NL2032742B1 - A surgical navigation system based on artificial intelligence and graph theory algorithm - Google Patents

A surgical navigation system based on artificial intelligence and graph theory algorithm Download PDF

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NL2032742B1
NL2032742B1 NL2032742A NL2032742A NL2032742B1 NL 2032742 B1 NL2032742 B1 NL 2032742B1 NL 2032742 A NL2032742 A NL 2032742A NL 2032742 A NL2032742 A NL 2032742A NL 2032742 B1 NL2032742 B1 NL 2032742B1
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module
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Chen Yahong
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Univ Lishui
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/20—Surgical navigation systems; Devices for tracking or guiding surgical instruments, e.g. for frameless stereotaxis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G06V10/757—Matching configurations of points or features
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
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    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
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    • G16H30/00—ICT specially adapted for the handling or processing of medical images
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/20—Surgical navigation systems; Devices for tracking or guiding surgical instruments, e.g. for frameless stereotaxis
    • A61B2034/2046—Tracking techniques
    • A61B2034/2065—Tracking using image or pattern recognition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B90/00—Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups A61B1/00 - A61B50/00, e.g. for luxation treatment or for protecting wound edges
    • A61B90/36—Image-producing devices or illumination devices not otherwise provided for
    • A61B90/37—Surgical systems with images on a monitor during operation
    • A61B2090/376—Surgical systems with images on a monitor during operation using X-rays, e.g. fluoroscopy
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
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    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10068—Endoscopic image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
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Abstract

The invention discloses a surgery navigation auxiliary system based on artificial intelligence and graph theory algorithm in the field of artificial intelligence, A calibration and positioning module for extracting a target area of a surgery so as to generate a central surgery point, a graph conversion module for vectorizing the target area of the surgery according to a CT image, a conversion module taking a cutting position in the surgery area as a node, and a path generation module for connecting the central surgery point and the node, wherein the graph theory algorithm module generates a weighted graph between the central surgery point and the node. According to the technical scheme, the imaging of the surgical region is combined with the path generation module, so that the optimal surgical path is converted.

