Automatic identification and processing of anatomical structures in anatomical maps

Abstract

A method includes computing a centerline tree map of a volume of an organ of a patient in a computerized anatomic map of the volume. A predefined number of main branches in the tree map are identified. Using the identified main branches, one or more known anatomic opening regions of the volume are identified in the anatomic map.

Description

TECHNICAL FIELD

[0001] The present invention relates generally to cardiac mapping, and in particular to analyzing anatomic cardiac maps. BACKGROUND

[0002] Some clinical procedures employ techniques for analyzing computerized anatomic maps of organs. For example, U.S. Patent Application Publication No. 2020 / 0065983 describes a method that includes computing a center of mass of a volume of a patient’s organ in a computerized anatomic map of the volume. In the anatomic map, a location on a surface of the volume that is farthest from the center of mass is found. The location is identified as a known anatomic opening of the organ. In an embodiment, the location includes computing paths from the center of mass to a plurality of locations on the surface of the volume, and finding a longest path of the plurality of paths. In another embodiment, the organ is a cardiac chamber and the opening is an opening of a pulmonary vein.

[0003] As another example, U.S. Patent Application Publication No. 2008 / 0044072 describes a method for labeling connected tubular objects within segmented image data, including receiving segmented image data, and labeling the segmented image data to identify a plurality of components in 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 are within a radius band, grouping the candidates based on their physical distance from each other and their radius estimates, partitioning the segmented image data according to the grouped candidates, and assigning a separate color label to each of a plurality of components that are different from each other. A particular model of an expected anatomic structure can be used to automatically identify arterial and venous trees.

[0004] U.S. Patent Application Publication No. 2008 / 0273777 describes a method and apparatus for generating a network of luminal surfaces by defining a set of mediatrices for a tubular structure, defining a series of cross-sections along a mediatrice of the set of mediatrices, generating a connectivity graph of the mediatrices, defining a plurality of surface representations based on the graph of mediatrices and the cross-sections, computing a volume defined by a first surface representation of the surface representations, defining a partitioning of the mediatrices, cross-sections, surfaces, and / or volume representations, and outputting the network of luminal surfaces.

[0005] U.S. Patent Application Publication No. 2007 / 0109299 describes a system and method for efficiently using surface data to compute a feature path of a virtual three-dimensional object, among other things. A surface mesh is constructed using segmented volume data representing the object. A geodesic distance (or a metric distance) from a reference point is computed for each shape element in the surface mesh. The geodesic distance values are used to generate loops. Loop centroids are computed and connected to form a feature path, which is optionally trimmed and smoothed. SUMMARY

[0006] Embodiments of the invention described below provide a method comprising computing a centerline tree map of a volume of a patient's organ in a computerized anatomical map of the volume. A predefined number of main branches in the tree map are identified. Using the identified main branches, one or more known anatomical opening regions of the volume are identified in the anatomical map.

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

[0008] In some embodiments, computing the centerline tree map comprises computing a centerline skeleton map of the volume, and defining a center point of the centerline skeleton map as a root of the centerline tree map.

[0009] In embodiments, identifying the main branches comprises removing secondary branches and circular branches from the tree map.

[0010] In another embodiment, identifying the one or more known anatomical opening regions comprises identifying the opening regions based on an antero-posterior orientation and depth of the opening regions in the anatomical map.

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

[0012] In some embodiments, the method further comprises deriving a cut curve of 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 cut curve thereof.

[0013] In embodiments, deriving the cut curve on the anatomical map comprises selecting points on the map to be included in the cut curve and using the selected points to derive the cut curve. In another embodiment, the derived cut curve on the anatomical map is one of a smooth cut curve and a polygonal cut curve. In yet another embodiment, deriving the cut curve on the anatomical map comprises: (i) segmenting the opening region on the map; (ii) using the segmented opening region, deriving a farthest median projection point on a main branch of the opening region; and (iii) defining a normal plane of the main branch at the farthest median projection point. An intersection between the opening region and the normal plane is defined as the cut curve.

