Mesh topology adaptation

Abstract

A concept is presented for adapting a predefined first mesh topology representing an organ to a different second mesh topology of the organ. One such concept includes identifying a correspondence between the first mesh topology and the second mesh topology based on a spectral matching of the first mesh topology and the second mesh topology. The predefined first mesh topology is aligned with the second mesh topology based on the identified correspondence between the first mesh topology and the second mesh topology.

Description

Technical Field

[0001] This invention relates to the field of mesh topology, and more specifically, to adapting a predefined first mesh topology representing an organ to a different second mesh topology for that organ. Background Technology

[0002] Annotated medical images are widely used in scientific medical research, such as for evaluating and training image segmentation techniques. A popular segmentation technique is model-based segmentation (MBS). Using this technique, triangular meshes representing organ boundaries can be fitted to medical images in a controlled manner, preserving the approximate shape of the organ and leveraging prior anatomical knowledge to refine the segmentation. The segmentation model must be trained using image data of the organ to be segmented and the corresponding ground truth mesh. Typically, these meshes are created manually by clinicians or researchers. However, this manual process is very time-consuming.

[0003] The process of generating realistic meshes can be accelerated by using existing models developed for the same organ, which have previously been trained on similar image data. Alternatively, realistic meshes for organs may already be available, previously depicted by clinicians for different segmentation models. In both cases, the topology of the available realistic mesh (from previous segmentation or a different model) may not match the existing model topology (e.g., in terms of the number of vertices and triangles), and for model-based segmentation, the meshes typically must have the same topology.

[0004] One way to obtain organ meshes with the same topology is to find the correspondence between vertices in two different meshes, which can be provided by well-known point matching methods such as Iterative Nearest Point (ICP) or Coherent Point Shift (CPD). However, although these methods take into account global transformations in the feature space between the two shapes, it is still necessary to incorporate spatial regularity into the mapping so that neighboring points in one mesh correspond to neighboring points in another mesh. Summary of the Invention

[0005] This invention is defined by the claims.

[0006] According to one aspect of the invention, a method is provided for adapting a predefined first mesh topology representing an organ to a different second mesh topology of the organ. The method includes: identifying a correspondence between the first and second mesh topologies based on spectral matching of the first and second mesh topologies; and aligning the predefined first and second mesh topologies based on the identified correspondence.

[0007] The proposed embodiments provide a concept for adapting a predefined first grid topology to a different second grid topology. For example, the embodiments can be used to adapt a real first grid of an organ to a new second grid topology of the organ. Specifically, the embodiments can employ spectral matching to align / adapt a predefined (i.e., real) first grid to a new second grid topology. In other words, spectral matching is proposed to align an existing grid of the same organ with a new topology. Such an approach can help relax requirements regarding annotated datasets.

[0008] For example, the proposed embodiments could provide a method for finding correspondences between existing mesh topologies (e.g., obtained from segmentations using a pre-trained model or from real meshes previously generated by the user) and newly defined mesh topologies using spectral matching. In this way, correspondences between meshes of organs with different topologies can be defined because direct vertex correspondences between the two meshes are not required due to the transformation to spectral space. Furthermore, spatial regularity can be ensured by using spectral embeddings (e.g., calculated based on the graph Laplacian of the surface mesh).

[0009] Implementation examples can be based on the idea of ​​using spectral matching to align existing meshes of the same organ with new topologies. In such examples, spectral matching is proposed to find the correspondence between existing mesh topologies and newly defined mesh topologies. This allows the transformation of previously defined real meshes into new, desired mesh topologies for the model (which can then be retrained for segmentation).

[0010] For example, identifying the correspondence between a first grid topology and a second grid topology based on spectral matching of the first and second grid topologies may include: obtaining a first spectrum derived from the first grid topology; obtaining a second spectrum derived from the second grid topology; decomposing the first and second spectra to determine spectral coordinates based on the first and second spectra; analyzing the determined spectral coordinates to identify a match between the first and second spectra; and identifying the correspondence between the first and second grid topologies based on the identified match. Therefore, embodiments may employ conventional / known spectrum generation and / or matching processes, thereby fully utilizing widely known and / or available processes.

[0011] In some embodiments, aligning a predefined first grid topology with a second grid topology may include aligning the spectral components of the first grid topology and the second grid topology. For example, aligning the spectral components may include processing the first grid topology on both grids using a point transformation method, such as the Coherent Point Shift (CPD) method or the Iterative Closest Point (ICP) method. Therefore, known point matching methods can be employed, thereby reducing, for example, the complexity and / or cost of the embodiments.

