CN113053494A - PACS (Picture archiving and communication System) based on artificial intelligence and design method thereof - Google Patents
PACS (Picture archiving and communication System) based on artificial intelligence and design method thereof Download PDFInfo
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Abstract
The invention relates to the technical field of medical image processing, in particular to a PACS (Picture archiving and communication System) based on artificial intelligence and a design method thereof. Wherein the system includes: a file storage system for storing image data transmitted by the medical diagnostic imaging apparatus; the service controller comprises a marking service module and a training service module; the marking service module is used for acquiring image data in the file storage system and determining marking information in the image data; classifying the image data according to the labeling information to form at least two data sets, and storing the data sets in a file storage system; the training service module is used for acquiring a data set in the file storage system; and training the image data in the data set according to the labeled data in the data set by a deep learning training method to form an artificial intelligence model, and storing the artificial intelligence model in a file storage system. The invention can fully mine and utilize the mass medical image data resources stored in the system.
Description
Technical Field
The invention relates to the technical field of medical image processing, in particular to a Picture Archiving and Communication System (PACS) based on artificial intelligence and a design method thereof.
Background
In diagnostic imaging, PACS is a computer system dedicated to storing, acquiring, transmitting and presenting medical images. The PACS technology is provided for realizing non-film storage and communication of medical images, improving the efficiency of overall medical operation and realizing networking, modernization and remote work of doctors, and is a universal solution in the field of current medical image storage and communication.
Referring to fig. 1 and 2, fig. 1 is a design and architecture scheme of a PACS system in the related art, and fig. 2 is a data flow diagram of the PACS system in the related art. In the related technology, the medical diagnosis imaging equipment acquires image data and transmits the image data to the PACS for storage, and the doctor processing terminal equipment can retrieve and modify the related image data by accessing the PACS.
However, the related technology only simply realizes the storage and retrieval of the medical images, mass medical image data resources stored in the system cannot be fully mined and utilized, and the system is in an idle state for most of time and cannot play a due role.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides the PACS system based on artificial intelligence and the design method thereof, which can fully mine and utilize mass medical image data resources stored in the system.
According to the technical solution provided by the present invention, as a first aspect of the present invention, there is provided a PACS system based on artificial intelligence, including:
a file storage system for storing image data transmitted by the medical diagnostic imaging apparatus;
the service controller comprises a marking service module and a training service module;
the marking service module is used for acquiring the image data in the file storage system and determining marking information in the image data; classifying the image data according to the labeling information to form at least two data sets, and storing the data sets in the file storage system;
the training service module is used for acquiring a data set in the file storage system; and training the image data in the data set according to the labeled data in the data set by a deep learning training method to form at least two artificial intelligence models corresponding to different categories, and storing the artificial intelligence models in the file storage system.
Optionally, the service controller further includes:
the model service module is used for acquiring a diagnosis film reading request, determining an artificial intelligence model matched with the diagnosis film reading request, extracting the characteristics of the image data needing diagnosis film reading through the artificial intelligence model, and judging the characteristics.
Optionally, the system further comprises a user graphical interface module, wherein a graphic block corresponding to the user graphical interface module is formed on the user graphical interface;
by triggering a specific graphic module, a corresponding service module in the service controller can be called through the user graphic interface module.
Optionally, the user graphical interface module includes:
the marking graphical interface corresponds to the marking service module in the service controller, and the marking service module can be called by triggering a marking picture block;
and the training graphical interface corresponds to the training service module in the service controller, and can call the training service module by triggering a training image block.
Optionally, a device access gateway is further included, which enables a terminal device including the medical diagnostic imaging device to access the PACS system.
Optionally, the device access gateway supports DICOM protocol and HTTP protocol.
As a second aspect of the present invention, there is provided a PACS design method based on artificial intelligence, comprising the steps of:
acquiring and storing image data transmitted by medical diagnostic imaging equipment;
determining the marking information in the image data;
classifying the image data according to the labeling information to form at least two data sets;
training the image data in the data set according to the labeled data in the data set by a deep learning training method to form at least two artificial intelligent models corresponding to different categories;
and storing the artificial intelligence model.
