CN111062977B - Sample data generation method and device, computer equipment and storage medium - Google Patents
Sample data generation method and device, computer equipment and storage medium Download PDFInfo
- Publication number
- CN111062977B CN111062977B CN201911280823.3A CN201911280823A CN111062977B CN 111062977 B CN111062977 B CN 111062977B CN 201911280823 A CN201911280823 A CN 201911280823A CN 111062977 B CN111062977 B CN 111062977B
- Authority
- CN
- China
- Prior art keywords
- image data
- expected
- reference image
- layer thickness
- sample data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
- G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
- G06T7/337—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods involving reference images or patches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
- G06T7/0014—Biomedical image inspection using an image reference approach
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
Abstract
The embodiment of the invention discloses a sample data generation method, a sample data generation device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring reference image data and expected image data corresponding to the reference image data, wherein the reference image data and the expected image data correspond to the same scanning object, the reference layer thickness of the reference image is smaller than the expected layer thickness of the expected image, and the reference image data contains lesion marking information; and establishing a mapping relation between the reference image data and the expected image data, mapping the focus marking information of the reference image data into the expected image data according to the mapping relation, and taking the expected image data containing the mapped focus marking information as expected sample data. The method solves the problem that the sample data acquisition method in the prior art cannot ensure the accuracy of the sample data.
Description
Technical Field
The embodiment of the invention relates to the field of medical image data processing, in particular to a sample data generation method and device, computer equipment and a storage medium.
Background
The rapid evolution of the medical image artificial intelligence needs not only theoretical research but also a large amount of data as support, and the accuracy of the artificial intelligence model is based on the accuracy of a large amount of sample data, so that the higher the accuracy of the sample data is, the better the model learning effect is. In order to make the model more robust, the model is required to have higher accuracy not only on thin-layer data, but also on thick-layer data, and therefore, doctors are required to manually identify the focus of the CT images with different layer thicknesses to be used as sample data for model training and testing.
However, clinically, taking the CT lung nodule diagnosis as an example, doctors generally consider that when they screen nodules through a thin-layer image during film reading, they have the characteristics of low missed diagnosis and easily-determined properties, while when they screen lesions through a thick-layer image, they are easily missed diagnosis, and cannot determine whether the lesions are nodules or blood vessels, so that it is difficult to grasp the properties of the nodules and easy to misdiagnose, and therefore the accuracy of lesion labeling of the thin-layer CT image is much higher than that of the thick-layer CT image, i.e. the sample data acquisition method in the prior art hardly ensures the accuracy of lesion labeling of the thick-layer CT image, and also cannot ensure the accuracy of sample data corresponding to the thick-layer CT image.
Disclosure of Invention
The embodiment of the invention provides a sample data generation method, a sample data generation device, computer equipment and a storage medium, and solves the problem that the sample data acquisition method in the prior art cannot ensure the accuracy of sample data.
In a first aspect, an embodiment of the present invention provides a sample data generation method, including:
acquiring reference image data and expected image data corresponding to the reference image data, wherein the reference image data and the expected image data correspond to the same scanning object, the reference layer thickness of the reference image is smaller than the expected layer thickness of the expected image, and the reference image data contains lesion marking information;
and establishing a mapping relation between the reference image data and the expected image data, mapping the focus marking information of the reference image data into the expected image data according to the mapping relation, and taking the expected image data containing the mapped focus marking information as expected sample data.
Further, before the establishing the mapping relationship between the reference image data and the expected image data, the method further includes:
determining a corresponding reference layer thickness of the reference image according to the reference image data;
determining a desired layer thickness corresponding to the desired image according to the desired image data;
correspondingly, the establishing of the mapping relationship between the reference image data and the expected image data includes:
a mapping relationship between the reference image data of the reference layer thickness and the desired image data of the desired layer thickness is established.
Further, the establishing a mapping relationship between the reference image data of the reference layer thickness and the desired image data of the desired layer thickness includes:
rigidly registering the reference image data of the reference layer thickness with the desired image data of the desired layer thickness;
a mapping relationship between the reference image data and the desired image data is established based on the registration result.