[0012] In an exemplary embodiment, aligning a predefined first mesh topology with a second mesh topology may include resampling the first mesh topology based on the identified correspondence between the first and second mesh topologies.

[0013] Because the proposed concept enables the conversion of a previously defined real mesh into a new desired mesh topology for the model (which can then be retrained for segmentation), it will be understood that embodiments of the invention can be used in image segmentation. Therefore, according to another aspect of the invention, a method for segmenting medical images of organs is provided, the method comprising: adapting a first mesh topology representing the organ to a different second mesh topology of the organ, according to the proposed embodiment; and performing model-based segmentation of the image using the adapted first mesh topology.

[0014] According to another aspect, a computer program product is provided for adapting a predefined first mesh topology representing an organ to a different second mesh topology of the organ, wherein the computer program product includes a computer-readable storage medium embodying computer-readable program code configured to perform all the steps of the proposed embodiments.

[0015] Therefore, a computer system may also be provided, comprising: a computer program product according to the proposed embodiments; and one or more processors adapted to perform the method according to the proposed concept by executing computer-readable program code of the computer program product.

[0016] According to another aspect of the invention, a system is provided for adapting a predefined first mesh topology representing an organ to a different second mesh topology of the organ, the system comprising: an analysis component configured to identify a correspondence between the first mesh topology and the second mesh topology based on spectral matching of the first mesh topology and the second mesh topology; and a mesh alignment component configured to align the predefined first mesh topology with the second mesh topology based on the identified correspondence between the first mesh topology and the second mesh topology.

[0017] This system can be located remotely from the user device to adapt a first mesh topology to a different second mesh topology. In this way, a user (such as a medical professional) can have a suitably positioned system that can receive information remotely from the system to adapt the first mesh topology to a different second mesh topology. Therefore, embodiments can enable a user to adapt the first mesh topology to a different second mesh topology using a local system (which may include, for example, portable display devices such as laptops, tablets, mobile phones, PDAs, etc.). For example, embodiments can provide an application for a mobile computing device, and this application can be executed and / or controlled by a user of the mobile computing device.

[0018] The system may also include: a server device including a system for adapting a first mesh topology to different second mesh topologies; and a client device including a user interface. Therefore, to adapt the first mesh topology to different second mesh topologies, a dedicated data processing component can be used, thereby reducing the processing requirements or capabilities of other components or devices in the system.

[0019] The system may also include client devices, which include grid alignment components and display units. In other words, a user (such as a doctor or medical professional) may have a suitably positioned client device (such as a laptop, tablet, mobile phone, PDA, etc.) that processes received data to adapt a first grid topology to a different second grid topology and generates display control signals. Purely by example, embodiments could therefore provide a grid-based annotation system capable of performing medical analysis on one or more objects (e.g., patients) from a single remote location, providing communication between the subject and a monitoring user (e.g., a nurse or doctor), and its functionality could be extended or modified, for example, based on the proposed concept.

[0020] It will be understood that, depending on predetermined constraints and / or the availability of processing resources, processing capacity can therefore be distributed in different ways throughout the system.

[0021] These and other aspects of the invention will become apparent and will be explained with reference to the embodiments described below. Attached Figure Description

[0022] To better understand the invention and to more clearly illustrate how to practice it, reference will now be made to the accompanying drawings by way of example only, wherein:

[0023] Figure 1This is a simplified block diagram of a proposed embodiment of a system for adapting a predefined first mesh topology representing an organ to a different second mesh topology for that organ;

[0024] Figure 2 A simplified flowchart of a proposed embodiment of a method for adapting a predefined first mesh topology representing an organ to a different second mesh topology for that organ; and

[0025] Figure 3 This is a simplified block diagram of a computer in which one or more parts of an embodiment may be employed. Detailed Implementation

[0026] The invention will be described with reference to the accompanying drawings.

[0027] It should be understood that while the detailed description and specific examples indicate exemplary embodiments of the apparatus, system, and method, they are for illustrative purposes only and are not intended to limit the scope of the invention. These and other features, aspects, and advantages of the apparatus, system, and method of the present invention will become better understood from the following description, the appended claims, and the accompanying drawings. The fact that certain measures are referenced in mutually different dependent claims does not indicate that combinations of these measures cannot be used advantageously.

[0028] Based on a study of the accompanying drawings, the disclosure, and the appended claims, those skilled in the art can understand and implement variations of the disclosed embodiments in practicing the claimed invention. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite articles "a" or "an" do not exclude a plurality.