Optionally, the method further includes:
receiving a diagnosis reading request;
acquiring image data to be diagnosed and read;
determining an artificial intelligence model matched with the diagnosis reading request;
and extracting the characteristics of the image data needing to be diagnosed and read through the artificial intelligence model, and judging the characteristics.
Optionally, the determining the labeling information in the image data includes:
determining a disease category or a scanning part of the medical diagnostic imaging device;
marking corresponding focuses according to the disease types or the scanning parts of the medical diagnosis imaging equipment;
storing the determined annotation information.
Optionally, the classifying the image data according to the labeling information to form at least two data sets includes:
determining a sample volume of the data set;
and classifying the image data according to the labeling information, so that the data set comprises a plurality of image data of specific labeling information, and the image data in each data set accords with the corresponding sample capacity limit.
From the above, the PACS system based on artificial intelligence and the design method thereof provided by the invention have the following advantages compared with the prior art: the marking information in the image data is determined, measurement, marking and diagnosis data generated by doctors reading medical images in daily diagnosis and scientific research work can be conveniently and quickly converted into marking information, and a data set is formed for storage, wherein the marking information comprises marking data and movie and television data; through the deep learning training method, the data set formed by the labeling service module can be used, and the artificial intelligence model aiming at the specific disease is generated by the deep learning training method. The invention trains the artificial intelligence model by using the massive medical image data stored in the PACS system, and fully excavates the medical image data, so that the massive data stored in the PACS system can play a greater role.
Drawings
In order to more clearly illustrate the detailed description of the present application or the technical solutions in the prior art, the drawings needed to be used in the detailed description of the present application or the prior art description will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a block diagram of a related art PACS system.
Fig. 2 is a flowchart of the operation of a related art PACS system.
Fig. 3 is a block diagram of the PACS system according to embodiment 1 of the present invention.
Fig. 4 is a block diagram of the PACS system according to the first aspect of the present invention in embodiment 2.
Fig. 5 is a flowchart of a PACS system according to a first aspect of the present invention, which relates to method embodiment 1.
Fig. 6 is a flowchart of a PACS system according to a first aspect of the present invention, which relates to method embodiment 2.
100. The system comprises a file storage system, 110, an image data storage module, 120, a labeling information storage module, 130, a data set storage module, 140, a model storage module, 200, a service controller, 210, a labeling service module, 220, a training service module, 230, a model service module, 300, a user graphical interface module, 310, a labeling graphical interface, 311, a labeling graphic block, 320, a training graphical interface, 321, a training graphic block, 330, a model graphical interface, 331, a model graphic block and 400, wherein the image data storage module is used for storing image data and image data.
Detailed Description
The technical solutions in the present application will be described clearly and completely with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present application. 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 application.
In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present application, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; the connection can be mechanical connection or electrical connection; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meaning of the above terms in the present application can be understood in a specific case by those of ordinary skill in the art.
In addition, the technical features mentioned in the different embodiments of the present application described below may be combined with each other as long as they do not conflict with each other.
As a first aspect of the present invention, an artificial intelligence based PACS system is provided.
Example 1
The PACS system based on artificial intelligence provided by this embodiment, with reference to fig. 3, includes: the file storage system 100 and the service controller 200, wherein the service controller 200 comprises an annotation service module 210 and a training service module 220;
the file storage system 100 comprises an image data storage module 110, a labeling information storage module 120, a data set storage module 130 and a model storage module 140; the image data storage module 110 is used for storing image data sent by the medical diagnostic imaging equipment; the medical diagnosis imaging device comprises an ultrasonic device, a CT device and a film reading device, when a doctor uses the medical diagnosis imaging device such as the ultrasonic device and the CT device to scan, the medical diagnosis imaging device can collect ultrasonic image data, and the medical diagnosis imaging device transmits the collected ultrasonic image data to the PACS system and stores the ultrasonic image data in a file storage system 100 of the PACS system.
