CN111599004A - 3D blood vessel imaging system, method and device - Google Patents
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Abstract
The invention relates to a 3D blood vessel imaging system, a method and a device, which can rapidly and noninvasively display the vein blood vessels and the surrounding tissue conditions of all parts on the body surface of a human body in a three-dimensional form, and provide effective basis for the judgment and operation of the vein conditions in clinical diagnosis and treatment; the blood vessel extraction module is used for extracting three-dimensional time sequence characteristics of blood vessels in the infrared image by combining TOF three-dimensional distance information, and the three-dimensional time sequence characteristics comprise a time sequence of blood vessel length, width, depth and peripheral tissue plane characteristics; and the three-dimensional reconstruction module generates a three-dimensional modeled blood vessel image according to the three-dimensional time sequence characteristic and the TOF three-dimensional distance information.
Description
Technical Field
The invention relates to the technical field of medical images, in particular to a 3D blood vessel imaging system, a method and a device.
Background
The principle of the infrared vein developing device is that a human body surface vein blood vessel is subjected to signal acquisition through an infrared light source, a light filter, a CCD camera, an image acquisition card and the like, but the signal acquisition is generally carried out at a fixed angle, and the acquired signal is restored into a two-dimensional plane picture which only can display the plane position of the vein blood vessel and cannot display the specific depth of the blood vessel and the accurate relation with the surrounding tissue condition.
Disclosure of Invention
The invention aims to solve the problem of 3D reconstruction of vein vessels on the surface of a human body and provides a 3D vessel imaging system, a method and a device.
In order to achieve the above object, an aspect of the present invention provides a 3D blood vessel imaging system comprising:
the image acquisition module is used for acquiring an infrared black-and-white image of the vein vessel on the body surface of the human body and three-dimensional distance point cloud information of the TOF on the body surface;
the feature extraction module is used for extracting three-dimensional time sequence features of blood vessels in the infrared image by combining TOF three-dimensional distance information, and the three-dimensional time sequence features comprise time sequence sequences of blood vessel length, width, depth and peripheral tissue plane features;
and the three-dimensional reconstruction module generates a three-dimensional modeled blood vessel image according to the three-dimensional time sequence characteristic and the TOF three-dimensional distance information.
Further, the feature extraction module includes:
the preprocessing module is used for sequentially classifying and preprocessing the infrared black-and-white image according to the time sequence state of TOF point cloud information acquisition, marking the spatial distance information of each pixel point, and manufacturing the preprocessed image into a data set according to classification;
the model creating module is used for creating a convolutional neural network model and/or selecting the existing model structure and setting pre-training parameters;
and the model training module is used for training, testing and verifying the convolutional neural network model or the existing model structure based on the data set to serve as the time sequence feature recognition model.
Further, the data set is divided into a training data set, a testing data set and a verification data set, the model training module adopts the training data set to train the model, then adopts the testing data set to test the trained model, then uses the verification set to verify the model, and if the model timing sequence feature recognition requirement is met, the model is used as the three-dimensional timing sequence feature recognition model; and if not, modifying the historical mode parameter setting, and training, testing and verifying the model again until the time sequence feature recognition requirement is met, and taking the model as a three-dimensional time sequence feature recognition model.
Furthermore, the three-dimensional reconstruction module performs three-dimensional reconstruction of the blood vessels by adopting a space reconstruction method based on TOF point cloud information and three-dimensional blood vessel characteristics, determines the length, width and depth of each blood vessel based on three-dimensional time sequence characteristics in the reconstruction process, and completes reconstruction of the relationship between the blood vessels and the peripheral tissues according to the peripheral tissue plane characteristics.
In another aspect, the invention also provides a 3D blood vessel imaging method based on the above system, which is characterized in that,
establishing an image acquisition module, wherein the image acquisition module is used for acquiring an infrared black-and-white image of a human body surface vein and body surface TOF three-dimensional distance point cloud information;
establishing a feature extraction module for extracting three-dimensional time sequence features of blood vessels in the infrared image by combining TOF three-dimensional distance information, wherein the three-dimensional time sequence features comprise time sequence sequences of blood vessel length, width, depth and peripheral tissue plane features;
and establishing a three-dimensional reconstruction module, and generating a three-dimensional modeled blood vessel image according to the three-dimensional time sequence characteristic and the TOF three-dimensional distance information by the three-dimensional reconstruction module.
