CN111276219A - Medical imaging processing method, device, equipment and medium - Google Patents
Medical imaging processing method, device, equipment and medium Download PDFInfo
- Publication number
- CN111276219A CN111276219A CN202010046517.XA CN202010046517A CN111276219A CN 111276219 A CN111276219 A CN 111276219A CN 202010046517 A CN202010046517 A CN 202010046517A CN 111276219 A CN111276219 A CN 111276219A
- Authority
- CN
- China
- Prior art keywords
- processing
- item
- protocol parameters
- processing item
- plug
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H30/00—ICT specially adapted for the handling or processing of medical images
- G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/445—Program loading or initiating
- G06F9/44521—Dynamic linking or loading; Link editing at or after load time, e.g. Java class loading
- G06F9/44526—Plug-ins; Add-ons
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
Landscapes
- Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Theoretical Computer Science (AREA)
- Medical Informatics (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Radiology & Medical Imaging (AREA)
- Data Mining & Analysis (AREA)
- Computing Systems (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Evolutionary Computation (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Public Health (AREA)
- Apparatus For Radiation Diagnosis (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
The embodiment of the invention discloses a medical imaging processing method, a medical imaging processing device, medical imaging processing equipment and a medical imaging processing medium. The method comprises the following steps: acquiring imaging processing protocol parameters, wherein the imaging processing protocol parameters comprise scanning protocol parameters and processing item protocol parameters; acquiring image data according to the scanning protocol parameters; and calling the corresponding processing item plug-in according to the scanning processing item protocol parameters to process the image data to obtain a processing result, so that the problem that the processing result cannot be completely consistent due to the fact that the processing flow of image post-processing and the processing flow of image processing by using built-in image processing software are usually inconsistent is solved, and the effects of reducing research and development cost and ensuring strict consistency of the processing effect are achieved.
Description
Technical Field
Embodiments of the present invention relate to medical image processing technologies, and in particular, to a medical imaging processing method, apparatus, device, and medium.
Background
With the continuous development of Artificial Intelligence (AI), it is increasingly applied to medical image processing such as image noise reduction, image enhancement or image artifact removal.
The method comprises the steps of carrying out scanning imaging through preset scanning parameters, carrying out simple image processing by using image processing software built in imaging equipment, and then operating corresponding intelligent processing item plug-ins according to needs to carry out image post-processing.
Disclosure of Invention
The embodiment of the invention provides a medical imaging processing method, a medical imaging processing device, medical imaging processing equipment and a medical imaging processing medium, so that automation of image scanning and image intelligent processing is realized, and the phenomenon of inconsistent processing results caused by manually setting parameters is avoided.
In a first aspect, an embodiment of the present invention provides a medical imaging processing method, including:
acquiring imaging processing protocol parameters, wherein the imaging processing protocol parameters comprise scanning protocol parameters and processing item protocol parameters, and the scanning protocol parameters are generated according to the designated intelligent processing item;
acquiring image data according to the scanning protocol parameters;
and operating corresponding intelligent calling corresponding processing item plug-ins according to the scanning processing item protocol parameters, and processing the image data to obtain a processing result.
In a second aspect, an embodiment of the present invention further provides a medical imaging processing apparatus, including:
the scanning protocol parameter generating and acquiring module is used for acquiring imaging processing protocol parameters, wherein the imaging processing protocol parameters comprise scanning protocol parameters and intelligent processing item generating scanning protocol parameters appointed by processing item protocol parameter data;
the image data acquisition module is used for acquiring image data according to the scanning protocol parameters;
and the image data processing module is used for calling a corresponding intelligent processing item plug-in unit which operates correspondingly according to the scanning processing item protocol parameter and processing the image data to obtain a processing result.
In a third aspect, an embodiment of the present invention further provides an apparatus, where the apparatus includes:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a medical imaging processing method as provided by any of the embodiments of the invention.
In a fourth aspect, embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the program, when executed by a processor, implements a medical imaging processing method as provided by any of the embodiments of the present invention.
The method comprises the steps of obtaining imaging processing protocol parameters, wherein the imaging processing protocol parameters comprise scanning protocol parameters and processing item protocol parameters, and the scanning protocol parameters are generated according to specified intelligent processing items;
acquiring image data according to the scanning protocol parameters;
and according to the scanning processing item protocol parameters, operating corresponding intelligent calling corresponding processing item plug-ins, processing the image data to obtain a processing result, solving the problem that the processing result cannot be completely consistent due to the fact that the processing flow of image post-processing and the processing flow of image processing by using built-in image processing software are usually inconsistent, and achieving the effects of reducing research and development costs and ensuring strict consistency of the processing result.
Drawings
FIG. 1 is a flow chart of a medical imaging processing method according to one embodiment of the invention;
FIG. 2 is a flow chart of a medical imaging processing method according to a second embodiment of the invention;
FIG. 3 is a block diagram of a medical imaging processing apparatus in a third embodiment of the invention;
fig. 4 is a schematic structural diagram of an apparatus in a third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a medical imaging processing method according to an embodiment of the present invention, where the embodiment is applicable to medical imaging and image processing, and the method may be executed by a medical imaging processing apparatus, and specifically includes the following steps:
s110, acquiring imaging processing protocol parameters, wherein the imaging processing protocol parameters comprise scanning protocol parameters and processing item protocol parameters.
Common medical images include: b-mode ultrasound images, color doppler ultrasound images, magnetic resonance images, computed tomography images, X-ray images, positron emission computed tomography images, multi-modal medical images, and the like. Before medical image scanning, imaging processing protocol parameters are required to be set, and the imaging processing protocol parameters comprise scanning protocol parameters and processing item protocol parameters. According to the acquisition principle of different medical images, different scanning protocol parameters are set. For example, when performing a magnetic resonance image scan, the scan protocol parameters that need to be set include, but are not limited to: repetition time, echo time, receive bandwidth, echo chain length, imaging field of view, frequency and phase encoding matrix, etc.
The processing item protocol parameters include: one or more of AI image processing, AI-assisted diagnostic reporting, and AI image quality assessment. AI image processing includes, but is not limited to, image processing items such as image noise reduction enhancement, image segmentation, registration, etc. that may improve the imaging effect of the image; the image intelligent diagnosis report comprises a report in the form of text, curve or chart generated by data measurement and automatic analysis thereof. The image quality evaluation refers to an evaluation result of evaluating whether the coverage area, the contrast, the artifact degree and the like of a group of images accord with the quality control standard or not according to the quality control standard. The image data to be processed and the name of the processing item need to be input when setting the protocol parameter of the processing item.
The acquiring of the imaging processing protocol parameters may be a scanning protocol parameter setting area for displaying the medical image on the device display interface and selectable items of each processing item, and the scanning protocol parameters and the processing item protocol parameters set by the user are determined by detecting a click operation of the user on the device display interface.
In some embodiments, the imaging processing protocol parameters may be acquired, or the medical image to be acquired and the shooting object of the medical image may be input into a preset learning model, the scanning protocol parameters and the processing item protocol parameters are generated and displayed through an equipment display interface, wherein an equipment operation user may edit the displayed scanning protocol parameters and the displayed processing item protocol parameters such as addition, deletion, and modification.
Before image data is acquired, the protocol parameters of the processing item are set together while the scanning protocol parameters are set, so that the problem of inconsistent processing results caused by more human intervention between the scanning image and the image post-processing is avoided, the medical image processing efficiency is improved, and the research and development cost is reduced.
And S120, acquiring image data according to the scanning protocol parameters.
After the scanning protocol parameters of the medical image are set, an imaging instruction is generated based on the scanning protocol parameters, the imaging instruction is sent to imaging equipment, and the imaging equipment acquires the medical image according to the scanning protocol parameters to obtain image data.
And S130, calling a corresponding processing item plug-in according to the processing item protocol parameter, and processing the image data to obtain a processing result.
The processing item plug-in is a functional module for completing one or more processing items, and may be set in the local device, a preset server, or a cloud. After the image data is acquired, corresponding processing item plug-ins are respectively called based on at least one processing item in the processing item protocol parameters. Optionally, the calling processing item plugin may send the processing item identifier (for example, the processing item name or the code) to the device in which the processing item plugin is stored, so that the device performs matching based on the processing item identifier, determines the processing item plugin corresponding to the processing item identifier, and feeds back the matching obtained processing item plugin. The device storing the processing item plug-in comprises a mapping relation between the processing item identification and the processing item plug-in.
Optionally, invoking a corresponding processing item plug-in according to the processing item protocol parameter includes: when the protocol parameters of the processing items comprise at least two processing items, determining the front-back dependency relationship between different processing items, and calling corresponding processing item plug-ins in sequence according to the front-back dependency relationship between the processing items. Illustratively, the front-back dependency between the processing items is that the processing item B needs to be executed according to the processing result of the processing item a after the processing item a is processed, the processing item plug-in corresponding to the processing item a is called first, and when the processing result of the processing item a is obtained, the calling of the processing item plug-in corresponding to the processing item B is triggered. When the front and back dependency relationship does not exist between different processing items, the processing item plug-ins corresponding to a plurality of processing items can be called in parallel, so that the processing efficiency is improved. Illustratively, the processing items comprise an image intelligent diagnosis report and an image quality evaluation, and the two processing items do not have a dependency relationship and can call corresponding processing item plug-ins in parallel.
Optionally, invoking the corresponding processing item plug-in includes: and calling and loading processing item plug-ins corresponding to the processing items based on a preset framework, wherein the processing item plug-ins are machine learning models. When the acquired image data is processed, processing item protocol parameters are set according to user requirements, and a corresponding processing item plug-in is automatically called by a system according to the processing item protocol parameters to process the image data. A fixed framework is preset in the system, the framework is a program code product developed for solving one or a class of problems, and a framework user generally only needs to use the class or the function provided by the framework to realize all functions. The frame can be multiplexed, and multiple times of calling of processing items can be realized. When the processing item plug-in is called, all the processing items are loaded by using the same framework.
And the processing item plug-in is a machine learning model, the image data is used as input, the processing result meeting the processing requirement is used as output to be trained to obtain the machine learning model, and the trained machine learning model is called to process the image data needing to be processed.
In this embodiment, the image data is processed, and the obtained processing result is a DICOM (Digital imaging and Communications in Medicine) image or a DICOM structured report. The content of DICOM images or DICOM structured reports includes: the scanning protocol parameters of the medical image, the processing item protocol parameters, the processing result obtained by processing the image data by the calling processing item plug-in unit, and the like. The processing result is transmitted to a target information system (such as an information system of a hospital), so that a user can check or modify and edit the processing result in the system, the processing result is more conveniently and rapidly applied to a diagnosis report, and the convenience and efficiency of clinical diagnosis work are improved.
According to the technical scheme of the embodiment, the imaging processing protocol parameters are acquired, wherein the imaging processing protocol parameters comprise scanning protocol parameters and processing item protocol parameters, and the scanning protocol parameters are generated according to the designated intelligent processing items; acquiring image data according to the scanning protocol parameters; according to the scanning processing item protocol parameters, corresponding intelligent calling corresponding processing item plug-ins are operated to process the image data to obtain a processing result, the problem that the processing result cannot be completely consistent due to the fact that the processing flow of image post-processing and the processing flow of image processing by using built-in image processing software are usually inconsistent is solved, and the effects of reducing research and development costs and ensuring strict consistency of the processing effect are achieved.
Example two
Fig. 2 is a flowchart of a medical imaging processing method according to a second embodiment of the present invention, which is further optimized based on the second embodiment, the processing result is detected, when the processing result does not meet the processing requirement, the processing item plugin is retrained, the image data is processed again by using the processing item plugin obtained by retraining, a new processing result is obtained, and the processing precision of the processing item plugin obtained by retraining is verified, so that the processing result can meet the processing requirement. As shown in fig. 2, the method specifically includes:
s210, acquiring imaging processing protocol parameters, wherein the imaging processing protocol parameters comprise scanning protocol parameters and processing item protocol parameters.
And S220, acquiring image data according to the scanning protocol parameters.
And S230, calling a corresponding processing item plug-in according to the processing item protocol parameter, and processing the image data to obtain a processing result.
And S240, detecting the processing result according to the processing requirement of the processing item.
Each processing item has its own specific processing requirements, which may vary from item to item. Illustratively, for the AI image processing item, the processing requirement is to improve the imaging effect, such as making the image clearer than before through noise reduction enhancement; through image segmentation, the target area can be clearly and visually observed, and the influence of other areas is eliminated. The processing requirements for the AI-assisted diagnostic report processing items include: whether the text, curve or diagram is accurately generated, whether the obtained diagnosis report is complete, and the like. The processing requirements for AI image quality assessment include: and whether the image quality is correctly and reasonably evaluated according to the quality control standard.
And S250, when the processing result does not meet the processing requirement, triggering retraining of the processing item plug-in, and processing the image data again based on the processing item plug-in obtained by retraining to obtain a new processing result.
The reason why the processing result does not meet the processing requirement may be that the processing precision of the processing item plug-in is poor, and at this time, the processing item plug-in needs to be retrained, so that the processing capability is improved, and the processing result of the processing item plug-in on the image data meets the processing requirement.
Optionally, triggering retraining of the processing item plug-in includes: training the processing item plug-in based on local sample data and the mark of the sample data. And calling sample data stored locally by the equipment according to the processing item of which the processing result does not meet the processing requirement, wherein the sample data is used for training the processing item plug-in, and the sample data is provided with a mark, and retraining the processing item plug-in based on the sample data and the mark.
Optionally, triggering retraining of the processing item plug-in includes: and sending a training trigger instruction of the processing item plug-in and the marked sample data to a cloud server in which the processing item plug-in is stored, so that the cloud server retrains the processing item plug-in and recalls the trained processing item plug-in. And sending processing item training information to the equipment in which the processing item plug-in is stored so that the equipment in which the processing item plug-in is stored retrains the processing item plug-in according to the stored sample data. And deleting the called processing item plug-in, recalling the retrained processing item plug-in, and processing the image data based on the recalled processing item plug-in to obtain a new processing result.
Optionally, the processing item plug-ins are trained locally based on the sample data of the multiple devices and the marks of the sample data, and new processing item plug-ins are generated; and sending the new processing item plug-ins to the cloud server for comprehensive collection according to the weight of the training data set data, and generating new processing item plug-ins for distribution and updating to each image device. The rendering device will then use the new processing item plug-in for processing of the image data.
Before the image data is reprocessed based on the retrained processing item plug-in, and a new processing result is obtained, the method further comprises: and verifying the processing precision of the processing item plug-in obtained by retraining.
And verifying the processing item model obtained after retraining the processing item of which the processing result does not meet the processing requirement, and judging whether the processing result obtained by processing the image data by the new processing item model meets the processing requirement or not. If the processing requirements are met, calling a new processing item model to process the image data; if the processing result of the new processing item model still does not meet the processing requirement, the processing item model is retrained until the processing item model with the processing result meeting the processing requirement is obtained.
According to the technical scheme, the processing result is detected, when the processing result does not meet the processing requirement, the processing item plug-in is retrained, the image data is processed again by the processing item plug-in obtained by retraining to obtain a new processing result, the processing precision of the processing item plug-in obtained by retraining is verified, the processing result can meet the processing requirement, and the accuracy and the efficiency of processing the graphic data are improved.
EXAMPLE III
Fig. 3 is a structural diagram of a medical imaging processing apparatus according to a third embodiment of the present invention, where the apparatus includes: a protocol parameter acquisition module 310, an image data acquisition module 320, and an image data processing module 330.
The protocol parameter acquiring module 310 is configured to acquire imaging processing protocol parameters, where the imaging processing protocol parameters include a scanning protocol parameter and a processing item protocol parameter; an image data obtaining module 320, configured to obtain image data according to the scan protocol parameter; and the image data processing module 330 is configured to call a corresponding processing item plugin according to the processing item protocol parameter to obtain a processing result.
Optionally, the processed result comprises a DICOM image or a DICOM structured report.
Optionally, the processing item protocol parameters include: one or more of AI image processing, AI-assisted diagnostic reporting, and AI image quality assessment.
In the above embodiment, the image data processing module 330 includes:
and the processing item plug-in calling unit is used for calling and loading the processing item plug-ins corresponding to the processing items based on a preset framework, wherein the processing item plug-ins are machine learning models.
In the above embodiment, the medical imaging processing apparatus further includes:
the processing result detection module is used for detecting the processing result according to the processing requirement of the processing item;
and the processing item plug-in training module is used for triggering retraining of the processing item plug-in when the processing result does not meet the processing requirement, and processing the image data again based on the processing item plug-in obtained by retraining to obtain a new processing result.
In the embodiment, the processing item plug-in training module includes:
and the local training unit is used for training the processing item plug-in based on local sample data and the mark of the sample data.
Optionally, the processing item plug-in training module further includes:
the server training unit is used for sending a training trigger instruction of the processing item plug-in to a cloud server in which the processing item plug-in is stored, so that the cloud server retrains the processing item plug-in and recalls the trained processing item plug-in.
In the above embodiment, the image data processing module 330 further includes:
and the processing precision verification module is used for verifying the processing precision of the processing item plug-in obtained by retraining.
According to the technical scheme of the embodiment of the invention, the imaging processing protocol parameters are obtained through a protocol parameter obtaining module, wherein the imaging processing protocol parameters comprise scanning protocol parameters and processing item protocol parameters; the image data acquisition module acquires image data according to the scanning protocol parameters; and the image data processing module calls the corresponding processing item plug-in according to the processing item protocol parameters to obtain a processing result. The problem that the processing result cannot be completely consistent due to the fact that the processing flow of image post-processing and the processing flow of image processing by using built-in image processing software are usually inconsistent is solved, and the effects of reducing research and development cost and ensuring strict and consistent processing effect are achieved.
The medical imaging processing device provided by the embodiment of the invention can execute the medical imaging processing method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Example four
Fig. 4 is a schematic structural diagram of an apparatus according to embodiment 4 of the present invention, as shown in fig. 4, the apparatus includes a processor 410, a memory 420, an input device 430, and an output device 440; the number of the processors 410 in the device may be one or more, and one processor 410 is taken as an example in fig. 4; the processor 410, the memory 420, the input device 430 and the output device 440 in the apparatus may be connected by a bus or other means, for example, in fig. 4.
The memory 420 serves as a computer-readable storage medium for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the medical imaging processing method in the embodiment of the present invention (for example, the protocol parameter acquisition module 310, the image data acquisition module 320, and the image data processing module 330 in the medical imaging processing apparatus). The processor 410 executes various functional applications of the device and data processing by executing software programs, instructions and modules stored in the memory 420, namely, implements the medical imaging processing method described above.
The memory 420 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 420 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, memory 420 may further include memory located remotely from processor 410, which may be connected to devices through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input means 430 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the apparatus. The output device 440 may include a display device such as a display screen.
EXAMPLE five
An embodiment of the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, perform a medical imaging processing method, the method including:
acquiring imaging processing protocol parameters, wherein the imaging processing protocol parameters comprise scanning protocol parameters and processing item protocol parameters;
acquiring image data according to the scanning protocol parameters;
and calling a corresponding processing item plug-in according to the processing item protocol parameter, and processing the image data to obtain a processing result.
Of course, the storage medium provided by the embodiment of the present invention contains computer-executable instructions, and the computer-executable instructions are not limited to the method operations described above, and may also execute the relevant operations in the medical imaging processing method provided by any embodiment of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
It should be noted that, in the embodiment of the medical imaging processing apparatus, the units and modules included in the embodiment are merely divided according to the functional logic, but are not limited to the above division as long as the corresponding functions can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A medical imaging processing method, comprising:
acquiring imaging processing protocol parameters, wherein the imaging processing protocol parameters comprise scanning protocol parameters and processing item protocol parameters;
acquiring image data according to the scanning protocol parameters;
and calling a corresponding processing item plug-in according to the processing item protocol parameter, and processing the image data to obtain a processing result.
2. The medical imaging processing method of claim 1, wherein the processing result comprises a DICOM image or a DICOM structured report.
3. The medical imaging processing method of claim 1, wherein the processing item protocol parameters include: one or more of AI image processing, AI-assisted diagnostic reporting, and AI image quality assessment.
4. The medical imaging processing method of claim 1, wherein the invoking the corresponding processing item plug-in comprises:
and calling and loading a processing item plug-in corresponding to each processing item based on a preset framework, wherein the processing item plug-in is a machine learning model.
5. The medical imaging processing method according to claim 1, further comprising, after said processing the image data to obtain a processing result:
detecting the processing result according to the processing requirement of the processing item;
and when the processing result does not meet the processing requirement, triggering retraining of the processing item plug-in, and processing the image data again based on the processing item plug-in obtained by retraining to obtain a new processing result.
6. The medical imaging processing method of claim 5, wherein the triggering retraining of the processing item plugin comprises:
training the processing item plug-in based on sample data and the mark of the sample data; alternatively, the first and second electrodes may be,
and sending a training trigger instruction of the processing item plug-in and the marked sample data to a cloud server in which the processing item plug-in is stored, so that the cloud server retrains the processing item plug-in and recalls the trained processing item plug-in.
7. The medical imaging processing method of claim 5, wherein the image data is reprocessed based on the retrained processing item plugin to obtain a new processing result, further comprising:
and verifying the processing precision of the processing item plug-in obtained by retraining.
8. A medical imaging processing apparatus, comprising:
the device comprises a protocol parameter acquisition module, a processing item protocol acquisition module and a processing item processing module, wherein the protocol parameter acquisition module is used for acquiring imaging processing protocol parameters, and the imaging processing protocol parameters comprise scanning protocol parameters and processing item protocol parameters;
the image data acquisition module is used for acquiring image data according to the scanning protocol parameters;
and the image data processing module is used for calling the corresponding processing item plug-in according to the processing item protocol parameters to obtain a processing result.
9. An apparatus, characterized in that the apparatus comprises:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the medical imaging processing method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a medical imaging processing method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010046517.XA CN111276219A (en) | 2020-01-16 | 2020-01-16 | Medical imaging processing method, device, equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010046517.XA CN111276219A (en) | 2020-01-16 | 2020-01-16 | Medical imaging processing method, device, equipment and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111276219A true CN111276219A (en) | 2020-06-12 |
Family
ID=71001648
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010046517.XA Pending CN111276219A (en) | 2020-01-16 | 2020-01-16 | Medical imaging processing method, device, equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111276219A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111863206A (en) * | 2020-07-24 | 2020-10-30 | 上海联影医疗科技有限公司 | Image preprocessing method, device, equipment and storage medium |
| CN114052793A (en) * | 2021-09-18 | 2022-02-18 | 武汉联影医疗科技有限公司 | Medical equipment assisting method and device and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070237380A1 (en) * | 2006-04-06 | 2007-10-11 | Terarecon, Inc. | Three-dimensional medical image display device equipped with pre-processing system implementing clinical protocol |
| US20140348401A1 (en) * | 2013-05-22 | 2014-11-27 | Kabushiki Kaisha Toshiba | Image processing apparatus, medical imaging device and image processing method |
| US20160364631A1 (en) * | 2015-06-12 | 2016-12-15 | Merge Healthcare Incorporated | Methods and Systems for Automatically Analyzing Clinical Images Using Rules and Image Analytics |
| CN108537774A (en) * | 2017-03-01 | 2018-09-14 | 西门子医疗有限公司 | Method for checking medical image and the method for adjusting inspection agreement |
| CN109690554A (en) * | 2016-07-21 | 2019-04-26 | 西门子保健有限责任公司 | Method and system for the medical image segmentation based on artificial intelligence |
| CN110379492A (en) * | 2019-07-24 | 2019-10-25 | 复旦大学附属中山医院青浦分院 | A kind of completely new AI+PACS system and its audit report construction method |
| CN110458817A (en) * | 2019-08-05 | 2019-11-15 | 上海联影医疗科技有限公司 | Qualitative forecasting method, device, equipment and the storage medium of medical image |