Further, after the sample data is expected to be obtained, the method further comprises:
and rechecking the expected sample data, and taking the rechecked expected sample data as final expected sample data.
Further, the reference image and the desired sample data are used as target sample data.
Further, the lesion marking information is gold standard information.
In a second aspect, an embodiment of the present invention further provides an apparatus for generating sample data, including:
an obtaining module, configured to obtain reference image data and expected image data corresponding to the reference image data, where the reference image data and the expected image data correspond to a same scanning object, a reference layer thickness of the reference image is smaller than an expected layer thickness of the expected image, and the reference image data includes lesion marking information;
and the mapping module is used for establishing a mapping relation between the reference image data and the expected image data, mapping the focus marking information of the reference image data into the expected image data according to the mapping relation, and taking the expected image data containing the mapped focus marking information as expected sample data.
In a third aspect, an embodiment of the present invention further provides a computer device, where the computer device includes:
one or more processors;
storage means for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the sample data generation method according to any embodiment of the present invention.
Further, the output device is configured to output a configuration interface and a display interface, the configuration interface being at least configured to output a desired layer thickness option, the display interface being configured to output desired sample data.
In a fourth aspect, embodiments of the present invention further provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform the sample data generation method according to any embodiment of the present invention.
The technical scheme of the sample data generation method provided by the embodiment of the invention comprises the following steps: acquiring reference image data and expected image data corresponding to the reference image data, wherein the reference image data and the expected image data correspond to the same scanning object, the reference layer thickness of the reference image is smaller than the expected layer thickness of the expected image, and the reference image data contains focus marking information; and establishing a mapping relation between the reference image data and the expected image data, mapping the lesion marking information of the reference image data to the expected image data according to the mapping relation, and taking the expected image data containing the mapped lesion marking information as expected sample data. Compared with the prior art, the method has the advantages that the lesion marking information with higher accuracy in the reference image of the thin layer is mapped to the expected image of the thick layer, so that the lesion marking information of the expected image of the thick layer also has higher accuracy, and the accuracy of sample data of the medical image of the thick layer is greatly improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a sample data generating method according to an embodiment of the present invention;
FIG. 2A is a schematic diagram of a configuration interface according to an embodiment of the present invention;
FIG. 2B is a schematic diagram of another configuration interface provided in accordance with an embodiment of the present invention;
FIG. 2C is a schematic view of lesion marking information according to an embodiment of the present invention;
fig. 3A is a block diagram of a sample data generating apparatus according to a second embodiment of the present invention;
fig. 3B is a block diagram of a sample data generating apparatus according to a second embodiment of the present invention;
fig. 4 is a block diagram of a computer device according to a third embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described through embodiments with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. 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.
Example one
Fig. 1 is a flowchart of a sample data generation method according to an embodiment of the present invention. The technical scheme of the embodiment is suitable for automatically generating the focus marking information of the thick-layer medical image. The method can be executed by the sample data generation device provided by the embodiment of the invention, and the device can be realized in a software and/or hardware mode and is configured to be applied in a processor. The method specifically comprises the following steps:
s101, obtaining reference image data and expected image data corresponding to the reference image data, wherein the reference image data and the expected image data correspond to the same scanning object, the reference layer thickness of the reference image is smaller than the expected layer thickness of the expected image, and the reference image data comprises focus marking information.
The reference image and the desired image are three-dimensional medical images commonly used in clinic, such as CT, MR, PET-CT, etc., and the present embodiment takes a CT image as an example for explanation. And the reference image and the expected image are both preprocessed images, such as desensitization, cleaning, denoising and the like.
In order to improve the robustness of the model, medical images with different layer thicknesses and carrying lesion marking information are required to be used as sample data to train the model. However, for the thin layer CT image and the thick layer CT image of the same patient, the probability of occurrence of the lesion is completely the same, but the information of the lesion visually displayed on the thin layer CT image and the thick layer CT image is completely different, so that a doctor can easily find the position of the lesion on the thin layer CT image and analyze the information of the property, the size and the like of the lesion, but the difficulty of finding the position of the lesion on the thick layer CT image and analyzing the information of the property, the size and the like of the lesion is relatively high.
