CN113053494B - PACS system based on artificial intelligence and design method thereof - Google Patents
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Abstract
The application relates to the technical field of medical image processing, in particular to an artificial intelligence-based PACS and a design method thereof. Wherein the system comprises: a file storage system for storing image data transmitted by the medical diagnostic imaging apparatus; the service controller comprises a labeling service module and a training service module; the labeling service module is used for acquiring the image data in the file storage system and determining labeling 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; training image data in a data set according to the labeling data in the data set by a training method of deep learning to form an artificial intelligent model, and storing the artificial intelligent model in a file storage system. The application can fully excavate and utilize the mass medical image data resources stored in the system.
Description
Technical Field
The application relates to the technical field of medical image processing, in particular to a medical image archiving and communication system (Picture archiving and communication system, PACS) based on artificial intelligence and a design method thereof.
Background
In image diagnostics, PACS is a computer system that is specifically used to store, acquire, send and display medical images. The PACS technology is provided for realizing the purposes of non-slicing storage and communication of medical images, improving the overall medical operation efficiency and realizing networking, modernization and remodelling of doctor work, and is a general solution in the current medical image storage and communication field.
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, image data are acquired through medical diagnosis imaging equipment and are transmitted to a PACS system for storage, and a doctor processing terminal device can retrieve and modify the related image data by accessing the PACS system.
However, the related art simply realizes the storage and retrieval of the medical images, and massive medical image data resources stored in the system cannot be fully mined and utilized, so that the medical images cannot play a role in idle state most of the time.
Disclosure of Invention
In order to solve the defects in the prior art, the application provides an artificial intelligence-based PACS system and a design method thereof, which can fully mine and utilize mass medical image data resources stored in the system.
According to the technical scheme provided by the application, as a first aspect of the application, there is provided an artificial intelligence based PACS system, comprising:
a file storage system for storing image data transmitted by the medical diagnostic imaging apparatus;
the service controller comprises a labeling service module and a training service module;
the labeling service module is used for acquiring the image data in the file storage system and determining labeling information in the image data; classifying the image data according to the marking 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; training the image data in the data set according to the labeling data in the data set through a training method of deep learning to form at least two artificial intelligent models corresponding to different categories, and storing the artificial intelligent models in the file storage system.
Optionally, the service controller further includes:
the model service module is used for acquiring the diagnosis film reading request, determining an artificial intelligent model matched with the diagnosis film reading request, extracting the characteristics of image data to be diagnosed and read through the artificial intelligent model, and judging the characteristics.
Optionally, the system further comprises a user graphical interface module, wherein the user graphical interface module is provided with a block corresponding to the user graphical interface module;
by triggering a specific graphic module, a corresponding service module in the service controller can be invoked 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 image 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, the device access gateway enabling 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 application, there is provided an artificial intelligence based PACS design method comprising the steps of:
acquiring and storing image data transmitted by a medical diagnostic imaging device;
determining labeling 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 labeling data in the data set by a training method of deep learning to form at least two artificial intelligent models corresponding to different categories;
the artificial intelligence model is stored.
Optionally, the method further comprises:
receiving a diagnostic reading request;
acquiring image data of a piece to be diagnosed;
determining an artificial intelligence model that matches the diagnostic reading request;
and extracting the characteristics of the image data to be diagnosed and read through the artificial intelligent model, and judging the characteristics.
Optionally, the determining the labeling information in the image data includes:
determining a disease type or a scanning site of the medical diagnostic imaging device;
marking a corresponding focus according to the disease type or the scanning part of the medical diagnostic imaging equipment;
and 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 size of the dataset;
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 application have the following advantages compared with the prior art: the marking information in the image data is determined, so that 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, a data set is formed and stored, and the marking information comprises marking data and movie data; through the training method of deep learning, the data set formed by the labeling service module can be used, and the artificial intelligent model aiming at the specific disease can be generated by utilizing the training method of deep learning. According to the application, the artificial intelligent model is trained by utilizing the mass medical image data stored in the PACS system, and the medical image data is fully mined, so that the mass data stored in the PACS system plays a larger role.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of a related art PACS system.