Further, in the process of establishing the feature extraction module, the method includes:
establishing a preprocessing module, sequentially classifying and preprocessing the infrared black-white images according to the time sequence state of TOF point cloud information acquisition, marking the spatial distance information of each infrared black-white pixel point, and manufacturing the preprocessed images into a data set according to classification;
establishing a model establishing module, establishing a convolutional neural network model and/or selecting the existing model structure and setting pre-training parameters;
and establishing a model training module, and training, testing and verifying the convolutional neural network model or the existing model structure based on the data set to be used as the time sequence feature recognition model.
Further, the data set is divided into a training data set, a testing data set and a verification data set, the model training module adopts the training data set to train the model, then adopts the testing data set to test the trained model, then uses the verification set to verify the model, and if the model timing sequence feature recognition requirement is met, the model is used as the three-dimensional timing sequence feature recognition model; and if not, modifying the historical mode parameter setting, and training, testing and verifying the model again until the time sequence feature recognition requirement is met, and taking the model as a three-dimensional time sequence feature recognition model.
Further, in the process of establishing the three-dimensional reconstruction module, the method further includes:
and generating a three-dimensional modeled blood vessel image based on the three-dimensional time sequence characteristics and TOF three-dimensional distance information by adopting a space reconstruction method, determining the length, width and depth of each blood vessel based on the three-dimensional time sequence characteristics in the reconstruction process, and completing the reconstruction of the relationship between the blood vessel and the peripheral tissues according to the planar characteristics of the peripheral tissues.
In another aspect, the present invention also provides a blood vessel imaging apparatus, comprising:
the bracket is used for fixing the detected part of the patient;
the infrared LED lamp is arranged above the bracket and used for providing an infrared light source;
the TOF image distance sensor is arranged above the bracket and used for shooting a TOF point cloud image of the detected part in front;
and the CCD image sensor is arranged above the bracket and is used for shooting the two-dimensional blood vessel infrared image of the detected part in the front.
And the 3D imaging processing host is in communication connection with the TOF distance sensor and the CCD image sensor and is used for receiving and processing TOF point cloud information and two-dimensional images so as to complete three-dimensional reconstruction of the blood vessel of the detected part of the patient.
Further, the 3D imaging processing host comprises a processor and a storage medium, wherein the storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by the processor to execute the steps in the 3D blood vessel imaging method.
The invention provides a 3D blood vessel imaging system, a method and a device, which can rapidly and noninvasively display the vein blood vessels and the surrounding tissue conditions of all parts on the body surface of a human body in a three-dimensional form, and provide effective basis for the judgment and operation of the vein conditions in clinical diagnosis and treatment; the blood vessel extraction module is used for extracting three-dimensional time sequence characteristics of blood vessels in the TOF image, and the three-dimensional time sequence characteristics comprise a time sequence of blood vessel length, width, depth and peripheral tissue plane characteristics; and the three-dimensional reconstruction module restores the two-dimensional TOF blood vessel image into a three-dimensional modeled blood vessel image according to the three-dimensional time sequence characteristics.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a system framework diagram of a 3D vessel imaging system according to an embodiment of the invention.
FIG. 2 is a system framework diagram of a feature extraction module of one embodiment of the invention.
Fig. 3 is a flowchart of a 3D blood vessel imaging method according to an embodiment of the present invention.
FIG. 4 is a system architecture diagram of a vascular imaging device in accordance with an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Fig. 1 is a system framework diagram of a 3D vessel imaging system according to an embodiment of the invention, as shown in fig. 1,
the invention provides a 3D blood vessel imaging system, which comprises an image acquisition module 10, a feature extraction module 20 and a three-dimensional reconstruction module 30.
