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PATH PLANNING Presented by : Mohamadreza Negahdar Supervisor : Dr. Ahmadian Co-Supervisor : Prof. Navab Tehran University of Medical Sciences October 5, 2005

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OUTLINE Introduction Introduction Path planning in medicine Path planning in medicine Automatic path generation Automatic path generation Skeleton & skeletonization Skeleton & skeletonization Skeletonization techniques Skeletonization techniques Medical applications Medical applications Path planning Path planning Roadmap Roadmap Cell decomposition Cell decomposition Potential field Potential field Virtual endoscopy Virtual endoscopy Navigation Navigation Applications Applications Our work Our work

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Introduction How can “see” inside the body to screen and cure? How can “see” inside the body to screen and cure? Centerline extraction is the basis to understand three dimensional structure of the organ Centerline extraction is the basis to understand three dimensional structure of the organ Given a map and goal location, identify trajectory to reach goal location Given a map and goal location, identify trajectory to reach goal location Strategic competence Strategic competence How do we combine these two competencies, along with localization, and mapping, into a coherent framework? How do we combine these two competencies, along with localization, and mapping, into a coherent framework?

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Path Planning in medicine Fly-through and navigation Fly-through and navigation General idea of the shape of the organ walls General idea of the shape of the organ walls Detect an abnormal shape Detect an abnormal shape Making measurements for locating abnormalities Making measurements for locating abnormalities Computing local distension and length Computing local distension and length Risk of infection or perforation of the anatomy being examined will be eliminated Risk of infection or perforation of the anatomy being examined will be eliminated

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Path Planning in medicine Bronchoscopy, Airway analysis Bronchoscopy, Airway analysis Colonoscopy Colonoscopy Esophagus Esophagus Neurosurgery, Stereotaxic radiosurgery Neurosurgery, Stereotaxic radiosurgery Liver surgery Liver surgery Angiography Angiography Needle steering Needle steering

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Architecture

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Restrictions of manually path planning Very time consuming Frustrating for a novice user Need to improve the performance and lower the cost For this reason, we provide the surgeon with an automatic path generation.

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Automatic Path Generation Surgeon loads a 3D model Surgeon loads a 3D model Defines a start and an end point Defines a start and an end point Program returns an optimal path centered inside the model Program returns an optimal path centered inside the model The user can fly-through the path and/or edit it manually The user can fly-through the path and/or edit it manually

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Input

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Output

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Automatic Path Planning for VE The goal is to automatically extract a fly-through trajectory for the endoscope that stays as far as possible from the organ walls in order to maximize the amount of information that the user sees during the fly-through The goal is to automatically extract a fly-through trajectory for the endoscope that stays as far as possible from the organ walls in order to maximize the amount of information that the user sees during the fly-through

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Skeletonization The aim of the skeletonization is to extract a region-based shape feature representing the general form of an object. The aim of the skeletonization is to extract a region-based shape feature representing the general form of an object. We have applied skeletonization to extract the central path of a 3D "tubular" object. We have applied skeletonization to extract the central path of a 3D "tubular" object.

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Skeleton of object The medial axis from an object called its skeleton The medial axis from an object called its skeleton Skeleton-based techniques first compute a digital skeleton of the entire tree Skeleton-based techniques first compute a digital skeleton of the entire tree Center locus of multi-tangent circles (in 2D) or balls (in 3D) Center locus of multi-tangent circles (in 2D) or balls (in 3D) The skeleton represents: local object symmetries, and the topological structure of the object. The skeleton represents: local object symmetries, and the topological structure of the object.

