CN114039995A - Method and system for distinguishing AI module function management based on DICOM IP port - Google Patents

Method and system for distinguishing AI module function management based on DICOM IP port Download PDF

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CN114039995A
CN114039995A CN202111092649.7A CN202111092649A CN114039995A CN 114039995 A CN114039995 A CN 114039995A CN 202111092649 A CN202111092649 A CN 202111092649A CN 114039995 A CN114039995 A CN 114039995A
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dicom
model
module
port
different
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贺长征
岳新
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BEIJING SILVER MEDICAL INFORMATION TECHNICAL CO LTD
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BEIJING SILVER MEDICAL INFORMATION TECHNICAL CO LTD
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/04—Network management architectures or arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Medical Treatment And Welfare Office Work (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

The invention discloses a DICOM-based IP port distinguished AI module function management method, which comprises the following steps: setting different AI models for AI servers with the same algorithm based on different clinical application purposes; setting model parameters matched with each AI model; deploying the AI server corresponding to each AI model on different ports of the same DICOM IP address; based on the purpose of clinical application, transmitting DICOM images of patients to matched AI models; the AI model generates diagnostic data based on the model parameters and sends the diagnostic data to the image structured report. The invention also discloses a function management system for distinguishing the AI module based on the DICOM IP port. The invention uses different ports of the same DICOM IP to manage AI applications with different clinical purposes, and an AI scheduling platform can realize the tabulation of routing strategies and is very easy to configure; the multiplexing of the AI algorithm is easier, and more logic judgment is not needed; more complex multi-AI cooperative applications can also be easily implemented through external platform scheduling.