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

[0015] According to another embodiment of the invention, a system including a memory and a processor is also provided. The memory is configured to store a computerized anatomical mapping of the volume of a patient's organ. The processor is configured to (a) calculate a central tree diagram of the anatomical mapping, (b) identify a predefined number of main branches in the tree diagram, and (c) use the identified main branches to identify one or more known anatomical opening regions of that volume in the anatomical mapping. Attached Figure Description

[0016] The invention will be more fully understood through the following detailed description of embodiments thereof, taken in conjunction with the accompanying drawings, wherein:

[0017] Figure 1 This is a schematic diagram of a system for electroanatomical mapping according to an exemplary embodiment of the present invention;

[0018] Figure 2 This is a schematic volumetric rendering of a surface mesh mapping of the left atrium according to an exemplary embodiment of the present invention, which illustrates the automatically identified pulmonary veins (PV) in the mapping.

[0019] Figure 3 This is an exemplary embodiment of the present invention. Figure 2 A schematic volumetric rendering of a semi-automatically generated pulmonary vein (PV) section in a surface mesh mapping map;

[0020] Figure 4 This is an exemplary embodiment of the present invention. Figure 2 A schematic volumetric rendering of cuts in the pulmonary vein (PV) automatically generated from the surface mesh mapping map;

[0021] Figure 5 For illustrative purposes, an exemplary embodiment of the invention for use in the left atrium is described. Figure 2 A flowchart of a method for identifying pulmonary veins (PV) in a surface mesh mapping map;

[0022] Figure 6 To illustrate, an exemplary embodiment of the present invention is shown from... Figure 2 A flowchart of a semi-automatic method for cutting pulmonary veins (PV) using surface mesh mapping; and

[0023] Figure 7 To illustrate, an exemplary embodiment of the present invention is shown from... Figure 2 A flowchart of an automated method for cutting pulmonary veins (PV) using surface mesh mapping. Detailed Implementation

[0024] SUMMARY

[0025] Catheter-based anatomical mapping techniques can generate computerized anatomical maps of the lumens (i.e., the surface of a volume) of an organ. In some cases, the mapping techniques are not aware of anatomical openings or passages in the volume. For example, a map of the left atrium (LA) of the heart can not represent the ostia of the four pulmonary veins (PVs) and the passage to the left atrial appendage (LAA). Thus, identifying features (e.g., opening regions) in such a map with known anatomical structures can require input from a trained qualified person (such as a radiologist or a cardiologist) to identify certain anatomical landmarks in the mapped volume that are hinted at the openings (such as, for example, the openings in the LA map) based on landmarks of the PV ostia. As another example, coherent coloring algorithms also require cutting the PVs from the left atrial geometry to produce accurate coloring.

[0026] Furthermore, anatomical maps of an organ are typically presented as if the organ is viewed from an external observer. Physicians understand such maps and, in particular, find it easy to identify a LA showing the four PVs and the LAA, and manage to identify the veins and the atrial appendage. However, future anatomical maps can present a view of the LA from inside the atrium, and this new type of view can make it difficult for physicians to identify the different aforementioned parts.

[0027] Some embodiments of the invention described herein provide methods for automatically finding and annotating opening regions in a computerized anatomical map of a volume (e.g., a lumen) of an organ of a patient. The embodiments described herein are primarily related to the LA, but the disclosed techniques can be generally used for mapping and visualization of other heart chambers and other organs.

[0028] In some embodiments, a processor receives an anatomical mapped volume of the LA of a heart, and automatically identifies the PV and atrial appendage opening regions on the anatomical map of the LA. To this end, the processor computes a centerline map (hereinafter also simply referred to as "skeleton") from the anatomical map. A point on the centerline of a volume can be defined as a point in a planar cross-section of the volume that has more than one nearest point on the resulting boundary in 2D in the cross-section plane. Initially called "topological skeleton", it was introduced in 1967 as a tool for biological shape recognition.

[0029] The processor then performs the following steps:

[0030] 1. Simplify the centerline map to include only "principal" branches. A principal branch is defined as a curve of the map that is open-ended and is one of the longest branches of a predefined number of longest branches that start from an intersection of curves of the map. Another possible definition is the angle between branches, where the angle between two principal branches is greater than a predefined threshold.