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Skeletonization techniques detecting ridges in distance map of the boundary points detecting ridges in distance map of the boundary points calculating the Voronoi diagram generated by the boundary points calculating the Voronoi diagram generated by the boundary points the layer by layer erosion called thinning the layer by layer erosion called thinning

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Comparison of Skeletonization Techniques In digital spaces, only an approximation to the "true skeleton" can be extracted. There are two requirements to be complied with: In digital spaces, only an approximation to the "true skeleton" can be extracted. There are two requirements to be complied with: topological (to retain the topology of the original object) topological (to retain the topology of the original object) geometrical (forcing the "skeleton" being in the middle of the object and invariance under the most important geometrical transformation including translation, rotation, and scaling) geometrical (forcing the "skeleton" being in the middle of the object and invariance under the most important geometrical transformation including translation, rotation, and scaling) methodgeometricaltopological Distance transform yesno Voronoi-skeletonyesyes Thinningnoyes

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Distance Transformation The original (binary) image is converted into feature and non-feature elements. The feature elements belong to the boundary of the object. The original (binary) image is converted into feature and non-feature elements. The feature elements belong to the boundary of the object. The distance map is generated where each element gives the distance to the nearest feature element. The distance map is generated where each element gives the distance to the nearest feature element. The ridges (local extremes) are detected as skeletal points. The ridges (local extremes) are detected as skeletal points. The distance map resulted by the distance transformation depends on the chosen distance. The original binary object (left) and its distance map (right). (The distance map is displayed as a surface where the ridge points belong to the skeleton.)

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Distance Transformation Chose of distance: Extracted feature points (left) and distance map using city block (or 4-neighbour) distance (right) Distance map using chess-board (or 8-neighbour) distance (left) and distance map using (3,4)- chamfer distance (right)

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Voronoi Diagram The Voronoi diagram of a discrete set of points (called generating points) is the partition of the given space into cells so that each cell contains exactly one generating point and the locus of all points which are nearer to this generating point than to other generating points. The Voronoi diagram of a discrete set of points (called generating points) is the partition of the given space into cells so that each cell contains exactly one generating point and the locus of all points which are nearer to this generating point than to other generating points. The 10 generating points (left) and their Voronoi diagram (right).

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Voronoi Diagram The Voronoi diagrams can be computed by an incremental construction: The Voronoi diagrams can be computed by an incremental construction: If the density of boundary points (as generating points) goes to infinity then the corresponding Voronoi diagram converges to the skeleton.

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Voronoi Skeleton Some border points of a rectangle form the set of generating points The skeleton (marked by red lines) is approximated by a subgraph of the Voronoi diagram

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Thinning Border points of a binary object are deleted in iteration steps until only the “skeleton” is left. In case of “near tubular”3D objects (e.g., airway, blood vessel, and gastro–intestinal tract), Thinning has a major advantage over the other skeletonization methods since curve thinning can produces medial lines easily

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Thinning

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Thinning The thinning has some beneficial properties: The thinning has some beneficial properties: It preserves the topology (retains the topology of the original object) It preserves the topology (retains the topology of the original object) It preserves the shape (significant feature suitable for object recognition or classification is extracted) It preserves the shape (significant feature suitable for object recognition or classification is extracted) It preserves the shape It preserves the shape It forces the "skeleton" being in the middle of the object It forces the "skeleton" being in the middle of the object It forces the "skeleton" being in the middle of the object It forces the "skeleton" being in the middle of the object It produces one pixel/voxel width "skeleton“ It produces one pixel/voxel width "skeleton“ It does not preserve the topology, since It does not preserve the topology, since an object is disconnected an object is disconnected an object is completely deleted an object is completely deleted cavity (white connected component surrounded by an object) is created/a hole is created cavity (white connected component surrounded by an object) is created/a hole is created a cavity/hole is merged with the background a cavity/hole is merged with the background two cavities/four holes are merged two cavities/four holes are merged

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Thinning

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shape preserving thinning The original object (top) and the result of the thinning (bottom). The text remains readable

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Example of 2D thinning Example of 3D thinning A segmented human ventricle as an original object (left) and its medial lines (right)