Fig. 2 is a flowchart of the operation of the related art PACS system.
Fig. 3 is a block diagram of an embodiment 1 of a PACS system according to a first aspect of the present application.
Fig. 4 is a block diagram of an embodiment 2 of a PACS system according to a first aspect of the present application.
Fig. 5 is a flowchart of a PACS system according to a first aspect of the present application related to method embodiment 1.
Fig. 6 is a flowchart of a PACS system according to a first aspect of the present application related to method embodiment 2.
100. The system comprises a file storage system, a 110 image data storage module, a 120 annotation information storage module, a 130 data set storage module, a 140 model storage module, a 200 service controller, a 210 annotation service module, a 220 training service module, a 230 model service module, a 300 user graphical interface module, a 310 annotation graphical interface, a 311 annotation block, a 320 training graphical interface, a 321 training block, a 330 model graphical interface, a 331 model block and a 400 gateway.
Detailed Description
The following description of the embodiments of the present application will be made more apparent and fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the application are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In the description of the present application, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific 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 should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; the two components can be directly connected or indirectly connected through an intermediate medium, or can be communicated inside the two components, or can be connected wirelessly or in a wired way. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
In addition, the technical features of the different embodiments of the present application described below may be combined with each other as long as they do not collide with each other.
As a first aspect of the present application, there is provided an artificial intelligence based PACS system.
Example 1
The PACS system based on artificial intelligence provided in this embodiment includes, with reference to fig. 3: a file storage system 100 and a service controller 200, the service controller 200 including an annotation service module 210 and a training service module 220;
the file storage system 100 includes 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 scans by using the medical diagnosis imaging device such as the ultrasonic device and the CT device, the medical diagnosis imaging device can acquire ultrasonic image data, and the medical diagnosis imaging device transmits the acquired ultrasonic image data to the PACS system and stores the acquired ultrasonic image data in a file storage system 100 of the PACS system.
The labeling service module 210, where the labeling service module 210 is configured to obtain image data in the file storage system 100, determine labeling information in the image data, and store the labeling information in the labeling information storage module 120 in the belonging file storage system 100; the labeling service module 210 is further configured to classify the image data in the file storage system 100 according to the labeling 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 annotation information; the type of the labeling information can be distinguished by the scanning position or disease type of the medical diagnosis imaging equipment, the main labeling content is also determined according to the training purpose, for example, auxiliary diagnosis is carried out, and the corresponding information such as focus and the like is required to be labeled.
The training service module 220, where the training service module 220 is configured to obtain a data set stored in the file storage system 100 by the labeling service module 210; the training service module 220 is capable of providing a deep learning training method; the training service module 220 performs the training on the image data in the data set according to the labeling data in the data set by using the training method of deep learning, so as to form at least two artificial intelligent models, wherein different artificial intelligent models correspond to different types of data sets, and stores the formed artificial intelligent models in the model storage module 140 in the affiliated file storage system 100.
As can be seen from the embodiment, the labeling service module 210 can conveniently and quickly convert measurement, labeling and diagnosis data generated by a doctor reading medical images in daily diagnosis and scientific research work into labeling information, and form a data set for storage, wherein the labeling information comprises labeling data and movie data; the training service module 220 can use the data set formed by the annotation service module 210 and generate artificial intelligence models for specific disease types using deep learning training methods. According to the application, the artificial intelligent model is trained by using the mass medical image data stored in the PACS system through the labeling service module 210 and the training service module 220, and the medical image data is fully mined, so that the mass data stored in the PACS system plays a larger 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 doctor diagnosis and scientific research work.
The present embodiment provides an artificial intelligence based PACS system, referring to fig. 4, which further includes a model service module 230 based on embodiment 1. The model service module 230 is configured to obtain a diagnostic reading request, determine an artificial intelligent model that matches the diagnostic reading request, extract, by using the artificial intelligent model, features of image data of the to-be-diagnosed reading, and determine the features.
