CN111816281A - Ultrasonic image inquiry unit - Google Patents
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Abstract
The invention relates to the technical field of ultrasonic image processing, in particular to an ultrasonic image inquiry device. The method comprises the following steps: the system comprises an image acquisition unit, a diagnosis unit and a diagnosis unit, wherein the image acquisition unit is used for acquiring an ultrasonic image to be diagnosed, and the type of the ultrasonic image to be diagnosed comprises an ultrasonic image or an ultrasonic video; the image matching unit is used for matching the ultrasonic image to be diagnosed with a plurality of matching images containing marking information in an image database and calculating an image matching value; the screening unit is used for determining the matched image with the image matching degree value exceeding a preset matching degree value in the image database as a target ultrasonic image according to the image matching degree value; and the determining unit is used for determining the diagnostic information of the ultrasonic image to be diagnosed at least according to the mark information contained in the target ultrasonic image. The method can directly obtain the diagnosis information of the ultrasonic image to be diagnosed in a retrieval and matching mode.
Description
Technical Field
The invention relates to the technical field of ultrasonic image processing, in particular to an ultrasonic image inquiry device.
Background
The ultrasonic diagnostic apparatus has wide application in clinical medicine, and can be used for ultrasonic image examination and diagnosis of various parts of a body from head to foot. At present, many primary hospitals lack professional doctors combining ultrasound departments with clinical departments, when a patient diagnoses an ultrasound image obtained by scanning for a primary doctor, the primary doctor has the situations that the primary doctor cannot judge whether a scanned organ corresponding to the ultrasound image exists, whether a focus exists or not, and how to make a diagnosis conclusion under the condition that the focus exists, and the doctor cannot quickly and timely issue an ultrasound diagnosis conclusion.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide an ultrasonic image query device capable of rapidly assisting a doctor to rapidly and timely issue an ultrasonic diagnosis conclusion.
Particularly, the present invention provides an ultrasound image query apparatus, comprising:
the system comprises an image acquisition unit, a diagnosis unit and a diagnosis unit, wherein the image acquisition unit is used for acquiring an ultrasonic image to be diagnosed, and the type of the ultrasonic image to be diagnosed comprises an ultrasonic image or an ultrasonic video;
the image matching unit is used for matching the ultrasonic image to be diagnosed with a plurality of matching images containing marking information in an image database and calculating an image matching value;
the screening unit is used for determining the matched image with the image matching degree value exceeding a preset matching degree value in the image database as a target ultrasonic image according to the image matching degree value;
and the determining unit is used for determining the diagnostic information of the ultrasonic image to be diagnosed at least according to the mark information contained in the target ultrasonic image.
Further, the display device further comprises a display unit configured to:
displaying the ultrasonic image to be diagnosed and the target ultrasonic image, wherein the displayed target ultrasonic image comprises the corresponding mark information;
and displaying the image matching value of the ultrasonic image to be diagnosed and the target ultrasonic image.
Further, the system also comprises a history query unit, wherein the history query unit is configured to:
acquiring the information of an inspection object corresponding to an ultrasonic image to be diagnosed;
inquiring historical diagnosis ultrasonic images of the examination object according to the examination object information corresponding to the ultrasonic images to be diagnosed, wherein the historical diagnosis ultrasonic images are stored in the image database;
and when the historical diagnosis ultrasonic images exist, arranging according to the diagnosis time of the historical diagnosis ultrasonic images, and using the arrangement as a reference basis for the determination unit to determine the diagnosis information of the ultrasonic images to be diagnosed.
Further, the system also comprises a homogeneous query unit, wherein the homogeneous query unit is configured to:
determining focus information according to the diagnosis information of the ultrasonic image to be diagnosed;
inquiring a matching image corresponding to the focus of the same kind in the image database according to the focus information;
and taking the marking information of the matched image corresponding to the similar focus as a reference basis for the determining unit to determine the diagnostic information of the ultrasonic image to be diagnosed.