In order to improve the accuracy of the lesion marking information of the medical images with different layer thicknesses, the present embodiment first acquires reference image data and desired image data corresponding to the reference image data. Referring to fig. 2A, the user selects reference image data through a reference image data import option and imports desired image data through a desired image data import option. The reference image data and the desired image data correspond to the same scanning object, and preferably image data with different layer thicknesses corresponding to the same scanning object in the same positioning state, and the difference between the reference image data and the desired image data is that the reference layer thickness of the reference image corresponding to the reference image data is smaller than the desired layer thickness of the desired image corresponding to the desired image data, and the desired layer thickness can be set to 5mm, taking a CT image as an example. The reference image data contains the lesion marking information, and the lesion marking information is preferably gold standard marking information, namely the most reliable method for diagnosing a certain disease.
Preferably, in order to make the annotation information carried by the reference image data be gold standard annotation information, in this embodiment, two or more professional doctors respectively mark the reference image data back to back, and then the more professional senior doctors arbitrate the marking result, and the lesion annotation information carried by the arbitrated reference image data is used as gold standard lesion annotation information.
S102, establishing a mapping relation between the reference image data and the expected image data, mapping the focus marking information of the reference image data to the expected image data according to the mapping relation, and taking the expected image data containing the mapped focus marking information as expected sample data.
In some embodiments, the reference image data and the desired image data carry layer thickness information, such as both being DICOM files. It is therefore possible to determine a reference layer thickness for the reference image from the reference image data, determine a desired layer thickness for the desired image from the desired image data, and then establish a mapping between the reference image data for the reference layer thickness and the desired image data for the desired layer thickness. After the mapping relationship is determined, the lesion marking information in the reference image data is mapped to the expected image data according to the mapping relationship, and the expected image data containing the mapped lesion marking information is used as expected sample data.
In some embodiments, after acquiring the reference image data and the desired image data, the reference layer thickness of the reference image corresponding to the reference image data input or selected by the user and the desired layer thickness of the desired image corresponding to the desired image data are also acquired (see fig. 2B), and then a mapping relationship between the reference image data of the reference layer thickness and the desired image data of the desired layer thickness is established. After the mapping relationship is determined, the lesion marking information in the reference image data is mapped to the expected image data according to the mapping relationship, and the expected image data including the mapped lesion marking information is used as expected sample data, which is shown in fig. 2C.
In some embodiments, in establishing the mapping relationship between the reference image data of the reference layer thickness and the desired image data of the desired layer thickness, the reference image data of the reference layer thickness and the desired image data of the desired layer thickness are rigidly registered, and then the mapping relationship between the reference image data and the desired image data is established based on the registration result.
In order to further improve the accuracy of the desired sample data, the present embodiment performs a recheck on the desired sample data, and then takes the desired sample data that passes the recheck as final desired sample data. Wherein, the rechecking method can be selected as follows: and submitting the mapped expected sample data to a senior capital doctor for checking, if the checking is passed, taking the expected sample data as final sample data, and if the checking is not passed, canceling the qualification of the expected sample data as the sample data.
After the desired sample data is obtained, the reference image data and the desired image data of the desired layer thickness are used as target sample data for model training and model testing.
The technical scheme of the sample data generation method provided by the embodiment of the invention comprises the following steps: acquiring reference image data and expected image data corresponding to the reference image data, wherein the reference image data and the expected image data correspond to the same scanning object, the reference layer thickness of the reference image is smaller than the expected layer thickness of the expected image, and the reference image data contains focus marking information; and establishing a mapping relation between the reference image data and the expected image data, mapping the lesion marking information of the reference image data to the expected image data according to the mapping relation, and taking the expected image data containing the mapped lesion marking information as expected sample data. Compared with the prior art, the method has the advantages that the lesion marking information with higher accuracy in the reference image of the thin layer is mapped to the expected image of the thick layer, so that the lesion marking information of the expected image of the thick layer also has higher accuracy, and the accuracy of sample data of the medical image of the thick layer is greatly improved.