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Medical Applications assessment of laryngotracheal stenosis assessment of laryngotracheal stenosis assessment of infrarenal aortic aneurysm assessment of infrarenal aortic aneurysm unravelling the colon unravelling the colon Each of the emerged three applications requires the cross-sectional profiles of the investigated tubular organs

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Procedure image acquisition by Spiral Computed Tomography (S-CT) (semiautomatic snake-based) segmentation (i.e., determining a binary object from the gray-level picture morphological filtering of the segmented object curve thinning (by using one of our 3D thinning algorithm) raster-to-vector conversion pruning the vector structure (i.e., removing the unwanted branches) smoothing the resulted central path calculation of the cross-sectional profile orthogonal to the central path

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Assessment of LTS The cross-sectional profiles (based on the central path) of the upper respiratory tract (URT) were calculated with proven LTS on fiberoptic endoscopy (FE). The cross-sectional profiles (based on the central path) of the upper respiratory tract (URT) were calculated with proven LTS on fiberoptic endoscopy (FE). Locations of LTS were determined on axial S-CT slices and compared to findings of fiberoptic endoscopy (FE) by Cohen's kappa statistics. Regarding the site of LTS an excellent correlation was found between FE and S-CT (z=7.44, p<0.005). Locations of LTS were determined on axial S-CT slices and compared to findings of fiberoptic endoscopy (FE) by Cohen's kappa statistics. Regarding the site of LTS an excellent correlation was found between FE and S-CT (z=7.44, p<0.005). The segmented URT, its central path

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Assessment of AAA Along the central path the cross-sectional profile was computed. Along the central path the cross-sectional profile was computed. The maximum diameter in 3D as well as the length of the proximal and distal neck of the aneurysma, Since size of the aneurysma is regarded to be a prognosticated factor. The maximum diameter in 3D as well as the length of the proximal and distal neck of the aneurysma, Since size of the aneurysma is regarded to be a prognosticated factor. The volume of the segmented aneurysma was determined too. The volume of the segmented aneurysma was determined too. Two phantoms and their central paths The segmented part of the infrarenal aorta, its central path

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Unravelling the Colon Unravelling the colon is a new method to visualize the entire inner surface of the colon without the need for navigation. Unravelling the colon is a new method to visualize the entire inner surface of the colon without the need for navigation. This is a minimally invasive technique that can be used for colorectal polyps and cancer detection. This is a minimally invasive technique that can be used for colorectal polyps and cancer detection. An algorithm for unravelling the colon which is to digitally straighten and then flatten using reconstructed spiral/helical computer tomograph (CT) images. An algorithm for unravelling the colon which is to digitally straighten and then flatten using reconstructed spiral/helical computer tomograph (CT) images. Comparing to virtual colonoscopy where polyps may be hidden from view behind the folds, the unravelled colon is more suitable for polyp detection, because the entire inner surface is displayed at one view. Comparing to virtual colonoscopy where polyps may be hidden from view behind the folds, the unravelled colon is more suitable for polyp detection, because the entire inner surface is displayed at one view.

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Unravelling the Colon The segmented volume of a part of the artificial phantom with two polyps (top) and the same part of the phantom after unravelling (bottom). The segmented volume of a part of the cadavric phantom with polyps (top) and the unravelled colon (bottom).

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PATH PLANNING Approaches; Roadmap road map using Meadow maps road map using visibility graph road map using Voronoi diagram RRRRRT Cell decomposition exact cell decomposition approximate cell decomposition adaptive cell decomposition Potential field

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Roadmap Building a network connection between the vertices of polygons Typically represent obstacles as polygons, and the camera as a point Appropriate for polygon-based dataset, has limitation in VC

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Path planning: road maps using Meadow maps Use a-priori map, transform free space into convex polygons, grow obstacles by robot size Construct path through polygon edges, from start to goal

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Path planning: road maps using Meadow maps Polygon generation is computationally complex Uses map artifacts to determine polygons Jagged paths - though can fix with path relaxation path relaxationpath relaxation How update map if robot discovers discrepancies

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Path Relaxation Path Relaxation is a method of planning safe paths around obstacles for mobile robots. It works in two steps: a global grid search that finds a rough path, a global grid search that finds a rough path, followed by a local relaxation step that adjusts each node on the path to lower the overall path cost. The representation used by Path Relaxation allows an explicit trade off among length of path, clearance away from obstacles, and distance traveled through unmapped areas.

