CN111612863A - CTA image processing method, device and storage medium - Google Patents
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Abstract
The invention discloses a CTA image processing method, a CTA image processing device and a computer readable storage medium, wherein the method comprises the following steps: CTA image data is obtained; according to the CTA image data, performing first data type matching by using a matching model to determine the data type of the CTA image; generating an image post-processing application calling instruction carrying the data type according to the data type; in response to the call instruction, calling the image post-processing application to reconstruct a reconstructed image corresponding to the CTA image. In the CTA image processing method, apparatus, and computer-readable storage medium according to embodiments of the present invention, a matching model defined and constructed by image data metadata is first used to perform data type matching, so as to better determine a data type of image data, and perform post-processing on CTA image data according to the data type, the post-processing being adapted to the data type of the CTA image data.
Description
Technical Field
The present invention relates to the field of medical imaging technologies, and in particular, to a CTA image processing method, device, and computer-readable storage medium.
Background
There are three general examination items for head and neck CTA (CT Computed Tomography) imaging: head and neck CTA, skull CTA, neck CTA, three examination items correspond to CT (Computed Tomography) scanning ranges respectively: joint skull and neck scanning, separate skull scanning, and separate neck scanning. Wherein the head and neck CTA scan range is a union of the head CTA and the neck CTA. After the head and neck CTA is scanned, a doctor manually inputs the scanning type according to the definite image of the name of the examination item, selects a head and neck CTA image post-processing method matched with the scanning type for image reconstruction, and diagnoses based on the output reconstructed image. Therefore, a large amount of work is brought to a doctor, and the possibility of input errors exists in the manual operation process, and if the input errors occur, the subsequent work is brought with great trouble.
In view of the above situation, the conventional processing method is to determine an automatic image matching post-processing method corresponding to the inspection items for the three inspection items of the head and neck CTA images by performing rule matching between the "inspection description" and the "sequence description" in dependence on the image metadata. However, in the practical application process, the image metadata "inspection description" and "sequence description" of three inspection items of the head and neck CTA adopt uniform definitions without distinction or respectively definitions with distinction, but the definitions are often wrong, thereby causing that the inspection items cannot be correctly identified.
Disclosure of Invention
Embodiments of the present invention provide a CTA image processing method, apparatus, and computer readable storage medium to solve the above problems in the CTA image processing process.
According to a first aspect of the present invention, there is provided a CTA image processing method, comprising: CTA image data is obtained; according to the CTA image data, performing first data type matching by using a matching model to determine the data type of the CTA image; generating an image post-processing application calling instruction carrying the data type according to the data type; in response to the call instruction, calling the image post-processing application to reconstruct a reconstructed image corresponding to the CTA image.
According to one embodiment of the present invention, the matching model is constructed from metadata definitions of CTA image data.
According to an embodiment of the invention, the method further comprises: detecting a first matching result of the first data type matching; and when the first matching result shows that the first data type matching fails, performing secondary data type matching by using a recognition model according to the CTA image data so as to determine the data type of the CTA image.
According to an embodiment of the present invention, performing secondary data type matching using a recognition model according to the CTA image data to determine the data type of the CTA image includes at least one of:
determining the data type of the CTA image by using a classification model constructed according to the type identification of the CTA image data;
the method comprises the steps of extracting the bone features of a CTA image by using a bone feature extraction model constructed according to CTA image data, and determining the data type of the CTA image according to the extracted bone features.
According to an embodiment of the invention, the method further comprises: detecting a second matching result of the secondary data type matching; and when the second matching result shows that the secondary data type matching fails, acquiring the data type determined based on the scanning range of the image data.
According to a second aspect of the present invention, there is provided a CTA image processing apparatus, comprising: the acquisition module is used for acquiring CTA image data; the type determining module is used for performing first-time data type matching according to the CTA image data by using a matching model so as to determine the data type of the CTA image; the application calling module is used for generating an image post-processing application calling instruction carrying the data type according to the data type; and the image processing module is used for responding to the calling instruction and calling the image post-processing application so as to reconstruct a reconstructed image corresponding to the CTA image.
According to one embodiment of the present invention, the matching model is constructed from metadata definitions of CTA image data.