[0031] The process of identifying the main branches is simpler if the anatomy is known, such as the anatomy of the LA, since the expected graph topology is known (three branches on the left and two on the right and three junctions in the intersection for the LA).

[0032] After the processor has simplified the graph by removing the "minor" branches and / or the circular segments from the graph, the main branches remain in the graph. Thus, the minor branches are open-ended lines in the graph that are too short to be part of the predefined number of branches. In case there are short branches in the middle of the graph, i.e. non open-ended branches, these are removed or merged with the larger branches. Depending on the anatomy of the volume, the predefined number of main branches is known. For the LA, this number is typically five (5). Note that the valve is part of the LA body and is not identified / represented in the skeleton.

[0033] 2. Define a hierarchical tree graph, where the center point of the centerline graph is defined as the tree graph root, the intersections of the centerline graph are defined as vertices (nodes), and the centerline segments between the intersections (or between the intersections and the open ends) are defined as arcs. Thus, the main branches are the remaining arcs of the lowest level of the tree graph that have open ends.

[0034] 3. Identify the five main branches of the tree graph.

[0035] 4. Rotate the map including the skeleton to a posterior-anterior (PA - back to front) orientation so that the right-hand side of the view corresponds to the right-hand side of the viewer and the left-hand side of the view is on the left-hand side of the viewer.

[0036] 5. Identify the two right PVs by the depth (z coordinate value) of their end points on the main branches of the anatomical map: the right inferior pulmonary vein (RIPV) has a lower depth than the right superior pulmonary vein (RSPV).

[0037] 6. Identify the left PV and the atrial appendage by the depth (z coordinate value) that decreases in the following order: left atrial appendage (LAA), left superior pulmonary vein (LSPV), left inferior pulmonary vein (LIPV).

[0038] One of the purposes of identifying the open regions, e.g. the PVs, in the anatomical map is to remove the distal part of the open regions (also referred to as "cutting the map"), e.g. the PVs, from the map. This helps the physician to visualize the volume (e.g. of the LA).

[0039] In particular, the disclosed technology can help a physician perform subsequent ablation of a PV by removing extraneous electro-physiological (EP) information that can confuse the physician: if a PV is not removed from the map, electrical activity will appear in the electro-physiological (EP) pattern of the map, e.g., in the form of map coloring. Such EP information, which is deemed irrelevant just prior to performing the ablation, can cause the physician to erroneously place the ablation catheter too deep into the PV (e.g., instead of placing the catheter at the ostium of the PV). Additionally, such cuts are important in coloring algorithms where EA activity should not propagate to the PV.

[0040] While cutting open regions can be beneficial, it is difficult to automatically define cutting curves for the relevant anatomical structures. This is particularly true for heart chambers, where the anatomical structure between the chamber (e.g., LA) and the open region (e.g., of a PV) can be complex, significantly increasing the required time. Thus, attempting to cut the PV of an anatomical map can result in an erroneous location and / or shape of the cut, and will require heavy work of the operator.

[0041] Some embodiments of the present invention provide a method for accurately deriving a cutting curve for a PV identified on an anatomical map using a "semi-automatic" method. To this end, the user clicks on a surface point on the LA map that should be on the proposed cutting curve. The processor computes another point on the skeleton that is closest to the surface point, and then defines a plane on the skeleton that passes through the computed point Q, orthogonal to the tangent of the branch at Q. The intersection of the plane with the surface map is used as a basis for deriving a cutting curve for the vein described below. In another embodiment, another algorithm for "semi-automatic" cutting is provided, which does not rely on the skeleton of the map.

[0042] In embodiments, in cases where the smooth cutting curve is not well defined by the above process (e.g., by being open-ended), the processor derives a best-fit polygonal cutting curve, also described below.

[0043] In some cases, due to the complexity of the chamber, even the disclosed "semi-automatic" method can be difficult to perform. Thus, some embodiments of the present invention disclose a method that fully and automatically derives a cutting curve for an identified known anatomical opening, such as in the ostial region of a PV.