Description

P1538/NLpd
A SURGICAL NAVIGATION SYSTEM BASED ON ARTIFICIAL INTELLIGENCE AND
GRAPH THEORY ALGORITHM
Technical field
The invention belongs to the field of artificial intelli- gence, and particularly relates to a surgical navigation auxiliary system based on artificial intelligence and a graph theory algo- rithm.
Background Technology
Traumatic surgery, as its name implies, is the operation of minor trauma. It refers to the use of laparoscopy, thoracoscopy and other modern medical devices and related equipment for sur- gery. The advantages of minimally invasive surgery are less trau- ma, less pain and faster recovery. It is the dream of every pa- tient who needs surgery. Minimally invasive surgery makes this dream come true. Early minimally invasive surgery refers to a new technology of performing surgery in the human body through lapa- roscopy, thoracoscopy and other endoscopes.
However, in minimally invasive surgery, due to the small opening area, the opening position will gradually lead to blood exudation in the process of surgery, which often blurs the visual field of the endoscope, and after the blood cover is removed, the shaking of the probe will lead to the deviation of the visual field, which makes the chief surgeon lose direction. Therefore, there is an urgent need for a surgical navigation assistant system to overcome the defect of path loss in minimally invasive surgery.
Summary of the invention
In order to solve the above problems, the object of the pre- sent invention is to provide a surgical navigation assistance sys- tem based on artificial intelligence and graph theory algorithm.
In order to realize the purpose, the technical scheme of the invention is as follows: the surgery navigation auxiliary system based on artificial intelligence and graph theory algorithm com- prises a graph theory algorithm module and an image module used for storing a human body image and a surgery track image, A cali-
bration and positioning module for extracting a target area of a surgery so as to generate a central surgery point, a graph conver- sion module for vectorizing the target area of the surgery accord- ing to a CT image, a conversion module taking a cutting position in the surgery area as a node, and a path generation module for connecting the central surgery point and the node, wherein the graph theory algorithm module generates a weighted graph between the central surgery point and the node.
Furthermore, the system also comprises an identification mod- ule, wherein the identification module divides the weighted graph into a plurality of dendrograms, and then the identification mod- ule finds a minimum spanning tree through the dendrogram as a sur- gical navigation path.
Furthermore, the system also comprises a comparison module, wherein the comparison module uses the minimum spanning tree to compare the CT image and the human body image, at this time, the node connecting line between the minimum spanning trees and the human body image are identified, and if the node connecting line passes through an aorta vessel, the comparison module gives an alarm to determine the minimum spanning tree.
Furthermore, the dendrograms close to the minimum spanning tree are compared one by one by the comparison module so as to plan the optimal dendrogram.
Furthermore, the discrimination module adopts a deep full convolution neural network structure U-Net.
Further, the image module includes placing the preoperative 3D image data and the intraoperative 2D image data in the same co- ordinate system, performing image registration, and providing im- age guidance during the operation.
Further, before 2D/3D registration, the image module needs to perform spatial transformation on the preoperative CT images to obtain DRR images at different angles, construct an objective function by calculating the similarity measure values of the DRR images and the intraoperative X-ray images, and optimize the ob- jective function to obtain the optimal spatial transformation pa- rameters. O that the similarity measure of the image to be regis- tered reaches an extreme value.
Further, the image registration includes performing coarse registration by using a deep learning network, and then performing fine registration by using a parameter optimization method.
Furthermore, the similarity measure between the segmented floating image and the reference image is calculated, and the pa- rameters are updated by Adam parameter optimization algorithm to complete the single-block registration.
By adopting the scheme, the invention realizes the following beneficial effects that: 1, in the technical scheme, the imaging of the operation area is combined with the path generation module so as to be converted into an optimal operation path. 2. Let G be a connected graph with n vertices, then starting from any vertex of G (the central surgery vertex), a depth-first search or breadth-first search can visit all n vertices of G, and then from a visited vertex to an unvisited adjacent vertex, an edge of G must be passed through. While the two methods for the
Each of the n vertices is visited only once, so except for the in- itial starting point, the remaining n-1 vertices are visited through a total of n-1 edges in G, thus connecting all the nodes. 3. Compared with the prior art for connecting all nodes, in the technical scheme, a weighted graph is divided into a plurality of dendrograms by using an identification module, and a minimum span- ning tree is used as a surgical navigation path. 4, Compared with the prior art for generating a surgical navi- gation path, in the technical scheme, the node connecting lines among the minimum spanning trees are identified from the human body image, and if the node connecting lines pass through an aor- tic blood vessel, the comparison module gives an alarm to deter- mine the minimum spanning tree. 5. According to the technical scheme, a 2D/3DCT hierarchical registration method based on a convolutional neural network is in- troduced by using an image module, and technical support is pro- vided for navigation problems in surgery.
Description of the drawings
Fig. 1 is a system flow chart of an embodiment of the present invention.
Description of the invention