Example two
Fig. 3A is a block diagram of a sample data generation apparatus according to an embodiment of the present invention. The device is used for executing the sample data generation method provided by any of the above embodiments, and the device can be implemented by software or hardware. The device includes:
an obtaining module 11, configured to obtain reference image data and expected image data corresponding to the reference image data, where the reference image data and the expected image data correspond to the same scanning object, a reference layer thickness of the reference image is smaller than an expected layer thickness of the expected image, and the reference image data includes lesion marking information;
the mapping module 12 is configured to establish a mapping relationship between the reference image data and the expected image data, map the lesion marking information of the reference image data into the expected image data according to the mapping relationship, and use the expected image data including the mapped lesion marking information as expected sample data.
Optionally, the mapping module 12 includes:
a layer thickness determining unit for determining a corresponding reference layer thickness of the reference image based on the reference image data;
and the mapping unit is used for determining the expected layer thickness corresponding to the expected image according to the expected image data. A mapping relationship between the reference image data of the reference layer thickness and the desired image data of the desired layer thickness is established.
Optionally, the mapping unit is specifically configured to perform rigid registration on the reference image data of the reference layer thickness and the desired image data of the desired layer thickness; a mapping relationship between the reference image data and the desired image data is established based on the registration result.
As shown in fig. 3B, the system further includes a rechecking module 13, configured to recheck the expected sample data, and use the expected sample data that passes the rechecking as final expected sample data.
According to the technical scheme of the sample data generation device provided by the embodiment of the invention, the reference image data and the expected image data corresponding to the reference image data are obtained through the obtaining module, wherein the reference image data and the expected image data correspond to the same scanning object, the reference layer thickness of the reference image is smaller than the expected layer thickness of the expected image, and the reference image data contain focus marking information; the mapping relation between the reference image data and the expected image data is established through the mapping module, the focus marking information of the reference image data is mapped to the expected image data according to the mapping relation, and the expected image data containing the mapped focus marking information is used as expected sample data. Compared with the prior art, the method has the advantages that the lesion marking information with higher accuracy in the reference image of the thin layer is mapped to the expected image of the thick layer, so that the lesion marking information of the expected image of the thick layer also has higher accuracy, and the accuracy of sample data of the medical image of the thick layer is greatly improved.
The sample data generation device provided by the embodiment of the invention can execute the sample data generation method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
EXAMPLE III
Fig. 4 is a schematic structural diagram of a computer apparatus according to a third embodiment of the present invention, as shown in fig. 4, the apparatus includes a processor 201, a memory 202, an input device 203, and an output device 204; the number of the processors 201 in the device may be one or more, and one processor 201 is taken as an example in fig. 4; the processor 201, the memory 202, the input device 203 and the output device 204 in the apparatus may be connected by a bus or other means, and fig. 4 illustrates the connection by a bus as an example.
The memory 202, as a computer-readable storage medium, may be used for storing software programs, computer-executable programs, and modules, such as program instructions/modules (e.g., the obtaining module 11 and the mapping module 12) corresponding to the sample data generating method in the embodiment of the present invention. The processor 201 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory 202, that is, implements the sample data generation method described above.
The memory 202 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 for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 202 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 202 may further include memory located remotely from the processor 201, which may be connected to the device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 203 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function controls of the apparatus.
The output means 204 may comprise a display device such as a display screen of a user terminal, for example, for outputting a configuration interface for outputting at least the desired layer thickness option and a display interface for outputting the desired sample data.
Example four
An embodiment of the present invention further provides a storage medium containing computer-executable instructions, where the computer-executable instructions are executed by a computer processor to perform a sample data generation method, and the method includes:
acquiring reference image data and expected image data corresponding to the reference image data, wherein the reference image data and the expected image data correspond to the same scanning object, the reference layer thickness of the reference image is smaller than the expected layer thickness of the expected image, and the reference image data contains lesion marking information;
and establishing a mapping relation between the reference image data and the expected image data, mapping the focus marking information of the reference image data into the expected image data according to the mapping relation, and taking the expected image data containing the mapped focus marking information as expected sample data.