[0044] In these embodiments, the processor first segments the surface map, addressing each segmented region, and then provides a cutting curve that does not encroach on a segmentation line around each PV branch of the skeleton, all without input from the physician. As described below, a segmentation line is a set of locations on the surface of the map that have the same distance from the central axis (skeleton) of two or more main branches in the main branches of the map surface.

[0045] Typically, the processor is programmed in software that contains the specific algorithms that enable the processor to perform each of the processor-related steps and functions described above.

[0046] The disclosed technology uses an automated post-processing method to analyze the anatomic maps, which can facilitate the diagnostic interpretation work required by physicians. The disclosed technology can speed up complex diagnostic procedures, such as those required in diagnostic catheterization, and in doing so can make the clinical diagnosis and subsequent treatment, such as catheter ablation, safer and more effective.

[0047] System Description

[0048] Figure 1 Schematic illustration of a system 21 for electroanatomic mapping according to an example embodiment of the present application. Figure 1 A physician 27 is depicted using an electroanatomic catheter 29 to perform electroanatomic mapping of a heart 23 of a patient 25. The catheter 29 includes one or more arms 20, which can be mechanically flexible, at its distal end, each coupled with one or more electrodes 22. During the mapping procedure, the electrodes 22 acquire and / or inject signals to tissue of the heart 23. A processor 28 receives these signals via an electrical interface 35 and uses the information contained in these signals to construct an anatomic map 31 of, for example, the surface of the left atrium. The processor 28 can display the anatomic map 31 on a display 26 during and / or after the procedure.

[0049] During the procedure, a tracking system is used to track the respective positions of the sensing electrodes 22, so that each of the signals can be associated with the signal acquisition location. For example, an 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 by reference herein, can be used. In the ACL system, the processor estimates the respective positions of the electrodes based on impedance measured between each sensing electrode 22 and a plurality of surface electrodes 24 coupled to the skin of the patient 25. For example, three surface electrodes 24 can be coupled to the patient’s chest, and another three surface electrodes can be coupled to the patient’s back. (For ease of illustration, Figure 1 only one surface electrode is shown in FIG. 1.) Current is transferred between the electrodes 22 and the surface electrodes 24 within the patient’s heart 23. The processor 28 calculates the estimated positions of all of the electrodes 22 within the patient’s heart based on the ratios between the resulting current amplitudes (or the impedances represented by these amplitudes) measured at the surface electrodes 24, and the known positions of the electrodes 24 on the patient’s body. Thus, the processor can associate any given impedance signal received from an electrode 22 with the location at which the signal was acquired.

[0050] Figure 1 The exemplary embodiments shown are chosen for clarity of concept. Other tracking methods can be used, for example, methods based on measuring voltage signals, as in the CARTO® system (manufactured by Biosense Webster). Other types of sensing catheters can likewise be employed, such as the LASSO® catheter (manufactured by Biosense Webster). Other types of electrodes, such as those used for ablation, can be fitted to the electrodes 22 to gather the required position data and used in a similar manner, as described above. 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 the physical contact between each electrode 22 and the inner surface of the heart chamber during the measurements.

[0051] The processor 28 typically comprises a general purpose computer having software programmed to perform the functions described herein. The software can be downloaded to the computer in electronic form, over a network, for instance, or it alternatively or additionally can be supplied and / or stored on non-transitory tangible media, such as magnetic, optical or electronic memory.

[0052] In particular, the processor 28 runs a special algorithm included in the Figures 5 to 7 as disclosed herein, which enables the processor 28 to perform the disclosed steps, as further described below.

[0053] Automatic identification of posterior mapping of pulmonary veins

[0054] To keep the presentation clear, Figure 2 the description (as well as Figures 3 to 4 the description) uses a schematic mesh surface map 40 of the anatomical map 31. The surface mesh map 40 is typically made of a triangular mesh (triangles are not shown for clarity).

[0055] Figure 2 A schematic volume rendering of the surface mesh map 40 of the left atrium according to an embodiment of the present application, which illustrates the automatically identified pulmonary veins (PVs) 411-412 and 414-415 in the map.

