Automatic identification and processing of anatomical structures in anatomical maps.

Abstract

To analyze anatomical cardiac maps.SOLUTION: A method includes calculating a medial-axis tree graph of volume of an organ of a patient in a computerized anatomical map of the volume. A predefined number of major branches in the tree graph are identified. Using the identified major branches, one or more known anatomical opening regions of the volume are identified in the anatomical map.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates generally to cardiac mapping, and more particularly to the analysis of anatomical cardiac maps. [Background technology]

[0002] Some clinical procedures employ techniques for analyzing computerized anatomical maps of organs. For example, U.S. Patent Application Publication No. 2020 / 0065983 describes a method that includes calculating the center of gravity of a volume of a patient's organ in a computerized anatomical map of the volume. On the anatomical map, a location on the surface of the volume that is furthest from the center of gravity is found. This location is identified as a known anatomical opening of the organ. In one embodiment, the location includes calculating multiple paths from the center of gravity to multiple locations on the surface of the volume and finding the longest path among the multiple paths. In another embodiment, the organ is a cardiac ventricle and the opening is that of a pulmonary vein.

[0003] As another example, U.S. Patent Application Publication No. 2008 / 0044072 describes a method for labeling connected tubular objects in segmented image data, including receiving segmented image data and labeling the segmented image data to identify multiple components within the segmented image data. The labeling includes processing the segmented image data to create a processed image representing centerlines and radius estimates of the connected tubular components, determining seed point candidates in the processed image that fall within a band of radii, grouping the candidates based on their physical distance from each other and their radius estimates, dividing the segmented image data according to the grouped candidates, and assigning a distinct color label to each of the multiple distinct components. A specific model of expected anatomical structures can be used for automatic identification of arterial and venous trees.

[0004] U.S. Patent Application Publication No. 2008 / 0273777 describes a method and apparatus for generating a network of endoluminal surfaces by defining a set of medial axes of a tubular structure, defining a series of cross sections along the medial axes in the set of medial axes, generating a connectivity graph of the medial axes, defining a plurality of surface representations based on the graph of the medial axes and the cross sections, calculating a volume defined by a first of the surface representations, defining segments of the medial axes, the cross sections, the surface and / or the volume representations, and outputting the network of endoluminal surfaces.

[0005] U.S. Patent Application Publication No. 2007 / 0109299 describes, among other things, a system and method for efficiently using surface data to calculate feature paths of a virtual three-dimensional object. A surface mesh is constructed using segmented volumetric data representing the object. A geodesic distance from a reference point is calculated for each feature element in the surface mesh. The geodesic distance values ​​are used to generate rings. Ring centroids are calculated and connected to form feature paths, which are optionally truncated and smoothed. Summary of the Invention [Means for solving the problem]

[0006] One embodiment of the present invention, described below, provides a method that includes calculating a medial axis tree graph of a volume of a patient's organ in a computerized anatomical map of the volume. A predetermined number of major branches are identified in the tree graph. Using the identified major branches, one or more known anatomical orifice regions of the volume are identified in the anatomical map.

[0007] In some embodiments, the volume includes the left atrium of the heart and the identified one or more orifice regions include the ostia of a pulmonary vein.

[0008] In some embodiments, computing the medial axis tree graph includes computing a medial axis skeletal graph of the volume and defining a center point of the medial axis skeletal graph as the root of the medial axis tree graph.

[0009] In one embodiment, identifying major bifurcations includes removing small bifurcations and circular bifurcations from the tree graph.

[0010] In another embodiment, identifying one or more known anatomical orifice regions includes identifying the orifice regions based on a posterior-anterior orientation and depth of the orifice regions in the anatomical map.

[0011] In some embodiments, the method further comprises presenting the identified one or more orifice regions to a user on an anatomical map.

[0012] In some embodiments, the method further includes deriving a cutting curve for the identified known anatomical opening region in the anatomical map, and cutting the identified known anatomical opening region in the anatomical map along the derived cutting curve.

[0013] In one embodiment, deriving a cutting curve on the anatomical map includes selecting points on the map to be included in the cutting curve and using the selected points in deriving the cutting curve. In another embodiment, the derived cutting curve on the anatomical map is one of a smooth cutting curve and a polygonal cutting curve. In yet another embodiment, deriving a cutting curve on the anatomical map includes (i) segmenting an orifice region on the map, (ii) using the segmented orifice region to derive a farthest central projection point on a major branch of the orifice region, and (iii) a normal plane defined at the farthest central projection point of the major branch. The intersection between the orifice region and the normal plane is defined as the cutting curve.