Further, the marking information at least includes scanned part information, lesion information and treatment information corresponding to the ultrasound image to be diagnosed.
Further, the image matching unit obtains the image matching value of the ultrasonic image to be diagnosed and each matching image through cosine similarity calculation.
Further, the image matching unit calculates and obtains an image matching value of the ultrasonic image to be diagnosed and each matching image through a trained matching neural network model.
Further, the matching neural network model includes:
the first neural network is used for identifying the scanning part of the ultrasonic image to be diagnosed and is obtained by training a plurality of ultrasonic images marked with the category of the scanning part;
the first neural network is obtained by training a plurality of ultrasonic images marked with focus information;
screening a neural network, and screening a corresponding matched image according to the information of the scanned part and the focus of the ultrasonic image to be diagnosed;
and the matching neural network is used for calculating the matching value of the ultrasonic image to be diagnosed and the matching image screened by the screening neural network.
Further, the preset matching degree value set by the screening unit enables at least one matching image to be determined as the target ultrasonic image.
Further, the image database is: one or more of a local image database, a hospital alliance image database, and a cloud image database.
According to the ultrasonic image query device, the image matching unit can be used for rapidly matching the ultrasonic image to be diagnosed with a plurality of matching images containing the mark information in the image database and calculating the image matching degree value, then the matching images with the image matching degree value exceeding the preset matching degree value in the image database are determined as target ultrasonic images according to the screening unit, and the diagnosis information of the ultrasonic image to be diagnosed is determined according to the mark information contained in the target ultrasonic images. The diagnostic information of the ultrasonic image to be diagnosed can be directly obtained in a retrieval matching mode.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a schematic structural diagram of an ultrasound image query device according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of an ultrasound image query device according to another embodiment of the present invention.
Fig. 3 is a schematic structural diagram of an ultrasound image query device according to another embodiment of the present invention.
Fig. 4 is a schematic structural diagram of an ultrasound image query device according to another embodiment of the present invention.
Fig. 5 is a flowchart illustrating an ultrasound image query method according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following detailed description and accompanying drawings. Wherein like elements in different embodiments are numbered with like associated elements. In the following description, numerous details are set forth in order to provide a better understanding of the present application. However, those skilled in the art will readily recognize that some of the features may be omitted or replaced with other elements, materials, methods in different instances. In some instances, certain operations related to the present application have not been shown or described in detail in order to avoid obscuring the core of the present application from excessive description, and it is not necessary for those skilled in the art to describe these operations in detail, so that they may be fully understood from the description in the specification and the general knowledge in the art. Furthermore, the features, operations, or characteristics described in the specification may be combined in any suitable manner to form various embodiments.
At present, many primary hospitals lack specialized doctors combining ultrasound departments with clinical departments, when an examination object carries out diagnosis on an ultrasound image obtained by scanning for primary doctors, the primary doctors have the situations that the scanning organs corresponding to the ultrasound image cannot be judged, whether a focus exists or not, and how to make a diagnosis conclusion under the condition that the focus exists, and the doctors cannot quickly and timely issue an ultrasound diagnosis conclusion.
As one aspect of the present invention, as shown in fig. 1, the present invention provides an ultrasound image query apparatus, including: image acquisition unit 100, image matching unit 200, screening unit 300, and determination unit 400. The image acquiring unit 100 is configured to acquire an ultrasound image to be diagnosed, where the type of the ultrasound image to be diagnosed includes an ultrasound image or an ultrasound video; the image matching unit 200 is configured to match the ultrasound image to be diagnosed with a plurality of matching images containing the label information in an image database, and calculate an image matching degree value, where the image database is pre-established and is used to store a plurality of ultrasound images marked with the label information by a doctor or an artificial intelligence model, and the ultrasound images are used as matching images matched with the ultrasound image to be diagnosed, and the matching images at least include one or more of matching ultrasound images, matching CT images, matching MR images, and matching PET images; the screening unit 300 is configured to determine, according to the image matching degree value, a matching image in the image database, for which the image matching degree value exceeds a preset matching degree value, as a target ultrasound image; the determining unit 400 is configured to determine, according to at least marker information included in the target ultrasound image, diagnostic information of the ultrasound image to be diagnosed, where the diagnostic information at least includes one or more of scanned region information, lesion information, and treatment information.