[0056] As seen, the processor 28 computes a “medial axis” skeleton S42 from the LA surface anatomical map 31. As a hierarchical tree map, the skeleton 42 has a root point M045. The root point M0 of the tree map divides the map into two main sub-trees, with the left main branch being on the same (left) side and the right main branch being on the same (right) side. For a predefined number of N main branches, M0 can be computed as the closest point on the skeleton to the center of mass of the map. ​the point where S i is the coordinate of the end point of branch j. The skeleton 42 (i.e. tree graph) comprises only five main branches (N=5), i.e. branches 441-445, since the processor has removed the secondary or circular branches (not shown) from the skeleton 42.

[0057] As further seen, the surface mesh map 40 (by the processor 28) is rotated to a posterior-anterior (from back to front) PA orientation, so that the right of the view corresponds to the right side of the viewer, and so that the left of the view is located at the left side of the patient.

[0058] The processor has identified two right PVs and two left PVs by their x values (on the x axis 46). The lower and upper structures are identified by their depth values (depth is marked on the depth (z value) axis 47): the right inferior pulmonary vein (RIPV) 412 has a depth 492 lower than the depth 491 of the right superior pulmonary vein (RSPV) 411. Similarly, the processor identifies the atrial appendage and the left PVs by their depth values. As seen, the depth values decrease for the following sequence: the left atrial appendage (LAA) 413 has a depth 493, the left superior pulmonary vein (LSPV) 414 has a depth 494, and the left inferior pulmonary vein (LIPV) 415 has a depth 495.

[0059] Cutting of posterior mapping of pulmonary veins

[0060] Figure 3 is a schematic illustration volume rendering of the semi-automatic generation of cuts of the pulmonary veins (PVs) 411 and 412 in the surface mesh map 40 according to an exemplary embodiment of the present application. Figure 2 is a schematic illustration volume rendering of the semi-automatic generation of cuts of the pulmonary veins (PVs) 411 and 412 in the surface mesh map 40 according to an exemplary embodiment of the present application.

[0061] In the illustrated embodiment, after the PVs are identified, the user clicks on the map on a point P 50 that should be on the proposed cut curve. The algorithm computes another point T 52 on the skeleton S 42 that is closest to the point P, and then defines a plane N (not shown) that passes through T 52, which is perpendicular to the branch 442 of the skeleton S 42 at the point T 52. The intersection of the plane N with the map forms the basis for finding a smooth cut curve 555 of the PVs. (The curve 555 is visible in the inset 75).

[0062] Finding a smooth cut curve can be achieved in several ways. In one embodiment, for example, the algorithm can apply the following steps:

[0063] i) define a circle 54 and a circumference 55 with a radius ||P-T'|| 57, where the point T' 53 is the projection of the point T on the plane N.

[0064] ii) Starting from three points with equal angular distances in circle 54, curve 555 is defined by the projections of these points onto the volume's calibration surface.

[0065] iii) If the curve is not closed, then:

[0066] • Increase your points and try again.

[0067] or,

[0068] • Add a point in the middle of the geodesic path, which connects each consecutive point that could not be connected.

[0069] In the implementation, when the smooth cutting curve 555 is not well defined, for example, by opening at the ends, the processor derives the best-fit polygon cutting curve, as described below. For example, the processor applies an algorithm that includes the following steps for finding intersecting planes N to cut RSPV 411:

[0070] i) Use plane N to cut the mapping map.

[0071] ii) Define the boundaries of the anatomical structures as the nearest closed polygon Q56 at the intersection.

[0072] By fitting a quadratic B-spline curve that interpolates the projection points onto the surface of the mapping map, and then sampling the smoothed curve and projecting it onto the surface to obtain a polygon, the nearest closed polygon can be defined. For this purpose, a metric can be used, for example, where the vertex coordinates of the closed polygon, given that sets {s} and {r} have the same number of points as the closed polygon, such that the metric M = ∑|r i -ρ i | 2 Minimize, where r i and ρ i These are the coordinates of a set of corresponding intersection points (not shown) of the polygon and the plane N with the surface, such as r. i It is its vertex. More generally, the distance or similarity between curves can be calculated using computational geometry. Therefore, any distance calculation method between curves can be used here. For example, Hausdorff distance or Frechet distance.

