CN116740768B - Navigation visualization method, system, equipment and storage medium based on nasoscope - Google Patents

Navigation visualization method, system, equipment and storage medium based on nasoscope Download PDF

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CN116740768B
CN116740768B CN202311008249.2A CN202311008249A CN116740768B CN 116740768 B CN116740768 B CN 116740768B CN 202311008249 A CN202311008249 A CN 202311008249A CN 116740768 B CN116740768 B CN 116740768B
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tumor
image
module
picture
shot
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CN116740768A (en
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蔡惠明
李长流
朱淳
潘洁
胡学山
卢露
倪轲娜
王玉叶
张岩
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Nanjing Nuoyuan Medical Devices Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/15—Biometric patterns based on physiological signals, e.g. heartbeat, blood flow
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B1/00—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
    • A61B1/313—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor for introducing through surgical openings, e.g. laparoscopes
    • A61B1/3135—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor for introducing through surgical openings, e.g. laparoscopes for examination of the epidural or the spinal space
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/54—Browsing; Visualisation therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/22—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/56—Extraction of image or video features relating to colour
    • 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
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
    • 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
    • A61B2034/107—Visualisation of planned trajectories or target regions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/03—Recognition of patterns in medical or anatomical images
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The application discloses a navigation visualization method, a system, equipment and a storage medium based on a nasoscope, which belong to the field of image data processing, wherein a shooting module shoots a tumor part to obtain a shooting image, the shooting image is imported into an image processing strategy to be subjected to sharpening processing to obtain a clear picture of the tumor part, the clear picture is substituted into an image recognition strategy, a tumor image in the picture is extracted, a tumor contour is recognized and extracted, the tumor contour is imported into data fitting software to perform contour curve fitting to obtain a tumor contour curve formula taking the position of the shooting module as an origin, a path of a safe distance from the tumor contour is planned according to the tumor contour curve formula, and data acquisition equipment performs data acquisition on the tumor according to the planned path.

Description

Navigation visualization method, system, equipment and storage medium based on nasoscope
Technical Field
The application belongs to the field of image data processing, and particularly relates to a navigation visualization method, a system, equipment and a storage medium based on a nose cranioscope.
Background
When brain tumor detection is carried out, the approximate position of a tumor is usually identified in advance through CT irradiation, the data acquisition equipment is inserted to the position which is at a safe distance from the tumor through the nasal cavity of a patient, video photographing is carried out on a tumor picture, but only partial tumor pictures are taken by video, when the panoramic picture of the tumor is taken, the track of the data acquisition module is controlled in a manual mode, the track of the data acquisition module cannot be planned and predicted in advance, so that the damage to the tumor caused by the extrusion of the data acquisition module and the tumor in the moving process is very easy to cause, and meanwhile, the quality of the taken video is also easy to be lower.
For example, chinese patent publication No. CN108921843B provides a system for detecting performance of a vehicle-mounted navigator, which includes an image acquisition device for acquiring and transmitting a standard picture; the processor is used for receiving the standard pictures and displaying the standard pictures through a screen of the vehicle-mounted navigator; the method is also used for acquiring a display picture of the vehicle navigator; comparing the display picture with the standard picture; if the comparison result meets the preset condition, judging that the vehicle navigator is qualified, otherwise, judging that the vehicle navigator is unqualified. Therefore, the system for detecting the performance of the vehicle-mounted navigator can automatically detect the performance of the vehicle-mounted navigator without manual detection, time and labor are saved, and the detection result is obtained according to the set standard and is very reliable, so that the detection accuracy is improved. The application also discloses a method for detecting the performance of the vehicle-mounted navigator, which has the same beneficial effects as the system for detecting the performance of the vehicle-mounted navigator.
The method acquires a picture of the current pose of a mobile carrier acquired by a camera, and determines a path planning area in the picture according to the movable range of the mobile carrier; and generating a navigation path from the mobile carrier to the target position in the path planning area according to the point coordinates of the target position in the picture coordinate system and a path planning algorithm. By the method, the path planning distance of the user is limited through the path planning area, so that the actual moving distance of the moving carrier is in an acceptable error range, and the phenomenon that the moving carrier is too large in yaw due to too large error is avoided; the navigation path is directly generated in the path planning area in the picture through the path planning algorithm, so that the calculated amount is reduced, the path calculation speed is increased, and the path display is more convenient.