The annotation service module 210, where the annotation service module 210 is configured to obtain image data in the file storage system 100, determine annotation information in the image data, and store the annotation information in an annotation information storage module 120 in the file storage system 100; the annotation service module 210 is further configured to classify the image data in the file storage system 100 according to the annotation information, divide the image data into at least two data sets, and store the data sets in the data set storage module 130 in the file storage system 100; each data set corresponds to a specific type of marking information; the types of the labeling information can be distinguished through the scanning part or the disease species of the medical diagnosis imaging equipment, and the main labeling content is also determined according to the training purpose, for example, the information such as corresponding focus needs to be labeled when the auxiliary diagnosis is performed.
The training service module 220, the training service module 220 is configured to obtain a data set stored in the file storage system 100 by the annotation service module 210; the training service module 220 can provide a deep learning training method; the training service module 220 performs the training of the data set on the image data in the data set according to the labeled data in the data set by the deep learning training method, so as to form at least two artificial intelligence models, wherein different artificial intelligence models correspond to different types of data sets, and the formed artificial intelligence models are stored in the model storage module 140 in the file storage system 100.
As can be seen from this embodiment, the annotation service module 210 can conveniently and quickly convert the measurement, annotation, and diagnosis data generated by the doctor reading the medical image in the daily diagnosis and scientific research into annotation information, and form a data set for storage, where the annotation information includes the label data and the movie data; the training service module 220 can use the data set formed by the annotation service module 210 to generate an artificial intelligence model for a specific disease species using a deep learning training method. According to the invention, the artificial intelligence model is trained by using the massive medical image data stored in the PACS through the marking service module 210 and the training service module 220, and the medical image data is fully mined, so that the massive data stored in the PACS plays a greater role.
Example 2
In order to enable the user to use the artificial intelligence model trained by the training service module 220, thereby improving the efficiency and quality of the doctor's diagnosis and research work.
The present embodiment provides an artificial intelligence based PACS system, which further includes a model service module 230 on the basis of embodiment 1, with reference to fig. 4. The model service module 230 is configured to obtain a diagnosis interpretation request, determine an artificial intelligence model matched with the diagnosis interpretation request, extract features of image data to be diagnosed, and determine the features.
Before the model service module 230 works specifically, a user sends a diagnosis reading request to the PACS system, where the diagnosis reading request includes: the method comprises the steps that identification ultrasonic image data with identification and marking information of the identification ultrasonic image data with identification are required to be diagnosed and read; when the model service module 230 works specifically, acquiring ultrasound image data with identification and labeling information of the ultrasound image data with identification in the diagnosis and interpretation request; determining matched artificial intelligence models according to the marking information of the ultrasonic image data with identification; and extracting the characteristics of the image data with identification of the film reading needing to be diagnosed through the artificial intelligence model to judge the characteristics.
In the embodiment, the model service module 230 is used for providing the functions of auxiliary diagnosis and reading of the film based on artificial intelligence by using the artificial intelligence model formed by training the training service module 220, so as to provide the user with the assistance in diagnosis and information processing, and improve the efficiency and quality of diagnosis and scientific research work of doctors.
For the above embodiments 1 and 2, the PACS system further includes a graphical interface module 300, and a user graphical interface has a tile corresponding to the graphical interface module 300. The user graphic interface module 300 includes: a label graphical interface 310, a training graphical interface 320, and a model graphical interface 330; the annotation graphical interface 310 can call the annotation service module 210 described in the above embodiment, that is, by triggering the annotation tile 311 displayed in a visual form, the annotation service module 210 described in the above embodiment can be called; the training gui interface 320 can call the training service module 220 described in the above embodiment, that is, the training service module 220 described in the above embodiment can be called by triggering the training tile 321 displayed in a visual form; the model gui interface 330 can call the model service module 230 described in the above embodiment 2, that is, the model service module 230 described in the above embodiment can be called by triggering the model tile 331 displayed in a visual form.