[0073] Figure 4 According to an exemplary embodiment of the present invention Figure 2 A schematic volumetric rendering of the automatic cutting 70 of the pulmonary vein (PV) in the surface mesh mapping diagram 40. In the illustrated embodiment, the processor 28 derives the cutting curve according to the algorithm (after identifying the PV, and calculates the skeleton 42 with the main branches 441-445 as described above).

[0074] The processor 28 identifies all surface points 60 (e.g., vertices on a triangular mesh representing the surface of the left atrium) to form a split line approximately equidistant from two adjacent skeletal main branches corresponding to the anatomical structure. Figure 4 The adjacent skeletal branches referred to above are branches 441-442, 442-445, 443-444, and 444-445. In other words, for each pair of the five lines 441-445 of the skeleton (starting from the vertices of the skeleton S42), the processor finds sets of points 60 on the surface that are equidistant from these lines and that are farthest from the vertices. The sets of surface points 60 effectively split the map into regions corresponding to the anatomical structure.

[0075] As used herein, the term“about” with reference to any numerical value or range of numbers indicates suitable dimensional tolerances that allow parts or assemblies to perform their intended purpose as described herein. More specifically,“about” can refer to a range of values ±20% of the recited value, e.g.,“about 90%” can refer to a range of values from 71% to 99%.

[0076] The PV is then able to automatically cut in the map 31 by lines that do not encroach on the junction points 60 using the processor 28, as follows:

[0077] a) Each found point 60 (that is associated with two skeletal branches) has a projection (58) point 60 on both branches. This is true for all of the above branch pairs and for each surface point (e.g., vertex).

[0078] b) Each branch has a (e.g., from the root point 45) farthest selected median projection (58) point Ti 68.

[0079] c) Each point Ti has a tangent Ni 65 that defines a desired cut plane C 70 on which the point Ti is embedded.

[0080] The required anatomical map, i.e., the cut anatomical map of the PV, is automatically derived, taking into account each of the five branches of the skeleton. The derivation steps necessary to understand the implementation are described below. Alternatively, the cut plane C 70 can be used to define a projection curve, such as Figure 3 the curve 555 of FIG. 5B, in this way defining a more realistic representation of the boundaries of the map of the anatomical structure.

[0081] Identification and cutting method of PV in anatomical map

[0082] Figure 5 To schematically describe the method for identifying the left atrium according to the embodiments of the present application, Figure 2FIG. 2 shows a flowchart of the method of the present application for automatically identifying the five main branches 441-445 of the pulmonary veins (PVs) 411, 412, 414, and 415 in the surface mesh map 40 of the left atrium (LA) of the heart 23 of a patient 25. According to the algorithm of the presented embodiment, a process is performed that begins at map receiving step 78 where the processor 28 receives a surface map of the LA of the heart 23 of a patient 25, such as the map 31 computed by the system 21.

[0083] At skeleton computing step 80, the processor 28 computes a "medial axis" skeleton 42 of the mapped surface of the LA.

[0084] Next, at skeleton cleaning step 82, the processor 28 removes secondary or circular branches from the skeleton 42.

[0085] Next, at tree map building step 84, the processor 28 builds a hierarchical tree map 42.

[0086] Then, at root identification step 86, the processor identifies the tree root as the center point 45 of the skeleton 42.

[0087] Next, at main branch identification step 88, the processor 28 identifies the five main branches 441-445 of the tree map.

[0088] To do so, at map rotation step 90, the processor 28 rotates the surface mesh map 40 to a posterior-anterior (posterior-to-anterior) PA orientation to align the coordinate system such that the x-axis distinguishes left from right, and the z-axis distinguishes depth (posterior- interior).