Specifically, the image acquisition module 10 acquires an infrared black-and-white image of the vein and blood vessel on the body surface of the human body and three-dimensional distance point cloud information of the body surface TOF. The infrared black-and-white image is obtained through a CCD image sensor, the CCD image sensor shoots a two-dimensional blood vessel infrared image of a detection part under the irradiation of an infrared light source, and a TOF point cloud image is obtained through a TOF image distance sensor.
The feature extraction module 20 is configured to extract three-dimensional time sequence features of blood vessels in the infrared image by combining TOF three-dimensional distance point cloud information, where the three-dimensional time sequence features include a time sequence of blood vessel length, width, depth, and peripheral tissue plane features.
Specifically, the TOF three-dimensional distance point cloud information of the present embodiment includes a depth value of each pixel of the two-dimensional blood vessel infrared image, which represents the distance between the blood vessel and the image capturing apparatus. The three-dimensional coordinates of each pixel point can be obtained by converting the depth value of the pixel. Time sequence sequences are constructed through the length, width and depth of the blood vessel and the planar characteristics of surrounding tissues, and the time sequence sequences are spliced together to form a complete three-dimensional point cloud model through point cloud pretreatment and point cloud registration, so that the reconstruction of the blood vessel three-dimensional model is completed.
In another embodiment of the present invention, after the three-dimensional time-series features of the blood vessel are extracted, the feature extraction module 20 further classifies the three-dimensional time-series features in a machine learning manner, so as to accurately complete the reconstruction of the three-dimensional model of the blood vessel.
Specifically, the feature extraction module 20 further includes: a preprocessing module 21, a model creation module 22 and a model training module 23.
The preprocessing module 21 sequentially classifies and preprocesses the infrared black and white image according to the time sequence state of TOF point cloud information acquisition, and simultaneously labels the spatial distance information of each pixel point, and the preprocessed image is made into a data set according to classification.
The model creating module 22 is used for creating a convolutional neural network model and/or selecting an existing model structure and setting pre-training parameters;
and the model training module 23 is used for training, testing and verifying the convolutional neural network model or the existing model structure based on the data set to serve as the time sequence feature recognition model.
Specifically, the data set is divided into a training data set, a testing data set and a verification data set, the model training module adopts the training data set to perform model training, then adopts the testing data set to test the trained model, and then uses the verification set to verify the model, if the model meets the time sequence feature recognition requirement, the model is used as the three-dimensional time sequence feature recognition model; if not, modifying the historical mode parameter setting, training, testing and verifying the model again until the time sequence feature recognition requirement is met, and taking the model as a three-dimensional time sequence feature recognition model
The three-dimensional reconstruction module 30 restores the two-dimensional TOF blood vessel image into a three-dimensional modeled blood vessel image according to the three-dimensional time sequence characteristics.
The three-dimensional reconstruction module 30 performs three-dimensional reconstruction of blood vessels on the TOF image by using a spatial reconstruction method, determines the length, width and depth of each blood vessel based on three-dimensional time sequence characteristics in the reconstruction process, and completes reconstruction of the relationship between the blood vessels and peripheral tissues according to the planar characteristics of the peripheral tissues.
Fig. 3 is a flowchart of a method of 3D blood vessel imaging according to an embodiment of the present invention, and as shown in fig. 3, the 3D blood vessel imaging method according to the embodiment of the present invention includes the following steps based on the 3D blood vessel imaging system:
s100, establishing an image acquisition module, wherein the image acquisition module is used for acquiring a TOF image of a vein vessel on the body surface of a human body;
s200, establishing a feature extraction module, wherein the blood vessel extraction module is used for extracting three-dimensional time sequence features of blood vessels in a TOF image, and the three-dimensional time sequence features comprise time sequence sequences of blood vessel length, width, depth and peripheral tissue plane features;
s300, establishing a three-dimensional reconstruction module, and restoring the two-dimensional TOF blood vessel image into a three-dimensional modeled blood vessel image according to the three-dimensional time sequence characteristics by the three-dimensional reconstruction module.
