CN115393338A - Biological tissue identification model construction method and device and electronic equipment - Google Patents
Biological tissue identification model construction method and device and electronic equipment Download PDFInfo
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Abstract
The invention provides a method and a device for constructing a biological tissue recognition model and electronic equipment, which relate to the technical field of medicine and have the characteristics that a biological tissue training sample is decomposed to obtain an image of the biological tissue training sample; constructing a mapping relation between the characteristics of the biological tissue training sample image and the tissue type of the training sample; training the biological tissue recognition model by utilizing a plurality of mapping relations to obtain initial parameters of the biological tissue recognition model; and inputting the information of the biological tissue support sample into a biological tissue identification model configured with initial parameters for parameter fine adjustment to obtain an optimized biological tissue identification model. The biological tissue image to be identified is acquired through the miniature ultrahigh frequency ultrasonic probe, and then the biological tissue image to be identified is input into the biological tissue identification model based on the acquisition, so that the biological tissue to be identified under the spinal endoscope is identified, and the identification efficiency of the tissue under the spinal endoscope is improved.
Description
Technical Field
The invention relates to the technical field of medicine, in particular to a method and a device for constructing a biological tissue identification model and electronic equipment.
Background
Spinal column related diseases are always one of important risk factors threatening the national health, and mainly comprise lumbar intervertebral disc protrusion, lumbar spinal stenosis and the like.
The risk of the spine minimally invasive surgery is lower than that of the traditional open surgery, but the spine minimally invasive surgery depends on the experience of a doctor, and meanwhile, the spine minimally invasive surgery is not limited by 'hand feeling' under the endoscope and the like due to narrow visual field. The existing ultrasonic probe (such as a liver) used in the operation is thick and cannot pass through a minimally invasive spinal channel; the case of applying the miniature ultrasonic probe in the gastrointestinal endoscope and the intravascular catheter is a side-scanning or annular-scanning probe, and only specific tissue structures such as gastrointestinal walls, vascular walls and the like can be distinguished, and the scanning range is small, the morphological recovery is difficult, and the miniature ultrasonic probe is only used for judging the nature and the range of pathological changes.
Therefore, a method and an apparatus for constructing a biological tissue recognition model, and an electronic device are provided.
Disclosure of Invention
The specification provides a biological tissue identification model construction method, a biological tissue identification model construction device and electronic equipment, so that identification of tissues under a spinal endoscope is realized, and identification efficiency of the tissues under the spinal endoscope is improved.
The present specification provides a biological tissue identification model construction method, including:
acquiring biological tissue training sample information and biological tissue supporting sample information, wherein the biological tissue training sample information comprises a biological tissue training sample image and a training sample tissue type;
decomposing the biological tissue training sample to obtain the characteristics of the biological tissue training sample image;
constructing a mapping relation between the characteristics of the biological tissue training sample image and the tissue type of the training sample;
training a biological tissue recognition model by using a plurality of mapping relations to obtain initial parameters of the biological tissue recognition model;
and inputting the biological tissue support sample information into the biological tissue identification model configured with the initial parameters for parameter fine adjustment to obtain the optimized biological tissue identification model.
Optionally, the training sample tissue types include bone, muscle, fat, blood vessel.
Optionally, the biological tissue support sample information includes a biological tissue support sample image, and a support sample tissue type, where the support sample tissue type includes nerve, nucleus pulposus, annulus fibrosus, and spinal cord.
Optionally, the decomposing the biological tissue training sample to obtain the characteristics of the biological tissue training sample image includes:
and performing wavelet decomposition on the biological tissue training sample by using one-dimensional haar wavelet transform to obtain the signal characteristics of the biological tissue training sample image.
Optionally, the inputting the information of the biological tissue support sample into the biological tissue identification model configured with the initial parameters for parameter fine tuning to obtain the optimized biological tissue identification model includes:
decomposing the biological tissue supporting sample to obtain the characteristics of the biological tissue supporting sample image;
constructing a mapping relation between the characteristics of the biological tissue supporting sample image and the tissue type of the supporting sample;
optimizing the biological tissue recognition model by using a plurality of mapping relations to obtain optimized parameters of the biological tissue recognition model;
determining the optimized biological tissue recognition model based on the optimization parameters.
Optionally, after determining the optimized biological tissue recognition model based on the optimization parameters, the method includes:
acquiring a biological tissue image to be identified through a miniature ultrahigh frequency ultrasonic probe;
and inputting the biological tissue image to be identified into the optimized biological tissue identification model to obtain the type of the biological tissue.