The ultrasonic image query device can quickly search through the image matching unit 200, match the ultrasonic image to be diagnosed with a plurality of matching images containing the mark information in the image database and calculate the image matching degree value, then determine the matching image of which the image matching degree value exceeds the preset matching degree value in the image database as a target ultrasonic image according to the screening unit 300, and determine the diagnosis information of the ultrasonic image to be diagnosed according to the mark information contained in the target ultrasonic image. The diagnostic information of the ultrasonic image to be diagnosed can be directly obtained in a retrieval matching mode.
In an embodiment, the image obtaining unit 100 of the present invention may directly obtain the ultrasound image to be diagnosed by scanning the scanned portion with the ultrasound probe of the ultrasound device; if the examination object needs to go across hospitals for a diagnosis, for example, go to a special hospital for hanging a specialist clinic, the ultrasound image to be diagnosed can be stored in a storage medium, which can be various computer-readable storage media capable of storing program codes, such as a usb disk, a mobile hard disk, a Read-Only Memory (ROM), an optical disk, or a cloud disk. It is to be understood that the ultrasound image to be diagnosed of the present invention may be one or more of a single frame ultrasound image, a multi-frame ultrasound image, or an ultrasound video. The "scanning site" in the present invention may include a human, an animal, a part of a human or a part of an animal. For example, the subject may include an organ or a blood vessel such as a liver, a heart, a uterus, a brain, a chest, an abdomen, and the like. Additionally, the term "scanning site" may include an artificial model. The artificial model represents a material having a volume very close to the density and effective atomic number of an organism, and may include a spherical artificial model having an emotion similar to a human body.
In order to provide a diagnosis reference basis for a doctor quickly, the method matches the ultrasonic image to be diagnosed with a plurality of matching images containing the mark information in an image database in a retrieval mode, and then screens the matching images with high matching degree with the ultrasonic image to be diagnosed as target ultrasonic images by calculating the image matching degree value. The doctor can use the marking information of the target ultrasonic image as a diagnosis reference basis. The marking information at least comprises scanning part information, focus information and treatment information corresponding to the ultrasonic image to be diagnosed. It is to be understood that the image database of the present invention is: one or more of a local image database, a hospital alliance image database, and a cloud image database.
In an embodiment, the image matching unit 200 of the present invention obtains the image matching value of the ultrasound image to be diagnosed and each of the matching images through a cosine similarity algorithm. The cosine similarity calculation method measures the similarity between the ultrasonic image to be diagnosed and the matched image by calculating the cosine value of the included angle between the image feature vector representing the ultrasonic image to be diagnosed and the inner product space of a plurality of matched images in the image database.
In order to further improve the speed and accuracy of the retrieval matching, in another embodiment, the image matching unit 200 of the present invention calculates and obtains the image matching degree value of the ultrasound image to be diagnosed and each of the matching images through a trained matching neural network model. The matching neural network model comprises: a first neural network, a second neural network, a screening neural network, and a matching neural network.