For the above embodiment, the PACS system further includes a device access gateway 400, the device access gateway 400 enabling terminal devices including the medical diagnostic imaging device to access the PACS system. The device access gateway 400 supports the DICOM protocol and the HTTP protocol. That is, medical diagnostic imaging devices such as ultrasound devices, CT devices, and image reading devices, can transmit image data to the file storage system 100 through the DICOM protocol via the gateway 400 to be stored in the DICOM format, and other user terminals can obtain influence data from the PACS system through the HTTP protocol.
As a second aspect of the present invention, a PACS design method based on artificial intelligence is provided.
Embodiment mode 1
The PACS design method based on artificial intelligence provided by the embodiment, referring to fig. 5, includes the following steps:
s11: acquiring and storing image data transmitted by medical diagnostic imaging equipment;
the medical diagnosis imaging device comprises an ultrasonic device, a CT device and a film reading device, when a doctor uses the medical diagnosis imaging device such as the ultrasonic device and the CT device to scan, the medical diagnosis imaging device can collect ultrasonic image data, and the medical diagnosis imaging device transmits the collected ultrasonic image data to the PACS system and stores the ultrasonic image data in a file storage system 100 of the PACS system.
S12: determining the marking information in the image data;
optionally, the step of determining the labeling information in the image data includes: s121: determining a disease category or a scanning part of the medical diagnostic imaging device; s122: marking corresponding focuses according to the disease types or the scanning parts of the medical diagnosis imaging equipment; s123: storing the determined annotation information.
The types of the labeling information can be distinguished through the scanning part or the disease species of the medical diagnosis imaging equipment, and the main labeling content is also determined according to the training purpose, for example, the information such as corresponding focus needs to be labeled when the auxiliary diagnosis is performed.
S13: classifying the image data according to the labeling information to form at least two data sets, and storing the data sets;
each of the data sets corresponds to a specific type of annotation information.
S14: training the image data in the data set according to the labeled data in the data set by a deep learning training method to form at least two artificial intelligent models corresponding to different categories; different artificial intelligence models correspond to different types of data sets;
optionally, the step S14 includes: determining a sample volume of the data set; and classifying the image data according to the labeling information, so that the data set comprises a plurality of image data of specific labeling information, and the image data in each data set accords with the corresponding sample capacity limit.
S15: and storing the artificial intelligence model.
As can be seen from the embodiment, the determination of the annotation information in the image data can conveniently and quickly convert the measurement, annotation and diagnosis data generated by a doctor reading medical images in daily diagnosis and scientific research into annotation information, and form a data set for storage, wherein the annotation information comprises the marking data and the movie and television data; through the deep learning training method, the data set formed by the annotation service module 210 can be used, and the artificial intelligence model for a specific disease is generated by the deep learning training method. The invention trains the artificial intelligence model by using the massive medical image data stored in the PACS system, and fully excavates the medical image data, so that the massive data stored in the PACS system can play a greater role.
Embodiment mode 2
The method aims to enable a user to use a trained artificial intelligence model so as to improve the efficiency and quality of doctor diagnosis and scientific research work.
The present embodiment provides an artificial intelligence based PACS method which is the PACS method according to embodiment 1, further including, with reference to fig. 6, the following steps performed after S15:
s26: receiving a diagnosis reading request;
s27: acquiring image data to be diagnosed and read;
s28: determining an artificial intelligence model matched with the diagnosis reading request;
s29: and extracting the characteristics of the image data needing to be diagnosed and read through the artificial intelligence model, and judging the characteristics.
In this embodiment, before S26 is executed, a user sends a diagnostic reading request to the PACS system, where the diagnostic reading request includes: the method comprises the steps that identification ultrasonic image data with identification and marking information of the identification ultrasonic image data with identification are required to be diagnosed and read; acquiring the ultrasonic image data with identification and the marking information of the ultrasonic image data with identification in the diagnosis reading request in the execution process of the embodiment; determining matched artificial intelligence models according to the marking information of the ultrasonic image data with identification; and extracting the characteristics of the image data with identification of the film reading needing to be diagnosed through the artificial intelligence model to judge the characteristics.