According to an embodiment of the invention, the apparatus further comprises: the first detection module is used for detecting a first matching result of the first data type matching; and the identification module is used for performing secondary data type matching according to the CTA image data by adopting a pre-constructed identification model when the first matching result shows that the first data type matching fails so as to determine the data type of the CTA image.
According to an embodiment of the present invention, the identification module includes at least one of: the identification determining submodule is used for determining the data type of the CTA image by utilizing a classification model constructed according to the type identification of the CTA image data; the extraction determining submodule is used for constructing a bone feature extraction model according to CTA image data, extracting bone features of the CTA image, and determining the data type of the CTA image according to the extracted bone features.
According to a third aspect of the present invention, there is provided a computer-readable storage medium comprising a set of computer-executable instructions, which when executed, perform the CTA image processing method described above.
In the CTA image processing method, apparatus, and computer-readable storage medium according to embodiments of the present invention, a matching model defined and constructed by image data metadata is first used to perform data type matching, so as to better determine a data type of image data, and perform post-processing on CTA image data according to the data type, the post-processing being adapted to the data type of the CTA image data. Meanwhile, when the data type matching of the CTA image data by the matching model fails, an identification model is constructed by using the 'inspection description' and the 'sequence description' according to the CTA metadata, and the image data type is further automatically identified. The accuracy of head and neck CTA image data type identification is improved to the maximum extent, and the data type identification efficiency of the CTA image data is effectively improved. In addition, after the data type matching by using the identification model fails, the data type determined according to the scanning range is directly obtained, so that all CTA image data can be subjected to image post-processing corresponding to the CTA image data type after the correct data type is determined. Therefore, according to the data type of the CTA image, the image data is processed in a targeted manner, and the accuracy is effectively improved.
It is to be understood that the teachings of the present invention need not achieve all of the above-described benefits, but rather that specific embodiments may achieve specific technical results, and that other embodiments of the present invention may achieve benefits not mentioned above.
Drawings
The above and other objects, features and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description read in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which:
in the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.
FIG. 1 is a schematic diagram illustrating a first flowchart of a CTA image processing method according to an embodiment of the invention;
FIG. 2 is a schematic diagram illustrating a second flowchart of a CTA image processing method according to an embodiment of the invention;
FIG. 3 is a flowchart illustrating an implementation of an exemplary CTA image processing method according to an embodiment of the present invention;
fig. 4 is a schematic diagram showing a configuration of a CTA image processing apparatus according to an embodiment of the present invention.
Detailed Description
The principles and spirit of the present invention will be described with reference to a number of exemplary embodiments. It is understood that these embodiments are given only to enable those skilled in the art to better understand and to implement the present invention, and do not limit the scope of the present invention in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The technical solution of the present invention is further elaborated below with reference to the drawings and the specific embodiments.
Fig. 1 is a schematic flow chart showing a first implementation of a CTA image processing method according to an embodiment of the present invention.
Referring to fig. 1, a CTA image processing method according to an embodiment of the present invention at least includes the following steps: operation 101, acquiring CTA image data; operation 102, performing first data type matching according to CTA image data by using a matching model to determine a data type of the CTA image; operation 103, generating an image post-processing application calling instruction carrying the data type according to the data type; at operation 104, in response to the call instruction, an image post-processing application is called to reconstruct a reconstructed image corresponding to the CTA image.
In operation 101, CTA image data is acquired.
CTA (CT angiography) combines a CT enhancement technology with a thin-layer, large-range and rapid scanning technology, and clearly displays details of blood vessels of all parts of the whole body through reasonable post-processing. Has the characteristics of no wound and simple and convenient operation, and has important values for vascular variation, vascular diseases and displaying pathological changes and vascular relations. Therefore, CTA image data is mainly acquired using CT (Computed Tomography) scanning technology.
In an embodiment of the present invention, the CT scanning apparatus may be directly connected to obtain CTA image data, or the cloud storage device may be used for data sharing, or the mobile storage device may be used for storing the CTA image data obtained by the CT scanning apparatus and then processing the CTA image data.
At operation 102, a first data type match is performed from the CTA image data using a matching model to determine the data type of the CTA image.
In one embodiment of the present invention, a matching model is constructed from the metadata definitions of CTA image data.
Specifically, the data type matching rule of CTA image data can be directly defined from "check description" and "sequence description" in the metadata definition of CTA image data. For example: in the metadata definition of CTA image data, "examination description" as "tlcta" means "skull CTA image".