The first neural network is used for identifying the scanning part of the ultrasonic image to be diagnosed and is obtained by training a plurality of ultrasonic images marked with the category of the scanning part; the first neural network is a convolutional neural network and comprises an input layer, a hidden layer and an output layer; the hidden layer comprises a plurality of convolution layers, a down-sampling layer and an up-sampling layer; the input ultrasonic image to be diagnosed is subjected to convolution operation and down-sampling operation respectively through a plurality of convolution layers and down-sampling layers, and is subjected to convolution operation and up-sampling operation respectively through a plurality of convolution layers and up-sampling layers; the input layer and the hidden layer, the hidden layers and the output layer of the first neural network are connected through weight parameters; the convolution layer in the first neural network is used for automatically extracting the characteristic vector in the ultrasonic image to be diagnosed. After a plurality of ultrasonic images marked with scanning part categories are trained, the scanning part corresponding to the ultrasonic image to be diagnosed can be quickly identified after the ultrasonic image to be diagnosed is input into the first neural network. It is understood that when the hospital stores the acquired ultrasound images in the local image database, the hospital union image database or the cloud image database, the hospital stores the ultrasound images in a classified manner, for example, all the ultrasound images related to the "heart" are stored in a sub-image set. The image database establishes corresponding sub-image sets according to different scanned positions, such as uterus, brain, chest, abdomen and the like. According to the invention, the first neural network can quickly identify the scanned part of the ultrasonic image to be diagnosed, so that the amount of retrieval matching can be reduced, and the speed of retrieval matching is improved. It should be understood that the first neural network can quickly identify the scanned part of the ultrasound image to be diagnosed, and can be used as a determination unit to determine the diagnostic information of the ultrasound image to be diagnosed, for example, when the information is scanned part information, the scanned part can be identified through the first neural network, and then reference confirmation is performed by combining the scanned part information in the marker information included in the target ultrasound image.
In order to further improve the matching speed of the image matching unit 200, the second neural network is used for identifying the focus information of the ultrasonic image to be diagnosed, and the second neural network is obtained by training a plurality of ultrasonic images marked with the focus information; the second neural network of the present invention is also a convolutional neural network. And predicting the focus information through a second neural network. It should be understood that the lesion information identified by the second neural network is the contour of the lesion, i.e., the shape of the lesion. The second neural network training method specifically comprises the following steps: inputting a plurality of ultrasonic image samples marked with focus information into a second neural network to predict focus areas in the ultrasonic image samples; determining a target lesion area corresponding to the predicted lesion area by using the predicted lesion area; and determining the sampling weight of the ultrasonic image sample according to the focus information of the ultrasonic image sample and the predicted focus area, and further obtaining a trained second neural network. The focus area of the ultrasonic image to be diagnosed can be quickly identified through the second neural network. It can be understood that the image database can establish corresponding sub-image sets according to different lesion areas of the same scanned part. The "lesion area" herein may be understood as a shape contour of a lesion. According to the method, the focus information of the ultrasonic image to be diagnosed can be rapidly identified through the second neural network, the searching matching amount can be reduced, and the searching matching speed is improved.
Screening a neural network, and screening a corresponding matched image according to the information of the scanned part and the focus of the ultrasonic image to be diagnosed; and the matching neural network is used for calculating the matching value of the ultrasonic image to be diagnosed and the matching image screened by the screening neural network. The specific training mode of the screening neural network and the matching neural network is similar to that of the first neural network and the second neural network, and is not repeated herein.
The screening unit 300 is configured to determine, according to the image matching degree value, a matching image in the image database, for which the image matching degree value exceeds a preset matching degree value, as a target ultrasound image; the preset matching degree value set by the screening unit 300 enables at least one matching image to be determined as the target ultrasound image. An operator can set a preset matching value through the input unit, and the input unit is used for inputting a control instruction of the operator. The input unit may be at least one of a keyboard, a trackball, a mouse, a touch panel, a handle, a dial, a joystick, and a foot switch. The input unit may also input a non-contact type signal such as a sound, a gesture, a line of sight, or a brain wave signal. The operator may set a specific preset matching value, for example, 95%, and more than 95% of the matching images in the image database may be screened by the screening unit 300. Several matching ultrasound images can also be determined as target ultrasound images by setting a screen, and the screening unit 300 performs the screening according to the degree of the image matching degree.
The determining unit 400 of the present invention determines the diagnostic information of the ultrasound image to be diagnosed at least according to the marker information included in the target ultrasound image. And deducing and determining the diagnosis information of the ultrasonic image to be diagnosed through the mark information contained in the target ultrasonic image. The marking information at least comprises scanning part information, focus information and treatment information corresponding to the ultrasonic image to be diagnosed.