The following is a further detailed description of specific embodiments:
The embodiment is basically as shown in Figure 1: a surgical navigation auxiliary system based on artificial intelligence and graph theory algorithm comprises a graph theory algorithm module and an image module for storing human body images and surgical trajectory images. A calibration and positioning module for ex- tracting a target area of a surgery so as to generate a central surgery point, a graphic conversion module for vectorizing the target area of the surgery according to a CT image, a conversion module taking a cutting position in the surgery area as a node, and a path generation module for connecting the central surgery point and the node, wherein the graph theory algorithm module gen- erates a weighted graph between the central surgery point and the node.
The identification module divides the weighted graph into a plurality of dendrograms, and then the identification module looks for the minimum spanning tree as a surgery navigation path through the dendrogram; and the comparison module compares the minimum spanning tree with the CT image and the human body image, and identifies a node connecting line between the minimum spanning trees and the human body image, If the node connection line passes through the aorta vessel, the comparison module sends an alarm to determine the minimum spanning tree.
The dendrograms close to the minimum spanning tree are com- pared one by one by the comparison module so as to plan the opti- mal dendrogram, and the discrimination module adopts a deep full convolution neural network structure U-Net.
The specific implementation process is as follows: firstly, a
CT image map and a human body structure simulation map are sub- jected to convolution operation, preoperative 3D image data and intracperative 2D image data are placed in the same coordinate system, image registration is carried out, image guidance can be provided during the operation, and then an image module and an im- age module need to carry out space transformation on a preopera- tive CT image before 2D/3D registration, DRR images at different angles are obtained, an objective function is constructed by cal- culating the similarity measure values of the DRR images and the intraoperative X-ray images, and the objective function is opti- mized to obtain the optimal spatial transformation parameters, so 5 that the similarity measure values of the images to be registered reach an extreme value, and image registration comprises rough registration by using a deep learning network. Secondly, the pa- rameter optimization method is used for fine registration, and the similarity measure between the segmented floating image and the reference image is calculated. The Adam parameter optimization al- gorithm is used to update the parameters and complete the single block registration.
Subsequently, a weighted graph is generated between a central operation point and a node, subsequently, the weighted graph is divided into a plurality of dendrograms, subsequently, an identi- fication module finds a minimum spanning tree as an operation nav- igation path through the dendrogram, the spanning tree definition of the weighted graph sets the graph to be a connected graph with n vertexes, then starting from any vertex of G (the central opera- tion point), By making a depth-first search or breadth-first search, all n vertices in G can be visited, and then from a visit- ed vertex to an unvisited adjacent vertex, an edge in G must be traversed. Each of the n vertices is visited only once, so except for the initial starting point, the remaining n-1 vertices are visited through a total of n-1 edges in G, thus connecting all the nodes.
A minimum spanning tree is generated from the dendrogram, node connecting lines among the minimum spanning trees are identi- fied from the human body image, and if the node connecting lines pass through an aortic vessel, a comparison module gives an alarm to judge whether the minimum spanning tree meets the operation re- quirement, If the minimum spanning tree is negated, the dendro- grams close to the minimum spanning tree are compared one by one by the comparison module so as to plan the optimal dendrogram.
It is noted that relational terms such as first and second and the like may be used herein only to distinguish one entity or operation from another entity or operation without necessarily re-
quiring or implying any such actual relationship or order between such entities or operations. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cov- er a non-exclusive inclusion, such that a process, method, arti- cle, or apparatus that comprises a list of elements does not in- clude only those elements but may include other elements not ex- pressly listed or inherent to such process, method, article, or apparatus.
The above is only an embodiment of the present invention, and the general knowledge such as the specific structure and charac- teristics known in the scheme is not described too much here.
Those of ordinary skill in the art know all the general technical knowledge of the technical field to which the invention belongs before the application date or the priority date, can learn all the prior art in this field, and have the ability to apply the conventional experimental means before this date. Those of ordi- nary skill in the art may perfect and implement the present solu- tion in combination with their own capabilities under the inspira- tion given by the present application, and some typical known structures or known methods shall not be an obstacle for those of ordinary skill in the art to implement the present application. It should be noted that for those skilled in the art, several modifi- cations and improvements can be made without departing from the structure of the present invention, which should also be regarded as the scope of protection of the present invention, and these will not affect the effect of the implementation of the invention and the utility of the patent. The scope of protection claimed in this application shall be subject to the content of the claims, and the specific implementation mode and other records in the de- scription may be used to interpret the content of the claims.