A matching model may also be pre-constructed according to the "inspection description" and the "sequence description" in the metadata definition of the CTA image data, and then the CTA image data obtained in operation 101 is matched by using the matching model, so as to obtain the data type of the CTA image data. For example: head CTA image, neck CTA image.
And 103-104, generating an image post-processing application calling instruction carrying the data type according to the data type. In response to the call instruction, the image post-processing application is called to reconstruct a reconstructed image corresponding to the CTA image.
After the data type of the CTA image is determined, there are differences in post-processing methods and operation flows taken for different types of CTA image data. For example: for multi-planar reconstruction (MPR) and curved surface reconstruction (CPR) taken from a two-dimensional CTA image, the parameters of the image data of interest are different for CTA image data of different parts (i.e., CTA image data of different data types), and the processing steps for the image data are also different.
In an embodiment of the present invention, after the data type of the CTA image is determined, an instruction for calling image post-processing is generated according to the data type of the CTA image. In essence, the data type is automatically selected in the CTA image post-processing application, so that the image post-processing application can correctly process the acquired CTA image by using the post-processing method corresponding to the data type.
The instruction for calling the image post-processing generated according to the data type of the CTA image can be an automatic input instruction of the data type in the image post-processing application, and the image post-processing application can process the CTA image data according to a post-processing method of the CTA image data corresponding to the instruction and further adopt a corresponding method to reconstruct a reconstructed image corresponding to the CTA image.
Fig. 2 is a schematic diagram illustrating an implementation flow of a CTA image processing method according to an embodiment of the present invention.
Referring to fig. 2, a CTA image processing method according to an embodiment of the present invention at least includes the following steps: operation 201, acquiring CTA image data; operation 202, performing first data type matching according to CTA image data by using a matching model to determine the data type of the CTA image; operation 203, detecting a first matching result of the first data type matching; in operation 204, when the first matching result shows that the first data type matching fails, performing secondary data type matching by using the recognition model according to the CTA image data to determine the data type of the CTA image; operation 205, generating an image post-processing application calling instruction carrying a data type according to the data type; at operation 206, in response to the call instruction, an image post-processing application is called to reconstruct a reconstructed image corresponding to the CTA image.
The specific implementation process of operations 201, 202, 205, and 206 is similar to the specific implementation process of operations 101, 102, 103, and 104 in the embodiment shown in fig. 1, and is not described here again.
In operations 203-204, a first matching result of the primary data type matching is detected, and when the first matching result shows that the primary data type matching fails, secondary data type matching is performed according to the CTA image data by using an identification model to determine the data type of the CTA image.
In one embodiment of the present invention, the detection of the matching result is automatically performed according to the following method: if the determined data type of the CTA image data is matched with the obtained CTA image data, the image post-processing application can be normally called; otherwise, the image post-processing application cannot be called normally.
In an embodiment of the present invention, a classification model constructed according to the type identifier of CTA image data is used as a recognition model to perform secondary data type matching on the CTA image data, thereby determining the data type of the CTA image.
Specifically, the types of a large amount of CTA image data are labeled, then an intelligent algorithm (such as a neural network algorithm) is used for performing recognition model training on the labeled CTA image data to obtain a recognition model, and then the recognition model is used for recognizing the data type of the obtained CTA image data.
In another embodiment of the present invention, a bone feature extraction model of a CTA image is constructed by using a large amount of CTA image data in advance, a bone feature of the CTA image is extracted, then the extracted bone feature is determined according to the CTA image data, and a data type of the CTA image is determined according to the extracted bone feature.
For example, based on a conventional algorithm, skull feature extraction is respectively performed on three types (skull CTA, neck CTA, head and neck CTA) of the head and neck CTA image data, and the data type of the obtained CTA image data is determined to be one of the three data types (skull CTA image, neck CTA image, head and neck CTA image) through skull feature judgment of the obtained CTA image data.
In an embodiment of the present invention, the CTA image data processing method further includes: detecting a second matching result of the secondary data type matching; and when the second matching result shows that the secondary data type matching fails, acquiring the data type determined based on the scanning range of the image data.
For example, the data type determined based on the scan range of the image data may be acquired in the following manner: the physician or related operator determines the data type of CTA image data based on the scan volume, for example: a head CTA image, a neck CTA image, etc. The data type of the CTA image is input through the manual selection entrance of the data type in the image post-processing application, so that the image post-processing application can perform proper image post-processing operation on the obtained CTA image.