The method and the device search the real case corresponding to the ultrasonic image to be diagnosed as the basis of diagnosis in a search matching mode, have high speed and high accuracy, and avoid misdiagnosis.
As shown in fig. 2, the present invention further includes a display unit 500, the display unit 500 being configured to: displaying the ultrasonic image to be diagnosed and the target ultrasonic image, wherein the displayed target ultrasonic image comprises the corresponding mark information; and displaying the image matching value of the ultrasonic image to be diagnosed and the target ultrasonic image. The display unit 500 in this embodiment is a display, and the number of displays is not limited. The displayed ultrasound image to be diagnosed, the target ultrasound image, the image matching degree value, and the like may be displayed on one display, or may be simultaneously displayed on a plurality of displays, which is not limited in this embodiment. In addition, the display also provides a graphical interface for human-computer interaction of a user while displaying, one or more controlled objects are arranged on the graphical interface, and the operator is provided with a human-computer interaction device to input an operation instruction to control the controlled objects, so that corresponding control operation is executed. For example, projection and VR glasses, but the display may also include an input device, for example, a touch input display screen, and a projector VR glasses for sensing motion. The icons displayed on the display can be operated by the man-machine interaction device to execute specific functions.
In order to inquire the historical data of the inspection object corresponding to the ultrasonic image to be diagnosed, the historical data is compared with the historical ultrasonic image (the closest ultrasonic image) to obtain trend judgment or difference information judgment. As shown in fig. 3, the present invention further includes a history querying unit 600, where the history querying unit 600 is configured to: acquiring the information of an inspection object corresponding to an ultrasonic image to be diagnosed; inquiring historical diagnosis ultrasonic images of the examination object according to the examination object information corresponding to the ultrasonic images to be diagnosed, wherein the historical diagnosis ultrasonic images are stored in the image database; when the historical diagnostic ultrasound images exist, the historical diagnostic ultrasound images are arranged according to the diagnostic time of the historical diagnostic ultrasound images and used as a reference for the determination unit 400 to determine the diagnostic information of the ultrasound images to be diagnosed. Trend judgment or discriminative information judgment can be obtained.
As shown in fig. 4, the present invention further includes a similar query unit 700, which can obtain disease information diagnosis conclusion, medication record, diagnosis and treatment effect, etc. of the examination object under the similar ultrasound image. The homogeneous query unit 700 is configured to:
determining focus information according to the diagnosis information of the ultrasonic image to be diagnosed;
inquiring a matching image corresponding to the focus of the same kind in the image database according to the focus information;
the marking information of the matching image corresponding to the similar focus is used as the reference basis for the determining unit 400 to determine the diagnostic information of the ultrasonic image to be diagnosed.
As a second aspect of the present invention, as shown in fig. 5, the present invention further provides an ultrasound image query method, including:
s100, obtaining an ultrasonic image to be diagnosed, wherein the type of the ultrasonic image to be diagnosed comprises an ultrasonic image or an ultrasonic video;
s200, matching the ultrasonic image to be diagnosed with a plurality of matching images containing marking information in an image database, and calculating an image matching value;
s300, according to the image matching degree value, determining a matched image of which the image matching degree value exceeds a preset matching degree value in the image database as a target ultrasonic image;
s400, determining the diagnosis information of the ultrasonic image to be diagnosed at least according to the mark information contained in the target ultrasonic image.
The ultrasonic image query method can rapidly match the ultrasonic image to be diagnosed with a plurality of matching images containing the mark information in the image database and calculate the image matching degree value, then determines the matching image of which the image matching degree value exceeds the preset matching degree value in the image database as the target ultrasonic image, and determines the diagnosis information of the ultrasonic image to be diagnosed according to the mark information contained in the target ultrasonic image. The diagnostic information of the ultrasonic image to be diagnosed can be directly obtained in a retrieval matching mode.
As a third aspect of the present invention, a computer storage medium is provided, wherein the computer storage medium is used for storing a program for executing the above-mentioned ultrasound image query method.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.
Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.