[0014] In some embodiments, defining the intersection includes deriving one of a smooth cutting curve and a polygonal cutting curve on the anatomical map using the normal plane.

[0015] According to another embodiment of the present invention, there is further provided a system including a memory and a processor, wherein the memory is configured to hold an electronic anatomical map of a volume of an organ of a patient, and the processor is configured to (a) calculate a medial axis tree graph of the anatomical map, (b) identify a predetermined number of major bifurcations in the tree graph, and (c) identify, in the anatomical map, one or more known anatomical opening regions of the volume using the identified major bifurcations. [Brief explanation of the drawings]

[0016] The present invention will be more fully understood from the following detailed description taken in conjunction with the drawings, in which: [Figure 1] 1 is a pictorial illustration of a system for electroanatomical mapping, in accordance with an exemplary embodiment of the present invention; [Figure 2] 1 is a schematic pictorial volume rendering of a surface mesh map of the left atrium illustrating automatically identified pulmonary veins (PVs) within the map, according to an exemplary embodiment of the present invention; [Figure 3] 3 is a schematic pictorial volume rendering of a semi-automatically generated cut of pulmonary veins (PV) in the surface mesh map of FIG. 2, according to an exemplary embodiment of the present invention. [Figure 4] 3 is a schematic pictorial volume rendering of an automatically generated cut of pulmonary veins (PV) in the surface mesh map of FIG. 2, according to an exemplary embodiment of the present invention. [Figure 5] 3 is a flowchart that schematically illustrates a method for identifying pulmonary veins (PVs) in the surface mesh map of FIG. 2 of the left atrium, in accordance with an exemplary embodiment of the present invention. [Figure 6] 3 is a flowchart that schematically illustrates a semi-automated method for cutting pulmonary veins (PVs) from the surface mesh map of FIG. 2, in accordance with an exemplary embodiment of the present invention. [Figure 7] 3 is a flowchart that schematically illustrates an automated method for cutting pulmonary veins (PVs) from the surface mesh map of FIG. 2, in accordance with an exemplary embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0017] Overview Catheter-based anatomical mapping techniques can create computerized anatomical maps of organ cavities (e.g., the surface of a volume). In some cases, the mapping techniques do not recognize anatomical openings or passageways within the volume. For example, a map of the left atrium (LA) of the heart may not represent the openings of the four pulmonary veins (PVs) and their passageways to the left atrial appendage (LAA). Therefore, identifying features (e.g., openings) in such maps with known anatomy may require input from a trained and qualified individual, such as a radiologist or cardiologist, to identify specific anatomical landmarks within the mapped volume that suggest openings, such as the opening area in a map of the LA based on PV ostia. As another example, coherent coloring algorithms also require the disconnection of PVs from the left atrial shape to generate accurate coloring.

[0018] Furthermore, anatomical maps of organs are typically presented as if the organ were viewed from the outside. Physicians understand such maps and find it easy to recognize the LA, particularly the four PVs and LAA, and to identify the veins and atrial appendages. However, future anatomical maps may present a view of the LA from within the atria, and this new type of view may make it difficult for physicians to distinguish between the different aforementioned parts.

[0019] Some embodiments of the invention described herein provide methods for automatically finding and representing orifice regions in computerized anatomical maps of patient organ volumes (e.g., cavities). While the embodiments described herein primarily refer to the LA, the disclosed techniques can be used for mapping and visualization of other cardiac chambers and other organs.

[0020] In some embodiments, a processor receives an anatomically mapped volume of the LA of the heart and automatically identifies the PV and atrial appendage orifice regions on the anatomical map of the LA. To this end, the processor calculates a medial axis graph (hereinafter also referred to as a "skeleton" for short) from the anatomical map. A point on the medial axis of the volume can be defined as a point in a planar cross-section of the volume that has two or more closest points on the resulting boundary in 2D within the cross-sectional plane. What was originally called a "topological skeleton" was introduced in 1967 as a tool for biometric shape recognition.

[0021] The processor then performs the following steps: 1. Simplify the medial axis graph to include only "dominant" branches. A dominant branch is defined as an open-ended curve in the graph that begins at the intersection of the curves in the graph and is one of a predetermined number of longest branches. A further possible definition is by the angle between the branches, where the angle between two dominant branches is greater than a predetermined threshold.

[0022] When an anatomical structure, such as the LA, is known, the process of identifying the major bifurcations is easier because the expected graph topology is known (for the LA, three branches to the left, two to the right, and three junctions at the intersection).