Description

Method and system for distinguishing AI module function management based on DICOM IP port
Technical Field
The invention relates to the field of medical information, in particular to a DICOM IP port based AI module distinguishing function management method and system.
Background
Based on the principle of DICOM protocol IP address management, 255 DICOM devices can exist in a service intranet at maximum. There may be 65535 different traffic ports per IP address, in accordance with the principles of the TCP/IP protocol. For the image transmission service of the video equipment/post-processing workstation/AI system, each IP address is usually occupied by an independent equipment and workstation, and 104 ports are used by default. ACR (american academy of radiology) considers that future imaging departments will have thousands of different AI application scenarios, and therefore it is necessary to use different IP ports to manage different AI models.
Because the AI algorithm has strong ability of transfer learning, a plurality of same algorithms are matched with different parameters to be used in different clinical scenes. AI enterprises typically integrate many AI applications on a DICOM IP port for multiplexing purposes. In order to make the output of an AI application on one port meet different clinical needs, there are generally two methods for scheduling management of this type of AI, but both have significant drawbacks. The first approach is to let the AI generate a full set of information under all scenarios, and let the subsequent applications select according to the clinical scenario. But the image sequence types of different service scenes are different, so that the internal programming of the AI is complicated, and information cannot be generated comprehensively; and all the parameter resource consumption is large, and the subsequent application also needs complex programming to judge the extracted information content. The second method is completely based on the DICOM image type judgment, but the judgment has logic holes and is not suitable for the service scene with the same image type but different diagnostic information needs to be extracted.
Disclosure of Invention
In view of the above, the main objective of the present invention is to provide a method and a system for differentiating AI module function based on DICOM IP ports, which use different ports of the same DICOM IP to manage AI applications with different clinical purposes, rather than integrating multiple AI applications with the same port.
In order to achieve the purpose, the technical scheme of the invention is as follows:
in one aspect, the invention provides a DICOM IP port based AI module distinguishing function management method, which comprises the following steps:
s101: setting different AI models for AI servers with the same algorithm based on different clinical application purposes; wherein each clinical application purpose corresponds to an AI model;
s102: setting model parameters matched with each AI model;
s103: deploying the AI server corresponding to each AI model on different ports of the same DICOM IP address;
s104: based on the purpose of clinical application, transmitting DICOM images of patients to matched AI models;
s105: the AI model generates diagnostic data based on the model parameters and sends the diagnostic data to the image structured report.
Preferably, the configuration of the model parameters further comprises: different image sequences are input according to the configuration, and images are filtered according to the sequence description.
Preferably, the diagnostic data includes measurement values and key images.
On the other hand, the invention also provides a DICOM IP port-based AI module distinguishing function management system, which comprises: the device comprises a configuration module, an execution module and a sending module. Wherein,
the configuration module, the execution module and the sending module are sequentially connected.
The configuration module is used for deploying AI servers with the same algorithm on different ports of the same DICOM IP address based on different clinical application purposes, setting different AI models for the AI servers, and setting model parameters matched with the AI models for each AI model, wherein each clinical application purpose corresponds to one AI model;
an execution module to receive a DICOM image of a patient and the AI model to generate diagnostic data based on the model parameters;
and the sending module is used for sending the diagnosis data to the image structured report.
Preferably, the configuring of the model parameters in the configuration module further comprises: different image sequences are input according to the configuration, and images are filtered according to the sequence description.
Preferably, the execution module further comprises: a sending unit and a calculating unit. Wherein, the sending unit sends the DICOM image of the patient to the selected AI model port; the calculation unit calls an AI model corresponding to the AI server to complete calculation and output diagnosis data.
Preferably, the diagnostic data includes measurement values and key images.
The invention has the beneficial effects that:
compared with the prior art, the invention can configure the AI modules with different clinical application purposes through different IP ports, and can obtain the following effects:
1. the AI scheduling platform can realize the tabulation of the routing strategy and is very easy to configure.
2. The AI product only occupies one DICOM IP address, the multiplexing of the AI algorithm is easier, different ports only need to be configured with different AI model parameters, and more logic judgment is not needed.
3. More complex multi-AI cooperative applications can be easily implemented through external platform scheduling.
Drawings
Fig. 1 is a schematic flowchart of a DICOM IP port-based AI module function management method according to embodiment 1.
Fig. 2 is a schematic configuration interface diagram of a DICOM device in the DICOM IP port based AI module function management method according to embodiment 1.
Fig. 3 is a schematic configuration interface diagram of an AI model and a PACS device ID in the DICOM IP port-based AI module function management method according to embodiment 1.
Fig. 4 is a schematic configuration interface diagram of AI model parameters in the DICOM IP port based AI module function management method according to embodiment 1.
Fig. 5 is a schematic view of an execution interface of an AI algorithm in the DICOM IP port based AI module function management method according to embodiment 1.
Fig. 6 is a schematic diagram of a transmission interface of a result in the DICOM IP port based AI module function management method according to embodiment 1.
Fig. 7 is a schematic flowchart of the DICOM IP port based AI module function management system according to embodiment 2.1.
Fig. 8 is a schematic configuration interface diagram of a DICOM device in the DICOM IP port differentiation AI module function management system according to embodiment 2.1.
Fig. 9 is a schematic configuration interface diagram of the DICOM IP port-based configuration for distinguishing the AI model from the PACS device ID in the AI module function management system according to embodiment 2.1.
Fig. 10 is a schematic configuration interface diagram of an AI model parameter in the DICOM IP port based AI module function management system according to embodiment 2.1.
Fig. 11 is a schematic view of an execution interface of an AI algorithm in the DICOM IP port based AI module differentiation function management system according to embodiment 2.1.
Fig. 12 is a schematic diagram of a transmission interface of the result in the DICOM IP port based AI module function management system according to embodiment 2.1.
Fig. 13 is a schematic flowchart of a DICOM IP port based AI module function management system according to embodiment 2.2.
Fig. 14 is a schematic view of an execution interface of an AI algorithm in the DICOM IP port based AI module differentiation function management system according to embodiment 2.2.
Fig. 15 is a schematic diagram of a transmission interface of the result in the DICOM IP port based AI module function management system according to embodiment 2.2.
Detailed Description
The invention provides a DICOM-based IP port distinguished AI module function management method, which comprises the following steps:
s101: setting different AI models for AI servers with the same algorithm based on different clinical application purposes; wherein,