Of course, the storage medium provided by the embodiment of the present invention contains computer-executable instructions, and the computer-executable instructions are not limited to the method operations described above, and may also perform related operations in the sample data generation method provided by any embodiment of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) to execute the sample data generation method according to the embodiments of the present invention.
It should be noted that, in the embodiment of the sample data generation apparatus, each included unit and module are only divided according to functional logic, but are not limited to the above division, as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (8)
1. A sample data generating method is characterized by comprising the following steps:
acquiring reference image data and expected image data corresponding to the reference image data, wherein the reference image data and the expected image data correspond to the same scanning object, the reference layer thickness corresponding to the reference image data is smaller than the expected layer thickness corresponding to the expected image data, the reference image data comprises focus labeling information, and the focus labeling information is gold standard information;
and establishing a mapping relation between the reference image data and the expected image data, mapping the focus marking information of the reference image data to the expected image data according to the mapping relation, taking the expected image data containing the mapped focus marking information as expected sample data, and taking the reference image data containing the focus marking information and the expected sample data as target sample data for model training.
2. The method of claim 1, wherein prior to establishing the mapping relationship between the reference image data and the desired image data, further comprising:
determining a corresponding reference layer thickness of the reference image according to the reference image data;
determining a desired layer thickness corresponding to the desired image according to the desired image data;
correspondingly, the establishing of the mapping relationship between the reference image data and the expected image data includes:
a mapping relationship between the reference image data of the reference layer thickness and the desired image data of the desired layer thickness is established.
3. The method of claim 2, wherein establishing a mapping between the reference image data for the reference layer thickness and the desired image data for the desired layer thickness comprises:
rigidly registering the reference image data of the reference layer thickness with the desired image data of the desired layer thickness;
a mapping relationship between the reference image data and the desired image data is established based on the registration result.
4. The method of claim 1, wherein after the sample data is expected to be obtained, further comprising:
and rechecking the expected sample data, and taking the rechecked expected sample data as final expected sample data.
5. A sample data generation apparatus, comprising:
an obtaining module, configured to obtain reference image data and expected image data corresponding to the reference image data, where the reference image data and the expected image data correspond to a same scanning object, a reference layer thickness corresponding to the reference image data is smaller than an expected layer thickness corresponding to the expected image data, the reference image data includes lesion marking information, and the lesion marking information is gold standard information;
and the mapping module is used for establishing a mapping relation between the reference image data and the expected image data, mapping the focus marking information of the reference image data into the expected image data according to the mapping relation, taking the expected image data containing the mapped focus marking information as expected sample data, and taking the reference image data containing the focus marking information and the expected sample data as target sample data for model training.
6. A computer device, characterized in that the computer device comprises:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the sample data generation method of any one of claims 1-4.
7. The computer apparatus of claim 6, further comprising an output device;
the output device is used for outputting a configuration interface and a display interface, the configuration interface is at least used for outputting the expected layer thickness option, and the display interface is used for outputting expected sample data.