[0023] After the processor simplifies the graph by removing "small" branches and / or circular portions from the graph, only major branches remain in the graph. Thus, minor branches are open-ended lines in the graph that are too short to be part of a predetermined number of branches. If short branches are in the middle of the graph, i.e., non-open ends, they are either removed or merged with larger branches. The predetermined number of major branches is known from the anatomy of the volume. For the LA, this number is typically 5. Note that the valves are part of the LA body and are not recognized / represented in the skeleton.

[0024] 2. Define a hierarchical tree graph by defining the central point of the medial axis graph as the tree graph root, the intersections of the medial axis graph as vertices (nodes), and the medial axis sections between intersections (or between intersections and open ends) as arcs. Thus, the main branches are the remaining arcs at the lowest level of the tree graph that have open ends.

[0025] 3. Identify the five major branches of the tree graph.

[0026] 4. Rotate the map containing the skeleton into a posterior-anterior (PA - posterior to anterior) orientation so that the right side of the view corresponds to the observer's right side and the left side of the view corresponds to the observer's left side.

[0027] 5. Identify the two right PVs by the depth (z coordinate value) of the end points of their major branches on the surface mesh map of the anatomical map, with the right inferior pulmonary vein (RIPV) having a lower depth than the right superior pulmonary vein (RSPV).

[0028] 6. Identify the left PVs and atrial appendages by decreasing depth (z coordinate value) in the following order: left atrial appendage (LAA), left superior pulmonary vein (LSPV), left inferior pulmonary vein (LIPV).

[0029] One purpose of identifying an orifice region, e.g., of a PV, in an anatomical map may be to remove the distal portion of the orifice region (e.g., of a PV) from the map (also called "cutting the map"), which helps the physician visualize the volume (e.g., of an LA).

[0030] In particular, the disclosed technology can help physicians perform subsequent ablation of PVs by removing irrelevant electrophysiological (EP) information that can confuse physicians. If a PV is not removed from the map, electrical activity will appear in the electrophysiological (EP) version of the map, for example, in the form of map coloring. This EP information, which was still considered irrelevant immediately before performing the ablation, can cause the physician to mistakenly place the ablation catheter deep inside that PV (e.g., instead of placing the catheter at the PV ostium). This disconnect is also crucial in the coloring algorithm, where EP activity should not propagate to the PV.

[0031] Although the cut opening area can be useful, automatically defining the cut curve for the relevant anatomical structures is very difficult. This is particularly true for the ventricles, where the anatomical structure between the ventricle (e.g., LA) and the opening area (e.g., of the PV) is complex and can significantly increase the time required. As a result, attempting to separate the PV from the anatomical map can result in incorrect cut locations and / or shapes, requiring difficult operator effort.

[0032] Some embodiments of the present invention provide a method for accurately deriving a cutting curve for an identified PV on an anatomical map using a "semi-automatic" technique. To this end, the user clicks on a surface point on the LA map that should lie on the proposed curve for cutting. The processor calculates another point on the skeleton that is closest to the surface point and then defines a plane through the calculated point Q on the skeleton that is perpendicular to the tangent to the branch at Q. The intersection of the plane with the surface map is used as the basis for deriving the cutting curve for the vein, as described below. In another embodiment, another algorithm for "semi-automatic" cutting is provided that does not rely on the map skeleton.

[0033] In one embodiment, if a smooth cutting curve is not well defined by the above process, for example because it is open-ended, the processor derives an optimal polygonal cutting curve, as also described below.

[0034] In some cases, the complexity of the ventricles can make even the disclosed "semi-automatic" method difficult to implement. Therefore, some embodiments of the present invention disclose a method for fully automatic derivation of cutting curves for identified known anatomical openings, such as within the PV ostium region.

[0035] In these embodiments, the processor first segments the surface map, addresses each segmented region, and then provides cutting curves around each PV branch of the skeleton that do not encroach on the segmentation lines, all without input from the physician. A segmentation line is a set of locations on the map surface that have the same distance from two or more of the major branches of the map surface central axis (skeleton), as described below.

[0036] Typically, the processor is programmed with software containing specific algorithms that enable the processor to perform each of the processor-related steps and functions outlined above.

[0037] The disclosed technology uses automated post-processing methods to analyze anatomical maps, facilitating the diagnostic interpretation required by physicians. Thus, the disclosed technology can expedite complex diagnostic procedures, such as those required for diagnostic catheterization, making clinical diagnosis and subsequent treatments, such as catheter ablation, safer and more efficient.