each clinical application purpose corresponds to one AI model;
s102: setting model parameters matched with each AI model;
in this step, AI model parameters are configured, which are mainly used to implement the same AI server, to input different image sequences (to filter images according to SQL conditional statements such as sequence descriptions) according to configuration, to output required results according to an XML template, to configure result storage locations, and to configure key image paths, etc.
S103: deploying the AI server corresponding to each AI model on different ports of the same DICOM IP address;
s104: based on the purpose of clinical application, transmitting DICOM images of patients to matched AI models;
in this step, a DICOM device is configured in the PACS for transmitting the image to the AI server. Specifically, the callqmservice. exe is executed to copy the case image of the PACS image center to the PACS device corresponding to the selected AI model application (i.e., the copied image is sent to the corresponding AI server port).
S105: the AI model generates diagnostic data based on the model parameters and sends the diagnostic data to the image structured report.
In the step, the system executes execmi.exe in a delayed mode, calls a corresponding model of an AI server, and completes calculation and output.
In an embodiment provided by the present invention, the configuration of the model parameters further includes: different image sequences are input according to the configuration, and images are filtered according to the sequence description.
In one embodiment, the diagnostic data includes measurement values and key images.
In one embodiment, a DICOM IP port based AI module function management system includes: the device comprises a configuration module, an execution module and a sending module. Wherein,
the configuration module, the execution module and the sending module are sequentially connected;
the configuration module is used for deploying AI servers with the same algorithm on different ports of the same DICOM IP address based on different clinical application purposes, setting different AI models for the AI servers, and setting model parameters matched with the AI models for each AI model, wherein each clinical application purpose corresponds to one AI model;
an execution module to receive a DICOM image of a patient and the AI model to generate diagnostic data based on the model parameters;
and the sending module is used for sending the diagnosis data to the image structured report.
In an embodiment provided by the present invention, the execution module further includes: a sending unit and a calculating unit. Wherein, the sending unit sends the DICOM image of the patient to the selected AI model port; the calculation unit calls the model corresponding to the AI server to complete calculation and output results.
In one embodiment, the results include the measurements and key images.
The following describes in detail a function management method and system for differentiating AI modules based on DICOM IP ports according to the present invention with reference to specific embodiments and accompanying drawings, it should be understood that the scope of the present invention is not limited to the embodiments, and all modifications or variations according to the spirit of the present invention belong to the scope of the present invention.
Example 1: function management method for distinguishing AI (Artificial Intelligence) modules based on DICOM (digital imaging and communications in medicine) IP (Internet protocol) port
In this embodiment, a method for managing AI module function based on DICOM IP port differentiation, as shown in fig. 1, includes:
s101: configuring DICOM devices, AI models and device IDs for sending images to an AI server;
(1) the DICOM equipment is configured in the PACS and used for sending the image to the AI server; as shown in fig. 2.
(2) Configuring an AI model and sending the image to a PACS equipment ID used by an AI server; as shown in fig. 3.
S102: setting AI model parameters matched with each AI model; configuring AI model parameters, mainly used for realizing the same AI server, inputting different image sequences according to configuration (filtering images according to SQL conditional statements such as sequence description) and outputting required results according to an XML template, configuring the storage positions of the results, the paths of key images and the like. As shown in fig. 4.
S103: after DICOM equipment, AI models and equipment IDs in the step S101 and AI model parameters in the step S102 are configured, DICOM images of patients are sent to matched AI models, and the AI models generate diagnosis data based on the model parameters; in the process, an AI model application is selected according to clinical requirements of a case, and two InterRIS tasks are added when clicking for determination: one task is to execute callqmservice. exe to copy the case image of the PACS image center to the PACS device corresponding to the selected AI model application (i.e. send the image to the corresponding AI server port after copying); one task is to delay execution of execai.exe to call a model corresponding to the AI server to complete calculation and output. As shown in fig. 5.
S104: the measured values and key images were sent to the visual structured report using HL 7. As shown in fig. 6.
Example 2: function management system for distinguishing AI (Artificial Intelligence) modules based on DICOM (digital imaging and communications in medicine) IP (Internet protocol) port
Example 2.1
A DICOM IP port based AI module function management system, as shown in fig. 7, includes: a configuration module 10, an execution module 20 and a sending module 30. Wherein,
the configuration module 10, the execution module 20 and the sending module 30 are sequentially connected;
the configuration module 10 deploys AI servers with the same algorithm on different ports of the same DICOM IP address based on different clinical application purposes, sets different AI models for the AI servers, and sets model parameters matched with each AI model for each AI model, wherein each clinical application purpose corresponds to one AI model; as shown in fig. 8-10. FIG. 8 illustrates a configuration interface diagram of a DICOM device; FIG. 9 shows a schematic diagram of a configuration interface for an AI model and PACS device IDs; fig. 10 shows a configuration interface diagram of AI model parameters.
An execution module 20 that receives DICOM images of a patient and the AI model generates diagnostic data based on the model parameters; as shown in fig. 11.
The sending module 30 sends the diagnostic data (measured values and key images) to the visual structured report using HL 7; as shown in fig. 12.
Example 2.2
A DICOM IP port based AI module function management system, as shown in fig. 13, includes: a configuration module 10, an execution module 20 and a sending module 30. Wherein,
the configuration module 10, the execution module 20 and the sending module 30 are sequentially connected;
the execution module 20 further includes a sending unit 201 and a calculating unit 202;
the configuration module 10 deploys the AI servers with the same algorithm on different ports of the same DICOM IP address based on different clinical application purposes, sets different AI models for the AI servers, and sets model parameters matched with each AI model for each AI model, wherein each clinical application purpose corresponds to one AI model.
The execution module 20 receives DICOM images of patients and the AI model generates diagnostic data based on the model parameters, as shown in fig. 14, wherein the transmission unit 201 copies case images of the PACS image center to PACS devices corresponding to the selected AI model application, and simultaneously transmits the copied images to corresponding AI server ports; the calculation unit 202 calls a model corresponding to the AI server to complete calculation and output a result.
The sending module 30 sends the diagnostic data (measured values and key images) to the visual structured report using HL 7; as shown in fig. 15.
The above description is only a preferred embodiment of the present disclosure and should not be interpreted as limiting the scope of the present disclosure, it should be noted that those skilled in the art can make various changes and modifications without departing from the spirit of the present disclosure, which falls within the protection scope of the present disclosure.