[0029] It should be understood that the accompanying drawings are merely schematic and not drawn to scale. It should also be understood that in all the accompanying drawings, the same reference numerals are used to indicate the same or similar parts.

[0030] A method is proposed for adapting a predefined first mesh topology representing an organ to a different second mesh topology for that organ. Therefore, embodiments can be used to transform a mesh from a defined (e.g., annotated ground truth values) first model space to another model space. This allows ground truth annotations from the first model space to be used to train new, different models (e.g., designed to solve new problems).

[0031] The embodiment proposes using spectral matching to find a correspondence between a predefined (e.g., pre-existing) first mesh topology of an organ (obtained from organ segmentation using a pre-trained model, or from a real mesh previously generated by a clinician) and a second mesh topology (e.g., newly defined). Therefore, this enables the transformation from a predefined real mesh to a new mesh topology in the model, which can then be retrained for segmentation.

[0032] Spectral matching is well-known, therefore a detailed description of it is omitted in this specification. However, briefly, the spectral decomposition of the graph Laplacian associated with complex shapes provides isometry-invariant eigenfunctions (modes). Each vertex on the shape can be uniquely represented by a combination of eigenmode values ​​at each point (sometimes called spectral coordinates). Spectral matching involves establishing point correspondences by pairing vertices on different shapes that have the most similar spectral coordinates. Therefore, spectral theory provides a solution for matching surface meshes with different topologies in the spectral domain, and thus can provide applications for matching different meshes to create atlases.

[0033] The inventors propose using spectral matching to incorporate previously defined meshes into new model topologies. In this way, the proposed embodiments can identify correspondences between meshes of the same organ but with different topologies (e.g., the brain, heart, kidney, liver, or lungs of two different subjects / patients). Specifically, due to the transformation to spectral space, direct vertex correspondences between meshes are not required. Furthermore, spatial regularity is ensured, for example, by using spectral embeddings calculated based on the graph Laplacian of the surface mesh.

[0034] Therefore, the proposed embodiments can be used to segment medical images of organs. This method may include: adapting a first mesh topology representing an organ to a different second mesh topology for that organ, according to the proposed embodiments. The adapted first mesh topology can then be used to perform model-based segmentation of the image. Thus, the proposed concepts(s) can facilitate semi-automatic real-world segmentation. By employing previously developed models, the process can be accelerated (because the user may only need to implement small corrections, rather than, for example, manually creating new meshes). Furthermore, versatile data can be provided, and this can be used, for example, for more projects where the same organ needs to be depicted but with different topologies.

[0035] As an example only, the illustrative embodiments can be used in many different types of clinical, medical or subject-related settings, such as hospitals, doctors' offices, medical research institutions, wards, nursing homes, private residences, etc.

[0036] To aid in understanding the proposed concepts(s), reference will now be made to... Figure 1 An exemplary embodiment of a system for adapting a predefined first mesh topology representing an organ to a different second mesh topology for that organ is described.

[0037] Figure 1This is a simplified block diagram of the proposed embodiment. System 100 according to the proposed embodiment is configured to obtain (e.g., received via an input interface) a predefined first mesh topology 110 representing an organ (such as the liver of a first subject). System 100 is also adapted to obtain (e.g., received via an input interface) a different second mesh topology 120 for that organ (such as the liver of a second subject). The system is configured to adapt the predefined first mesh topology 110 to the second mesh topology 120. In other words, system 100 is configured to transform the mesh from a defined (e.g., ground truth values ​​with annotations) first model space to another model space. This allows ground truth annotations from the first model space to be used to train new, different models.

[0038] More specifically, system 100 includes an analysis component 130 configured to perform spectral matching of a first mesh topology 110 and a second mesh topology 120 to identify a correspondence between the first mesh topology 110 and the second mesh topology 120. Then, a mesh alignment component 135 of the system is configured to align a predefined first mesh topology 110 and the second mesh topology 120 based on the correspondence between the first and second mesh topologies (identified by the analysis component 130).

[0039] The aligned mesh topology 140 is output from system 100 (e.g., via an output interface) and is adapted to the second mesh topology 120. In this way, the annotations of the first mesh topology 110 are carried over to the aligned mesh topology 140, but also aligned with the second mesh topology.

[0040] With further explanation and description, reference will now be made. Figure 2 An exemplary method according to one embodiment is described.

