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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* - PowerPoint PPT Presentation


  • Path Planning in Virtual BronchoscopyMohamadreza Negahdar

    Supervisor : Dr. AhmadianCo-supervisor : Prof. Navab

    Tehran University of Medical SciencesJanuary 2006 Progress Report

  • 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

  • IntroductionHigh-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 BronchoscopyVB is a computer-based approach for navigating virtually through airways captured in a 3-D MDCT image

    VB 3-D image analysis:Guidance of bronchoscopyHuman lung-cancer assessment Planning and guiding bronchoscopic biopsiesQuantitative airway analysis noninvasively-Smooth virtual navigation

    A suitable method must:Provide a detailed, smooth structure of the airway trees central axesRequire little human interactionFunction over a wide range of conditions as observed in typical lung-cancer patients

  • Virtual BronchoscopyA 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: dont use gray-scale information,

  • Virtual BronchoscopyQuicksee-Basic operation:Load Data3D radiologic imageDo Automatic AnalysisComputePaths (axes) through airwaysExtract regions (airways)Save results for interactive navigationPerform Interactive navigation/assessmentView, Edit, create paths through 3D imageView structure; get quantitative dataMany visual aids and viewers available

  • OUR WORKGoals: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 fasterNeeds less interactionBe more robust and reproducible

  • PathPath through a tubular structure defines a trajectory along tubes central axis

    A Path denoted as:Medial (central) axes of branchesPreserve homotopy of structureContinuous for smooth visualizationPath is spine of cylinder

  • Previous Path-Finding MethodsAutomated approaches:Segmentation followed by 3D skeletonizationActive contour modelsMorphological operationsEstimation of principal eigenvectorsVector fields

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

  • Our Method 2D

    Morphological Operations (Algorithm I)

    Distance Transformation (Algorithm II)


    A Combination of Methods with Novelty

    PhantomHuman airways

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

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

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

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

  • 2-D (branching shape-Algorithm I)SkeletonizationFalse Branches

  • 2-D (branching shape-Algorithm I)Length-based EliminationEnd PointsBranch Points

  • 2-D (basic shape-Algorithm II)Distance Transformation (Chamfer Distance)Start PointStart Point

  • Distance TransformationCity block dist4(p,q) = | px qx | + | py qy |Chess board dist8(p,q) = max { | px qx |,| py qy | }Chamfer distcha(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 II)End Point Detection & Shortest PathSteepest DescentLocal MaximaStart PointEnd PointEnd PointsShortest Path

  • 3DOur Procedure

    Prepare the Data

    Start Point Detection

    Boundary Extraction

    End Points Detection

    Path Initialization



  • 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

  • Start Point Detection

  • Boundary ExtractionMorphological Operations Boundary = Dilated Image Original Image

    Boundary = Original Image Eroded Image

    Distance Transformation from boundary to middle

    Boundary = ( DT == 1 )

  • End Point DetectionDistance Transformation

    Assigns larger number to voxels with region growing in comparison to exact Euclidean metricMore accurate approximation of true Euclidean distance metricAllocate integer values to voxels which speeds up the next computations

    EDT< 1 , 2 , 3 >< 3 , 4 , 5 >

  • Chamfer Distance Transformation

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

  • End Point DetectionNeighboring Window

  • End Point Detection

  • Path InitializationNeighboring

  • Path InitializationStart PointEnd PointsFarest End Point

  • CenteringWhat is a snake?An energy minimizing spline guided by external constraint forces and pulled by image forces toward features:Edge detectionSubjective contoursMotion trackingStereo matching

    Basically, snakes are trying to match a deformable model to animage by means of energy minimization.


  • CenteringEnergy & Gradient of ImageD = 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) )

    DGMiddle axis has minimum of Gradient

  • CenteringSnakePath 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 termsImage termDecreasing function of the image gradient

  • CenteringImage 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

  • CenteringStart PointEnd PointsFarest End Point

  • Refinements

    Length-based Elimination

    In Path Initialization Stage:Remove branches which has length less than 10 voxelAfter Centering Stage:Remove branches which has length less than 5 voxel

  • Refinements

    Continuous Path

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

  • & now Virtual navigation and virtual endoscopySegmentation & RegistrationVirtual-guided bronchoscopy & BiopsyQuantification of anatomical structuresSurgical planningRadiation treatmentCurved planner reformationStenosis detectionAneurism and wall bronchia classification detectionDeforming volumes

  • Virtual Bronchoscopy

  • DiscussionNo single method is good for everything then we use combination of distance field & potential field

    Fully automatedwithout any interaction by physician

    No miss branch , No false branch42 branch out of 42

    Robustless sensitivity to noise

    Too fastless than 1 minute for (512 x 512 x 416) (0.59-0.59-0.50 mm)

  • Future workEvaluate 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

  • My thanks to Dr. Alireza AhmadianProf. Nassir NavabDr. Joerg Traub& My Family

    For nothing is hidden, except to be revealed;Nor has been secret, but that is should come to light.

  • Questions . Suggestions . Comments . Ideas . ?

    [email protected] [email protected]

    In the name of God Mohamadreza Negahdar hails from Tehran , He is currently pursuing his M.Sc. in Biomedical Engineering at Tehran University of Medical Science (TUMS) , Tehran , Iran . His research interests include Image processing , Image Analysis, Wavelet & watermarking , Bioinformatics , Medical decision-making , Fuzzy logic & Neuro-fuzzy , Robotics & tele-surgery , Path planning , Virtual endoscopy , Medical instruments , Astronomy , Musicology and etc. .Virtual navigation & Virtual endoscopy, data analysis, reduced dimensionalityMultidetector computed-tomography (MDCT)The advantage of VB is that airway analysis can be done noninvasively, thus enabling more careful assessment and follow-on procedure planning.Local measurements on airway branchesFor this reason, we provide the surgeon with an automatic path faster: computing time is below the minute on a standard PC needs less interaction: only one user defined point needed for the complete trajectory is more robust and reproducible: segment and compute the trajectory at the same time, dos not rely on a previously segmented object.Centered, Homotopic, Connected, Invariant under isometric transformations, Robust (weak sensitivity to noise), ThinNeuheitBlum,1967Moving through z dimensionEthinThe shortest path between the two pointsP(v), forcing the snake to move towards the edges in the imageExploits the centeredness propertyReliability ensures that the physician has the possibility of fully examine the interior of the organ.AcknowledgementThis presentation designed and presented by Mohamadreza Negahdar at Tehran University of Medical Science (TUMS).E-mail : [email protected] , [email protected]