Before the model service module 230 works specifically, a user sends a diagnosis and film reading request to the PACS system, where the diagnosis and film reading request includes: the method comprises the steps of identifying ultrasonic image data and marking information of the ultrasonic image data needing to be diagnosed and read; when the model service module 230 specifically works, acquiring the ultrasonic image data with identification and the labeling information of the ultrasonic image data with identification in the diagnosis and reading request; determining a matched artificial intelligent model through the labeling information of the ultrasonic image data with identification; and extracting the characteristics of the image data with identification to be diagnosed and read through the artificial intelligent model, and judging the characteristics.
The embodiment utilizes the artificial intelligence model formed by training by the training service module 220 through the model service module 230, thereby providing the auxiliary diagnosis and film reading functions based on artificial intelligence, providing the auxiliary diagnosis and information processing for the user, and improving the efficiency and quality of doctor diagnosis and scientific research work.
For embodiments 1 and 2 above, the pacs system further includes a graphical interface module 300 on which tiles corresponding to the user graphical interface module 300 are formed. The user graphical interface module 300 includes: labeling graphical interface 310, training graphical interface 320, and model graphical interface 330; the labeling graphical interface 310 can call the labeling service module 210 described in the above embodiment, that is, by triggering the labeling tile 311 displayed in a visual form, the labeling service module 210 described in the above embodiment can be called; the training graphical interface 320 can invoke the training service module 220 described in the above embodiment, that is, by triggering the training tile 321 displayed in a visual form, the training service module 220 described in the above embodiment can be invoked; the model graphical interface 330 is capable of invoking the model service module 230 described in the above embodiment 2, i.e. by triggering the model tiles 331 displayed in visual form, i.e. the model service module 230 described in the above embodiment can be invoked.
For the above embodiments, the PACS system further comprises a device access gateway 400, said device access gateway 400 enabling a terminal device comprising said medical diagnostic imaging device to access said PACS system. The device access gateway 400 supports the DICOM protocol and the HTTP protocol. Namely medical diagnostic imaging equipment such as ultrasonic equipment, CT equipment, film reading equipment and the like, can transmit image data to the file storage system 100 through the gateway 400 through the DICOM protocol and store the image data in the DICOM format, and other user terminals can acquire influence data from the PACS system through the HTTP protocol.
As a second aspect of the present application, there is provided a PACS design method based on artificial intelligence.
Embodiment 1
The PACS design method based on artificial intelligence provided in this embodiment, referring to fig. 5, includes the following steps:
s11: acquiring and storing image data transmitted by a medical diagnostic imaging device;
the medical diagnosis imaging device comprises an ultrasonic device, a CT device and a film reading device, when a doctor scans by using the medical diagnosis imaging device such as the ultrasonic device and the CT device, the medical diagnosis imaging device can acquire ultrasonic image data, and the medical diagnosis imaging device transmits the acquired ultrasonic image data to the PACS system and stores the acquired ultrasonic image data in a file storage system 100 of the PACS system.
S12: determining labeling information in the image data;
optionally, the step of determining the labeling information in the image data includes: s121: determining a disease type or a scanning site of the medical diagnostic imaging device; s122: marking a corresponding focus according to the disease type or the scanning part of the medical diagnostic imaging equipment; s123: and storing the determined annotation information.
The type of the labeling information can be distinguished by the scanning position or disease type of the medical diagnosis imaging equipment, the main labeling content is also determined according to the training purpose, for example, auxiliary diagnosis is carried out, and the corresponding information such as focus and the like is required to be labeled.
S13: classifying the image data according to the marking information to form at least two data sets, and storing the data sets;
each of the datasets corresponds to a particular type of annotation information.
S14: training the image data in the data set according to the labeling data in the data set by a training method of deep learning to form at least two artificial intelligent models corresponding to different categories; different artificial intelligence models correspond to different classes of data sets;
optionally, the step S14 includes: determining a sample size of the dataset; 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: the artificial intelligence model is stored.
According to the embodiment, the marking information in the image data is determined, so that 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 the marking information, a data set is formed and stored, and the marking information comprises marking data and movie data; through the training method of deep learning, the data set formed by the annotation service module 210 can be used, and the artificial intelligence model for a specific disease species can be generated by utilizing the training method of deep learning. According to the application, the artificial intelligent model is trained by utilizing the mass medical image data stored in the PACS system, and the medical image data is fully mined, so that the mass data stored in the PACS system plays a larger role.