[0041] Figure 2This is a simplified flowchart of a proposed embodiment of a method 200 for adapting a predefined first mesh topology representing an organ to a different second mesh topology for that organ. Method 200 includes a first step 210: identifying a correspondence between the first and second mesh topologies based on spectral matching of the first and second mesh topologies. More specifically, step 210 of identifying the correspondence between the first and second mesh topologies based on spectral matching includes: obtaining 220 a first spectrum derived from the first mesh topology; and obtaining 230 a second spectrum derived from the second mesh topology. Note that the first and second spectra can be obtained in any order. Furthermore, obtaining the spectra may include generating the spectra, or alternatively, simply receiving the generated spectra from an external component. After obtaining the first and second spectra, step 210 of identifying the correspondence between the first and second mesh topologies further includes a step 235 of decomposing the first and second spectra to determine spectral coordinates. The spectral coordinates are then analyzed in step 240 to identify a match between the first and second spectra. Based on the identified matches, the correspondence between the first grid topology and the second grid topology is then identified in step 250.

[0042] Method 200 then includes a second step 260: aligning a predefined first grid topology with a second grid topology based on the identified correspondence between the first and second grid topologies. Here, step 260 of aligning the predefined first grid topology with the second grid topology includes aligning the spectral components of the first grid topology and the second grid topology. More specifically, in this exemplary embodiment, aligning the spectral components includes processing the first grid topology using a point transformation method (such as a coherent point drift method or an iterative nearest point (ICP) method). Alternatively or additionally, aligning the predefined first grid topology with the second grid topology may include resampling the first grid topology based on the identified correspondence between the first and second grid topologies.

[0043] From the exemplary embodiments described above, it will be understood that the proposed concept can facilitate the generation of consistent mesh-based annotations for images using spectral matching. Specifically, the proposed embodiments employ spectral matching to find correspondences between existing mesh topologies and newly defined mesh topologies. Instead of direct vertex correspondences, a spectral embedding calculated based on a graph Laplacian of the surface mesh is proposed.

[0044] Therefore, a method is proposed for using spectral matching to find correspondences between existing mesh topologies (either obtained from organ segmentation using a pre-trained model or from real meshes previously generated by clinicians) and newly defined mesh topologies. This is achieved by using independently computed spectral embeddings of two shapes and finding matches between the defined mesh (i.e., the ground truth) and the template (i.e., the new mesh topology). After the matches are defined, the ground truth mesh can be resampled in the new topology. This is supported by spectral matching, which provides point-to-point mesh correspondences.

[0045] Figure 3 An example of a computer 300 in which one or more portions of an embodiment may be employed is shown. The various operations discussed above can utilize the capabilities of computer 300. For example, one or more portions of a system for providing a subject-specific user interface can be incorporated into any element, module, application, and / or component discussed herein. It should be understood that system functional blocks may run on a single computer or may be distributed across several computers and locations (e.g., connected via the Internet).

[0046] Computer 300 includes, but is not limited to, PCs, workstations, laptops, PDAs, handheld devices, servers, storage devices, etc. Typically, in terms of hardware architecture, computer 300 may include one or more processors 310, memory 320, and one or more I / O devices 370 communicatively coupled via a local interface (not shown). The local interface may be, for example, but not limited to, one or more buses or other wired or wireless connections, as known in the art. The local interface may have additional elements, such as controllers, buffers (caches), drivers, repeaters, and receivers, to enable communication. Furthermore, the local interface may include address, control, and / or data connections to enable appropriate communication between the aforementioned components.

[0047] Processor 310 is a hardware device for executing software that can be stored in memory 320. Processor 310 can actually be any custom or commercially available processor, central processing unit (CPU), digital signal processor (DSP), or auxiliary processor among several processors associated with computer 300, and processor 310 can be a semiconductor-based microprocessor (in the form of a microchip) or microprocessor.

[0048] Memory 320 may include any one or a combination of volatile memory elements (e.g., random access memory (RAM) such as dynamic random access memory (DRAM), static random access memory (SRAM), etc.) and non-volatile memory elements (e.g., ROM, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic tape, compact optical disc read-only memory (CD-ROM), magnetic disk, floppy disk, cassette tape, etc.). Furthermore, memory 320 may incorporate electrical, magnetic, optical, and / or other types of storage media. Note that memory 320 may have a distributed architecture, where various components are geographically separated but accessible to processor 310.