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Path Relaxation Path Relaxation Property Let p = v 0, v 1,..., v k be a shortest path from s = v 0 to v k.If we relax, in order, (v 0, v 1 ), (v 1, v 2 ),..., (v k-1, v k ), even intermixed with other relaxations, then d [v k ] = δ(s, v k ). Proof Induction to show that d[v i ] = δ(s, v i ) after (v i-1, v i ) is relaxed. Basis: i = 0. Initially, d [v 0 ] = 0 = δ(s, v 0 ) = δ(s, s). Inductive step: Assume d[v i-1 ] = δ(s, v i-1 ). Relax (v i-1, v i ). By convergence property, d [v i ] = δ(s, v i ) afterward and d [vi ] never changes.

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Path planning: road map using visibility graph Consists of edges/roads joining all pairs of vertices that can see each other (including start and goal positions) Implies edges along the side of polygons Finds shortest sequence of roads from start to goal

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Path planning: road map using visibility graph Brings robot very close to objects Generates shortest path length Fairly simple implementation Inefficient in densely populated environments Create visibility graphPlan shortest path

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Path planning: road map using Voronoi diagram Edges/roads formed by points that are equidistant from two or more obstacles Finds shortest sequence of roads from start to goal

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Path planning: road map using Voronoi diagram Tends to maximize distance from obstacles Can be a problem for short-range sensors if they can not detect the obstacles, and hence the robot can not localize No need to grow obstacles as robot stays “in the middle” Important advantage is that the control system using range sensors can follow Voronoi lines directly Maximize the readings of local minima in current sensor values Can be used to actually create Voronoi diagrams of unknown environments

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Voronoi diagram

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Rapidly-exploring Random trees Begin at the start state Attempt to grow into the goal state By exploring the vehicle’s state space Search from both sides Goal Start

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RRT

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RRT

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Cell decomposition Divide space into simple connected regions called cells Construct connectivity graph from adjacent open cells Find cells containing start and goal locations, and search for path between them in the connectivity graph Compute path within each cell found in path above, e.g. Pass through midpoints of cell boundaries Sequence of wall-following motions and straight line movements

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Path planning: exact cell decomposition Computational complexity directly depends on density and complexity of elements in environment Sparse is good, even for very geometrically large areas

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Path planning: approximate cell decomposition Popular due to popularity of grid-based maps Low computational complexity Potentially large memory requirements

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Path planning: approximate cell decomposition NF1, ‘grassfire’, algorithm Minima-free Wavefront expansion from goal outwards Each cell visited once - computational complexity linear in number of cells, not environment complexity

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Path planning: adaptive cell decomposition

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Potential field This approaches is simplified to a point such as a camera model in computer graphics The camera moves under the influence of a set of potentials produced by the attraction and repulsion potentials The attraction potential pulls robot toward the goal and the repulsion potential pushes it away from the obstacles The variation of potentials create the attraction and repulsion forces

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Path planning: potential fields CreateCreate an artificial field on robot’s map, and treat Create Robot as point under influence of field Goal as the low point (attractive force) Obstacles as peaks (repulsive forces)

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Potential Field Generation Generation of potential field function U(q) attracting (goal) and repulsing (obstacle) fields summing up the fields functions must be differentiable Generate artificial force field F(q) Set robot speed (v x, v y ) proportional to the force F(q) generated by the field the force field drives the robot to the goal if robot is assumed to be a point mass