The problems proposed in the background art exist in the above patents: when brain tumor detection is carried out, the approximate position of a tumor is usually identified in advance through CT irradiation, a data acquisition device is inserted into a position which is at a safe distance from the tumor through the nasal cavity of a patient, video photographing is carried out on a tumor picture, but only partial tumor pictures are taken in the video, when panoramic pictures of the tumor are taken, the track of the data acquisition module is controlled in a manual mode, the track of the data acquisition module cannot be planned and predicted in advance, so that the damage to the tumor caused by the extrusion of the data acquisition module and the tumor in the moving process is extremely easy to cause, and the quality of the photographed video is also easy to be lower.
Disclosure of Invention
Aiming at the defects of the prior art, the application provides a navigation visualization method, a system, equipment and a storage medium based on a naso-cranioscope, wherein a data acquisition device inserts into a naso-cranio tumor position through a nasal cavity, a shooting module shoots a tumor part to obtain a shooting image, the shooting image is guided into an image processing strategy to be subjected to sharpening processing to obtain a sharpening picture of the tumor part, the sharpening picture is substituted into an image recognition strategy, a tumor image in the picture is extracted, a tumor contour is recognized and extracted, the tumor contour is guided into data fitting software to perform contour curve fitting to obtain a tumor contour curve formula taking the position of the shooting module as an origin, a path away from a tumor contour safety distance is planned according to the tumor contour curve formula, the data acquisition device performs data acquisition on the tumor according to the planned path, and the damage to the tumor during data acquisition is avoided while the shooting quality is improved.
In order to achieve the above purpose, the present application provides the following technical solutions:
a navigation visualization method based on a nasoscope comprises the following specific steps:
s1, inserting the data acquisition equipment into a tumor position of the nasal cranium through a nasal cavity, and photographing the tumor position by a camera module to obtain a photographed image;
s2, importing the shot image into an image processing strategy for sharpness processing to obtain a sharp image of the tumor part;
s3, substituting the clear picture into an image recognition strategy, extracting a tumor image in the picture, and recognizing and extracting a tumor contour;
s4, importing the tumor contour into data fitting software to fit a contour curve, and obtaining a tumor contour curve formula taking the position of the camera module as an origin;
s5, planning a path of a safe distance from the tumor contour according to a tumor contour curve formula;
and S6, the data acquisition equipment acquires data of the tumor according to the planned path.
Specifically, the specific steps of S1 are as follows:
s101, identifying the approximate position of a tumor through CT irradiation in advance, and inserting the data acquisition equipment to a position at a safe distance from the tumor through the nasal cavity of a patient;
s102, the shooting module shoots the tumor part at a position which is at a safe distance from the tumor, and a shooting picture of the tumor part is obtained.
Specifically, the image processing strategy of S2 includes the following specific steps:
s201, extracting the intensity of at least one color channel of each pixel of a shot picture of a shot tumor part to form a foggy color intensity set, acquiring the humidity of a shot position through a humidity detection assembly, acquiring the temperature of the shot position through a temperature detection assembly, and acquiring the distance data from a shooting module to the tumor position through a distance acquisition module;
s202, taking the foggy color intensity set, the distance data from the image pickup module to the tumor position, the temperature data of the image pickup position and the humidity data of the image pickup position as the input of the constructed machine learning model, and obtaining a foggy clear picture output by the machine learning model.
Specifically, the training manner of the machine learning model in S202 is as follows:
s2021, acquiring tumor images in foggy and non-foggy environments by using an image pickup module to obtain at least one group of foggy shooting pictures and non-foggy clear pictures, and simultaneously obtaining humidity of at least one group of shooting positions, temperature of the shooting positions and distance data from the image pickup module to the tumor positions;
s2022, combining the foggy shot picture, the humidity of the shot position, the temperature of the shot position and the distance data from the shooting module to the tumor position into feature vectors, wherein the set of all feature vectors is used as the input of a machine learning model, the machine learning model takes a foggy clear picture predicted by each group of feature vectors as the output, takes an actual foggy clear picture corresponding to each group of feature vectors as a prediction target, and takes the sum of the prediction accuracy of all predicted foggy clear pictures as a training target;
s2023, a calculation formula of prediction accuracy is as follows:wherein the subscript i is the number of groups of feature vectors, the superscript j is the j-th pixel in the shot picture, M is the predicted haze-free clear picture, M is the shot haze-free clear picture, n is the total number of pixels of the shot picture, and->Training a machine learning model for the prediction accuracy between the i-th group of feature vector predicted haze-free clear pictures and the shot haze-free clear pictures until the sum of the prediction accuracy reaches convergence;
the machine learning model is any one of a deep neural network model and a deep belief network model.