The present specification provides a biological tissue identification model construction device including:
the acquisition module is used for acquiring biological tissue training sample information and biological tissue supporting sample information, wherein the biological tissue training sample information comprises a biological tissue training sample image and a training sample tissue type;
the decomposition module is used for decomposing the biological tissue training sample to obtain the characteristics of the biological tissue training sample image;
the construction module is used for constructing a mapping relation between the characteristics of the biological tissue training sample image and the tissue type of the training sample;
the training module is used for training a biological tissue recognition model by utilizing a plurality of mapping relations to obtain initial parameters of the biological tissue recognition model;
and the fine adjustment module is used for inputting the biological tissue support sample information into the biological tissue identification model configured with the initial parameters for parameter fine adjustment to obtain the optimized biological tissue identification model.
Optionally, the training sample tissue types include bone, muscle, fat, blood vessel.
Optionally, the biological tissue support sample information includes a biological tissue support sample image, and a support sample tissue type, where the support sample tissue type includes nerve, nucleus pulposus, annulus fibrosus, and spinal cord.
Optionally, the decomposition module includes:
and performing wavelet decomposition on the biological tissue training sample by using one-dimensional haar wavelet transform to obtain the signal characteristics of the biological tissue training sample image.
Optionally, the fine tuning module includes:
decomposing the biological tissue supporting sample to obtain the characteristics of the biological tissue supporting sample image;
constructing a mapping relation between the characteristics of the biological tissue supporting sample image and the tissue type of the supporting sample;
optimizing the biological tissue identification model by using a plurality of mapping relations to obtain optimized parameters of the biological tissue identification model;
determining the optimized biological tissue recognition model based on the optimization parameters.
Optionally, after determining the optimized biological tissue recognition model based on the optimization parameters, the method includes:
acquiring a biological tissue image to be identified through a miniature ultrahigh frequency ultrasonic probe;
and inputting the biological tissue image to be identified into the optimized biological tissue identification model to obtain the type of the biological tissue.
The present specification also provides an electronic device, wherein the electronic device includes:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform any of the methods described above.
The present specification also provides a computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement any of the methods described above.
In this specification, a biological tissue recognition model is trained by using a small sample, training of the biological tissue recognition model is completed by using a large amount of data that is easy to obtain, and the biological tissue recognition model is optimized by using a small amount of data that is difficult to obtain. And scanning the tissues under the spinal endoscope by using the miniature ultrahigh frequency ultrasonic probe and the optimized biological tissue identification model in a conventional endoscope operation channel, so that the tissues under the spinal endoscope are identified, and the identification efficiency of the tissues under the spinal endoscope is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic diagram illustrating a method for constructing a biological tissue recognition model according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a biological tissue identification model building apparatus provided in an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure;
fig. 4 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.
Detailed Description
The following description is presented to disclose the invention so as to enable any person skilled in the art to practice the invention. The preferred embodiments described below are by way of example only, and other obvious variations will occur to those skilled in the art. The basic principles of the invention, as defined in the following description, may be applied to other embodiments, variations, modifications, equivalents, and other technical solutions without departing from the spirit and scope of the invention.
Exemplary embodiments of the present invention are described more fully below with reference to the accompanying figures 1-4. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals denote the same or similar elements, components, or parts in the drawings, and thus their repetitive description will be omitted.
Features, structures, characteristics or other details described in a particular embodiment do not preclude the fact that the features, structures, characteristics or other details may be combined in a suitable manner in one or more other embodiments in accordance with the technical idea of the invention.
In describing particular embodiments, the present invention has been described with reference to features, structures, characteristics or other details that are within the purview of one skilled in the art to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.
The flowcharts shown in the figures are illustrative only and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The term "and/or" and/or "includes all combinations of any one or more of the associated listed items.
Fig. 1 is a schematic diagram of a method for constructing a biological tissue recognition model according to an embodiment of the present disclosure, where the method may include:
s110: acquiring biological tissue training sample information and biological tissue supporting sample information, wherein the biological tissue training sample information comprises a biological tissue training sample image and a training sample tissue type;
optionally, the training sample tissue types include bone, muscle, fat, blood vessel.
Optionally, the biological tissue support sample information includes a biological tissue support sample image and a support sample tissue type, and the support sample tissue type includes nerve, nucleus pulposus, annulus fibrosus and spinal cord.