[0038] System Description FIG. 1 is a pictorial representation of a system 21 for electroanatomical mapping, in accordance with an exemplary embodiment of the present invention. FIG. 1 illustrates a physician 27 using an electroanatomical catheter 29 to perform electroanatomical mapping of a heart 23 of a patient 25. The catheter 29 includes one or more arms 20, which may be mechanically flexible, at its distal end, with one or more electrodes 22 coupled to each arm. During a mapping procedure, the electrodes 22 acquire signals from and / or inject signals into tissue of the heart 23. A processor 28 receives these signals via an electrical interface 35 and uses information contained in these signals to construct an anatomical map 31, for example, of the surface of the left atrium. During and / or after the procedure, the processor 28 can display the anatomical map 31 on a display 26.

[0039] During treatment, a tracking system can be used to track the position of each of the sensing electrodes 22, thereby correlating each signal with the location where the signal was acquired. For example, the Active Current Location (ACL) system manufactured by Biosense-Webster (Irvine, California), described in U.S. Patent No. 8,456,182, the disclosure of which is incorporated herein by reference, may be used. In the ACL system, a processor estimates the location of each of the sensing electrodes 22 based on the impedance measured between each of the sensing electrodes 22 and multiple surface electrodes 24 coupled to the skin of the patient 25. For example, three surface electrodes 24 may be coupled to the patient's chest and three surface electrodes 24 may be coupled to the patient's back. (For ease of illustration, only one surface electrode is shown in FIG. 1.) Currents are passed between the electrodes 22 and 24 within the patient's heart 23. The processor 28 calculates the estimated locations of all of the electrodes 22 within the patient's heart based on the ratio between the current amplitudes (or the impedances indicated by these amplitudes) measured at the surface electrodes 24 and the known locations of the electrodes 24 on the patient's body. In this way, the processor can associate any given impedance signal received from the electrodes 22 with the location where the signal was obtained.

[0040] The exemplary embodiment shown in FIG. 1 has been chosen purely for conceptual clarity. Other tracking methods, such as those based on voltage signal measurement, can be used, similar to the Carto® 4 system (manufactured by Biosense Webster). Other types of sensing catheters, such as the Lasso® catheter (manufactured by Biosense Webster), could equally well be employed. A contact sensor may be attached to the distal end of the electroanatomical catheter 29. As noted above, other types of electrodes, such as those used for ablation, can be adapted to electrodes 22 and utilized in a similar manner to obtain the necessary position data. Thus, in this case, the ablation electrodes used to collect the position data are considered sensing electrodes. In an optional embodiment, the processor 28 is further configured to indicate the quality of physical contact between each of the electrodes 22 and the inner surface of the ventricle during measurement.

[0041] Processor 28 typically comprises a general-purpose computer with software programmed to perform the functions described herein. The software may be downloaded to the computer in electronic form, for example over a network, or alternatively or additionally may be provided and / or stored on a non-transitory tangible medium, such as magnetic, optical, or electronic memory.

[0042] In particular, processor 28 executes the dedicated algorithms disclosed herein, which are included in Figures 5-7, and which enable processor 28 to perform the disclosed steps, as further described below.

[0043] Automatic identification of pulmonary veins after mapping To keep the presentation clear, the illustration in Figure 2 (and in Figures 3-4) uses a schematic mesh surface map 40 representation of the anatomical map 31. The surface mesh map 40 is typically made of a triangulated mesh (triangles not shown for clarity).

[0044] FIG. 2 is a schematic pictorial volume rendering of a surface mesh map 40 of the left atrium illustrating automatically identified pulmonary veins (PV) 411-412 and 414-415 within the map, according to an embodiment of the present invention.

[0045] As shown, processor 28 has computed a "medial axis" skeleton S 42 from LA surface anatomical map 31. As a hierarchical tree graph, skeleton 42 has a root point M0 45. The tree graph's root point M0 divides the graph into two major subtrees whose left major branches are on the same (left) side and whose right major branches are on the same (right) side. For a given number of major branches, N, M0 is the point

[0046]

number

[0047] As can be further seen, the surface mesh map 40 is rotated (by the processor 28) in the posterior-anterior (back to front) PA direction so that the right side of the view corresponds to the observer's right side and the left side of the view is on the patient's left side.

[0048] The two right PVs and two left PVs have been identified by the processor by their x-values ​​(on the x-axis 46). The inferior and superior structures have been identified by their depth values ​​(depths are marked on the depth (z-value) axis 47), with the right inferior pulmonary vein (RIPV) 412 having a depth 492 that is lower than the depth 491 of the right superior pulmonary vein (RSPV) 411. Similarly, the processor has identified the atrial appendages and left PVs by their depth values. As can be seen, the depth values ​​decrease for the sequence of the left atrial appendage (LAA) 413 at depth 493, the left superior pulmonary vein (LSPV) 414 at depth 494, and the left inferior pulmonary vein (LIPV) 415 at depth 495.