Further, in the process of establishing the feature extraction module, the method comprises the following steps:
s201, establishing a preprocessing module, wherein the preprocessing module sequentially classifies and preprocesses TOF images according to time sequence states of the TOF images during acquisition, and the preprocessed images are made into data sets according to classification;
s202, establishing a model establishing module, establishing a convolutional neural network model and/or selecting the existing model structure and setting pre-training parameters;
s203, establishing a model training module, and training, testing and verifying the convolutional neural network model or the existing model structure based on the data set to be used as the time sequence feature recognition model.
The model training module adopts the training data set to carry out model training, then adopts the testing data set to test the trained model, then uses the verification set to verify the model, and if the model meets the time sequence feature recognition requirement, the model is taken as the three-dimensional time sequence feature recognition model; and if not, modifying the historical mode parameter setting, and training, testing and verifying the model again until the time sequence feature recognition requirement is met, and taking the model as a three-dimensional time sequence feature recognition model.
Specifically, a space reconstruction method is adopted in the three-dimensional reconstruction module process to carry out blood vessel three-dimensional reconstruction on the TOF image, the length, the width and the depth of each blood vessel are determined based on three-dimensional time sequence characteristics in the reconstruction process, and the reconstruction of the relation between the blood vessel and the peripheral tissues is completed according to the peripheral tissue plane characteristics.
FIG. 4 is a system architecture diagram of a vascular imaging device in accordance with an embodiment of the present invention. As shown in fig. 4, the blood vessel imaging apparatus of the present embodiment is based on the above-described 3D blood vessel imaging system. The method comprises the following steps: support frame
In another aspect, the present invention also provides a blood vessel imaging apparatus, comprising: the device comprises an infrared LED lamp 1a, a TOF image sensor 1b, a CCD image sensor 1c, a support 1D and a 3D imaging processing host 1 e.
Specifically, the support 1d is used to fix the detected part of the patient. The infrared LED lamp 1a is installed above the bracket to provide an infrared light source. The TOF image sensor 1b is mounted above the gantry to take TOF images of the examined area at the front. The CCD image sensor 1d is installed above the stand to take a two-dimensional image of the detected portion on the front side. And the 3D imaging processing host 1e is in communication connection with the TOF image sensor 1b and the CCD image sensor 1c and is used for receiving and processing the TOF image and the two-dimensional image so as to complete three-dimensional reconstruction of the blood vessel of the detected part of the patient.
The local, 3D imaging processing host comprises a processor and a storage medium, wherein the storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by the processor, and the steps in the 3D blood vessel imaging method are described above. Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer 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 computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer 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. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer 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 computer readable signal medium may also be any computer readable medium that is not a computer 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 computer readable 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.
Computer 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, Smalltalk, C + +, 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 computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or terminal. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., 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 invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A 3D vascular imaging system, comprising:
the image acquisition module is used for acquiring an infrared black-and-white image of the vein vessel on the body surface of the human body and three-dimensional distance point cloud information of the TOF on the body surface;
the feature extraction module is used for extracting three-dimensional time sequence features of blood vessels in the infrared image by combining TOF three-dimensional distance point cloud information, and the three-dimensional time sequence features comprise time sequence sequences of blood vessel length, width, depth and peripheral tissue plane features;
and the three-dimensional reconstruction module generates a three-dimensional modeled blood vessel image according to the three-dimensional time sequence characteristic and the TOF three-dimensional distance information.
2. A 3D vessel imaging system according to claim 1, wherein the feature extraction module comprises:
the preprocessing module is used for sequentially classifying and preprocessing the infrared black-and-white image according to the time sequence state of TOF point cloud information acquisition, marking the spatial distance information of each pixel point, and manufacturing the preprocessed image into a data set according to classification;
the model creating module is used for creating a convolutional neural network model and/or selecting the existing model structure and setting pre-training parameters;
and the model training module is used for training, testing and verifying the convolutional neural network model or the existing model structure based on the data set to serve as the time sequence feature recognition model.
3. The 3D vessel imaging system according to claim 2, wherein the data set is divided into a training data set, a testing data set and a verification data set, the model training module performs model training using the training data set, then tests the trained model using the testing data set, and then verifies the model using the verification set, and if the model meets the time sequence feature recognition requirement, the model is used as the three-dimensional time sequence feature recognition model; and if not, modifying the historical mode parameter setting, and training, testing and verifying the model again until the time sequence feature recognition requirement is met, and taking the model as a three-dimensional time sequence feature recognition model.