In the embodiments of the present specification, small-sample learning (raw-shot learning FSL) is a type of machine learning method, and existing machine learning and deep learning tasks rely on a large amount of labeled data for training. The learning process of human is not the same, and human can utilize the knowledge learned in the past, and can learn well on new problems only by a small number of samples. Small sample learning is one such process, and it is expected that just a few samples are needed for new problems, like humans, with some a priori knowledge. The application field of the existing small sample learning method focuses on the aspect of images, is widely applied to the problems of image classification, reinforcement learning and the like, has a plurality of problems in the aspect of determining the type of biological tissues, and is lack of a mature model.
Collecting a large amount of sectional views of bones, muscles, fat, blood vessels and the like and marking the types of the sectional views, and collecting a small amount of sectional views of nerves, fibrous rings, spinal cords and the like and marking the types of the sectional views. Bones, muscles, fat, blood vessels and the like are relatively superficial and are easy to obtain; meanwhile, the shapes, sizes and the like of bones, muscles, fat, blood vessels and the like are easy to obtain, so that a large amount of data of bones, muscles, fat, blood vessels and the like are used as biological tissue training sample information. The data such as nerve, nucleus pulposus, annulus fibrosus, spinal cord, because in human backbone passageway, it is great to obtain the degree of difficulty, also difficult discernment, and the data bulk that can obtain is less, supports sample information as biological tissue.
S120: decomposing the biological tissue training sample to obtain the characteristics of the biological tissue training sample image;
optionally, the decomposing the biological tissue training sample to obtain the characteristics of the biological tissue training sample image includes:
and performing wavelet decomposition on the biological tissue training sample by using one-dimensional haar wavelet transform to obtain the signal characteristics of the biological tissue training sample image.
In the specific implementation manner of the present specification, a one-dimensional haar wavelet transform is used to perform wavelet decomposition on an ultrasonic scanning signal of a biological tissue training sample to obtain a signal characteristic of each biological tissue training sample. The biological tissue training sample image includes a range of tissue depths under scan.
S130: constructing a mapping relation between the characteristics of the biological tissue training sample image and the tissue type of the training sample;
s140: training a biological tissue recognition model by using a plurality of mapping relations to obtain initial parameters of the biological tissue recognition model;
s150: and inputting the biological tissue support sample information into the biological tissue identification model configured with the initial parameters for parameter fine adjustment to obtain the optimized biological tissue identification model.
Optionally, the inputting the information of the biological tissue supporting sample into the biological tissue identification model configured with the initial parameters for performing parameter fine tuning to obtain the optimized biological tissue identification model includes:
decomposing the biological tissue supporting sample to obtain the characteristics of the biological tissue supporting sample image;
constructing a mapping relation between the characteristics of the biological tissue supporting sample image and the supporting sample tissue type;
optimizing the biological tissue identification model by using a plurality of mapping relations to obtain optimized parameters of the biological tissue identification model;
determining the optimized biological tissue recognition model based on the optimization parameters.
In the specific implementation manner of the present specification, a one-dimensional haar wavelet transform is used to perform wavelet decomposition on the ultrasound scanning signal of the biological tissue support sample, so as to obtain the signal characteristics of each biological tissue support sample. The biological tissue support sample image includes a range of tissue depths under scan. For example: the signal characteristics of nucleus pulposus and spine include homogeneity and small acoustic impedance; the signal characteristics of the nerve comprise a point-shaped surface and the acoustic impedance is small; the signal characteristics of the fiber loop include large acoustic impedance.
Optionally, after determining the optimized biological tissue recognition model based on the optimization parameters, the method includes:
acquiring a biological tissue image to be identified through a miniature ultrahigh frequency ultrasonic probe;
and inputting the biological tissue image to be identified into the optimized biological tissue identification model to obtain the type of the biological tissue.
In the specific implementation mode of the specification, the diameter range of the miniature ultrahigh frequency ultrasonic probe is within 6mm, and the preferred scheme is 4mm; the orientation angle range of the miniature ultrahigh frequency ultrasonic probe is forward scanning and 0-60-degree inward scanning by deflecting towards the side, so that the miniature ultrahigh frequency ultrasonic probe is adapted to the use scene of the spinal endoscope, the ultrasonic scanning of soft tissues along a minimally invasive channel is realized, and unnecessary injury to a human body in the operation process is reduced as much as possible.