Fig. 3 is a schematic flow chart illustrating an implementation example of a CTA image processing method according to an embodiment of the present invention.
Referring to fig. 3, the CTA image processing method according to the embodiment of the present invention is applied to the head and neck CTA image processing. The image data of the head and neck CTA is mainly classified into the following three data types: head and neck CTA images, skull CTA images, neck CTA images. As shown in fig. 3, a specific application example of the CTA image processing method according to the embodiment of the present invention includes the following steps:
in operation 301, head and neck CTA image data is obtained.
In operation 302, metadata rule matching is performed on the obtained CTA image data, and a matching result is detected. If the matching is successful, the data type of the CTA image data is obtained, and operation 303 is executed; otherwise, operation 304 is performed.
Specifically, "metadata rule matching is performed on the acquired CTA image data" means that data type matching is performed on the acquired CTA image data using a matching model constructed from "check description" and "sequence description" in the metadata definition of the CTA image data. Data types for CTA image data are obtained, for example: head and neck CTA images, skull CTA images, neck CTA images.
For detecting the matching result, the detection of the matching result can be automatically performed according to the following method: if the determined data type of the CTA image data is matched with the obtained CTA image data, the image post-processing application can be normally called; otherwise, the image post-processing application cannot be called normally.
In operation 303, an image post-processing application is invoked according to the data type of the CTA image data, and a corresponding image post-processing operation is performed.
In operation 304, the data type of the CTA image data is further identified by using a data type automatic identification method, and an identification result of the data type identification is detected.
The automatic data type identification can adopt the following two methods: 1. the method comprises the steps of marking the types of a large amount of CTA image data, then carrying out recognition model training on the marked CTA image data by using an intelligent algorithm (such as a neural network algorithm) to obtain a recognition model, and then carrying out data type recognition on the obtained CTA image data by using the recognition model. 2. Based on a traditional algorithm, skull feature extraction is respectively carried out on three types (skull CTA, neck CTA and head and neck CTA) of the head and neck CTA image data, and the data type of the obtained CTA image data is determined to be one of the three data types (skull CTA image, neck CTA image and head and neck CTA image) through skull feature judgment of the obtained CTA image data.
In operation 305, a data type determined from the scan range of the CTA image data is obtained.
The data type input or selection entrance of the image data can be reserved, and when the data type of the CTA image data cannot be automatically identified in the above operation steps, a doctor or a related operator can manually input the data type of the CTA image data to be processed.
In operation 306, an image post-processing operation corresponding to the data type of the CTA image data is performed on the CTA image data by using the image post-processing application, and a corresponding reconstructed image is output.
In the CTA image processing method, apparatus, and computer-readable storage medium according to embodiments of the present invention, a matching model defined and constructed by image data metadata is first used to perform data type matching, so as to better determine a data type of image data, and perform post-processing on CTA image data according to the data type, the post-processing being adapted to the data type of the CTA image data. Meanwhile, when the data type matching of the CTA image data by the matching model fails, an identification model is constructed by using the 'inspection description' and the 'sequence description' according to the CTA metadata, and the image data type is further automatically identified. The accuracy of head and neck CTA image data type identification is improved to the maximum extent, and the data type identification efficiency of the CTA image data is effectively improved. In addition, after the data type matching by using the identification model fails, the data type determined according to the scanning range is directly obtained, so that all CTA image data can be subjected to image post-processing corresponding to the CTA image data type after the correct data type is determined. Therefore, according to the data type of the CTA image, the image data is processed in a targeted manner, and the accuracy is effectively improved.
Similarly, based on the CTA image processing method, an embodiment of the present invention further provides a computer-readable storage medium, where a program is stored, and when the program is executed by a processor, the processor at least performs the following steps: operation 102, performing first data type matching according to CTA image data by using a matching model to determine a data type of the CTA image; operation 103, generating an image post-processing application calling instruction carrying the data type according to the data type; at operation 104, in response to the call instruction, an image post-processing application is called to reconstruct a reconstructed image corresponding to the CTA image.
