“ D(i,j-1,k) )

Gz = 0.5 ( D(i,j,k+1) â€“ D(i,j,k-1) )

G

D

Middle axis has minimum of Gradient

- Snake
Path is considered as a parameterized curve (snake)

v(s) = ( x(s),y(s),z(s) )T s [0,1]

The Snake evolves in order to minimize an energy defined as:

Smoothing terms

Image term

Decreasing function of the image gradient

- Image force
v(i) is the discrete representation of the curve v

- In our experiments, the snake converges in a few iterations (< 20) and stabilizes itself very robustly

Start Point

End Points

Farest End Point

- Length-based Elimination
- In Path Initialization Stage:
Remove branches which has length less than 10 voxel

- After Centering Stage:
Remove branches which has length less than 5 voxel

- In Path Initialization Stage:

- Continuous Path
- Lose of continuity after centering
- Detect of discontinuity and make continue the path

ïƒ¼

- Virtual navigation and virtual endoscopy
- Segmentation & Registration
- Virtual-guided bronchoscopy & Biopsy
- Quantification of anatomical structures
- Surgical planning
- Radiation treatment
- Curved planner reformation
- Stenosis detection
- Aneurism and wall bronchia classification detection
- Deforming volumes
- â€¦

- No single method is good for everything â€¦
then we use combination of distance field & potential field

- Fully automated
without any interaction by physician

- No miss branch , No false branch
42 branch out of 42

- Robust
less sensitivity to noise

- Too fast
less than 1 minute for (512 x 512 x 416) â€“ (0.59-0.59-0.50 mm)

- Evaluate our method with more dataset
- Test the final path in a virtual environment
- More refinements of the path planning method
- Comparing of our method with others

Thank You!

My thanks to â€¦

Dr. Alireza Ahmadian

Prof. Nassir Navab

Dr. Joerg Traub

& My Family

For nothing is hidden, except to be revealed;

Nor has been secret, but that is should come to light.

Questions â€¦. Suggestions â€¦. Comments â€¦. Ideas â€¦. ?