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Attractive Potential Field Parabolic function representing the Euclidean distance to the goal Attracting force converges linearly towards 0 (goal)

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Repulsion Potential Field Should generate a barrier around all the obstacle strong if close to the obstacle not influence if far from the obstacle : minimum distance to the object : minimum distance to the object Field is positive or zero and tends to infinity as q gets closer to the object

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Path planning: potential fields Fairly easy to implement Set robot speed proportional to force Field drives robot to the goal Movement is similar to a ball rolling down a hill Is also a control law as robot can always determine next required action (assuming robot can localize position with respect to its map and the field) Local minima can be problematic Concave objects can generate oscillations More complicated if robot is not treated as point mass

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Path planning: potential field, extensions Rotation potential field Repulsive force also a function of orientation, e.g. an obstacle parallel to robot’s direction of travel Enhanced wall following

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Virtual Endoscopy - Idea Input a high-resolution 3D radiologic image Input a high-resolution 3D radiologic image virtual copy of anatomy Use computer to explore virtual anatomy Use computer to explore virtual anatomy permits unlimited navigation exploration In each frame, the view on the left is the virtual CT rendering and on the right is the endoscope image

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Navigation The camera automatically moves from the source point towards the target point The camera automatically moves from the source point towards the target point User can interactively modified the camera position and direction User can interactively modified the camera position and direction The camera stays away from the surface The camera stays away from the surface The camera should never penetrate through the surface The camera should never penetrate through the surface The physician can change source and target positions The physician can change source and target positions

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Virtual Navigator - Architecture

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APPLICATIONS

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Virtual colonoscopy ssimplification of the colonic surface by decimation tthinning of the decimated colon to create a preliminary centerline sselection of equally spaced points on the preliminary centerline ggrouping neighboring points mmapping them back to rings in the original colon Gheorghe Iordanescu, PhD, Ronald M. Summers, MD, PhD

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Virtual colonoscopy Start: The starting point is the original colon surface (S OC ), produced from the CTC images using a region growing segmentation and isosurface extraction. The threshold for the isosurface extraction is a specific Hounsfield unit (HU). The “marching cubes” algorithm was used to extract the isosurface. The surface is composed of many small triangles, which are described by their vertices and edges. Start: The starting point is the original colon surface (S OC ), produced from the CTC images using a region growing segmentation and isosurface extraction. The threshold for the isosurface extraction is a specific Hounsfield unit (HU). The “marching cubes” algorithm was used to extract the isosurface. The surface is composed of many small triangles, which are described by their vertices and edges. Decimation: The S OC is simplified using decimation to minimize the number of operations performed in subsequent steps. Decimation keeps the general appearance and topology of the colon but reduce the number of vertices in region of the surface with low curvature. The result of this step is the decimated colon surface (S DC ). Decimation: The S OC is simplified using decimation to minimize the number of operations performed in subsequent steps. Decimation keeps the general appearance and topology of the colon but reduce the number of vertices in region of the surface with low curvature. The result of this step is the decimated colon surface (S DC ). Gheorghe Iordanescu, PhD, Ronald M. Summers, MD, PhD startdecimationthinningmodelingremapping Centerline computation Final remapping

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Virtual colonoscopy Thinning: We compute the thinned colon surface (S TC ) by iteratively averaging the distances between colon vertices in the S DC. Thinning: We compute the thinned colon surface (S TC ) by iteratively averaging the distances between colon vertices in the S DC. The thinning is equivalent to applying a Laplacian operator to each vertex V[i]. If vertex V[i] has N[i] neighbors and all of these neighbors are in a neighborhood N i, the formula for thinning is: Gheorghe Iordanescu, PhD, Ronald M. Summers, MD, PhD startdecimationthinningmodelingremapping Centerline computation Final remapping Thinning evolution of a 3D colon surface (a) Three-dimensional surface of the human colon reconstructed from a CT colonography dataset. (b) Thinned 3D surface of the human colon; 250 (dark red), 500 (green), and 1,000 (blue) iterations.