Further, based on the above CTA image processing method, an embodiment of the present invention further provides a CTA image processing apparatus, as shown in fig. 4, where the apparatus 40 includes: an obtaining module 401, configured to obtain CTA image data; a type determining module 402, configured to perform first data type matching according to CTA image data by using a matching model to determine a data type of the CTA image; an application calling module 403, configured to generate an image post-processing application calling instruction carrying a data type according to the data type; the image processing module 404 is configured to, in response to the call instruction, call an image post-processing application to reconstruct a reconstructed image corresponding to the CTA image.
According to one embodiment of the present invention, the matching model is constructed from metadata definitions of CTA image data.
According to an embodiment of the present invention, the apparatus 40 further comprises: the first detection module is used for detecting a first matching result of the first data type matching; and the identification module is used for performing secondary data type matching according to the CTA image data by adopting a pre-constructed identification model when the first matching result shows that the first data type matching fails so as to determine the data type of the CTA image.
According to an embodiment of the present invention, the identification module includes at least one of: the identification determining submodule is used for determining the data type of the CTA image by utilizing a classification model constructed according to the type identification of the CTA image data; and the extraction determining submodule is used for constructing a bone feature extraction model according to the CTA image data, extracting the bone features of the CTA image and determining the data type of the CTA image according to the extracted bone features.
Here, it should be noted that: the above description of the embodiment of the CTA image processing apparatus is similar to the description of the method embodiment shown in fig. 1 to 3, and has similar beneficial effects to the method embodiment shown in fig. 1 to 3, and therefore, the description thereof is omitted. For technical details not disclosed in the embodiment of the CTA image processing apparatus of the present invention, please refer to the description of the method embodiment shown in fig. 1 to 3 for understanding, and therefore, for brevity, will not be described again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of a unit is only one logical function division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read Only Memory (ROM), a magnetic disk, or an optical disk.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A CTA image processing method, comprising:
CTA image data is obtained;
according to the CTA image data, performing first data type matching by using a matching model to determine the data type of the CTA image;
generating an image post-processing application calling instruction carrying the data type according to the data type;
in response to the call instruction, calling the image post-processing application to reconstruct a reconstructed image corresponding to the CTA image.
2. The method of claim 1, wherein the matching model is constructed from metadata definitions of CTA image data.
3. The method of claim 1, further comprising:
detecting a first matching result of the first data type matching;
and when the first matching result shows that the first data type matching fails, performing secondary data type matching by using a recognition model according to the CTA image data so as to determine the data type of the CTA image.
4. The method of claim 3, wherein performing a secondary data type match from the CTA image data using a recognition model to determine the data type of the CTA image comprises at least one of:
determining the data type of the CTA image by using a classification model constructed according to the type identification of the CTA image data;
the method comprises the steps of extracting the bone features of a CTA image by using a bone feature extraction model constructed according to CTA image data, and determining the data type of the CTA image according to the extracted bone features.
5. The method of claim 3, further comprising:
detecting a second matching result of the secondary data type matching;
and when the second matching result shows that the secondary data type matching fails, acquiring the data type determined based on the scanning range of the image data.
6. A CTA image processing apparatus, comprising:
the acquisition module is used for acquiring CTA image data;
the type determining module is used for performing first-time data type matching according to the CTA image data by using a matching model so as to determine the data type of the CTA image;
the application calling module is used for generating an image post-processing application calling instruction carrying the data type according to the data type;
and the image processing module is used for responding to the calling instruction and calling the image post-processing application so as to reconstruct a reconstructed image corresponding to the CTA image.
7. The apparatus of claim 6, wherein the matching model is constructed from metadata definitions of CTA image data.
8. The apparatus of claim 6, further comprising:
the first detection module is used for detecting a first matching result of the first data type matching;
and the identification module is used for performing secondary data type matching according to the CTA image data by adopting a pre-constructed identification model when the first matching result shows that the first data type matching fails so as to determine the data type of the CTA image.
9. The apparatus of claim 6, wherein the identification module comprises at least one of:
the identification determining submodule is used for determining the data type of the CTA image by utilizing a classification model constructed according to the type identification of the CTA image data;
the extraction determining submodule is used for constructing a bone feature extraction model according to CTA image data, extracting bone features of the CTA image, and determining the data type of the CTA image according to the extracted bone features.
10. A computer-readable storage medium comprising a set of computer-executable instructions that, when executed, perform the CTA image processing method of any one of claims 1-5.
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