Moreover, those skilled in the art will appreciate that aspects of the present description may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, aspects of this description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data blocks," modules, "" units, "" components, "or" systems. Furthermore, aspects of the present description may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the operation of various portions of this specification may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran2003, Perl, COBOL2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or processing device. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing processing device or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.
Claims (10)
1. An ultrasound image query device, comprising:
the system comprises an image acquisition unit, a diagnosis unit and a diagnosis unit, wherein the image acquisition unit is used for acquiring an ultrasonic image to be diagnosed, and the type of the ultrasonic image to be diagnosed comprises an ultrasonic image or an ultrasonic video;
the image matching unit is used for matching the ultrasonic image to be diagnosed with a plurality of matching images containing marking information in an image database and calculating an image matching value;
the screening unit is used for determining the matched image with the image matching degree value exceeding a preset matching degree value in the image database as a target ultrasonic image according to the image matching degree value;
and the determining unit is used for determining the diagnostic information of the ultrasonic image to be diagnosed at least according to the mark information contained in the target ultrasonic image.
2. The ultrasound image query device of claim 1, further comprising a display unit configured to:
displaying the ultrasonic image to be diagnosed and the target ultrasonic image, wherein the displayed target ultrasonic image comprises the corresponding mark information;
and displaying the image matching value of the ultrasonic image to be diagnosed and the target ultrasonic image.
3. The ultrasound image query device of claim 1, further comprising a history query unit configured to:
acquiring the information of an inspection object corresponding to an ultrasonic image to be diagnosed;
inquiring historical diagnosis ultrasonic images of the examination object according to the examination object information corresponding to the ultrasonic images to be diagnosed, wherein the historical diagnosis ultrasonic images are stored in the image database;
and when the historical diagnosis ultrasonic images exist, arranging according to the diagnosis time of the historical diagnosis ultrasonic images, and using the arrangement as a reference basis for the determination unit to determine the diagnosis information of the ultrasonic images to be diagnosed.
4. The ultrasound image query device of claim 1, further comprising a homogeneous query unit configured to:
determining focus information according to the diagnosis information of the ultrasonic image to be diagnosed;
inquiring a matching image corresponding to the focus of the same kind in the image database according to the focus information;
and taking the marking information of the matched image corresponding to the similar focus as a reference basis for the determining unit to determine the diagnostic information of the ultrasonic image to be diagnosed.
5. The ultrasound image query device according to claim 1, wherein the marking information at least includes scanned region information, lesion information and treatment information corresponding to the ultrasound image to be diagnosed.
6. The ultrasound image query device according to any one of claims 1 to 5, wherein the image matching unit obtains an image matching degree value of the ultrasound image to be diagnosed and each of the matching images through cosine similarity calculation.
7. The ultrasound image query device according to any one of claims 1 to 5, wherein the image matching unit obtains an image matching degree value of the ultrasound image to be diagnosed and each matching image through a trained matching neural network model calculation.
8. The ultrasound image query device of any one of claims 1-5, wherein the matching neural network model comprises:
the first neural network is used for identifying the scanning part of the ultrasonic image to be diagnosed and is obtained by training a plurality of ultrasonic images marked with the category of the scanning part;
the first neural network is obtained by training a plurality of ultrasonic images marked with focus information;
screening a neural network, and screening a corresponding matched image according to the information of the scanned part and the focus of the ultrasonic image to be diagnosed;
and the matching neural network is used for calculating the matching value of the ultrasonic image to be diagnosed and the matching image screened by the screening neural network.
9. The ultrasound image query device according to any one of claims 1-5, wherein the preset matching degree value set by the screening unit enables at least one matching image to be determined as the target ultrasound image.