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Virtual colonoscopy Modeling: We create a model of the S TC by taking equally spaced vertices from the S TC. The model of the S TC will be composed of straight segments that connect these approximately equally spaced vertices from the S TC. To obtain the correct vertices, we use a region growing strategy in which we start from a seed point and identify vertices connected to the seed. Modeling: We create a model of the S TC by taking equally spaced vertices from the S TC. The model of the S TC will be composed of straight segments that connect these approximately equally spaced vertices from the S TC. To obtain the correct vertices, we use a region growing strategy in which we start from a seed point and identify vertices connected to the seed. Gheorghe Iordanescu, PhD, Ronald M. Summers, MD, PhD startdecimationthinningmodelingremapping Centerline computation Final remapping Modeling the colon by an ordered set of 3D point. (a) Portion of the thinned colon, its vertices (red crosses) and their connectivity are displayed. (b) Some vertices are selected to model the thinned colon (green circles). (c) Model of the tinned colon, piece-wise linear curve instead of a surface mesh. (d) Portion of the tinned colon and its piece-wise linear model.

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Virtual colonoscopy Remapping: We use the model to compute slices through the S TC that correspond to rings in the S DC. Using the one-to-one mapping of the vertices on the S TC and S DC, the indices of the vertices from the same slice in the S TC are used to get a ring in the S DC. Remapping: We use the model to compute slices through the S TC that correspond to rings in the S DC. Using the one-to-one mapping of the vertices on the S TC and S DC, the indices of the vertices from the same slice in the S TC are used to get a ring in the S DC. The result is vertices from the S DC that are grouped in ring-like areas, where each ring is approximately perpendicular to the colon centerline. Gheorghe Iordanescu, PhD, Ronald M. Summers, MD, PhD startdecimationthinningmodelingremapping Centerline computation Final remapping Steps in the centerline algorithm. (a) Original 3D colonic surface from prone CTC. (b) Blue curve shows the preliminary centerline after thinning and modeling. Preliminary centerlines may not lie within the colonic lumen. Green curve shows the final centerline after remapping. The final centerline lies within the colonic lumen. (c) Detail of sigmoid colon (blue box in (a)). (d) Segmented colon. Colon rings (cross-sections) are colored.

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Virtual colonoscopy Centerline Computation: The centers-of-mass of the edges of adjacent rings of the S DC is the local centerline point. We determine the local centerline point by averaging the vertices at the rings’ edges. The resulting points at the center of each ring are again interpolated for purpose of display using spline functions. The ring centers and interpolated points constitute the final centerline. For interpolation we use the Catmull-Rom spline. Centerline Computation: The centers-of-mass of the edges of adjacent rings of the S DC is the local centerline point. We determine the local centerline point by averaging the vertices at the rings’ edges. The resulting points at the center of each ring are again interpolated for purpose of display using spline functions. The ring centers and interpolated points constitute the final centerline. For interpolation we use the Catmull-Rom spline. Catmull-Rom spline Catmull-Rom spline Gheorghe Iordanescu, PhD, Ronald M. Summers, MD, PhD startdecimationthinningmodelingremapping Centerline computation Final remapping Detail of the centerline in different colonic segments. Portions of the (a) splenic flexure, (b) hepatic flexure, and (c) transverse colon are shown.

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Catmull-Rom Spilnes Splines are a mathematical means of representing a curve, by specifying a series of points at intervals along the curve and defining a function that allows additional points within an interval to be calculated. Splines are a mathematical means of representing a curve, by specifying a series of points at intervals along the curve and defining a function that allows additional points within an interval to be calculated. The points that define a spline are known as "Control Points". One of the features of the Catmull-Rom spline is that the specified curve will pass through all of the control points - this is not true of all types of splines. The points that define a spline are known as "Control Points". One of the features of the Catmull-Rom spline is that the specified curve will pass through all of the control points - this is not true of all types of splines. To calculate a point on the curve, two points on either side of the desired point are required, as shown on the next. The point is specified by a value t that signifies the portion of the distance between the two nearest control points. To calculate a point on the curve, two points on either side of the desired point are required, as shown on the next. The point is specified by a value t that signifies the portion of the distance between the two nearest control points.

