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. Clinical background (Motivation). Lung cancer is the most common cause of cancer related death*

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Path Planning in Virtual Bronchoscopy

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Path planning in virtual bronchoscopy

Path Planning in Virtual Bronchoscopy

Mohamadreza Negahdar

Supervisor : Dr. Ahmadian

Co-supervisor : Prof. Navab

Tehran University of Medical Sciences

January 2006

  • Progress Report


Clinical background motivation

Clinical background (Motivation)

  • 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


Introduction

Introduction

  • 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


Virtual bronchoscopy

Virtual Bronchoscopy

  • 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 –noninvasively-

    • Smooth virtual navigation

  • A suitable method must:

    • Provide a detailed, smooth structure of the airway tree’s central axes

    • Require little human interaction

    • Function over a wide range of conditions as observed in typical lung-cancer patients


Virtual bronchoscopy1

Virtual Bronchoscopy

  • 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: don’t use gray-scale information,


Virtual bronchoscopy2

Virtual Bronchoscopy

  • Quicksee-Basic operation:

    • Load Data

      • 3D radiologic image

    • Do Automatic Analysis

      • Compute

        • Paths (axes) through airways

        • Extract regions (airways)

      • Save results for interactive navigation

    • Perform Interactive navigation/assessment

      • View, Edit, create paths through 3D image

      • View structure; get quantitative data

      • Many visual aids and viewers available


Our work

OUR WORK

  • 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


Path planning in virtual bronchoscopy

Path

  • Path through a tubular structure defines a trajectory along tube’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


Previous path finding methods

Previous Path-Finding Methods

  • 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


Our method

Our Method

  • 2D

    • Morphological Operations (Algorithm I)

    • Distance Transformation (Algorithm II)

  • 3D

    • A Combination of Methods with Novelty

      • Phantom

      • Human airways


2 d basic shape algorithm i

2-D(basic shape-Algorithm I)

  • Load an Object


2 d basic shape algorithm i1

2-D(basic shape-Algorithm I)

  • Distance from Boundary


2 d basic shape algorithm i2

2-D(basic shape-Algorithm I)

  • Gradient of DT from Boundary

Gradient < 0


2 d basic shape algorithm i3

2-D(basic shape-Algorithm I)

  • Thinning


2 d branching shape algorithm i

2-D(branching shape-Algorithm I)

  • Skeletonization

False Branches


2 d branching shape algorithm i1

2-D(branching shape-Algorithm I)

  • Length-based Elimination

Branch Points

End Points


2 d basic shape algorithm ii

Distance Transformation (Chamfer Distance)

2-D(basic shape-Algorithm II)

Start Point

Start Point


Distance transformation

Distance Transformation

  • City block

    dist4(p,q) = | px – qx | + | py – qy |

  • Chess board

    dist8(p,q) = max { | px – qx |,| py – qy | }

  • Chamfer

    distcha<A,B>(p,q) = A. max { | px – qx |,| py – qy | } +

    (B-A). min { | px – qx |,| py – qy | }

  • Euclidean

    diste(p,q) = √( ( px – qx )2 + ( py – qy )2 )

  • Squared Euclidean

    distE(p,q) = ( px – qx )2 + ( py – qy )2


2 d basic shape algorithm ii1

End Point Detection & Shortest Path

2-D(basic shape-Algorithm II)

Shortest Path

End Point

Start Point

Steepest Descent

Local Maxima

End Points


Path planning in virtual bronchoscopy

3D

  • Our Procedure

    • Prepare the Data

    • Start Point Detection

    • Boundary Extraction

    • End Points Detection

    • Path Initialization

    • Centering

    • Refinements


Prepare the input

Prepare the Input

  • 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


Load data

Load Data


Start point detection

Start Point Detection


Boundary extraction

Boundary Extraction

  • Morphological Operations

    Boundary = Dilated Image – Original Image

    Boundary = Original Image – Eroded Image

  • Distance Transformation from boundary to middle

     Boundary = ( DT == 1 )


End point detection

End Point Detection

  • 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

EDT

< 1 , 2 , 3 >

< 3 , 4 , 5 >


Chamfer distance transformation

Chamfer Distance Transformation

  • distcha<A,B,C>(p,q) = A. max { | px – qx |,| py – qy |,|pz – qz | } +

    (B-A). max{ min{ | px – qx |,| py – qy | },

    min{ | px – qx |,| pz – qz | },

    min{ | py – qy |,| pz – qz | } } +

    (C-B-A). min{ | px – qx |,| py – qy |,|pz – 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)


End point detection1

End Point Detection

  • Neighboring Window


End point detection2

End Point Detection


Path initialization

Path Initialization

  • Neighboring


Path initialization1

Path Initialization

Start Point

End Points

Farest End Point


Centering

Centering

  • 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

    • Basically, snakes are trying to match a deformable model to an

      image by means of energy minimization.

G

D


Centering1

Centering

  • 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) – D(i-1,j,k) )

    Gy = 0.5 ( D(i,j+1,k) – D(i,j-1,k) )

    Gz = 0.5 ( D(i,j,k+1) – D(i,j,k-1) )

G

D

Middle axis has minimum of Gradient


Centering2

Centering

  • 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


Centering3

Centering

  • 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


Centering4

Centering

Start Point

End Points

Farest End Point


Refinements

Refinements

  • 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


Refinements1

Refinements

  • Continuous Path

    • Lose of continuity after centering

    • Detect of discontinuity and make continue the path


Path planning in virtual bronchoscopy

& now …

  • 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


Virtual bronchoscopy3

Virtual Bronchoscopy


Discussion

Discussion

  • No single method is good for everything …

    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) – (0.59-0.59-0.50 mm)


Future work

Future work

  • 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


Path planning in virtual bronchoscopy

Thank You!

My thanks to …

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.


Path planning in virtual bronchoscopy

Questions …. Suggestions …. Comments …. Ideas …. ?

[email protected] [email protected]


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