[0049] Automatic identification of pulmonary veins after mapping and sectioning FIG. 3 is a schematic pictorial volume rendering of a semi-automatically generated cut of pulmonary veins (PV) 411 and 412 in surface mesh map 40 of FIG. 2, according to an exemplary embodiment of the present invention.

[0050] In the illustrated embodiment, after a PV is identified, the user clicks on point P50 on the map, which should be on the proposed cutting curve. The algorithm calculates another point T52 on skeleton S42 that is closest to point P, and then defines a plane N (not shown) ~ T52 that is perpendicular to branch 442 of skeleton S42 at point T52. The intersection of plane N with the map forms the basis for finding a smooth cutting curve 555 for the PV. (Curve 555 can be seen in inset 75.)

[0051] Finding a smooth cutting curve can be performed in several ways. In one embodiment, for example, the algorithm can apply the following steps: i) Define a circle 54 with radius ||P-T'|| 57 and a circumference 55 on which point T' 53 is the projection of point T on plane N. ii) Starting from three points with equal angular distance on the circle 54, define a curve 555 using the projection of these points onto the mapped surface of the volume. iii) If the curve is not closed, Increase the number of points and try again. Or, Add a point in the middle of the geodesic path connecting each consecutive point that failed to connect.

[0052] In one embodiment, if the smooth cutting curve 555 is not well defined, for example by being open ended, the processor derives an optimal polygonal cutting curve, as described below. For example, the processor applies an algorithm that includes the following steps to find an intersection plane N for cutting the RSPV 411: i) Cut the map with plane N. ii) Define the anatomical structure boundary as the closest closed polygon Q 56 of the intersection.

[0053] The closest closed polygon can be defined by interpolating the projected points on the map surface, then sampling the smooth curve and fitting a quadratic B-spline curve that projects onto the surface to obtain the polygon. For this purpose, for example, if sets {s} and {r} have the same number of points, then the coordinates of the vertices are given by the metric

[0054]

number

[0055] 4 is a schematic pictorial volume rendering of an automatically created cut 70 of the pulmonary veins (PVs) in the surface mesh map 40 of FIG. 2, in accordance with an exemplary embodiment of the present invention. In the illustrated embodiment, the processor 28 derives the cut curves according to an algorithm (after the PVs have been identified and the skeleton 42 has been calculated using the major branches 441-445 as described above).

[0056] Processor 28 identifies all of the surface points 60 (e.g., vertices on the triangular mesh representing the surface of the left atrium) to form segmentation lines that are approximately equidistant from two adjacent major skeletal branches corresponding to the anatomical structures. The adjacent skeletal branches referenced in FIG. 4 are branches 441-442, 442-445, 443-444, and 444-445. In other words, for each pair of five skeletal lines 441-445 (starting from a vertex of skeleton S42), processor 28 finds the set of points 60 that are equidistant from the lines, lie on the surface, and are furthest from the vertices. The set of surface points 60 effectively segments the map into regions corresponding to the anatomical structures.

[0057] The term "approximately" used in connection with any numerical value or range of values ​​herein indicates a reasonable dimensional tolerance that enables a portion of a component or a collection of components to function for its intended purpose as described herein. More specifically, "approximately" may refer to a range of values ​​of ±20% of the recited value; for example, "approximately 90%" may refer to a range of values ​​of 71% to 99%.

[0058] Using processor 28, PV can then automatically cut map 31 by not encroaching on the line connecting points 60, as shown below. a) Each of the found points 60 (associated with two skeletal branches) has a projection (58) point 60 onto the two branches. This is true for all of the above branch pairs, and for each surface point (e.g., vertex). b) Each branch has its furthest (eg, from the root point 45) central projection (58) point Ti 68 selected. c) Each point Ti has a tangent line Ni 65 that defines the cross section C 70 to be explored in which the point Ti is embedded.

[0059] Taking into account each of the five branches of the skeleton, the necessary anatomical map, i.e., the cuts of the PVs, is automatically derived. The derivation steps essential for understanding the embodiment are described below. Alternatively, the cut plane C 70 can be used to define a projected curve, such as curve 555 in FIG. 3, thus defining a more realistic representation of the boundaries of the map of the anatomical structures.

[0060] Methods for identifying and segmenting PVs on anatomical maps 5 is a flow chart that schematically illustrates a method for identifying pulmonary veins (PV) 411, 412, 414, and 415 in the surface mesh map 40 of FIG. 2 of the left atrium, in accordance with an embodiment of the present invention. The algorithm according to the presented embodiment performs a process that begins with processor 28 receiving a surface map of the LA of heart 23 of patient 25, such as map 31 calculated by system 21, in a map receiving step 78.