10. The ultrasound image query device of any one of claims 1-5, wherein the image database is: one or more of a local image database, a hospital alliance image database, and a cloud image database.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112381006A (en) * | 2020-11-17 | 2021-02-19 | 深圳度影医疗科技有限公司 | Ultrasonic image analysis method, storage medium and terminal equipment |
| CN112837790A (en) * | 2021-01-27 | 2021-05-25 | 海纳医信（北京）软件科技有限责任公司 | Display method and device of image inspection information |
| CN113409924A (en) * | 2021-07-01 | 2021-09-17 | 上海市第一人民医院 | Artificial intelligence-based lung examination image auxiliary marking method and system |
| CN113553460A (en) * | 2021-08-13 | 2021-10-26 | 北京安德医智科技有限公司 | Image retrieval method and device, electronic device and storage medium |
| CN114549458A (en) * | 2022-02-20 | 2022-05-27 | 湖南康润药业股份有限公司 | Ultrasonic scanning normative evaluation method and system |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU1132899A (en) * | 1999-01-14 | 2000-07-20 | Motorola Australia Pty Ltd | A method and apparatus for quantising and dequantising data |
| CN101714153A (en) * | 2009-11-16 | 2010-05-26 | 杭州电子科技大学 | Visual perception based interactive mammography image searth method |
| CN103942337A (en) * | 2014-05-08 | 2014-07-23 | 北京航空航天大学 | Video search system based on image recognition and matching |
| WO2014141247A1 (en) * | 2013-03-11 | 2014-09-18 | Tylerton International Holdings Inc. | Modeling the autonomous nervous system and uses thereof |
| CN106779084A (en) * | 2016-09-30 | 2017-05-31 | 广州英康唯尔互联网服务有限公司 | Machine learning system and method |
| CN107644419A (en) * | 2017-09-30 | 2018-01-30 | 百度在线网络技术（北京）有限公司 | Method and apparatus for analyzing medical image |
| CN107820138A (en) * | 2017-11-06 | 2018-03-20 | 广东欧珀移动通信有限公司 | Video broadcasting method, device, terminal and storage medium |
| CN108345909A (en) * | 2018-02-27 | 2018-07-31 | 广东欧珀移动通信有限公司 | It is a kind of to search the method, apparatus of picture personage, computer readable storage medium |
| CN108717709A (en) * | 2018-05-24 | 2018-10-30 | 东北大学 | Image processing system and image processing method |
| CN109034380A (en) * | 2018-06-08 | 2018-12-18 | 四川斐讯信息技术有限公司 | A kind of distributed image identification system and its method |
| EP3528179A1 (en) * | 2018-02-15 | 2019-08-21 | Koninklijke Philips N.V. | Training a neural network |
| CN110197722A (en) * | 2019-05-31 | 2019-09-03 | 贵州精准健康数据有限公司 | AI-CPU system platform |
| CN110310271A (en) * | 2019-07-01 | 2019-10-08 | 无锡祥生医疗科技股份有限公司 | Property method of discrimination, storage medium and the Vltrasonic device of carotid plaques |
| CN110584714A (en) * | 2019-10-23 | 2019-12-20 | 无锡祥生医疗科技股份有限公司 | Ultrasonic fusion imaging method, ultrasonic device, and storage medium |
| CN110648327A (en) * | 2019-09-29 | 2020-01-03 | 无锡祥生医疗科技股份有限公司 | Method and equipment for automatically tracking ultrasonic image video based on artificial intelligence |
| CN111079802A (en) * | 2019-12-02 | 2020-04-28 | 易思维（杭州）科技有限公司 | Matching method based on gradient information |
| CN111292301A (en) * | 2018-12-07 | 2020-06-16 | 北京市商汤科技开发有限公司 | Focus detection method, device, equipment and storage medium |
| CN111292298A (en) * | 2020-01-21 | 2020-06-16 | 无锡祥生医疗科技股份有限公司 | Breast cancer pathological typing determination method, device and storage medium |
-
2020
- 2020-06-23 CN CN202010581882.0A patent/CN111816281B/en active Active