The tissue under the spinal endoscope is scanned through a miniature ultrahigh frequency ultrasonic probe in a conventional endoscope operation channel. The miniature ultrahigh frequency ultrasonic probe is connected with the imaging equipment through a wire and used for exciting an electric pulse signal, collecting the electric pulse signal converted from ultrasonic echo, realizing the transmission and the reception of phased array ultrasonic waves by changing ultrasonic parameters, and imaging the ultrasonic detection content through the imaging equipment.
In this specification, a biological tissue recognition model is trained by using a small sample, training of the biological tissue recognition model is completed by using a large amount of data that is easy to obtain, and the biological tissue recognition model is optimized by using a small amount of data that is difficult to obtain. And scanning the tissues under the spinal endoscope by using the miniature ultrahigh frequency ultrasonic probe and the optimized biological tissue identification model in a conventional endoscope operation channel, so that the tissues under the spinal endoscope are identified, and the identification efficiency of the tissues under the spinal endoscope is improved.
Fig. 2 is a schematic diagram of a biological tissue identification model building apparatus provided in an embodiment of the present disclosure, where the apparatus may include:
the acquisition module 10 is configured to acquire biological tissue training sample information and biological tissue support sample information, where the biological tissue training sample information includes a biological tissue training sample image and a training sample tissue type;
the decomposition module 20 is configured to decompose the biological tissue training sample to obtain characteristics of the biological tissue training sample image;
a construction module 30, configured to construct a mapping relationship between characteristics of the biological tissue training sample image and the tissue type of the training sample;
a training module 40, configured to train a biological tissue recognition model using a plurality of mapping relationships to obtain initial parameters of the biological tissue recognition model;
and a fine-tuning module 50, configured to input the information of the biological tissue support sample into the biological tissue identification model configured with the initial parameters to perform parameter fine tuning, so as to obtain the optimized biological tissue identification model.
Optionally, the training sample tissue types include bone, muscle, fat, blood vessel.
Optionally, the biological tissue support sample information includes a biological tissue support sample image and a support sample tissue type, and the support sample tissue type includes nerve, nucleus pulposus, annulus fibrosus and spinal cord.
Optionally, the decomposition module includes:
and performing wavelet decomposition on the biological tissue training sample by using one-dimensional haar wavelet transform to obtain the signal characteristics of the biological tissue training sample image.
Optionally, the fine-tuning module includes:
decomposing the biological tissue supporting sample to obtain the characteristics of the biological tissue supporting sample image;
constructing a mapping relation between the characteristics of the biological tissue supporting sample image and the tissue type of the supporting sample;
optimizing the biological tissue recognition model by using a plurality of mapping relations to obtain optimized parameters of the biological tissue recognition model;
determining the optimized biological tissue recognition model based on the optimization parameters.
Optionally, after determining the optimized biological tissue recognition model based on the optimization parameters, the method includes:
acquiring a biological tissue image to be identified through a miniature ultrahigh frequency ultrasonic probe;
and inputting the biological tissue image to be identified into the optimized biological tissue identification model to obtain the type of the biological tissue.
The functions of the apparatus in the embodiment of the present invention have been described in the above method embodiments, so that reference may be made to the related descriptions in the foregoing embodiments for details that are not described in the present embodiment, and further details are not described herein.
Based on the same inventive concept, the embodiment of the specification further provides the electronic equipment.
In the following, embodiments of the electronic device of the present invention are described, which may be regarded as specific physical implementations for the above-described embodiments of the method and apparatus of the present invention. Details described in the embodiments of the electronic device of the invention should be considered supplementary to the embodiments of the method or apparatus described above; for details which are not disclosed in embodiments of the electronic device of the invention, reference may be made to the above-described embodiments of the method or the apparatus.
Fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure. An electronic device 300 according to this embodiment of the invention is described below with reference to fig. 3. The electronic device 300 shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 3, electronic device 300 is in the form of a general purpose computing device. The components of electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one memory unit 320, a bus 330 that couples various system components including the memory unit 320 and the processing unit 310, a display unit 340, and the like.
Wherein the storage unit stores program code executable by the processing unit 310 to cause the processing unit 310 to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned processing method section of the present specification. For example, the processing unit 310 may perform the steps shown in fig. 1.
The storage unit 320 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM) 3201 and/or a cache memory unit 3202, and may further include a read-only memory unit (ROM) 3203.