Specifically, the specific steps of the image recognition strategy in S3 are as follows:
s301, importing an image obtained after the definition processing into gray processing software, and performing gray processing on the image to obtain a gray processing image;
s302, dividing the gray processing image into pixels, calculating the difference value between the gray value of each pixel and the gray value of the adjacent pixel above to obtain a vertical gradient value of the pixel, and calculating the difference value between the gray value of each pixel and the gray value of the adjacent pixel on the left to obtain a horizontal gradient value of the pixel;
s303, respectively comparing the vertical gradient value and the horizontal gradient value with a gradient threshold, taking the corresponding pixel point connecting line of which the vertical gradient value and the horizontal gradient value are larger than the gradient threshold as a boundary, dividing the graying processing image into at least one region, obtaining the image after the clearing processing of the regions, extracting the color and the shape of the region images, importing the color and the shape of the region images into a similarity comparison formula with the stored tumor color and shape, calculating the similarity of the region images and the tumor image, positioning the tumor position, and extracting the tumor contour in the image.
Specifically, the specific step of S4 is as follows:
s401, importing the shot tumor outline picture and the distance from the outline to the camera module into three-dimensional model construction software to construct a tumor three-dimensional image corresponding to the tumor outline picture, and extracting the tumor three-dimensional image and the position of the corresponding camera module;
s402, taking a connecting line between a tumor three-dimensional image and the closest point of a corresponding image pickup module as an x-axis, taking a line which is vertical to the x-axis in a horizontal plane where the x-axis is located as a y-axis, and guiding the positions of the tumor three-dimensional image and the corresponding image pickup module into data fitting software to perform fitting of contour curves to obtain a tumor contour curve formula taking the position of the image pickup module as an originWherein->The position coordinates of the root of the tumor three-dimensional image relative to the camera module.
Specifically, the specific step of S5 is as follows:
s501, collecting a tumor contour curve, and solving a tangent equation of each point of the tumor contour curveWherein->Is in +.>Dot derivative value, wherein->For tumor root image +.>The point on the tangent of the point is found the equation of +.>，/>The equation expression of (2) is +.>Simplifying to obtainWherein->To be in the +.>The tangent to the dot is perpendicular and passes +>Equation of line (2)>Is a point of (2);
s502, calculatingThe track equation of the point which is different from the tangent point by a set safety distance d is calculated by the following steps:，/>substituting it into +.>Obtaining a path planning track equation->And after the track is calculated, the track points positioned in the tumor are removed.
The navigation visualization system based on the nasoscope is realized based on the navigation visualization method based on the nasoscope, and comprises a control module, a camera shooting module, an image processing module, a contour fitting module, a path planning module and a display module, wherein the control module is used for controlling data acquisition equipment to run according to a planned path, the camera shooting module is used for shooting a tumor part to obtain a shooting image, the image processing module is used for importing the shooting image into an image processing strategy to conduct sharpening processing to obtain a clear picture of the tumor part, the contour fitting module is used for importing a tumor contour into data fitting software to conduct contour curve fitting to obtain a tumor contour curve formula taking the position of the camera shooting module as an origin, and the path planning module is used for planning a path with a safe distance from the tumor contour according to the tumor contour curve formula, and the display module is used for displaying the shooting tumor video of the camera shooting module in real time.
An electronic device, comprising: the system comprises a server, an acquisition terminal and a memory, wherein a computer program which can be called by a processor is stored in the memory; the acquisition terminal acquires image data, and the server executes the navigation visualization method based on the rhinoscope by calling the computer program stored in the memory.
A computer readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a rhinoscope-based navigation visualization method as described above.