Claims (7)

1. A DICOM IP port-based AI module function distinguishing management method is characterized by comprising the following steps:
s101: setting different AI models for AI servers with the same algorithm based on different clinical application purposes; wherein each of the clinical application goals corresponds to one of the AI models;
s102: setting model parameters matched with the AI model for each AI model;
s103: deploying the AI server corresponding to each AI model on different ports of the same DICOM IP address;
s104: based on the clinical application purpose, sending DICOM images of the patient to the matched AI model;
s105: and the AI model generates diagnosis data based on the model parameters and sends the diagnosis data to an image structured report.
2. The DICOM-based IP port differentiated AI module function management method of claim 1, wherein the model parameters setting further comprises: different image sequences are input according to the configuration, and images are filtered according to the sequence description.
3. The DICOM IP port based AI module functionality management method of claim 1, wherein the diagnostic data includes measurement values and key images.
4. A DICOM-based IP port differentiated AI module function management system is characterized by comprising: the device comprises a configuration module, an execution module and a sending module. Wherein,
the configuration module, the execution module and the sending module are sequentially connected.
The configuration module deploys AI servers with the same algorithm on different ports of the same DICOM IP address based on different clinical application purposes, sets different AI models for the AI servers, and sets model parameters matched with the AI models for each AI model, wherein each clinical application purpose corresponds to one AI model;
the execution module to receive a DICOM image of a patient and the AI model to generate diagnostic data based on the model parameters;
and the sending module is used for sending the diagnosis data to an image structured report.
5. The DICOM IP Port differentiated AI module function management system of claim 4, wherein the configuration of model parameters in the configuration module further comprises: different image sequences are input according to the configuration, and images are filtered according to the sequence description.
6. The DICOM IP port differentiated AI module function management system of claim 4, wherein the execution module further comprises: a transmitting unit and a calculating unit; wherein the sending unit is used for sending the DICOM image of the patient to the selected AI model port; and the computing unit calls the AI model corresponding to the AI server to complete computation and output diagnosis data.
7. The DICOM IP port differentiated AI module function management system of claim 4, wherein the diagnostic data includes measurements and key images.
CN202111092649.7A 2021-09-17 2021-09-17 Method and system for distinguishing AI module function management based on DICOM IP port Pending CN114039995A (en)

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Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140278530A1 (en) * 2013-03-15 2014-09-18 WISC Image (MD) LLC Associating received medical imaging data to stored medical imaging data
CN111404833A (en) * 2020-02-28 2020-07-10 华为技术有限公司 Data stream type recognition model updating method and related equipment
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