[0089] Finally, at PV identification steps 92 and 94, on the surface mesh map 40, the processor 28 identifies two right PVs and two left PVs by their left-right positions on the map, and identifies the inferior and superior PVs (and auricles) by their depth values on the surface mesh map 40 (such as shown in FIG. 1). Figure 2

[0090] Figure 6 To illustrate the semi-automatic method of cutting pulmonary veins (PVs) from a surface mesh map 40 according to an embodiment of the present application, a flowchart is shown in FIG. 2. While the illustrated embodiment provides for 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 heart chambers) that need to be cut from their maps. Figure 2 In another embodiment (not shown in FIG. 3), another algorithm for "semi-automatic" cutting is provided that does not rely on the skeleton of the map.

[0091] Figure 6

[0092] ​​​A process is executed according to the algorithm of the presented implementation scheme, which begins at PV identification step 601, where processor 28, for example, uses... Figure 5 The steps described identify four PVs 411, 412, 414 and 415, as well as the auricle, in the surface grid mapping map 40.

[0093] Next, at the surface point selection step 603, the user clicks on the point on the cavity mapping map (for example, on mapping map 40) that should be on the cutting curve.

[0094] In calculation step 605, processor 28 derives (e.g., calculates) a smooth cutting curve, such as... Figure 3 The curve is 555. Then, at inspection step 607, the processor checks whether the smooth cutting curve derived by the processor is closed.

[0095] If the smooth cutting curve is closed, then at cutting step 609, processor 28 cuts the PV in question along the smooth curve. On the other hand, if the smooth curve has open ends, then at polygon cutting curve derivation step 611, processor 28 calculates the best-fit polygon cutting curve, such as... Figure 3 Curve 56. Then, at cutting step 613, processor 28 cuts the PV in question along the polygon curve.

[0096] Figure 7 To illustrate the embodiments of the present invention, the following is a schematic diagram. Figure 2 A flowchart of an automated method for cutting pulmonary veins (PVs) from surface mesh mapping map 40. While the illustrated embodiment sets the PV as the anatomical structure to be cut, in general, the disclosed single-click technique is applicable to other anatomical structures of other organs (such as other cardiac chambers) that require cutting from their mapping maps. A process is executed according to the algorithm of the presented embodiment, starting at PV identification step 701, where processor 28, for example, uses... Figure 5 The steps described identify four PVs, 411, 412, 414, and 415, as well as the auricle, in the surface grid mapping map 40.

[0097] Next, in the mapping map segmentation step 703, the processor 28 segments the mesh mapping map 40 using segmentation lines 60, as follows: Figure 4 As stated above.

[0098] In export step 705, using the dividing line 60, processor 28 calculates the farthest median projection point within each PV under discussion (e.g., Figure 4 Point 68). Then, in the cutting plane derivation step 707, the processor 28 defines the normal plane of the skeleton 42 at the farthest median projection point, such as Figure 4plane 70. Then, at a cut step 709, the processor 28 cuts the PV in question along the polygonal curve. In embodiments, as an alternative to using plane 70, the processor uses the farthest median projection point as a basis for computing a smooth cut curve, such as curve 555, or a polygonal curve, such as curve 56, as described above with respect to steps 605-613. Figure 3 and Figure 6 In other embodiments, other statistical functions, such as maximum, minimum, mean, etc., can be utilized in addition to the median.

[0099] Figures 5 to 7 The exemplary flowchart shown is chosen purely for conceptual clarity. In optional embodiments, various additional steps can be performed, for example to automatically register the opening into the LA of the PV being identified and cut with a medical image.

[0100] While the embodiments described herein primarily address the identification of known anatomical openings in a mapped volume, such as the pulmonary vein ostia, the various methods and systems described herein can also be used for other applications. For example, the disclosed methods can be used to identify regions in a cavity that are not proportionally sized and / or shaped. While the disclosed embodiments relate to cardiac applications, the disclosed methods can also be applied to a mapped volume of any organ cavity. For example, the algorithms described herein can be applied to any geometry having tubular structures, such as all other cavities of the heart. As another example, the methods can be applied to an otolaryngology map.