Compared with the prior art, the application has the beneficial effects that:
the method comprises the steps that a data acquisition device inserts into a tumor position of a nasal cranium through a nasal cavity, a shooting module shoots a tumor position to obtain a shooting image, the shooting image is imported into an image processing strategy to be subjected to sharpening processing to obtain a sharpening picture of the tumor position, the sharpening picture is substituted into an image recognition strategy, a tumor image in the picture is extracted, a tumor contour is recognized and extracted, the tumor contour is imported into data fitting software to perform contour curve fitting to obtain a tumor contour curve formula taking the position of the shooting module as an origin, a path away from a tumor contour safety distance is planned according to the tumor contour curve formula, the data acquisition device performs data acquisition on the tumor according to the planned path, and the damage to the tumor during data acquisition is avoided while shooting quality is improved.
Drawings
FIG. 1 is a flow chart of a method for visualizing a nasoscope-based navigation system according to the present application;
FIG. 2 is a schematic diagram of the components of a nasoscope-based navigation visualization system of the present application;
FIG. 3 is a schematic diagram of data transmission between a server and an acquisition terminal according to the present application;
fig. 4 is a schematic diagram of an acquisition trajectory of a data acquisition device of a nasoscope-based navigation visualization system of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments.
Example 1
Referring to fig. 1, an embodiment of the present application is provided: a navigation visualization method based on a nasoscope comprises the following specific steps:
s1, inserting the data acquisition equipment into a tumor position of the nasal cranium through a nasal cavity, and photographing the tumor position by a camera module to obtain a photographed image;
in this step, the specific steps are:
s101, identifying the approximate position of a tumor through CT irradiation in advance, and inserting the data acquisition equipment to a position at a safe distance from the tumor through the nasal cavity of a patient;
s102, photographing a tumor part at a position which is at a safe distance from the tumor by using a photographing module to obtain a photographed picture of the tumor part;
it should be noted that, the identification of the approximate position of the tumor by CT irradiation is a conventional technique for acquiring the tumor position, but the present application is not limited to CT irradiation and may be any detection means for acquiring the tumor position.
S2, importing the shot image into an image processing strategy for sharpness processing to obtain a sharp image of the tumor part;
in this step, the image processing strategy of S2 includes the following specific steps:
s201, extracting the intensity of at least one color channel of each pixel of a shot picture of a shot tumor part to form a foggy color intensity set, acquiring the humidity of a shot position through a humidity detection assembly, acquiring the temperature of the shot position through a temperature detection assembly, and acquiring the distance data from a shooting module to the tumor position through a distance acquisition module;
s202, taking a foggy color intensity set, distance data from an imaging module to a tumor position, temperature data of an imaging position and humidity data of the imaging position as inputs of a constructed machine learning model, and obtaining a foggy clear picture output by the machine learning model;
it should be noted that the humidity is definitely different for each human skull, so that different fog is easily generated on the surface of the lens, and the video is blurred;
the training method of the machine learning model in S202 is as follows:
s2021, acquiring tumor images in foggy and non-foggy environments by using an image pickup module to obtain at least one group of foggy shooting pictures and non-foggy clear pictures, and simultaneously obtaining humidity of at least one group of shooting positions, temperature of the shooting positions and distance data from the image pickup module to the tumor positions;
s2022, combining the foggy shot picture, the humidity of the shot position, the temperature of the shot position and the distance data from the shooting module to the tumor position into feature vectors, wherein the set of all feature vectors is used as the input of a machine learning model, the machine learning model takes a foggy clear picture predicted by each group of feature vectors as the output, takes an actual foggy clear picture corresponding to each group of feature vectors as a prediction target, and takes the sum of the prediction accuracy of all predicted foggy clear pictures as a training target;
s2023, a calculation formula of prediction accuracy is as follows:wherein the subscript i is the number of groups of feature vectors, the superscript j is the j-th pixel in the shot picture, M is the predicted haze-free clear picture, M is the shot haze-free clear picture, n is the total number of pixels of the shot picture, and->Training a machine learning model for the prediction accuracy between the i-th group of feature vector predicted haze-free clear pictures and the shot haze-free clear pictures until the sum of the prediction accuracy reaches convergence;