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Catmull-Rom Spilnes Given the control points P 0, P 1, P 2, and P 3, and the value t, the location of the point can be calculated as (assuming uniform spacing of control points): Given the control points P 0, P 1, P 2, and P 3, and the value t, the location of the point can be calculated as (assuming uniform spacing of control points): [ ] [P 0 ] [ ] [P 0 ] [ ] [P 1 ] [ ] [P 1 ] q(t) = 0.5 * (1.0f, t, t^2, t^3) * * [ ] [P 2 ] [ ] [P 2 ] [ ] [P 3 ] [ ] [P 3 ] To put that another way: To put that another way: q(t) = 0.5( (2 * P 1 ) + (-P 0 + P 2 ) * t + (2*P 0 – 5*P 1 + 4*P 2 – P 3 ) * t^2 + (-P 0 + 3P 1 – 3*P 2 = P 3 ) * t^3 )

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Catmull-Rom Spilnes This formula gives Catmull-Rom spline the following characteristics: This formula gives Catmull-Rom spline the following characteristics: The spline passes through all of the control points. The spline passes through all of the control points. The spline is C1 continuous, meaning that there are no discontinuities in the tangent direction and magnitude. The spline is C1 continuous, meaning that there are no discontinuities in the tangent direction and magnitude. The spline is not C2 continuous. The second derivative is linearly interpolated within each segment, causing the curvature to vary linearly over the length of the segment. The spline is not C2 continuous. The second derivative is linearly interpolated within each segment, causing the curvature to vary linearly over the length of the segment. Points on a segment may lie outside of the domain of P 1 -> P 2. Points on a segment may lie outside of the domain of P 1 -> P 2.

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Virtual colonoscopy Mapping the S DC to the S OC : A second mapper associates vertices in the S DC with the vertices in the S OC based on minimum distance criterion between the vertices of the two surfaces. Based on the correspondence of the vertices on the S DC and S OC we can segment the S OC and split the surface into rings. Mapping the S DC to the S OC : A second mapper associates vertices in the S DC with the vertices in the S OC based on minimum distance criterion between the vertices of the two surfaces. Based on the correspondence of the vertices on the S DC and S OC we can segment the S OC and split the surface into rings. To limit the search space and improve computational efficiency in performing this second mapping, a process called “vertex classification” is performed, vertices in the S DC and S OC are grouped into classes according to their spatial coordinates. Because only vertices in neighboring classes need to be searched, there is a substantial performance improvement. Gheorghe Iordanescu, PhD, Ronald M. Summers, MD, PhD startdecimationthinningmodelingremapping Centerline computation Final remapping

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Virtual colonoscopy

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Virtual Endoscopy (based on potential fields) The idea is to utilize a hierarchical analysis of attractors to determine principal attractors We combine potentials derived from the distance between source and target positions and from the distance to the colon surface to guide path search process, the paths far away from the colon wall and in the direction of target position Advantages: Eliminating small-undesired branches during the attractor analysis. Warranty of connectivity between start and target points. Search of paths just between principal attractors and do not waste time in connecting the small attractors. Rui C. H. Chiou, Arie E. Kaufman, Zhengrong Liang, Lichan Hong and Miranda Achniotou

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Virtual Endoscopy (based on potential fields) Calculate distance from surface and target Detect attractors Analyze hierarchical attractors Analyze attractors according to their contribution to principal skeleton Calculate distance from next consecutive principal attractor The idea is to take the most powerful attractor and scan its influence zone by 3D distance region growing The process repeats for the next most powerful attractor until the source target principal attractors are found Search paths between principal attractors Rui C. H. Chiou, Arie E. Kaufman, Zhengrong Liang, Lichan Hong and Miranda Achniotou