[0101] It should therefore be understood that the embodiments described above are cited by way of example, and that the present application is not limited to what has been particularly shown and described hereinabove. Rather, the scope of the present application includes both combinations and sub-combinations of the various features described hereinabove, as well as variations and modifications thereof which would occur to persons of ordinary skill in the art upon reading the foregoing description and which are not disclosed in the prior art. Documents incorporated by reference in the present patent application are to be considered an integral part of the application except that to the extent any terms are defined in such incorporated documents in a manner that conflicts with the definitions made explicit herein, the definitions made explicit in this application shall control.

Claims

1. A method for automatically identifying and processing anatomical structures in an anatomical map, the method comprising: computing a centerline tree map of a volume of an organ of a patient in a computerized anatomical map of the volume; identifying a predefined number of main branches in the tree map; using the identified main branches, identifying one or more known anatomical opening regions of the volume in the anatomical map; deriving a cut curve of the identified known anatomical opening region on the anatomical map, comprising segmenting the known anatomical opening region on the anatomical map, using the segmented known anatomical opening region, deriving a farthest median projection point on the main branch of the known anatomical opening region and defining a normal plane of the main branch at the farthest median projection point, and defining an intersection between the known anatomical opening region and the normal plane as the cut curve; cutting the identified known anatomical opening region along the cut curve derived in the anatomical map.

2. The method of claim 1, wherein the volume comprises a left atrium of a heart and the identified one or more known anatomical opening regions comprise pulmonary vein ostia.

3. The method of claim 1, wherein computing the centerline tree map comprises computing a centerline skeleton map of the volume and setting a center point of the centerline skeleton map as a root of the centerline tree map.

4. The method of claim 1, wherein identifying the main branches comprises removing secondary branches and circular branches from the tree map.

5. The method of claim 1, wherein identifying the one or more known anatomical opening regions comprises identifying the one or more known anatomical opening regions based on a posterior-anterior orientation and depth of the one or more known anatomical opening regions in the anatomical map.

6. The method of claim 1, wherein the method comprises presenting the identified one or more known anatomical opening regions to a user on the anatomical map.

7. The method of claim 1, wherein deriving the cut curve on the anatomical map comprises selecting points on the anatomical map to be included in the cut curve and using the selected points in deriving the cut curve.

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

9. The method of claim 1, wherein defining the intersection comprises using the normal plane to derive one of a smooth cut curve and a polygonal cut curve on the anatomical map.

10. A system for automatically identifying and processing anatomical structures in an anatomical map, the system comprising a memory and a processor, the memory configured to hold a computerized anatomical map of a volume of an organ of a patient, the processor configured to: compute a centerline tree map of the anatomical map; identify a predefined number of main branches in the tree map; using the identified main branches, identify one or more known anatomical opening regions of the volume in the anatomical map; deriving a cut curve of the identified known anatomical opening region on the anatomical map, including segmenting the known anatomical opening region on the anatomical map, using the segmented known anatomical opening region, deriving a farthest median projection point on the main branch of the known anatomical opening region and defining a normal plane of the main branch at the farthest median projection point, and defining an intersection between the known anatomical opening region and the normal plane as the cut curve; cutting the identified known anatomical opening region along the cut curve derived in the anatomical map.

11. The system of claim 10, wherein the volume comprises a left atrium and the one or more identified known anatomical opening regions are each a pulmonary vein ostium.

12. The system of claim 10, wherein the processor is configured to compute the medial axis tree graph by computing a medial axis skeleton of the volume and setting a center point of the medial axis skeleton as a root of the medial axis tree graph.

13. The system of claim 10, wherein the processor is configured to identify the main branch by removing secondary branches and circular branches from the tree graph.

14. The system of claim 10, wherein the processor is configured to identify the one or more known anatomical opening regions based on an antero-posterior orientation and depth of the one or more known anatomical opening regions in the anatomical map.

15. The system of claim 10, wherein the processor is further configured to present the one or more identified known anatomical opening regions to a user on the anatomical map.

16. The system of claim 10, wherein the processor is configured to derive the cut curve by selecting points on the anatomical map to be included in the cut curve and using the selected points in deriving the cut curve.

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

18. The system of claim 10, wherein the processor is further configured to derive one of a smooth cut curve and a polygonal cut curve on the anatomical map using the normal plane.

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