the machine learning model is any one of a deep neural network model and a deep belief network model;
s3, substituting the clear picture into an image recognition strategy, extracting a tumor image in the picture, and recognizing and extracting a tumor contour;
in this step, the specific steps of the image recognition strategy in S3 are:
s301, importing an image obtained after the definition processing into gray processing software, and performing gray processing on the image to obtain a gray processing image;
s302, dividing the gray processing image into pixels, calculating the difference value between the gray value of each pixel and the gray value of the adjacent pixel above to obtain a vertical gradient value of the pixel, and calculating the difference value between the gray value of each pixel and the gray value of the adjacent pixel on the left to obtain a horizontal gradient value of the pixel;
s303, respectively comparing the vertical gradient value and the horizontal gradient value with a gradient threshold value, taking the corresponding pixel point connecting line of which the vertical gradient value and the horizontal gradient value are larger than the gradient threshold value as a boundary, dividing a graying treatment image into at least one region, acquiring the image after the sharpening treatment of the region, extracting the color and the shape of the region image, importing the color and the shape of the region image and the stored tumor color and shape into a similarity comparison formula, calculating the similarity of the region image and the tumor image, positioning the tumor position, and extracting the tumor contour in the image;
s4, importing the tumor contour into data fitting software to fit a contour curve, and obtaining a tumor contour curve formula taking the position of the camera module as an origin;
in this step, the specific steps of S4 are:
s401, importing the shot tumor outline picture and the distance from the outline to the camera module into three-dimensional model construction software to construct a tumor three-dimensional image corresponding to the tumor outline picture, and extracting the tumor three-dimensional image and the position of the corresponding camera module;
s402, taking a connecting line between the tumor three-dimensional image and the closest point of the corresponding image pickup module as an x-axis, taking a line perpendicular to the x-axis in a horizontal plane where the x-axis is located as a y-axis, and importing the positions of the tumor three-dimensional image and the corresponding image pickup module into data fitting software to perform data fittingFitting the line profile curve to obtain a tumor profile curve formula taking the position of the camera module as an originWherein->The position coordinates of the root of the tumor three-dimensional image relative to the camera module are obtained;
the coordinate construction mode is shown in fig. 4;
s5, planning a path of a safe distance from the tumor contour according to a tumor contour curve formula;
in this step, the specific steps of S5 are:
s501, collecting a tumor contour curve, and solving a tangent equation of each point of the tumor contour curveWherein->Is in +.>Derivative of the point, wherein->For tumor root image +.>The point on the tangent of the point is found the equation of +.>，/>The equation expression of (2) is +.>Simplifying to obtainWherein->To be in the +.>The tangent to the dot is perpendicular and passes +>Equation of line (2)>Is a point of (2);
s502, calculatingThe track equation of the point which is different from the tangent point by a set safety distance d is calculated by the following steps:，/>substituting it into +.>Obtaining a path planning track equation->After the track is calculated, removing the track points positioned in the tumor;
the set safety distance d is obtained by means of expert evaluation;
in this case, as shown in fig. 4, since there are two points that are different from each other by the set safety distance d, the calculation of the specific trajectory requires that the trajectory points located inside the tumor be removed after the calculation of the trajectory.
And S6, the data acquisition equipment acquires data of the tumor according to the planned path.
Through this embodiment: the method comprises the steps that a data acquisition device inserts into a tumor position of a nasal cranium through a nasal cavity, a shooting module shoots a tumor position to obtain a shooting image, the shooting image is imported into an image processing strategy to be subjected to sharpening processing to obtain a sharpening picture of the tumor position, the sharpening picture is substituted into an image recognition strategy, a tumor image in the picture is extracted, a tumor contour is recognized and extracted, the tumor contour is imported into data fitting software to perform contour curve fitting to obtain a tumor contour curve formula taking the position of the shooting module as an origin, a path away from a tumor contour safety distance is planned according to the tumor contour curve formula, the data acquisition device performs data acquisition on the tumor according to the planned path, and the damage to the tumor during data acquisition is avoided while shooting quality is improved.