[0061] In a skeleton calculation step 80, processor 28 calculates the "medial axis" skeleton 42 of the mapped surface of the LA.

[0062] Processor 28 then removes small or circular branches from skeleton 42 in a skeleton cleanup step 82 .

[0063] Processor 28 then constructs hierarchical tree graph 42 in a tree graph construction step 84 .

[0064] The processor then identifies the tree root as the center point 45 of the state tree 42 in an identification step 86 .

[0065] Processor 28 then, in a major branch identification step 88, identifies five major branches 441-445 of the tree graph.

[0066] To this end, processor 28 rotates surface mesh map 40 in a back-to-front (back to front) PA direction in map rotation step 90 to align the coordinate system so that the x-axis distinguishes left from right and the z-axis distinguishes depth (rear-to-interior).

[0067] Finally, in PV identification steps 92 and 94, processor 28 identifies two right and two left PVs by their left-right positions on the map, and identifies the inferior and superior PVs (and atrial appendages) on surface mesh map 40 by their depth values ​​on surface mesh map 40 as shown in FIG. 2.

[0068] 6 is a flow chart that schematically illustrates a semi-automated method for cutting pulmonary veins (PVs) from the surface mesh map 40 of FIG. 2, in accordance with an embodiment of the present invention. While the illustrated embodiment includes PVs as the anatomical structures to be cut, in general, the disclosed one-click technique is applicable to other anatomical structures of other organs, such as other ventricles, that need to be cut from their maps.

[0069] In another embodiment (not shown in FIG. 6), another algorithm for "semi-automatic" cutting is provided that does not rely on the map skeleton.

[0070] The algorithm according to the presented embodiment executes a process beginning with a PV identification step 601, where the processor 28 identifies the four PVs 411, 412, 414 and 415 and the atrial appendages in the surface mesh map 40, for example using the steps described in FIG. 5.

[0071] Next, in a surface point selection step 603, the user clicks on a point on the ventricular map (eg, on map 40) that should be on the cutting curve.

[0072] In a calculating step 605, processor 28 derives (e.g., calculates) a smooth cutting curve, such as curve 555 of Figure 3. Then, in a checking step 607, processor checks whether the smooth cutting curve derived by the processor is closed.

[0073] If the smooth cutting curve is closed, processor 28 cuts the PV in question along the smooth curve, in a cutting step 609. On the other hand, if the smooth curve is open-ended, processor 28 calculates a best-fit polygonal cutting curve, such as curve 56 in Figure 3, in a polygonal cut curve derivation step 611. Processor 28 then cuts the PV in question along the polygonal curve, in a cutting step 613.

[0074] 7 is a flow chart that schematically illustrates an automated method for cutting pulmonary veins (PVs) from the surface mesh map 40 of FIG. 2, in accordance with an embodiment of the present invention. While the illustrated embodiment includes PVs as the anatomical structures to be cut, the disclosed one-click technique is generally applicable to other anatomical structures of other organs, such as other ventricles, that need to be cut from their maps. The algorithm according to the presented embodiment performs a process beginning with a PV identification step 701, in which the processor 28 identifies the four PVs 411, 412, 414, and 415 and the atrial appendages within the surface mesh map 40, for example, using the steps described in FIG. 5.

[0075] Next, in a map segmentation step 703, processor 28 segments mesh map 40 using segmentation lines 60, as illustrated in FIG.

[0076] Using segmentation line 60, processor 28 calculates the farthest central projection point (e.g., point 68 in FIG. 4) within each PV of interest, in a derivation step 705. Processor 28 then defines a normal plane, such as plane 70 in FIG. 4, to skeleton 42 at the farthest central projection point, in a cutting plane derivation step 707. Processor 28 then cuts the PV of interest along a polygonal curve, in a cutting step 709. In one embodiment, instead of using plane 70, processor 28 uses the farthest central projection point as the basis for calculating a smooth cutting curve, such as curve 555, or a polygonal curve, such as curve 56, as described in steps 605-613 of FIGS. 3 and 6. In other embodiments, other statistical functions besides median, such as maximum, minimum, or average, can be utilized.

[0077] The exemplary flowcharts shown in Figures 5-7 have been chosen purely for purposes of conceptual clarity. In any embodiment, various additional steps may be performed, for example, to automatically register the opening of the identified and cut PV to the LA with the medical image.

