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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). Lung cancer is the most common cause of cancer related death*

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Path Planning in Virtual Bronchoscopy

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  1. Path Planning in Virtual Bronchoscopy Mohamadreza Negahdar Supervisor : Dr. Ahmadian Co-supervisor : Prof. Navab Tehran University of Medical Sciences January 2006 • Progress Report

  2. 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

  3. 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

  4. 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

  5. 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,

  6. 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

  7. 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 

  8. 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

  9. 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

  10. Our Method • 2D • Morphological Operations (Algorithm I) • Distance Transformation (Algorithm II) • 3D • A Combination of Methods with Novelty • Phantom • Human airways

  11. 2-D(basic shape-Algorithm I) • Load an Object

  12. 2-D(basic shape-Algorithm I) • Distance from Boundary

  13. 2-D(basic shape-Algorithm I) • Gradient of DT from Boundary Gradient < 0

  14. 2-D(basic shape-Algorithm I) • Thinning

  15. 2-D(branching shape-Algorithm I) • Skeletonization False Branches

  16. 2-D(branching shape-Algorithm I) • Length-based Elimination Branch Points End Points

  17. Distance Transformation (Chamfer Distance) 2-D(basic shape-Algorithm II) Start Point Start Point

  18. 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

  19. End Point Detection & Shortest Path 2-D(basic shape-Algorithm II) Shortest Path End Point Start Point Steepest Descent Local Maxima End Points

  20. 3D • Our Procedure • Prepare the Data • Start Point Detection • Boundary Extraction • End Points Detection • Path Initialization • Centering • Refinements

  21. 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

  22. Load Data

  23. Start Point Detection

  24. Boundary Extraction • Morphological Operations Boundary = Dilated Image – Original Image Boundary = Original Image – Eroded Image • Distance Transformation from boundary to middle  Boundary = ( DT == 1 )

  25. 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 >

  26. 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) 

  27. End Point Detection • Neighboring Window 

  28. End Point Detection

  29. Path Initialization • Neighboring  

  30. Path Initialization Start Point End Points Farest End Point

  31. 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

  32. 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

  33. 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

  34. 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

  35. Centering Start Point End Points Farest End Point

  36. 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

  37. Refinements • Continuous Path • Lose of continuity after centering • Detect of discontinuity and make continue the path 

  38. & 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 • …

  39. Virtual Bronchoscopy

  40. 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)

  41. 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

  42. 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.

  43. Questions …. Suggestions …. Comments …. Ideas …. ? mrnegahdar@razi.tums.ac.ir mrnus@yahoo.com

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