[0049] The software in memory 320 may include one or more individual programs, each including an ordered list of executable instructions for implementing logical functions. According to an exemplary embodiment, the software in memory 320 includes a suitable operating system (O / S) 350, a compiler 340, source code 330, and one or more applications 360. As shown, application 360 includes multiple functional components for implementing features and operations of the exemplary embodiment. According to the exemplary embodiment, application 360 of computer 300 may represent various applications, computing units, logic, functional units, processes, operations, virtual entities, and / or modules; however, application 360 is not intended to be limiting.

[0050] Operating system 350 controls the execution of other computer programs and provides scheduling, input / output control, file and data management, memory management, and communication control and related services. The inventors anticipate that application 360 for implementing the exemplary embodiments can be applied to all commercially available operating systems.

[0051] Application 360 can be a source program, an executable program (object code), a script, or any other entity including a set of instructions to be executed. When it is a source program, the program is typically translated by a compiler (such as compiler 340), assembler, interpreter, etc. (which may or may not be included in memory 320) to operate correctly in conjunction with O / S 350. Furthermore, application 360 can be written in an object-oriented programming language with classes of data and methods, or a procedural programming language with routines, subroutines, and / or functions, such as, but not limited to, C, C++, C#, Pascal, BASIC, API calls, HTML, XHTML, XML, ASP scripts, JavaScript, FORTRAN, COBOL, Perl, Java, ADA, .NET, etc.

[0052] I / O device 370 may include input devices, such as, but not limited to, a mouse, keyboard, scanner, microphone, camera, etc. Furthermore, I / O device 370 may also include output devices, such as, but not limited to, a printer, monitor, etc. Finally, I / O device 370 may also include devices for transmitting input and output, such as, but not limited to, a NIC or modulator / demodulator (for accessing remote devices, other files, devices, systems, or networks), radio frequency (RF) or other transceivers, telephone interfaces, bridges, routers, etc. I / O device 370 also includes components for communication over various networks, such as the Internet or intranets.

[0053] If the computer 300 is a PC, workstation, intelligent device, etc., the software in the memory 320 may also include a Basic Input / Output System (BIOS) (omitted for simplicity). The BIOS is a collection of basic software routines that initialize and test the hardware at startup, boot the O / S 350, and support data transfer between hardware devices. The BIOS is stored in some type of read-only memory (such as ROM, PROM, EPROM, EEPROM, etc.) so that it can be executed when the computer 300 is activated.

[0054] When the computer 300 is running, the processor 310 is configured to execute software stored in the memory 320 to transfer data to and from the memory 320, and to control the overall operation of the computer 300 according to the software. Applications 360 and O / S 350 are read, possibly buffered, and then executed by the processor 310, in whole or in part.

[0055] When Application 360 is implemented as software, it should be noted that Application 360 can be stored on virtually any computer-readable medium for use by or in conjunction with any computer-related system or method. In the context of this document, a computer-readable medium can be an electronic, magnetic, optical, or other physical device or component that can contain or store computer programs for use by or in conjunction with a computer-related system or method.

[0056] Application 360 can be embodied in any computer-readable medium for use by or in conjunction with an instruction execution system, apparatus, or device (such as a computer-based system, a processor-containing system, or other system that can fetch and execute instructions from and from an instruction execution system, apparatus, or device). In the context of this document, "computer-readable medium" can be any component that can store, transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. Computer-readable media can be, for example, but not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, devices, or propagation media.

[0057] This invention can be a system, method, and / or computer program product. The computer program product may include a computer-readable storage medium or multiple computer-readable storage media having computer-readable program instructions thereon for causing a processor to perform aspects of the invention.

[0058] Computer-readable storage media can be tangible devices capable of retaining and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable optical disc read-only memory (CD-ROM), digital versatile disc (DVD), memory sticks, floppy disks, mechanical encoding devices (such as punched cards or raised structures in grooves on which instructions are recorded), and any suitable combination of the foregoing. As used herein, computer-readable storage media should not be construed as being, in itself, a transient signal, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0059] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a suitable computing / processing device, or downloaded via a network (e.g., the Internet, a local area network, a wide area network, and / or a wireless network) to an external computer or external storage device. The network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to a computer-readable storage medium within the suitable computing / processing device.

[0060] Computer-readable program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and traditional procedural programming languages ​​such as "C" or similar programming languages. The computer-readable program instructions may execute entirely on a user's computer, partially on a user's computer, as a standalone software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry (including, for example, programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs)) may execute the computer-readable program instructions by utilizing state information from the computer-readable program instructions to personalize the electronic circuitry, thereby performing aspects of the invention.

[0061] This document describes aspects of the invention with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0062] A single processor or other unit can perform the functions listed in the claims.