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU1132899A (en) * | 1999-01-14 | 2000-07-20 | Motorola Australia Pty Ltd | A method and apparatus for quantising and dequantising data |
| CN101714153A (en) * | 2009-11-16 | 2010-05-26 | 杭州电子科技大学 | Visual perception based interactive mammography image searth method |
| WO2014141247A1 (en) * | 2013-03-11 | 2014-09-18 | Tylerton International Holdings Inc. | Modeling the autonomous nervous system and uses thereof |
| CN103942337A (en) * | 2014-05-08 | 2014-07-23 | 北京航空航天大学 | Video search system based on image recognition and matching |
| CN106779084A (en) * | 2016-09-30 | 2017-05-31 | 广州英康唯尔互联网服务有限公司 | Machine learning system and method |
| CN107644419A (en) * | 2017-09-30 | 2018-01-30 | 百度在线网络技术（北京）有限公司 | Method and apparatus for analyzing medical image |
| CN107820138A (en) * | 2017-11-06 | 2018-03-20 | 广东欧珀移动通信有限公司 | Video broadcasting method, device, terminal and storage medium |
| EP3528179A1 (en) * | 2018-02-15 | 2019-08-21 | Koninklijke Philips N.V. | Training a neural network |
| CN108345909A (en) * | 2018-02-27 | 2018-07-31 | 广东欧珀移动通信有限公司 | It is a kind of to search the method, apparatus of picture personage, computer readable storage medium |
| CN108717709A (en) * | 2018-05-24 | 2018-10-30 | 东北大学 | Image processing system and image processing method |
| CN109034380A (en) * | 2018-06-08 | 2018-12-18 | 四川斐讯信息技术有限公司 | A kind of distributed image identification system and its method |
| CN111292301A (en) * | 2018-12-07 | 2020-06-16 | 北京市商汤科技开发有限公司 | Focus detection method, device, equipment and storage medium |
| CN110197722A (en) * | 2019-05-31 | 2019-09-03 | 贵州精准健康数据有限公司 | AI-CPU system platform |
| CN110310271A (en) * | 2019-07-01 | 2019-10-08 | 无锡祥生医疗科技股份有限公司 | Property method of discrimination, storage medium and the Vltrasonic device of carotid plaques |
| CN110648327A (en) * | 2019-09-29 | 2020-01-03 | 无锡祥生医疗科技股份有限公司 | Method and equipment for automatically tracking ultrasonic image video based on artificial intelligence |
| CN110584714A (en) * | 2019-10-23 | 2019-12-20 | 无锡祥生医疗科技股份有限公司 | Ultrasonic fusion imaging method, ultrasonic device, and storage medium |
| CN111079802A (en) * | 2019-12-02 | 2020-04-28 | 易思维（杭州）科技有限公司 | Matching method based on gradient information |
| CN111292298A (en) * | 2020-01-21 | 2020-06-16 | 无锡祥生医疗科技股份有限公司 | Breast cancer pathological typing determination method, device and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 周玉金等: ""基于卷积神经网络的左心室超声图像特征点定位"", 《计算机应用》, vol. 1, 20 May 2019 (2019-05-20), pages 1201 - 1207 * |
| 杨长兴等: "《医学计算机应用基础》", vol. 1, 中国铁道出版社, pages: 314 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112381006A (en) * | 2020-11-17 | 2021-02-19 | 深圳度影医疗科技有限公司 | Ultrasonic image analysis method, storage medium and terminal equipment |
| CN112837790A (en) * | 2021-01-27 | 2021-05-25 | 海纳医信（北京）软件科技有限责任公司 | Display method and device of image inspection information |
| CN112837790B (en) * | 2021-01-27 | 2024-04-19 | 海纳医信（北京）软件科技有限责任公司 | Method and device for displaying image inspection information |
| CN113409924A (en) * | 2021-07-01 | 2021-09-17 | 上海市第一人民医院 | Artificial intelligence-based lung examination image auxiliary marking method and system |
| CN113553460A (en) * | 2021-08-13 | 2021-10-26 | 北京安德医智科技有限公司 | Image retrieval method and device, electronic device and storage medium |
| CN114549458A (en) * | 2022-02-20 | 2022-05-27 | 湖南康润药业股份有限公司 | Ultrasonic scanning normative evaluation method and system |
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