Example 2
Referring to fig. 2, in a second embodiment of the present application, a navigation visualization system based on a nasoscope is disclosed, which is implemented based on the above-mentioned navigation visualization method based on a nasoscope, and includes a control module, a camera module, an image processing module, a contour fitting module, a path planning module and a display module, where the control module is used to control the data acquisition device to run according to the planned path, the camera module is used to photograph a tumor site to obtain a photographed image, the image processing module is used to introduce the photographed image into an image processing strategy to perform a sharpening process to obtain a clear picture of the tumor site, the contour fitting module is used to introduce a tumor contour into data fitting software to perform fitting of a contour curve to obtain a tumor contour curve formula with the position of the camera module as an origin, the path planning module is used to plan a path away from a tumor contour safety distance according to the tumor contour curve formula, and the display module is used to display the photographed tumor video of the camera module in real time.
It should be noted that, the control module is connected with the camera module, the image processing module, the contour fitting module, the path planning module and the display module in a wired or wireless manner, and the connection relationship among the camera module, the image processing module, the contour fitting module, the path planning module and the display module corresponds to the information transmission relationship, and the camera module is not limited to the camera at the end of the naso-cranioscope.
Example 3
As shown in fig. 3, the present embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor performs a rhinoscope-based navigation visualization method as described above by invoking a computer program stored in the memory.
The electronic device may vary greatly in configuration or performance, and can include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memories store at least one computer program that is loaded and executed by the processors to implement a rhinoscope-based navigation visualization method provided by the above-described method embodiments. The electronic device can also include other components for implementing the functions of the device, for example, the electronic device can also have wired or wireless network interfaces, input-output interfaces, and the like, for inputting and outputting data. The present embodiment is not described herein.
Example 4
The present embodiment proposes a computer-readable storage medium having stored thereon an erasable computer program;
the computer program, when run on a computer device, causes the computer device to perform a rhinoscope-based navigation visualization method as described above.
For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, etc.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by way of wired or/and wireless networks from one website site, computer, server, or data center to another. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely one, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the application disclosed above are intended only to assist in the explanation of the application. The preferred embodiments are not intended to be exhaustive or to limit the application to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the application and the practical application, to thereby enable others skilled in the art to best understand and utilize the application. The application is limited only by the claims and the full scope and equivalents thereof.

Claims (7)

1. The navigation visualization method based on the nasoscope is characterized by comprising the following specific steps of:
s1, inserting the data acquisition equipment into a tumor position of the nasal cranium through a nasal cavity, and photographing the tumor position by a camera module to obtain a photographed image;
s2, importing the shot image into an image processing strategy for sharpness processing to obtain a sharp image of the tumor part;
s3, substituting the clear picture into an image recognition strategy, extracting a tumor image in the picture, and recognizing and extracting a tumor contour;
s4, importing the tumor contour into data fitting software to fit a contour curve, and obtaining a tumor contour curve formula taking the position of the camera module as an origin;
s5, planning a path of a safe distance from the tumor contour according to a tumor contour curve formula;
s6, the data acquisition equipment acquires data of the tumor according to the planned path; the specific steps of the S1 are as follows:
s101, identifying the approximate position of a tumor through CT irradiation in advance, and inserting the data acquisition equipment to a position at a safe distance from the tumor through the nasal cavity of a patient;
s102, photographing a tumor part at a position which is at a safe distance from the tumor by using a photographing module to obtain a photographed picture of the tumor part; the image processing strategy of S2 comprises the following specific steps:
s201, extracting the intensity of at least one color channel of each pixel of a shot picture of a shot tumor part to form a foggy color intensity set, acquiring the humidity of a shot position through a humidity detection assembly, acquiring the temperature of the shot position through a temperature detection assembly, and acquiring the distance data from a shooting module to the tumor position through a distance acquisition module;
s202, taking a foggy color intensity set, distance data from an imaging module to a tumor position, temperature data of an imaging position and humidity data of the imaging position as inputs of a constructed machine learning model, and obtaining a foggy clear picture output by the machine learning model; the training manner of the machine learning model in S202 is as follows:
s2021, acquiring tumor images in foggy and non-foggy environments by using an image pickup module to obtain at least one group of foggy shooting pictures and non-foggy clear pictures, and simultaneously obtaining humidity of at least one group of shooting positions, temperature of the shooting positions and distance data from the image pickup module to the tumor positions;