Embodiment 2
In order to enable a user to use the trained artificial intelligence model, the efficiency and quality of doctor diagnosis and scientific research work are improved.
The present embodiment provides an artificial intelligence-based PACS method, which is based on embodiment 1, referring to fig. 6, and further includes the following steps performed after S15:
s26: receiving a diagnostic reading request;
s27: acquiring image data of a piece to be diagnosed;
s28: determining an artificial intelligence model that matches the diagnostic reading request;
s29: and extracting the characteristics of the image data to be diagnosed and read through the artificial intelligent model, and judging the characteristics.
Before executing S26 in this embodiment, the user sends a diagnostic reading request to the PACS system, where the diagnostic reading request includes: the method comprises the steps of identifying ultrasonic image data and marking information of the ultrasonic image data needing to be diagnosed and read; acquiring identification ultrasonic image data and labeling information of the identification ultrasonic image data in the diagnosis film reading request in the implementation process of the embodiment; determining a matched artificial intelligent model through the labeling information of the ultrasonic image data with identification; and extracting the characteristics of the image data with identification to be diagnosed and read through the artificial intelligent model, and judging the characteristics.
The embodiment utilizes the artificial intelligence model formed by training, thereby providing the auxiliary diagnosis and film reading functions based on artificial intelligence, providing the assistance in diagnosis and information processing for users and improving the efficiency and quality of doctor diagnosis and scientific research work.
Those of ordinary skill in the art will appreciate that: the above embodiments are merely illustrative of the present application and are not intended to limit the present application, and any modifications, equivalent substitutions, improvements, etc. within the spirit of the present application should be included in the scope of the present application.
Claims (9)
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 labeling service module and a training service module;
the labeling service module is used for acquiring the image data in the file storage system and determining labeling information in the image data; classifying the image data according to the marking 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; training image data in the data set according to the labeling data in the data set by a training method of deep learning to form at least two artificial intelligent models corresponding to different categories, and storing the artificial intelligent models in the file storage system;
the file storage system comprises an image data storage module, a labeling information storage module, a data set storage module and a model storage module;
a user graphical interface module, wherein a graphic block corresponding to the user graphical interface module is formed on a user graphical interface;
by triggering a specific graphic module, a corresponding service module in the service controller can be invoked through the user graphic interface module.
2. The artificial intelligence based PACS system of claim 1, wherein the service controller further comprises:
the model service module is used for acquiring the diagnosis film reading request, determining an artificial intelligent model matched with the diagnosis film reading request, extracting the characteristics of image data to be diagnosed and read through the artificial intelligent model, and judging the characteristics.
3. The artificial intelligence based PACS system of claim 1, 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 image 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.
4. The artificial intelligence-based PACS system of claim 1, further comprising a device access gateway that enables a terminal device comprising the medical diagnostic imaging device to access the PACS system.
5. The artificial intelligence-based PACS system of claim 4, wherein the device access gateway supports DICOM protocol and HTTP protocol.
6. The PACS design method based on the artificial intelligence is characterized by comprising the following steps of:
acquiring and storing image data transmitted by a medical diagnostic imaging device;
determining labeling 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 labeling data in the data set by a training method of deep learning to form at least two artificial intelligent models corresponding to different categories;
the artificial intelligence model is stored.
7. The artificial intelligence based PACS design method as recited in claim 6, further comprising:
receiving a diagnostic reading request;
acquiring image data of a piece to be diagnosed;
determining an artificial intelligence model that matches the diagnostic reading request;
and extracting the characteristics of the image data to be diagnosed and read through the artificial intelligent model, and judging the characteristics.
8. The artificial intelligence based PACS design method of claim 6, wherein the determining labeling information in the image material includes:
determining a disease type or a scanning site of the medical diagnostic imaging device;
marking a corresponding focus according to the disease type or the scanning part of the medical diagnostic imaging equipment;
and storing the determined annotation information.
9. The artificial intelligence based PACS design method of claim 6, wherein classifying the image material according to the labeling information to form at least two data sets includes:
determining a sample size of the dataset;
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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