Claims (9)

CONCLUSIESCONCLUSIONS 1. Chirurgisch navigatiesysteem op basis van kunstmatige intelli- gentie en een grafiektheoriealgoritme, gekenmerkt door een gra- fiektheoriealgoritmemodule, een beeldmodule voor het opslaan van beelden van het menselijk lichaam en beelden van het operatie- spoor, een kalibratie- en positioneringsmodule voor het extraheren van een doelgebied van een operatie om een centraal operatiepunt te genereren, een grafiekomzettingsmodule voor het vectoriseren van het doelgebied van de operatie volgens een CT-beeld, een om- zettingsmodule die een snijpositie in het operatiegebied als knooppunt neemt, en een padgeneratiemodule voor het verbinden van het centrale operatiepunt en het knooppunt, waarbij de grafiekthe- orie-algoritmemodule een gewogen grafiek genereert tussen het cen- trale operatiepunt en het knooppunt.A surgical navigation system based on artificial intelligence and a graph theory algorithm, comprising a graph theory algorithm module, an image module for storing images of the human body and images of the surgical track, a calibration and positioning module for extracting an operation target area to generate a central operation point, a graph conversion module for vectorizing the operation target area according to a CT image, a conversion module taking a cut position in the operation area as a node, and a path generation module for connecting the central point of operation and the node, wherein the graph theory algorithm module generates a weighted graph between the central point of operation and the node. 2. Chirurgisch navigatiesysteem op basis van kunstmatige intelli- gentie en een grafiektheoriealgoritme volgens conlcusie 1, geken- merkt door verder een identificatiemodule te omvatten, waarin de identificatiemodule de gewogen grafiek in een veelvoud van dendro- grammen verdeelt, en dan vindt de identificatiemodule een minimale overbruggingsboom door het dendrogram als een chirurgisch naviga- tiepad.A surgical navigation system based on artificial intelligence and a graph theory algorithm according to claim 1, characterized by further comprising an identification module, wherein the identification module divides the weighted graph into a plurality of dendrograms, and then the identification module finds a minimum bridging tree through the dendrogram as a surgical navigation path. 3. Chirurgisch navigatiesysteem op basis van kunstmatige intelli- gentie en een grafiektheoriealgoritme volgens conclusie 2, geken- merkt door verder het omvatten van een vergelijkingsmodule, waarin de vergelijkingsmodule de minimum het overspannen boom gebruikt om het CT-beeld en het menselijke lichaamsbeeld te vergelijken, op dit ogenblik, wordt de knooppunt verbindende lijn tussen de mini- mum het overspannen bomen geïdentificeerd met het menselijke 1i- chaamsbeeld, en als de knooppunt verbindende lijn door het aorta- vaartuig gaat, geeft de vergelijkingsmodule een alarm om de mini- mum het overspannen boom te bepalen.The surgical navigation system based on artificial intelligence and a graph theory algorithm according to claim 2, characterized by further comprising a comparison module, wherein the comparison module uses the minimum spanning tree to compare the CT image and the human body image, at this time, the node connecting line between the minimum spanning trees is identified with the human body image, and if the node connecting line passes through the aortic vessel, the comparator module alarms to minimize the spanning determine tree. 4. Chirurgisch navigatiesysteem op basis van kunstmatige intelli-4. Surgical navigation system based on artificial intelligence gentie en een grafiektheoriealgoritme volgens conclusie 3, die in zoverre wordt gekenmerkt dat het optimale dendrogram wordt gepland door de dendrogrammen dicht bij de minimum spanning tree één voor één door een vergelijkingsmodule te vergelijken.genesis and a graph theory algorithm according to claim 3, characterized in that the optimal dendrogram is planned by comparing the dendrograms close to the minimum spanning tree one by one through a comparison module. 5. Chirurgisch navigatiesysteem op basis van kunstmatige intelli- gentie en een grafiektheoriealgoritme volgens conclusie 2, geken- merkt doordat de discriminatiemodule een diepe volledige convolu- tie neurale netwerkstructuur U-Net toepast.A surgical navigation system based on artificial intelligence and a graph theory algorithm according to claim 2, characterized in that the discrimination module applies a deep full convolution neural network structure U-Net. 6. Chirurgisch navigatiesysteem op basis van kunstmatige intelli- gentie en een grafiektheoriealgoritme volgens conclusie 1, geken- merkt doordat de beeldmodule de preoperatieve 3D beeldgegevens en de intraoperatieve 2D beeldgegevens in hetzelfde coördinatensys- teem plaatst, beeldregistratie uitvoert, en beeldbegeleiding tij- dens de operatie biedt.The surgical navigation system based on artificial intelligence and a graph theory algorithm according to claim 1, characterized in that the image module places the preoperative 3D image data and the intraoperative 2D image data in the same coordinate system, performs image registration, and image guidance during surgery offers. 7. Chirurgisch navigatiesysteem op basis van kunstmatige intelli- gentie en een grafiektheoriealgoritme volgens conclusie 6, geken- merkt doordat vóór de 2D/3D-registratie de beeldmodule een ruimte- lijke transformatie uitvoert op een preoperatief CT-beeld om DRR- beelden onder verschillende hoeken te verkrijgen, en een objectie- functie wordt geconstrueerd door een similariteitsmaatstafwaarde te berekenen tussen het DRR-beeld en een intraoperatief röntgen- beeld, waarbij de objectieve functie wordt geoptimaliseerd om de optimale ruimtelijke transformatieparameters te verkrijgen, zodat de similariteitsmaatstaf van het te registreren beeld een extreme waarde bereikt.A surgical navigation system based on artificial intelligence and a graph theory algorithm according to claim 6, characterized in that before the 2D/3D registration the image module performs a spatial transformation on a preoperative CT image to generate DRR images at different angles and an objective function is constructed by calculating a similarity measure value between the DRR image and an intraoperative X-ray image, optimizing the objective function to obtain the optimal spatial transformation parameters so that the similarity measure of the image to be registered is a extreme value reached. 8. Chirurgisch navigatiesysteem op basis van kunstmatige intelli- gentie en een grafiektheoriealgoritme volgens conclusie 7, waarbij de beeldregistratie bestaat uit het uitvoeren van grove registra- tie door gebruik te maken van een diep lerend netwerk, en vervol- gens het uitvoeren van fijne registratie door gebruik te maken van een parameteroptimalisatiemethode.The surgical navigation system based on artificial intelligence and a graph theory algorithm according to claim 7, wherein the image registration consists of performing coarse registration by using a deep learning network, and then performing fine registration by using a parameter optimization method. 9. Chirurgisch navigatiesysteem op basis van kunstmatige intelli-9. Surgical navigation system based on artificial intelligence gentie en een grafiektheoriealgoritme volgens conclusie 8, geken- merkt doordat de gelijkenismaat tussen het gesegmenteerde drijven- de beeld en het referentiebeeld wordt berekend, en de parameters door het Adam-parameteroptimaliseringsalgoritme worden bijgewerkt om de registratie van één blok te voltooien.genesis and a graph theory algorithm according to claim 8, characterized in that the similarity measure between the segmented floating image and the reference image is calculated, and the parameters are updated by the Adam parameter optimization algorithm to complete the registration of one block.
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