s2022, combining the foggy shot picture, the humidity of the shot position, the temperature of the shot position and the distance data from the shooting module to the tumor position into feature vectors, wherein the set of all feature vectors is used as the input of a machine learning model, the machine learning model takes a foggy clear picture predicted by each group of feature vectors as the output, takes an actual foggy clear picture corresponding to each group of feature vectors as a prediction target, and takes the sum of the prediction accuracy of all predicted foggy clear pictures as a training target;
s2023, a calculation formula of prediction accuracy is as follows:wherein the subscript i is the number of groups of feature vectors, the superscript j is the j-th pixel in the shot picture, M is the predicted haze-free clear picture, M is the shot haze-free clear picture, n is the total number of pixels of the shot picture, and->For the prediction accuracy between the i-th group of feature vector predicted haze-free clear pictures and the photographed haze-free clear pictures, a machine is providedTraining the learning model until the sum of the prediction accuracy reaches convergence, and stopping training; the specific steps of the image recognition strategy in the step S3 are as follows:
s301, importing an image obtained after the definition processing into gray processing software, and performing gray processing on the image to obtain a gray processing image;
s302, dividing the gray processing image into pixels, calculating the difference value between the gray value of each pixel and the gray value of the adjacent pixel above to obtain a vertical gradient value of the pixel, and calculating the difference value between the gray value of each pixel and the gray value of the adjacent pixel on the left to obtain a horizontal gradient value of the pixel;
s303, respectively comparing the vertical gradient value and the horizontal gradient value with a gradient threshold value, taking the corresponding pixel point connecting line of which the vertical gradient value and the horizontal gradient value are larger than the gradient threshold value as a boundary, dividing a graying treatment image into at least one region, acquiring the image after the sharpening treatment of the region, extracting the color and the shape of the region image, importing the color and the shape of the region image and the stored tumor color and shape into a similarity comparison formula, calculating the similarity of the region image and the tumor image, positioning the tumor position, and extracting the tumor contour in the image; the specific steps of the S4 are as follows:
s401, importing the shot tumor outline picture and the distance from the outline to the camera module into three-dimensional model construction software to construct a tumor three-dimensional image corresponding to the tumor outline picture, and extracting the tumor three-dimensional image and the position of the corresponding camera module;
s402, taking a connecting line between the tumor three-dimensional image and the closest point of the corresponding camera module as an x-axis, taking a line which is vertical to the x-axis in a horizontal plane where the x-axis is located as a y-axis, and guiding the positions of the tumor three-dimensional image and the corresponding camera module into data fitting software to perform fitting of contour curves, so as to obtain a tumor contour curve formula taking the position of the camera module as an origin.
2. The method for visualizing a nasal cranioscope-based navigation according to claim 1, wherein the specific step of S5 is:
s501, collecting a tumor contour curve, solving a tangent equation of each point of the tumor contour curve, and solving an equation perpendicular to a tangent of each point of the tumor contour curve and passing through each corresponding tangent point；
S502, calculatingAnd (3) a track equation of a point which is different from the tangent point by a set safety distance d is provided, and after the track is calculated, the track point positioned in the tumor is removed.
3. The nasoscope-based navigation visualization method of claim 2, wherein the machine learning model is any one of a deep neural network model or a deep belief network model.
4. A nasoscope-based navigation visualization system, which is realized based on the nasoscope-based navigation visualization method according to any one of claims 1 to 3, and is characterized by comprising a control module, a camera module, an image processing module, a contour fitting module, a path planning module and a display module, wherein the control module is used for controlling a data acquisition device to run according to the planned path, the camera module is used for photographing a tumor part to obtain a photographed image, and the image processing module is used for importing the photographed image into an image processing strategy to perform sharpness processing to obtain a sharp picture of the tumor part.
5. The nasoscope-based navigation visualization system of claim 4, wherein the profile fitting module is configured to guide a tumor profile into data fitting software to perform profile curve fitting, obtain a tumor profile curve formula with a position of the camera module as an origin, the path planning module is configured to plan a path from a safe distance of the tumor profile according to the tumor profile curve formula, and the display module is configured to display a captured tumor video of the camera module in real time.
6. An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor performs a rhinoscope-based navigation visualization method of any of claims 1-3 by invoking a computer program stored in the memory.
7. A computer-readable storage medium, characterized by: instructions stored thereon which, when executed on a computer, cause the computer to perform a method of nasal cranioscope-based navigation visualization according to any one of claims 1 to 3.
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