The storage unit 320 may also include a program/utility 3204 having a set (at least one) of program modules 3205, such program modules 3205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 300 may also communicate with one or more external devices 400 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 300, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 300 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interface 350. Also, electronic device 300 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via network adapter 360. Network adapter 360 may communicate with other modules of electronic device 300 via bus 330. It should be appreciated that although not shown in FIG. 3, other hardware and/or software modules may be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments of the present invention described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned method according to the present invention. The computer program, when executed by a data processing apparatus, enables the computer readable medium to implement the above-described method of the invention, namely: such as the method shown in fig. 1.
Fig. 4 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.
A computer program implementing the method shown in fig. 1 may be stored on one or more computer readable media. The computer readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in embodiments consistent with the present invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP). The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.
All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of the present application shall be included in the scope of the claims of the present application.
Claims (10)
1. A method for constructing a biological tissue recognition model, comprising:
acquiring biological tissue training sample information and biological tissue supporting sample information, wherein the biological tissue training sample information comprises a biological tissue training sample image and a training sample tissue type;
decomposing the biological tissue training sample to obtain the characteristics of the biological tissue training sample image;
constructing a mapping relation between the characteristics of the biological tissue training sample image and the tissue type of the training sample;
training a biological tissue recognition model by using a plurality of mapping relations to obtain initial parameters of the biological tissue recognition model;
and inputting the biological tissue support sample information into the biological tissue identification model configured with the initial parameters for parameter fine adjustment to obtain the optimized biological tissue identification model.
2. The method of claim 1, wherein the training sample tissue types include bone, muscle, fat, and blood vessels.
3. The method of constructing a biological tissue recognition model according to claim 2, wherein the biological tissue support sample information includes an image of a biological tissue support sample, a type of support sample tissue including nerve, nucleus pulposus, annulus fibrosus, spinal cord.
4. The method for constructing a biological tissue recognition model according to claim 3, wherein decomposing the biological tissue training sample to obtain the characteristics of the image of the biological tissue training sample comprises:
and performing wavelet decomposition on the biological tissue training sample by using one-dimensional haar wavelet transform to obtain the signal characteristics of the biological tissue training sample image.
5. The method according to claim 4, wherein the inputting the information of the biological tissue support sample into the biological tissue identification model configured with the initial parameters for parameter fine tuning to obtain the optimized biological tissue identification model comprises:
decomposing the biological tissue supporting sample to obtain the characteristics of the biological tissue supporting sample image;
constructing a mapping relation between the characteristics of the biological tissue supporting sample image and the tissue type of the supporting sample;
optimizing the biological tissue identification model by using a plurality of mapping relations to obtain optimized parameters of the biological tissue identification model;
determining the optimized biological tissue recognition model based on the optimization parameters.
6. The method of constructing a biological tissue recognition model according to claim 5, wherein after determining the optimized biological tissue recognition model based on the optimization parameters, the method comprises:
acquiring a biological tissue image to be identified through a miniature ultrahigh frequency ultrasonic probe;
and inputting the biological tissue image to be identified into the optimized biological tissue identification model to obtain the type of the biological tissue.
7. A biological tissue recognition model construction apparatus, comprising:
the acquisition module is used for acquiring biological tissue training sample information and biological tissue supporting sample information, wherein the biological tissue training sample information comprises a biological tissue training sample image and a training sample tissue type;
the decomposition module is used for decomposing the biological tissue training sample to obtain the characteristics of the biological tissue training sample image;
the construction module is used for constructing a mapping relation between the characteristics of the biological tissue training sample image and the tissue type of the training sample;
the training module is used for training a biological tissue recognition model by utilizing a plurality of mapping relations to obtain initial parameters of the biological tissue recognition model;
and the fine adjustment module is used for inputting the biological tissue support sample information into the biological tissue identification model configured with the initial parameters for parameter fine adjustment to obtain the optimized biological tissue identification model.
8. The biological tissue recognition model construction apparatus of claim 7, wherein the training sample tissue types include bone, muscle, fat, blood vessels.
9. An electronic device, wherein the electronic device comprises:
a processor; and (c) a second step of,
a memory storing computer-executable instructions that, when executed, cause the processor to perform the method of any of claims 1-6.
10. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-6.
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| CN202211069992.4A CN115393338A (en) | 2022-09-02 | 2022-09-02 | Biological tissue identification model construction method and device and electronic equipment |
| PCT/CN2023/116036 WO2024046408A1 (en) | 2022-09-02 | 2023-08-31 | Biological tissue recognition model construction method and apparatus, and electronic device |
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