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Virtual Endoscopy

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Virtual Bronchoscopy VB is a computer-based approach for navigating virtually through airways captured in a 3-D MDCT image VB is a computer-based approach for navigating virtually through airways captured in a 3-D MDCT image VB 3-D image analysis: VB 3-D image analysis: Guidance of bronchoscopy Guidance of bronchoscopy Human lung-cancer assessment Human lung-cancer assessment Planning and guiding bronchoscopic biopsies Planning and guiding bronchoscopic biopsies Quantitative airway analysis –noninvasively- Quantitative airway analysis –noninvasively- Smooth virtual navigation Smooth virtual navigation A suitable method must: A suitable method must: Provide a detailed, smooth structure of the airway tree’s central axes Provide a detailed, smooth structure of the airway tree’s central axes Require little human interaction Require little human interaction Function over a wide range of conditions as observed in typical lung- cancer patients Function over a wide range of conditions as observed in typical lung- cancer patients A. P. Kiraly, J. P. Helferty, E. A. Hoffman, G. McLennan, and W. E. Higgins

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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 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 The method: The method: Define the skeleton of a given segmented 3-d chest image Define the skeleton of a given segmented 3-d chest image Perform a multistage refinement of the skeleton to arrive at a final tree structure Perform a multistage refinement of the skeleton to arrive at a final tree structure A. P. Kiraly, J. P. Helferty, E. A. Hoffman, G. McLennan, and W. E. Higgins

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Virtual Bronchoscopy Quicksee-Basic operation: Quicksee-Basic operation: 1. Load Data 3D radiologic image 3D radiologic image 2. Do Automatic Analysis Compute Compute Paths (axes) through airways Paths (axes) through airways Extract regions (airways) Extract regions (airways) Save results for interactive navigation Save results for interactive navigation 3. Perform Interactive navigation/assessment View, Edit, create paths through 3D image View, Edit, create paths through 3D image View structure; get quantitative data View structure; get quantitative data Many visual aids and viewers available Many visual aids and viewers available A. P. Kiraly, J. P. Helferty, E. A. Hoffman, G. McLennan, and W. E. Higgins

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Virtual Bronchoscopy A. P. Kiraly, J. P. Helferty, E. A. Hoffman, G. McLennan, and W. E. Higgins Segmented Segmented Airway Tree I S and Root Site Stage 1: 3D Skeletonization Stage 2: 1. Length-based Elimination 2. Simple Centering 3. Line-based Elimination 4. Sphere-based Elimination Stage 3: 1. Site Elimination 2. Sub-voxel Centering 3. Spline Fitting Stage 4: Direction Setting Final Tree T = (V,B,P)

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3D Airway Segmentation Overview Lung Region Definition 2D Candidate Labeling 3D Reconstruction Modified 3D Region Growing Optional Filter 3D image I Airway Segmentation I S Optional Filter

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Virtual Bronchoscopy A. P. Kiraly, J. P. Helferty, E. A. Hoffman, G. McLennan, and W. E. Higgins

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OUR WORK Goals: Goals: The aim of our work is to build trajectories for virtual endoscopy inside 3D medical images, using the most automatic way. 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). 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 present. In my thesis, an automatic centerline determination algorithm for three dimensional virtual bronchoscopy CT image will be present. We try that our method: We try that our method: Be faster Be faster Needs less interaction Needs less interaction Be more robust and reproducible Be more robust and reproducible

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OUR WORK Method: Method: Skeletonization False branch Elimination and Simple Centering Tree RefinementDirection Setting Pre-segmented Airway Tree Bronchi Tree

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Virtual Bronchoscopy Thomas Deschamps

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Virtual Bronchoscopy Thomas Deschamps

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

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