4. The 3D vessel imaging system according to claim 3, wherein the three-dimensional reconstruction module performs three-dimensional reconstruction of the vessel based on the TOF point cloud information and the three-dimensional vessel characteristics by using a spatial reconstruction method, determines the length, width and depth of each vessel based on three-dimensional time sequence characteristics during reconstruction, and completes reconstruction of the relationship between the vessel and the surrounding tissue according to the surrounding tissue plane characteristics.
5. The method for 3D vascular imaging of any of the systems of claims 1 to 4, comprising the steps of:
establishing an image acquisition module, wherein the image acquisition module is used for acquiring an infrared black-and-white image of a human body surface vein and body surface TOF three-dimensional distance point cloud information;
establishing a feature extraction module for extracting three-dimensional time sequence features of blood vessels in the infrared image by combining TOF three-dimensional distance information, wherein the three-dimensional time sequence features comprise time sequence sequences of blood vessel length, width, depth and peripheral tissue plane features;
and establishing a three-dimensional reconstruction module, and generating a three-dimensional modeled blood vessel image according to the three-dimensional time sequence characteristic and the TOF three-dimensional distance information by the three-dimensional reconstruction module.
6. The 3D blood vessel imaging method as claimed in claim 5, wherein in the process of establishing the feature extraction module, the method comprises the following steps:
establishing a preprocessing module, sequentially classifying and preprocessing the infrared black-white images according to the time sequence state of TOF point cloud information acquisition, marking the spatial distance information of each infrared black-white pixel point, and manufacturing the preprocessed images into a data set according to classification;
establishing a model establishing module, establishing a convolutional neural network model and/or selecting the existing model structure and setting pre-training parameters;
and establishing a model training module, and training, testing and verifying the convolutional neural network model or the existing model structure based on the data set to be used as the time sequence feature recognition model.
7. The 3D vessel imaging method according to claim 6, wherein the data set is divided into a training data set, a testing data set and a verification data set, the model training module performs model training using the training data set, then tests the trained model using the testing data set, and then verifies the model using the verification set, if the model timing feature recognition is required, the model is used as the three-dimensional timing feature recognition model; and if not, modifying the historical mode parameter setting, and training, testing and verifying the model again until the time sequence feature recognition requirement is met, and taking the model as a three-dimensional time sequence feature recognition model.
8. The 3D vessel imaging method as claimed in claim 7, further comprising, during the establishing of the three-dimensional reconstruction module:
and generating a three-dimensional modeled blood vessel image based on the three-dimensional time sequence characteristics and TOF three-dimensional distance information by adopting a space reconstruction method, determining the length, width and depth of each blood vessel based on the three-dimensional time sequence characteristics in the reconstruction process, and completing the reconstruction of the relationship between the blood vessel and the peripheral tissues according to the planar characteristics of the peripheral tissues.
9. A vascular imaging device, comprising:
the bracket is used for fixing the detected part of the patient;
the infrared LED lamp is arranged above the bracket and used for providing an infrared light source;
the TOF image distance sensor is arranged above the bracket and used for shooting a TOF point cloud image of the detected part in front;
and the CCD image sensor is arranged above the bracket and is used for shooting the two-dimensional blood vessel infrared image of the detected part in the front.
And the 3D imaging processing host is in communication connection with the TOF distance sensor and the CCD image sensor and is used for receiving and processing TOF point cloud information and two-dimensional images so as to complete three-dimensional reconstruction of the blood vessel of the detected part of the patient.
10. An apparatus as claimed in claim 9, wherein the 3D imaging processing host comprises a processor and a storage medium, the storage medium storing instructions adapted to be loaded by the processor to perform the steps of a 3D vessel imaging method as claimed in any one of claims 5 to 8.
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| CN114287880A (en) * | 2021-12-08 | 2022-04-08 | 四川大学华西医院 | Early stage tumor formation monitoring method for animal experiment tumor based on infrared image processing |
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