8. A storage medium containing computer executable instructions for performing the method of sample data generation according to any one of claims 1 to 4 when executed by a computer processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911280823.3A CN111062977B (en) | 2019-12-13 | 2019-12-13 | Sample data generation method and device, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911280823.3A CN111062977B (en) | 2019-12-13 | 2019-12-13 | Sample data generation method and device, computer equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111062977A CN111062977A (en) | 2020-04-24 |
| CN111062977B true CN111062977B (en) | 2021-05-04 |
Family
ID=70300968
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911280823.3A Active CN111062977B (en) | 2019-12-13 | 2019-12-13 | Sample data generation method and device, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111062977B (en) |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006054271A2 (en) * | 2004-11-19 | 2006-05-26 | Koninklijke Philips Electronics, N.V. | False positive reduction in computer -assisted detection ( cad) |
| CA2897614C (en) * | 2013-03-14 | 2019-05-28 | Ventana Medical Systems, Inc. | Whole slide image registration and cross-image annotation devices, systems and methods |
| US10169871B2 (en) * | 2016-01-21 | 2019-01-01 | Elekta, Inc. | Systems and methods for segmentation of intra-patient medical images |
| CN107481276B (en) * | 2016-06-08 | 2020-02-14 | 北京天智航医疗科技股份有限公司 | Automatic identification method for marker point sequence in three-dimensional medical image |
| CN109523512B (en) * | 2018-10-17 | 2019-10-29 | 哈尔滨理工大学 | A kind of Automatic medical image segmentation method based on multichannel chromatogram tag fusion |
| CN109635646B (en) * | 2018-11-02 | 2023-02-10 | 清影医疗科技（深圳）有限公司 | Head image processing method, system, equipment and storage medium |
| CN109712176A (en) * | 2018-11-28 | 2019-05-03 | 众安信息技术服务有限公司 | A kind of mask method and system of multi-sequence image |
-
2019
- 2019-12-13 CN CN201911280823.3A patent/CN111062977B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN111062977A (en) | 2020-04-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10755411B2 (en) | Method and apparatus for annotating medical image | |
| KR101818074B1 (en) | Artificial intelligence based medical auto diagnosis auxiliary method and system therefor | |
| CN107123137B (en) | Medical image processing method and equipment | |
| CN103841894B (en) | The image segmentation of organ and anatomical structure | |
| CN111080583B (en) | Medical image detection method, computer device, and readable storage medium | |
| WO2019146357A1 (en) | Medical image processing device, method, and program, and diagnosis assistance device, method, and program | |
| CN111627508A (en) | Medical image reading method and device and computer readable medium | |
| CN109741312A (en) | A kind of Lung neoplasm discrimination method, device, equipment and medium | |
| JP2019008349A (en) | Learning data generation support apparatus and learning data generation support method and learning data generation support program | |
| JP2020171687A (en) | Systems and methods for processing 3d anatomical volumes based on localization of 2d slices thereof | |
| CN111062390A (en) | Region-of-interest labeling method, device, equipment and storage medium | |
| JPWO2019176407A1 (en) | Learning support device, learning support method, learning support program, area of interest discrimination device, area of interest discrimination method, area of interest discrimination program and trained model | |
| CN111863204A (en) | Mammary gland disease AI auxiliary diagnosis method and system based on molybdenum target X-ray photographic examination | |
| CA3011141A1 (en) | Confidence determination in a medical imaging video clip measurement based upon video clip image quality | |
| CN111782529A (en) | Test method and device for auxiliary diagnosis system, computer equipment and storage medium | |
| JP7170000B2 (en) | LEARNING SYSTEMS, METHODS AND PROGRAMS | |
| CN113888566A (en) | Target contour curve determining method and device, electronic equipment and storage medium | |
| CN111144506B (en) | Liver bag worm identification method based on ultrasonic image, storage medium and ultrasonic equipment | |
| CN111062977B (en) | Sample data generation method and device, computer equipment and storage medium | |
| CN111192664A (en) | Image marking method, system, equipment and storage medium | |
| CN109493396B (en) | CT image display method, device, equipment and medium | |
| JP7216660B2 (en) | Devices, systems, and methods for determining reading environments by synthesizing downstream needs | |
| CN112767314A (en) | Medical image processing method, device, equipment and storage medium | |
| CN111311587A (en) | Medical image data processing method, medical image data processing device, medical information system and medium | |
| CN110689112A (en) | Data processing method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Address after: Room B401, 4 / F, building 1, No. 12, shangdixin Road, Haidian District, Beijing 100085 Applicant after: Tuxiang Medical Technology Co., Ltd Address before: Room B401, 4 / F, building 1, No. 12, shangdixin Road, Haidian District, Beijing 100085 Applicant before: Beijing Tuoxiang Technology Co.,Ltd. |
|
| CB02 | Change of applicant information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |