CN113889236B - Medical image processing method and device and computer readable storage medium - Google Patents
Medical image processing method and device and computer readable storage medium Download PDFInfo
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- CN113889236B CN113889236B CN202111172866.7A CN202111172866A CN113889236B CN 113889236 B CN113889236 B CN 113889236B CN 202111172866 A CN202111172866 A CN 202111172866A CN 113889236 B CN113889236 B CN 113889236B
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- G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
Abstract
The invention discloses a method and a device for processing medical images in a structured report and a computer readable storage medium, wherein the method comprises the following steps: and acquiring the medical image, the tissue corresponding to the medical image and the tissue description information corresponding to the tissue from the structured report. And establishing an association relation between the medical image and the tissue description information according to the tissue. And when the preset operation on the tissue description information is detected, acquiring a target medical image corresponding to the tissue description information from the medical image according to the association relation. And displaying the target medical image at the terminal. According to the scheme, the incidence relation between the medical image and the tissue description information is established in advance according to the tissue, and after the preset operation on the tissue description information is detected, the target medical image corresponding to the tissue description information is obtained and displayed from the medical image according to the incidence relation, so that the medical image processing efficiency is improved.
Description
Technical Field
The present invention relates to the field of image processing technologies, and in particular, to a method and an apparatus for processing a medical image, and a computer-readable storage medium.
Background
The information science and the computer technology are comprehensively applied to the management of medical and medical health careers in the informatization of the hospital, the traditional medical health care industry is technically improved, and the medical modernization is realized. The Structured Reporting (SR) can support traditional free documents and Structured information, and improve the functions of accuracy, classification, and recording of numerical values in medical documents. In addition, the SR also eliminates the disadvantage of separating the image and the image related data information, and can generate a highly normative and standard pictorial and luxurious inspection report.
When a doctor reads the structured report, the doctor needs to find the corresponding medical image according to the description in the text information, and the process is complicated.
Disclosure of Invention
The embodiment of the invention provides a medical image processing method and device and a computer readable storage medium, which can improve the medical image processing efficiency.
In a first aspect, an embodiment of the present invention provides a method for processing a medical image, including:
acquiring a medical image, a tissue corresponding to the medical image and tissue description information corresponding to the tissue from a structured report;
establishing an association relation between the medical image and the tissue description information according to the tissue;
when the preset operation on the tissue description information is detected, acquiring a target medical image corresponding to the tissue description information from the medical image according to the association relation;
and displaying the target medical image at a terminal.
In a second aspect, an embodiment of the present invention provides a medical image processing apparatus, which includes:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a medical image, a tissue corresponding to the medical image and tissue description information corresponding to the tissue from a structured report;
the establishing module is used for establishing an incidence relation between the medical image and the tissue description information according to the tissue;
the second acquisition module is used for acquiring a target medical image corresponding to the tissue description information from the medical images according to the association relationship after the preset operation on the tissue description information is detected;
and the display module is used for displaying the target medical image at a terminal.
In one embodiment, the establishing module comprises:
the first establishing submodule is used for establishing a first incidence relation between the tissue and the medical image and establishing a second incidence relation between the tissue and the tissue description information;
and the second establishing submodule is used for establishing the association relationship between the medical image and the organization description information according to the first association relationship and the second association relationship.
In one embodiment, the first establishing sub-module is configured to:
determining whether the medical image includes the tissue;
when the medical image includes the tissue, a first association of the tissue and the medical image is established.
In one embodiment, the first establishing sub-module is configured to:
acquiring an original medical sequence corresponding to the tissue and position information of the tissue;
and acquiring a target medical sequence corresponding to the position information of the tissue from the original medical sequence, setting the medical sequence as the medical image, and obtaining the first association relation between the tissue and the medical image.
In one embodiment, the organization description information includes diagnostic information, and the first establishing sub-module is configured to:
acquiring a diagnosis object corresponding to the diagnosis information;
when the diagnosis object is the tissue, establishing the second association relationship between the tissue and the tissue description information.
In one embodiment, the display module comprises:
the image type acquisition sub-module is used for acquiring the image type of the target medical image;
and the image display submodule is used for determining a display area corresponding to the image type in the terminal and displaying the target medical image in the display area.
In one embodiment, the tissue description information includes lesion identification, and the presentation module includes:
a focus area obtaining submodule, configured to obtain a focus area corresponding to the focus identifier from the target medical image;
and the amplification display sub-module is used for amplifying and displaying the focus area.
In an embodiment, the preset operation includes a selection operation, and the second obtaining module includes:
the identification acquisition submodule is used for acquiring a target lesion identification meeting a preset lesion degree from the plurality of lesion identifications when the selection operation of the plurality of lesion identifications is detected;
and the medical image acquisition sub-module is used for acquiring a target medical image corresponding to the target lesion mark from the medical image.
In a third aspect, the embodiment of the present invention further provides a computer-readable storage medium, in which processor-executable instructions are stored, and the processor provides the medical image processing method as described above by executing the instructions.
According to the medical image processing method, the medical image processing device and the computer-readable storage medium, the association relationship between the medical image and the tissue description information is pre-established according to the tissue, and after the preset operation on the tissue description information is detected, the target medical image corresponding to the tissue description information is acquired from the medical image and displayed according to the association relationship, so that the medical image processing efficiency is improved.
Drawings
The technical solution and other advantages of the present invention will become apparent from the following detailed description of specific embodiments of the present invention, which is to be read in connection with the accompanying drawings.
Fig. 1 is a schematic view of a scene of a medical image processing system according to an embodiment of the present invention.
Fig. 2 is a flowchart illustrating a medical image processing method according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of an example of a structured report provided by an embodiment of the present invention.
Fig. 4 is a display diagram of an interface of a terminal according to an embodiment of the present invention.
Fig. 5 is a schematic structural diagram of processing of a medical image according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention relates to the technical field of Artificial Intelligence (AI). Artificial intelligence is a theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technique of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence. Artificial intelligence is the research of the design principle and the realization method of various intelligent machines, so that the machines have the functions of perception, reasoning and decision making.
The artificial intelligence technology is a comprehensive subject and relates to the field of extensive technology, namely the technology of a hardware level and the technology of a software level. The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
Embodiments of the present invention also relate to Machine Learning (ML). Machine learning is a multi-field cross discipline, and relates to a plurality of disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and the like. The special research on how a computer simulates or realizes the learning behavior of human beings so as to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve the performance of the computer. Machine learning is the core of artificial intelligence, is the fundamental approach for computers to have intelligence, and is applied to all fields of artificial intelligence. Machine learning and deep learning generally include techniques such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and formal education learning.
Embodiments of the present invention provide a system, a method, an apparatus, a server, a terminal, and a computer-readable storage medium for processing a medical image, which will be described in detail below.
Referring to fig. 1, fig. 1 is a schematic view of a scene of a medical image processing system according to an embodiment of the present invention. The system may include a user side device and a service side device, and the user side device and the service side device are connected through the internet formed by various gateways and the like, which are not described in detail. The user side device includes a terminal 11, and the service side device includes a server 12.
The terminal 11 includes, but is not limited to, a portable terminal such as a mobile phone or a tablet, a fixed terminal such as a computer, a query machine or an advertisement machine, and various virtual terminals. The server 12 includes a local server and/or a remote server, etc.
In advance, the server 12 obtains the medical image, the tissue corresponding to the medical image, and the tissue description information corresponding to the tissue from the structured report, and then establishes an association relationship between the medical image and the tissue description information according to the tissue for storage. After the preset operation on the organization description information is detected through the terminal 11, the target medical image corresponding to the organization description information is obtained from the medical image according to the association relation, finally the target medical image is sent to the terminal 11, and the target medical image is displayed on the interface of the terminal 11.
In the embodiment of the present invention, description will be made from the viewpoint of a processing apparatus of a medical image, which may be specifically integrated in a server.
In the prior art, when generating a graphic report, a single function button is usually adopted, and different information and images are correspondingly displayed by clicking the function button, so that the method is complicated and the operation amount is large. In addition, when the structured report needs to be displayed, the structured report needs to be generated according to the text information and the image and the preset rule, and the process is repeated and complicated.
The scheme aims to provide a medical image processing method, which acquires a medical image, a tissue corresponding to the medical image and tissue description information corresponding to the tissue from a structured report. And establishing an association relation between the medical image and the tissue description information according to the tissue. And when the preset operation on the tissue description information is detected, acquiring a target medical image corresponding to the tissue description information from the medical image according to the association relation. And displaying the target medical image at the terminal.
Referring to fig. 2, fig. 2 is a flowchart illustrating a medical image processing method according to an embodiment of the present invention. The method can comprise the following steps:
step S101, acquiring a medical image, a tissue corresponding to the medical image and tissue description information corresponding to the tissue from the structured report.
In which medical images are obtained of tissue in a non-invasive manner. The tissue includes not only normal tissues such as normal blood vessels, lungs, liver, and the like, but also diseased tissues such as hemangiomas, tuberculosis lesions, and the like. Thus, normal tissue and lesion tissue, such as lesion markings, lesion areas, etc., are included in the medical image. The type of the medical image may be a VR (Virtual Reality) image, a CPR (curved reconstruction) image, a probe image, a straightened image, or the like, and the type of the medical image is not specifically limited herein.
The tissue description information is used to describe the tissue in the medical image, including identification and properties of the tissue, such as position information, size information, etc. of the tissue.
The structured report in step S101 of the present scheme is a structured report of textual description information and/or image information for a physiological tissue in a medical image, which is constructed through an AI diagnosis result and a structured report template, and the structured report can be displayed on a user terminal, and part of the content in the structured report can be modified by a doctor. The AI diagnostic result may include, among others: basic information of the patient, information of the part to be diagnosed, and information of the diagnosis of the disease condition. The structured report template adopts a text typesetting and/or image display template supporting personalized setting.
Step S102, a first incidence relation between the tissue and the medical image is established, and a second incidence relation between the tissue and the tissue description information is established.
In one embodiment, the step of establishing a first association of the tissue and the medical image comprises: it is determined whether the medical image includes tissue. When the medical image includes tissue, a first association of the tissue and the medical image is established.
In one embodiment, the step of establishing a first association of the tissue and the medical image comprises: and acquiring an original medical sequence corresponding to the tissue and position information of the tissue. And acquiring a target medical sequence corresponding to the position information of the tissue from the original medical sequence, and setting the medical sequence as a medical image to obtain a first association relation between the tissue and the medical image.
Specifically, a tissue will be described as an example of a blood vessel. Firstly, an original medical sequence corresponding to a blood vessel is obtained, a blood vessel central line is extracted from the original medical sequence, a target medical sequence is obtained according to the original image sequence and the blood vessel central line, the target medical sequence is set as a medical image, and a first association relation between the blood vessel and the medical image is obtained.
In one embodiment, the tissue description information includes diagnostic information, and the step of establishing a second association between the tissue and the tissue description information includes: and acquiring a diagnosis object corresponding to the diagnosis information. And when the diagnosis object is a tissue, establishing a second association relation between the tissue and the tissue description information.
For example, when the diagnostic information included in the tissue-describing information a is "the right coronary artery originates from the right sinus. Calcified plaque, severe stenosis of lumen and muscle bridge are visible on the proximal wall of the Right Coronary Artery (RCA). When no plaque or obvious stenosis is found in the middle section of the vessel wall, the diagnostic object corresponding to the diagnostic information is a tissue, namely the right coronary artery. Therefore, a second association relationship of the right coronary artery tissue with the above-described tissue specification information a can be established.
Step S103, establishing an association relation between the medical image and the organization description information according to the first association relation and the second association relation.
Specifically, since there is a first correlation between the tissue and the medical image, the medical image corresponds to the tissue. And the organization description information have a second association relationship, namely the organization description information corresponds to the organization. Therefore, the association relationship between the medical image and the tissue description information can be established by taking the tissue as a bridge.
In the scheme, part of the content in the structured report can be set to be modifiable based on the medical image, and the text description of other formats or the text description of basic information can be set to be non-modifiable. Therefore, the probability of the error modification operation of the user can be reduced to a certain degree, and the uniformity and the accuracy of the report format are ensured. For example, the textual content in the structured report is "the diagnosis location is a coronary vessel of the heart,within the region A3 to A6 of the blood vessel, there is a calcified region of 3mm which appears Semi-circle shapeOther vessel segments are temporarily not abnormal ". In the above text example, the underlined region is very important information for diagnosis of a disease, and the region is used as a modifiable region, and when the doctor considers that the AI diagnosis result is incorrect, the content of the region can be modified in time.
In order to make the structured report more standardized, the words and descriptions used in the structured report can be called through standard terms and sentence libraries, and combined with AI diagnosis results, the structured report is generated. An example of a deep learning neural network learning structured report description may also be utilized to form a report generation model. And generating a structured report by using the report generation model and the AI diagnosis result.
In order to facilitate the user to quickly find the region available for modification, keywords (representative information such as lesion types and paragraph names) in the structured report can be used as identification bases to identify the region available for modification by the user. Alternatively, a separator may be used as paragraph division of the editable area. Namely, the user can quickly lock the position which can be modified by using the key words, key separators and other modes, thereby shortening the time for a doctor to browse the structured report and improving the speed of correcting AI diagnosis results and diagnosing focuses.
In addition, if the structured report contains multiple types of content descriptions, when the first type of content description is locked, other types of content and images are not displayed to the user. For example, the structured report includes a description of the dominant content of the vessel and a description of the fractional flow reserve, FFR, content of the vessel. At this time, when the user searches or actively selects to view the description of the blood vessel dominant type related content, the dominant type related image of the blood vessel is directly displayed for the user, and the FFR related image of the blood flow reserve fraction of the blood vessel is not displayed on the user terminal interface.
And step S104, after the preset operation on the tissue description information is detected, acquiring a target medical image corresponding to the tissue description information from the medical image according to the association relation.
In an embodiment, the preset operation includes a selection operation, and after the preset operation on the tissue description information is detected, the step of acquiring a target medical image corresponding to the tissue description information from the medical image according to the association relationship includes: when the selection operation of the plurality of focus marks is detected, a target focus mark meeting the preset lesion degree is obtained from the plurality of focus marks. And acquiring a target medical image corresponding to the target lesion mark from the medical image.
Specifically, it is assumed that the tissue description information includes three calcified plaques, namely, a first calcified plaque, a second calcified plaque, and a third calcified plaque. Wherein, the calcification degree of the three calcified plaques is increased sequentially. When the selection operation of the three calcified plaques is detected, a third calcified plaque with the most severe calcification is selected from the three calcified plaques to be used as a target calcified plaque, and then a target medical image corresponding to the target calcified plaque is obtained from the medical image.
As shown in fig. 3, a diagram of the left anterior descending coronary artery (segment of vessel LAD) with lesions is shown. Between the vessel centerline points 130-160 corresponding to the middle of the LAD, there is a stenosis, which is severe. According to the shape, size and other sign information of the image refocusing, the focus is determined to be non-calcified. Analysis of lesion situation in images by automated aided diagnosis, the description in the structured report is: "patient name: plum certain; sex: male; age: age 65. Diagnosis site: the coronary arteries of the heart. The patient isModerate stenosis, non-calcification, in the middle of the LAD. ". The underlined content in the structured report may be available for editing by the user. When the user audits the structured report, the auxiliary diagnosis is foundThe degree of stenosis in the result of the discontinuity is erroneous. The user can manually modify the 'moderate' into the 'severe', at the moment, the corresponding source image is automatically displayed according to the content modified by the user in the structured report, and the source image is enabled to enter an editable state for a doctor to observe or adjust the image. For example, when the user finds that the severity of the lesion should be severe by observing the lesion, and the "severe" may be manually modified to "severe", the LAD medical image in fig. 3 enters an editable state, and the user may manually modify "stenosis narrow 0.75" to "stenosis narrow 0.9".
It should be noted here that the dotted lines in fig. 3 are what the information in the present solution is obtained from to illustrate the text part, and are not the ones in the structured report.
And step S105, displaying the target medical image at the terminal.
In one embodiment, the step of presenting the target medical image at the terminal comprises:
an image type of the target medical image is acquired.
And determining a display area corresponding to the image type in the terminal, and displaying the target medical image in the display area.
Specifically, as shown in fig. 4, the terminal interface includes five display areas. The first display area is arranged at the upper left corner of the interface and used for displaying the original image. The second display area is arranged at the lower left corner of the interface and used for displaying VR images. The third display area is disposed on the right side of the first display area and the second display area for displaying CPR images. The fourth display area is arranged at the right side of the third display area and is used for displaying the probe picture. The fifth display area is arranged at the right side of the fourth display area and is used for displaying the straightening picture. In the actual medical image display process, if the type of the acquired target medical image is a VR image, the target medical image is displayed in a second display area.
In one embodiment, the tissue description information includes a lesion identifier, and the step of presenting the target medical image at the terminal includes: and acquiring a focus area corresponding to the focus identification from the target medical image. And amplifying and displaying the lesion area.
For example, if the tissue description information includes a pulmonary tuberculosis, a pulmonary tuberculosis region corresponding to the pulmonary tuberculosis may be obtained from the target medical image, and then the pulmonary tuberculosis region may be displayed in an enlarged manner for the user to observe.
According to the medical image processing method, the incidence relation between the medical image and the tissue description information is established in advance according to the tissue, and after the preset operation on the tissue description information is detected, the target medical image corresponding to the tissue description information is obtained and displayed from the medical image according to the incidence relation, so that the medical image processing efficiency is improved.
The present embodiment will be further described from the perspective of a processing apparatus of medical images, which may be integrated in a server, according to the method described in the above embodiment.
Referring to fig. 5, fig. 5 is a structural diagram of a medical image processing apparatus according to an embodiment of the present invention, the apparatus 1 includes: a first obtaining module 11, a building module 12, a second obtaining module 13 and a display module 14. The first obtaining module 11 is configured to obtain a medical image, a tissue corresponding to the medical image, and tissue description information corresponding to the tissue from the structured report. The establishing module 12 is configured to establish an association relationship between the medical image and the tissue description information according to the tissue. The second obtaining module 13 is configured to, after detecting a preset operation on the tissue description information, obtain a target medical image corresponding to the tissue description information from the medical image according to the association relationship. The presentation module 14 is used for presenting the target medical image at the terminal.
In which medical images are obtained of tissue in a non-invasive manner. The tissue includes not only normal tissues such as normal blood vessels, lungs, liver, and the like, but also diseased tissues such as hemangiomas, tuberculosis lesions, and the like. Thus, normal tissue and lesion tissue, such as lesion markings, lesion areas, etc., are included in the medical image. The type of the medical image may be a VR (Virtual Reality) image, a CPR (curved reconstruction) image, a probe image, a straightened image, or the like, and the type of the medical image is not specifically limited herein.
The tissue description information is used to describe the tissue in the medical image, including identification and properties of the tissue, such as position information, size information, etc. of the tissue.
In one embodiment, the setup module 12 includes: a first setup submodule 121 and a second setup submodule 122. The first establishing submodule 121 is configured to establish a first association relationship between the tissue and the medical image, and establish a second association relationship between the tissue and the tissue description information. The second establishing submodule 122 is configured to establish an association relationship between the medical image and the tissue description information according to the first association relationship and the second association relationship.
In one embodiment, the first establishing submodule 121 is configured to: it is determined whether the medical image includes tissue. When the medical image includes tissue, a first association of the tissue and the medical image is established.
In an embodiment, the tissue comprises a blood vessel, and the first establishing sub-module 121 is configured to: and acquiring an original medical sequence corresponding to the tissue and position information of the tissue. And acquiring a target medical sequence corresponding to the position information of the tissue from the original medical sequence, setting the medical sequence as a medical image, and obtaining a first association relation between the tissue and the medical image.
Specifically, a tissue will be described as an example of a blood vessel. The first establishing submodule 121 first obtains an original medical sequence corresponding to a blood vessel, extracts a blood vessel center line from the original medical sequence, obtains a target medical sequence according to the original image sequence and the blood vessel center line, sets the target medical sequence as a medical image, and obtains a first association relationship between the blood vessel and the medical image.
In one embodiment, the organization description information includes diagnostic information, and the first establishing sub-module 121 is configured to: and acquiring a diagnosis object corresponding to the diagnosis information. And when the diagnosis object is a tissue, establishing a second association relation between the tissue and the tissue description information.
For example, when the diagnostic information included in the tissue-describing information a is "the right coronary artery originates from the right sinus. Calcified plaque, severe stenosis of lumen and muscle bridge are visible on the proximal wall of the Right Coronary Artery (RCA). When no plaque or obvious stenosis is found in the middle section of the vessel wall, the diagnostic object corresponding to the diagnostic information is a tissue, namely the right coronary artery. Accordingly, the first establishing sub-module 121 may establish a second association relationship of the right coronary artery tissue with the above-described tissue description information a.
In an embodiment, the preset operation includes a selection operation, and the second obtaining module 13 includes: an identification acquisition sub-module 131 and a medical image acquisition sub-module 132. The identifier obtaining sub-module 131 is configured to, when a selection operation on multiple lesion identifiers is detected, obtain a target lesion identifier that satisfies a preset lesion degree from the multiple lesion identifiers. The medical image obtaining sub-module 132 is configured to obtain a target medical image corresponding to the target lesion identifier from the medical image.
Specifically, it is assumed that the tissue description information includes three calcified plaques, namely a first calcified plaque, a second calcified plaque, and a third calcified plaque. Wherein, the calcification degree of the three calcified plaques is increased sequentially. When the identification acquisition submodule 131 detects a selection operation on three calcified plaques, a third calcified plaque with the most severe calcification is selected from the three calcified plaques as a target calcified plaque, and then the medical image acquisition submodule 132 acquires a target medical image corresponding to the target calcified plaque from the medical image.
In one embodiment, display module 14 includes: an image type acquisition sub-module 141 and an image presentation sub-module 142. The image type acquiring submodule 141 is used to acquire the image type of the target medical image. The image display sub-module 142 is configured to determine a display area corresponding to the image type in the terminal, and display the target medical image in the display area.
Specifically, as shown in fig. 4, the terminal interface includes five display areas. The first display area is arranged at the upper left corner of the interface and used for displaying the original image. The second display area is arranged at the lower left corner of the interface and used for displaying VR images. The third display area is disposed on the right side of the first display area and the second display area for displaying CPR images. The fourth display area is arranged at the right side of the third display area and is used for displaying the probe picture. The fifth display area is arranged at the right side of the fourth display area and is used for displaying the straightening picture. In the actual medical image display process, if the type of the target medical image acquired by the image type acquiring submodule 141 is the VR image, the image display submodule 142 displays the target medical image in the second display area.
In one embodiment, the tissue description information includes lesion identification, and presentation module 14 includes: a lesion area acquisition sub-module 143 and a magnified presentation sub-module 144. And the lesion area obtaining sub-module 143 is configured to obtain a lesion area corresponding to the lesion identifier from the target medical image. And the amplification display sub-module 144 is used for performing amplification display on the focus area.
For example, if the tissue description information includes tuberculosis, the lesion region obtaining sub-module 143 may obtain a tuberculosis region corresponding to the tuberculosis from the target medical image, and then the enlarged display sub-module 144 enlarges and displays the tuberculosis region for the user to observe.
According to the medical image processing device, the incidence relation between the medical image and the tissue description information is established in advance according to the tissue, and after the preset operation on the tissue description information is detected, the target medical image corresponding to the tissue description information is acquired from the medical image and displayed according to the incidence relation, so that the medical image processing efficiency is improved.
Correspondingly, the embodiment of the present invention further provides a server, specifically:
the server may include components such as processors of one or more processing cores, memory of one or more computer-readable storage media, power and input units, and the like. Those skilled in the art will appreciate that the server architectures depicted above are not meant to be limiting, and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components. Wherein:
the processor is a control center of the server, connects various parts of the whole server by various interfaces and lines, and performs various functions of the server and processes data by running or executing software programs and/or modules stored in the memory and calling data stored in the memory, thereby performing overall monitoring of the server. Optionally, the processor may include one or more processing cores; preferably, the processor may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor.
The memory may be used to store software programs and modules, and the processor may execute various functional applications and data processing by operating the software programs and modules stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to the use of the server, and the like. For example, legal keywords may be stored, and some data obtained from a third-party server may also be stored. Further, the memory 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 volatile solid state storage device. Accordingly, the memory may also include a memory controller to provide the processor access to the memory.
The server also comprises a power supply for supplying power to each component, preferably, the power supply can be logically connected with the processor through a power management system, so that the functions of charging, discharging, power consumption management and the like can be realized through the power management system. The power supply may also include any component of one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like.
The server may further include an input unit operable to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.
Although not shown, the server may further include a display unit and the like, which will not be described in detail herein. Specifically, in this embodiment, the processor in the server loads the executable file corresponding to the process of one or more application programs into the memory according to the following instructions, and the processor runs the application programs stored in the memory, thereby implementing various functions as follows:
and acquiring the medical image, the tissue corresponding to the medical image and the tissue description information corresponding to the tissue from the structured report. And establishing an association relation between the medical image and the tissue description information according to the tissue. And when the preset operation on the tissue description information is detected, acquiring a target medical image corresponding to the tissue description information from the medical image according to the association relation. And displaying the target medical image at the terminal.
The server may implement the effective effect that can be achieved by any medical image processing apparatus provided in the embodiments of the present invention, which is detailed in the foregoing embodiments and not described herein again.
According to the server provided by the embodiment of the invention, the incidence relation between the medical image and the organization description information is pre-established according to the organization, and after the preset operation on the organization description information is detected, the target medical image corresponding to the organization description information is acquired from the medical image and displayed according to the incidence relation, so that the medical image processing efficiency is improved.
Various operations of embodiments are provided herein. In one embodiment, the one or more operations may constitute computer readable instructions stored on one or more computer readable media, which when executed by a base station, will cause a computing device to perform the operations. The order in which some or all of the operations are described should not be construed as to imply that these operations are necessarily order dependent. Those skilled in the art will appreciate alternative orderings having the benefit of this description. Moreover, it should be understood that not all operations are necessarily present in each embodiment provided herein.
Also, although the disclosure has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art based upon a reading and understanding of this specification and the annexed drawings. The present disclosure includes all such modifications and alterations, and is limited only by the scope of the appended claims. In particular regard to the various functions performed by the above described components (e.g., elements, resources, etc.), the terms used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure which performs the function in the herein illustrated exemplary implementations of the disclosure. In addition, while a particular feature of the disclosure may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for a given or particular application. Furthermore, to the extent that the terms "includes," has, "" contains, "or variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term" comprising.
Each functional unit in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium. The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Each apparatus or system described above may perform the method in the corresponding method embodiment.
In summary, although the present invention has been disclosed in the foregoing embodiments, the serial numbers before the embodiments are used for convenience of description only, and the sequence of the embodiments of the present invention is not limited. Furthermore, the above embodiments are not intended to limit the present invention, and those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, therefore, the scope of the present invention shall be limited by the appended claims.
Claims (8)
1. A method for processing medical images in a structured report is characterized by comprising the following steps:
acquiring a medical image, a tissue corresponding to the medical image and tissue description information corresponding to the tissue from a structured report;
acquiring an original medical sequence corresponding to the tissue and position information of the tissue;
acquiring a target medical sequence corresponding to the position information of the tissue from the original medical sequence, setting the target medical sequence as the medical image, and obtaining a first association relation between the tissue and the medical image;
under the condition that the organization description information comprises diagnosis information, acquiring a diagnosis object corresponding to the diagnosis information;
when the diagnosis object is the tissue, establishing a second association relation between the tissue and description information of the focus attribute in the tissue description information;
when the preset operation on the tissue description information is detected, acquiring a target medical image corresponding to the description information of the focus attribute in the tissue description information from the medical image according to the first association relation and the second association relation, and enabling the description information of the focus attribute and the target medical image to enter an editable state; or, after the preset operation on the medical image is detected, acquiring description information of a lesion attribute corresponding to the medical image from the tissue description information according to the first association relation and the second association relation, and enabling the description information of the lesion attribute and the target medical image to enter an editable state;
and displaying the target medical image at a terminal.
2. The method of claim 1, wherein the step of establishing an association between the medical image and the tissue description information according to the tissue comprises:
establishing a first incidence relation between the tissue and the medical image, and establishing a second incidence relation between the tissue and the tissue description information;
and establishing an association relation between the medical image and the organization description information according to the first association relation and the second association relation.
3. The method of claim 2, wherein the step of establishing a first relationship between the tissue and the medical image comprises:
determining whether the medical image includes the tissue;
when the medical image includes the tissue, a first association of the tissue and the medical image is established.
4. The method of processing medical images in a structured report according to claim 1, wherein said step of presenting said target medical image in a terminal comprises:
acquiring an image type of the target medical image;
and determining a display area corresponding to the image type in the terminal, and displaying the target medical image in the display area.
5. The method of claim 1, wherein the tissue description information includes a lesion identifier, and the step of displaying the target medical image at the terminal comprises:
acquiring a focus area corresponding to the focus identification from the target medical image;
and carrying out amplification display on the focus area.
6. The method according to claim 1, wherein the preset operation comprises a selecting operation, and the step of acquiring a target medical image corresponding to the tissue description information from the medical image according to the association relationship after the preset operation on the tissue description information is detected comprises:
when the selection operation of a plurality of focus marks is detected, acquiring a target focus mark meeting a preset lesion degree from the plurality of focus marks;
and acquiring a target medical image corresponding to the target lesion mark from the medical image.
7. A device for processing medical images in a structured report, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a medical image, a tissue corresponding to the medical image and tissue description information corresponding to the tissue from a structured report;
the establishing module is used for acquiring an original medical sequence corresponding to the tissue and the position information of the tissue; acquiring a target medical sequence corresponding to the position information of the tissue from the original medical sequence, setting the target medical sequence as the medical image, and obtaining a first association relation between the tissue and the medical image;
under the condition that the organization description information comprises diagnosis information, acquiring a diagnosis object corresponding to the diagnosis information; when the diagnosis object is the tissue, establishing a second association relation between the tissue and description information of the focus attribute in the tissue description information;
a second obtaining module, configured to obtain, from the medical image according to the first association relationship and the second association relationship, a target medical image corresponding to description information of a lesion attribute in the tissue description information after a preset operation on the tissue description information is detected, where the description information of the lesion attribute and the target medical image enter an editable state; or, after the preset operation on the medical image is detected, acquiring description information of a lesion attribute corresponding to the medical image from the tissue description information according to the first association relation and the second association relation, and enabling the description information of the lesion attribute and the target medical image to enter an editable state;
and the display module is used for displaying the target medical image at a terminal.
8. A computer readable storage medium having stored therein processor executable instructions, the processor providing a method of processing medical images in a structured report according to any of claims 1-6 by executing said instructions.
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