1 / 45

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*

decima
Download Presentation

Path Planning in Virtual Bronchoscopy

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related