| CN110569083A (en) * | 2019-08-07 | 2019-12-13 | 上海联影智能医疗科技有限公司 | Image segmentation processing method and device, computer equipment and storage medium |
-
2020
- 2020-01-16 CN CN202010046517.XA patent/CN111276219A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070237380A1 (en) * | 2006-04-06 | 2007-10-11 | Terarecon, Inc. | Three-dimensional medical image display device equipped with pre-processing system implementing clinical protocol |
| US20140348401A1 (en) * | 2013-05-22 | 2014-11-27 | Kabushiki Kaisha Toshiba | Image processing apparatus, medical imaging device and image processing method |
| US20160364631A1 (en) * | 2015-06-12 | 2016-12-15 | Merge Healthcare Incorporated | Methods and Systems for Automatically Analyzing Clinical Images Using Rules and Image Analytics |
| CN109690554A (en) * | 2016-07-21 | 2019-04-26 | 西门子保健有限责任公司 | Method and system for the medical image segmentation based on artificial intelligence |
| CN108537774A (en) * | 2017-03-01 | 2018-09-14 | 西门子医疗有限公司 | Method for checking medical image and the method for adjusting inspection agreement |
| CN110379492A (en) * | 2019-07-24 | 2019-10-25 | 复旦大学附属中山医院青浦分院 | A kind of completely new AI+PACS system and its audit report construction method |
| CN110458817A (en) * | 2019-08-05 | 2019-11-15 | 上海联影医疗科技有限公司 | Qualitative forecasting method, device, equipment and the storage medium of medical image |
| CN110569083A (en) * | 2019-08-07 | 2019-12-13 | 上海联影智能医疗科技有限公司 | Image segmentation processing method and device, computer equipment and storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111863206A (en) * | 2020-07-24 | 2020-10-30 | 上海联影医疗科技有限公司 | Image preprocessing method, device, equipment and storage medium |
| CN114052793A (en) * | 2021-09-18 | 2022-02-18 | 武汉联影医疗科技有限公司 | Medical equipment assisting method and device and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10810735B2 (en) | Method and apparatus for analyzing medical image | |
| CN110428415B (en) | Medical image quality evaluation method, device, equipment and storage medium | |
| US11797846B2 (en) | Learning assistance device, method of operating learning assistance device, learning assistance program, learning assistance system, and terminal device | |
| JP2018183583A (en) | System and method for cloud type medical image analysis | |
| CN111862044A (en) | Ultrasonic image processing method and device, computer equipment and storage medium | |
| CN110458817A (en) | Qualitative forecasting method, device, equipment and the storage medium of medical image | |
| CN111276219A (en) | Medical imaging processing method, device, equipment and medium | |
| CN109102501B (en) | Joint image processing method and image processing equipment | |
| JP2002253552A (en) | Method and device for connecting image and report in remote browsing station | |
| CN111882559B (en) | ECG signal acquisition method and device, storage medium and electronic device | |
| CN113658175A (en) | Method and device for determining symptom data | |
| CN112185522A (en) | Information processing method and device and terminal | |
| CN112614570A (en) | Sample set labeling method, pathological image classification method and classification model construction method and device | |
| US6679848B2 (en) | Method for allowing plug-in architecture for digital echocardiography lab image processing applications | |
| CN111951316A (en) | Image quantization method and storage medium | |
| CN108511052B (en) | Method for determining a projection data set and projection determination system | |
| CN111144506A (en) | Ultrasound image-based hydatid identification method, storage medium and ultrasound equipment | |
| CN115861283A (en) | Medical image analysis method, device, equipment and storage medium | |
| CN113467867B (en) | Information processing method, information processing device, electronic equipment and storage medium | |
| CN113568735B (en) | Data processing method and system | |
| CN112006713B (en) | Dynamic reconstruction, scan object positioning, count rate display method and PET scanning device | |
| WO2019058963A1 (en) | Medical image processing device, medical image processing method, and processing program used for same | |
| US20090009512A1 (en) | Imaging system | |
| JP7387457B2 (en) | Medical image processing devices, programs, and medical image processing systems | |
| CN114782364A (en) | Image detection method, device and system and detection equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Address after: 201807 Shanghai City, north of the city of Jiading District Road No. 2258 Applicant after: Shanghai Lianying Medical Technology Co., Ltd Address before: 201807 Shanghai City, north of the city of Jiading District Road No. 2258 Applicant before: SHANGHAI UNITED IMAGING HEALTHCARE Co.,Ltd. |
|
| CB02 | Change of applicant information |