[0078] While the embodiments described herein primarily address the identification of known anatomical openings, such as pulmonary vein ostia, within a mapped volume, the methods and systems described herein may be used in other applications. For example, the disclosed methods may be utilized to identify zones within a cavity that have disproportionate size and / or shape. While the disclosed embodiments refer to cardiac applications, the disclosed methods may be applied to mapped volumes of any organ cavity. For example, the algorithms described herein may be applied to any shape having a tubular structure, such as all other chambers of the heart. As another example, the methods may be applied to ear-nose-throat maps.

[0079] Accordingly, it will be understood that the above-described embodiments are cited by way of example, and that the present invention is not limited to what has been particularly shown and described in the foregoing specification. Rather, the scope of the present invention includes both combinations and subcombinations of the various features described in the above specification, as well as variations and modifications thereof not disclosed in the prior art that would occur to one skilled in the art upon reading the foregoing description. Documents incorporated by reference into this patent application are to be deemed an integral part of this application, except that if any term is defined in these incorporated documents in a way that contradicts the definition expressly or impliedly given herein, then only the definition in this specification shall be considered.

[0080] [Embodiment] (1) A method for automatic identification and processing of anatomical structures in an anatomical map, comprising: calculating a medial axis tree graph of a volume of a patient's organ on a computerized anatomical map of said volume; identifying a predetermined number of major branches in the tree graph; using the identified major bifurcations to identify, in the anatomical map, one or more known anatomical opening regions of the volume; A method comprising: (2) The method of embodiment 1, wherein the volume includes the left atrium of the heart and the identified one or more opening regions include the pulmonary vein ostium. (3) The method of embodiment 1, wherein computing the medial axis tree graph includes computing a medial axis skeletal graph of the volume and defining a central point of the medial axis skeletal graph as the root of the medial axis tree graph. (4) The method of embodiment 1, wherein identifying the major branches includes removing small branches and circular branches from the tree graph. (5) The method of embodiment 1, wherein identifying the one or more known anatomical opening regions includes identifying the opening regions based on a posterior-anterior orientation and depth of the opening regions in the anatomical map.

[0081] (6) The method of embodiment 1, further comprising presenting the identified one or more opening regions to a user on the anatomical map. (7) The method of embodiment 1, comprising deriving a cutting curve for an identified known anatomical opening region in the anatomical map, and cutting the identified known anatomical opening region in the anatomical map along the derived cutting curve. (8) The method of embodiment 7, wherein deriving the cutting curve on the anatomical map includes selecting points on the map that are included in the cutting curve and using the selected points in deriving the cutting curve. (9) The method described in embodiment 7, wherein the derived cutting curve on the anatomical map is one of a smooth cutting curve and a polygonal cutting curve. (10) Deriving the cutting curve on the anatomical map includes: segmenting the opening regions on the map; using the segmented aperture region to derive a farthest central projection point on the major branch of the aperture region and define a normal plane to the major branch at the farthest central projection point; defining an intersection between the opening area and the normal plane as the cutting curve; 8. The method of embodiment 7, comprising:

[0082] (11) The method of embodiment 10, wherein defining the intersection includes deriving one of a smooth cutting curve and a polygonal cutting curve on the anatomical map using the normal plane. (12) A system for automatic identification and processing of anatomical structures in an anatomical map, comprising: a memory configured to hold a computerized anatomical map of a volume of an organ of the patient; a processor, the processor comprising: Compute a medial axis tree graph of said anatomical map; identifying a predetermined number of major branches in the tree graph; Using the identified major bifurcations, identify in the anatomical map one or more known anatomical opening regions of the volume. The system is configured as follows: (13) The system of embodiment 12, wherein the volume includes the left atrium and the identified one or more opening regions are each a pulmonary vein ostium. (14) The system of embodiment 12, wherein the processor is configured to calculate the medial axis tree graph by calculating a medial axis skeletal graph of the volume and defining a center point of the medial axis skeletal graph as the root of the medial axis tree graph. (15) The system of embodiment 12, wherein the processor is configured to identify the major branches by removing small branches and circular branches from the tree graph.

[0083] (16) The system of embodiment 12, wherein the processor is configured to identify the one or more known anatomical opening regions based on a posterior-anterior orientation and depth of the opening region in the anatomical map. (17) The system of embodiment 12, wherein the processor is further configured to present the identified one or more opening regions to a user on the anatomical map. (18) The system of embodiment 12, wherein the processor is further configured to derive a cutting curve for the identified known anatomical opening region in the anatomical map and cut the identified known anatomical opening region in the anatomical map along the derived cutting curve. (19) The system of embodiment 18, wherein the processor is configured to derive the cutting curve by selecting points on the map that are included in the cutting curve and using the selected points to derive the cutting curve. (20) The system described in embodiment 18, wherein the derived cutting curve on the anatomical map is one of a smooth cutting curve and a polygonal cutting curve.