The embodiment utilizes the artificial intelligence model formed by training, thereby providing the functions of auxiliary diagnosis and film reading based on artificial intelligence, providing the assistance in the aspects of diagnosis and information processing for users, and improving the efficiency and quality of diagnosis and scientific research work of doctors.
Those of ordinary skill in the art will understand that: the above description is only exemplary of the present invention and should not be construed as limiting the present invention, and any modifications, equivalents, improvements and the like made within the spirit of the present invention should be included in the scope of the present invention.
Claims (10)
1. A PACS system based on artificial intelligence, comprising:
a file storage system for storing image data transmitted by the medical diagnostic imaging apparatus;
the service controller comprises a marking service module and a training service module;
the marking service module is used for acquiring the image data in the file storage system and determining marking information in the image data; classifying the image data according to the labeling information to form at least two data sets, and storing the data sets in the file storage system;
the training service module is used for acquiring a data set in the file storage system; and training the image data in the data set according to the labeled data in the data set by a deep learning training method to form at least two artificial intelligence models corresponding to different categories, and storing the artificial intelligence models in the file storage system.
2. The artificial intelligence based PACS system of claim 1, wherein the service controller further comprises:
the model service module is used for acquiring a diagnosis film reading request, determining an artificial intelligence model matched with the diagnosis film reading request, extracting the characteristics of the image data needing diagnosis film reading through the artificial intelligence model, and judging the characteristics.
3. The artificial intelligence based PACS system according to claim 1 or 2, further comprising a user graphical interface module, a user graphical interface having tiles formed thereon corresponding to the user graphical interface module;
by triggering a specific graphic module, a corresponding service module in the service controller can be called through the user graphic interface module.
4. The artificial intelligence based PACS system of claim 3, wherein the user graphical interface module comprises:
the marking graphical interface corresponds to the marking service module in the service controller, and the marking service module can be called by triggering a marking picture block;
and the training graphical interface corresponds to the training service module in the service controller, and can call the training service module by triggering a training image block.
5. The artificial intelligence based PACS system of claim 1, further comprising a device access gateway that enables terminal devices including the medical diagnostic imaging device to access the PACS system.
6. The artificial intelligence based PACS system of claim 5, wherein the device access gateway supports the DICOM protocol and the HTTP protocol.
7. A PACS design method based on artificial intelligence is characterized by comprising the following steps:
acquiring and storing image data transmitted by medical diagnostic imaging equipment;
determining the marking information in the image data;
classifying the image data according to the labeling information to form at least two data sets;
training the image data in the data set according to the labeled data in the data set by a deep learning training method to form at least two artificial intelligent models corresponding to different categories;
and storing the artificial intelligence model.
8. The artificial intelligence based PACS design method of claim 7, further comprising:
receiving a diagnosis reading request;
acquiring image data to be diagnosed and read;
determining an artificial intelligence model matched with the diagnosis reading request;
and extracting the characteristics of the image data needing to be diagnosed and read through the artificial intelligence model, and judging the characteristics.
9. The artificial intelligence based PACS design method of claim 7, wherein said determining annotation information in the image material comprises:
determining a disease category or a scanning part of the medical diagnostic imaging device;
marking corresponding focuses according to the disease types or the scanning parts of the medical diagnosis imaging equipment;
storing the determined annotation information.
10. The artificial intelligence based PACS design method of claim 7, wherein said classifying said image material according to said labeling information to form at least two data sets comprises:
determining a sample volume of the data set;
and classifying the image data according to the labeling information, so that the data set comprises a plurality of image data of specific labeling information, and the image data in each data set accords with the corresponding sample capacity limit.
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| RU2797879C1 (en) * | 2022-08-24 | 2023-06-09 | Общество с ограниченной ответственностью "РОБОСКАН" | Automated ultrasound method |
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