path planning in virtual bronchoscopy

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)

3D

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

Centering

Refinements

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.

DG

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 generation.is 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]