[0084] (21) The processor: segmenting the opening regions on the map; using the segmented aperture region to derive a farthest central projection point on the major branch of the aperture region and define a normal plane to the major branch at the farthest central projection point; defining an intersection between the opening area and the normal plane as the cutting curve; The cutting curve is derived by The system of embodiment 18 is configured as follows: (22) The system of embodiment 21, wherein the processor is further configured to derive one of a smooth cutting curve and a polygonal cutting curve on the anatomical map using the normal plane.

Claims

1. 1. A system for automatic identification and processing of anatomical structures in an anatomical map, comprising: a memory configured to hold a computerized anatomical map of a volume of an organ of the patient; a processor, the processor comprising: Compute a medial axis tree graph of said anatomical map; identifying a predetermined number of major branches in the tree graph; using the identified major bifurcations to identify, in the anatomical map, one or more known anatomical opening regions of the volume; Segmenting the anatomical orifice region on the anatomical map; using the segmented anatomical opening region to derive a farthest central projection point on the major branch of the anatomical opening region and define a normal plane to the major branch at the farthest central projection point; defining an intersection between the anatomical opening region and the normal plane as a cutting curve of the anatomical opening region; The cutting curve is derived by cutting the anatomical opening region along the cutting curve in the anatomical map; The system is configured as follows:

2. The system of claim 1 , wherein the volume includes the left atrium and the identified anatomical orifice regions are each a pulmonary vein ostium.

3. 2. The system of claim 1, wherein the processor is configured to compute the medial axis tree graph by computing a medial axis skeletal graph of the volume and defining a center point of the medial axis skeletal graph as the root of the medial axis tree graph.

4. The system of claim 1 , wherein the processor is configured to identify the major bifurcations by removing small bifurcations and circular bifurcations from the tree graph.

5. The system of claim 1 , wherein the processor is configured to identify the anatomical opening region based on a posterior-anterior orientation and a depth of the anatomical opening region in the anatomical map.

6. The system of claim 1 , wherein the processor is further configured to present the identified anatomical orifice regions to a user on the anatomical map.

7. 2. The system of claim 1, wherein the processor is configured to derive the cutting curve by selecting points on the map that are to be included in the cutting curve and using the selected points to derive the cutting curve.

8. The system of claim 1 , wherein the derived cutting curve on the anatomical map is one of a smooth cutting curve and a polygonal cutting curve.

9. The system of claim 1 , wherein the processor is further configured to derive one of a smooth cutting curve and a polygonal cutting curve on the anatomical map using the normal plane.

10. 1. A method for automatic identification and processing of anatomical structures in an anatomical map, comprising: calculating a medial axis tree graph of a volume of a patient's organ on a computerized anatomical map of said volume; identifying a predetermined number of major branches in the tree graph; identifying, in the anatomical map, one or more known anatomical opening regions of the volume using the identified major bifurcations; Segmenting the anatomical orifice region on the anatomical map; deriving a cutting curve by using the segmented anatomical opening region to derive a farthest central projection point on the major branch of the anatomical opening region, defining a normal plane to the major branch at the farthest central projection point, and defining an intersection between the anatomical opening region and the normal plane as a cutting curve for the anatomical opening region; cutting the anatomical opening region in the anatomical map along the cutting curve; A method comprising:

11. The method of claim 10 , wherein the volume includes the left atrium of the heart and the identified anatomical opening region includes a pulmonary vein ostium.

12. 11. The method of claim 10, wherein computing the medial axis tree graph comprises computing a medial axis skeletal graph of the volume and defining a center point of the medial axis skeletal graph as the root of the medial axis tree graph.

13. The method of claim 10 , wherein identifying the major bifurcations comprises removing small bifurcations and circular bifurcations from the tree graph.

14. 11. The method of claim 10, wherein identifying the anatomical opening region comprises identifying the anatomical opening region based on a posterior-anterior orientation and a depth of the anatomical opening region in the anatomical map.

15. The method of claim 10 , comprising presenting the identified anatomical orifice regions to a user on the anatomical map.

16. 11. The method of claim 10, wherein deriving the cutting curve on the anatomical map comprises selecting points on the map that are to be included in the cutting curve, and using the selected points in deriving the cutting curve.

17. The method of claim 10 , wherein the derived cutting curve on the anatomical map is one of a smooth cutting curve and a polygonal cutting curve.

18. The method of claim 10 , wherein defining the intersection includes deriving one of a smooth cutting curve and a polygonal cutting curve on the anatomical map using the normal plane.

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