[0063] Computer programs can be stored / distributed on suitable media (such as optical storage media or solid-state media provided with or as part of other hardware), but can also be distributed in other forms (such as via the Internet or other wired or wireless telecommunications systems).

[0064] These computer-readable program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create parts for implementing the functions / actions specified in one or more blocks of a flowchart and / or block diagram. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and / or other equipment to operate in a particular manner, such that the computer-readable storage medium in which the instructions are stored includes an article of writing containing instructions that implement aspects of the functions / actions specified in one or more blocks of a flowchart and / or block diagram.

[0065] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device, thereby producing a computer-implemented process, such that the instructions, which execute on the computer, other programmable apparatus or other device, perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0066] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of instructions, including one or more executable instructions for implementing a specified logical function(s). In some alternative embodiments, the functions marked in the blocks may occur in a non-concurrent order. For example, two blocks shown consecutively may actually execute substantially concurrently, or these blocks may sometimes execute in reverse order, depending on the functions involved. It will also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a system based on dedicated hardware that performs the specified function or action or executes a combination of dedicated hardware and computer instructions.

Claims

1. A method (200) for adapting a predefined first mesh topology representing an organ to a different second mesh topology of the organ, the method comprising: (210) Identify the correspondence between the first grid topology and the second grid topology based on the spectral matching of the first grid topology and the second grid topology; as well as Based on the identified correspondence between the first and second mesh topologies, the predefined first and second mesh topologies are aligned (260), wherein aligning the predefined first and second mesh topologies (260) includes: resampling the first mesh topology based on the identified correspondence between the first and second mesh topologies.

2. The method according to claim 1, wherein, Identifying (210) the correspondence between the first and second grid topologies based on spectral matching of the first and second grid topologies includes: Obtain (220) the first spectrum derived from the first grid topology; Obtain (230) the second spectrum derived from the second grid topology; Decompose (235) the first spectrum and the second spectrum to determine the spectral coordinates based on the first spectrum and the second spectrum; The spectral coordinates determined by analysis (240) are used to identify matches between the first and second spectra; and (250) The correspondence between the first mesh topology and the second mesh topology is identified based on the identified match.

3. The method according to claim 1 or 2, wherein, Aligning the predefined first mesh topology with the second mesh topology (260) includes: Align the spectral components of the first grid topology with the spectral components of the second grid topology.

4. The method of claim 3, wherein aligning the spectral components includes processing the first grid topology using a point transformation method.

5. The method according to claim 4, wherein the point transformation method includes a coherent point drift method.

6. A method for segmenting medical images of organs, the method comprising: According to any one of claims 1 to 5, a first mesh topology representing the organ is adapted (200) to a different second mesh topology of the organ; and Model-based segmentation of the image is performed using an adapted first mesh topology.

7. A computer program product comprising computer program code components, wherein when the computer program product is run on a computer, the computer program code components are adapted to implement the method according to any one of claims 1 to 6.

8. A system (100) for adapting a predefined first mesh topology representing an organ to a different second mesh topology of the organ, the system comprising: Analysis component (130) is configured to identify the correspondence between the first grid topology and the second grid topology based on spectral matching of the first grid topology and the second grid topology; as well as The mesh alignment (135) component is configured to align a predefined first mesh topology with the second mesh topology based on the identified correspondence between the first mesh topology and the second mesh topology. The mesh alignment component (135) includes a resampling component configured to resample the first mesh topology based on the identified correspondence between the first mesh topology and the second mesh topology.

9. The system of claim 8, wherein the analysis component (130) comprises: The spectrum component is configured to obtain a first spectrum derived from the first grid topology and a second spectrum derived from the second grid topology; An analysis component is configured to decompose the first spectrum and the second spectrum to determine spectral components, and to identify matches between the first spectrum and the second spectrum based on the determined spectral components; as well as The processor is configured to identify the correspondence between the first mesh topology and the second mesh topology based on the identified match.

10. The system according to claim 8 or 9, wherein the grid alignment component (135) is configured to align the spectral components of the first grid topology and the spectral components of the second grid topology.

11. The system of claim 10, wherein the mesh alignment component (135) includes a point transformation component configured to process the first mesh topology using a point transformation method.

12. The system of claim 11, wherein the point transformation method includes a coherent point drift method.

13. A system for segmenting medical images of organs, the system comprising: System (100) for adapting a first grid topology representing the organ to a different second grid topology of the organ according to any one of claims 8 to 12; as well as The segmentation component is configured to perform model-based segmentation of the image using an adapted first grid topology.

Images