CN114463336A - Cutting method and system for image and pixel level segmentation marking data thereof - Google Patents
Cutting method and system for image and pixel level segmentation marking data thereof Download PDFInfo
- Publication number
- CN114463336A CN114463336A CN202111642603.8A CN202111642603A CN114463336A CN 114463336 A CN114463336 A CN 114463336A CN 202111642603 A CN202111642603 A CN 202111642603A CN 114463336 A CN114463336 A CN 114463336A
- Authority
- CN
- China
- Prior art keywords
- image
- data matrix
- cut
- annotation
- labeling
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000005520 cutting process Methods 0.000 title claims abstract description 34
- 230000011218 segmentation Effects 0.000 title claims abstract description 29
- 239000011159 matrix material Substances 0.000 claims abstract description 63
- 238000002372 labelling Methods 0.000 claims abstract description 41
- 238000004590 computer program Methods 0.000 claims description 14
- 238000003860 storage Methods 0.000 claims description 10
- 238000006243 chemical reaction Methods 0.000 claims description 9
- 238000010586 diagram Methods 0.000 description 13
- 230000006870 function Effects 0.000 description 8
- 230000008569 process Effects 0.000 description 6
- 238000012545 processing Methods 0.000 description 6
- 238000012549 training Methods 0.000 description 6
- 230000001575 pathological effect Effects 0.000 description 4
- 238000003745 diagnosis Methods 0.000 description 3
- 238000003709 image segmentation Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 2
- 238000013136 deep learning model Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000010827 pathological analysis Methods 0.000 description 2
- 206010028980 Neoplasm Diseases 0.000 description 1
- 208000005718 Stomach Neoplasms Diseases 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 201000011510 cancer Diseases 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 206010017758 gastric cancer Diseases 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 201000011549 stomach cancer Diseases 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- 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
- G06T2207/10004—Still image; Photographic image
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Image Analysis (AREA)
Abstract
The invention belongs to the field of image cutting, and provides a cutting method and a cutting system for image and pixel level segmentation marking data thereof. The method comprises the steps of obtaining an original image, and converting the original image into an image data matrix; cutting the image data matrix according to the set pixels to obtain a cut image; acquiring an annotation file corresponding to the original image, and converting the annotation file into an annotation data matrix; cutting the label data matrix to obtain a cut label data matrix, and cutting in a mode of keeping the same size as the image so as to enable the cut image to correspond to the cut label file one by one; and determining labeling boundary coordinates one by one according to the labeling types aiming at the cut labeling data matrix, and storing the labeling boundary coordinates into corresponding labeling files.
Description
Technical Field
The invention belongs to the field of image clipping, and particularly relates to a clipping method and a clipping system for image and pixel level segmentation annotation data thereof.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the development of deep learning technology, more and more intelligent products enter our lives, but the current deep learning-based method has higher requirements on computer hardware and is difficult to process images with very high pixels. Particularly, in pathological diagnosis, many intelligent auxiliary diagnosis products are applied to actual scenes, but in pathological diagnosis, many scenes are needed to observe cells of pathological sections under a microscope, such as cancer diagnosis. The use of computer vision for automated diagnosis can greatly reduce the workload of pathologists. Researchers have also achieved significant success in this area in recent years.
The computer vision is used for solving the scenes, pathological sections need to be scanned under a high-power mirror, the pixel size of a scanned image is often over one hundred million or even billions, and the burden of the image on a computer is large, so that a neural network model cannot be trained. In addition, the labels required by the training network also need to be labeled on the complete slice scanning images, because the cut small images can destroy the integrity of the structure, which may lead to misjudgment of a pathologist. In addition, the depth learning model uses lower image pixels, and if an image with higher pixels is used, more time is needed for extracting image features, and more detailed information is lost; the image with higher pixels has high requirements on computer hardware, and a computer used in daily life cannot meet the requirements of large images easily.
In view of this, the invention provides a method for clipping an ultra-high pixel image, and can accurately clip the corresponding image segmentation label to ensure the effectiveness of the label.
Disclosure of Invention
In order to solve the technical problems in the background art, the present invention provides a clipping method and system for image and pixel level segmentation labeling data thereof, which can accurately clip corresponding image segmentation labels to ensure the effectiveness of the labels.
In order to achieve the purpose, the invention adopts the following technical scheme:
a first aspect of the present invention provides a cropping method for segmentation annotation data for an image and its pixel level segmentation.
A cropping method for image and pixel level segmentation annotation data thereof comprises the following steps:
acquiring an original image, and converting the original image into an image data matrix;
cutting the image data matrix according to the set pixels to obtain a cut image;
acquiring an annotation file corresponding to an original image, and converting the annotation file into an annotation data matrix;
cutting the label data matrix to obtain a cut label data matrix, and cutting in a mode of keeping the same size as the image so as to enable the cut image to correspond to the cut label file one by one;
and determining labeling boundary coordinates one by one according to the labeling types aiming at the cut labeling data matrix, and storing the labeling boundary coordinates into corresponding labeling files.
A second aspect of the invention provides a cropping system for an image and its pixel-level segmentation annotation data.
A cropping system for images and their pixel-level segmentation annotation data, comprising:
a first conversion module configured to: acquiring an original image, and converting the original image into an image data matrix;
a first cropping module configured to: cutting the image data matrix according to the set pixels to obtain a cut image;
a second conversion module configured to: acquiring an annotation file corresponding to the original image, and converting the annotation file into an annotation data matrix;
a second cropping module configured to: cutting the label data matrix to obtain a cut label data matrix, and cutting in a mode of keeping the same size as the image so as to enable the cut image to correspond to the cut label file one by one;
an annotation module configured to: and determining labeling boundary coordinates one by one according to the labeling types aiming at the cut labeling data matrix, and storing the labeling boundary coordinates into corresponding labeling files.
A third aspect of the invention provides a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in the cropping method for image and its pixel-level segmentation annotation data as described in the first aspect above.
A fourth aspect of the invention provides a computer apparatus.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the program implementing the steps in the cropping method for segmentation of annotation data for an image and its pixel level as described in the first aspect above.
Compared with the prior art, the invention has the beneficial effects that:
the invention can cut the image with higher pixels into the image with the pixel size required by an individual, can cut the image segmentation label in the original large image and corresponds to the cut small image one by one, and ensures the integrity and the accuracy of the label.
For the condition of label overlapping, the invention can also effectively process and ensure the precision of the label.
The method can be used for the creation process of the deep learning model training data set, and mainly aims at the training problem of the ultrahigh pixel image.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart of a cropping method for an image and its pixel-level segmentation annotation data shown in the present invention;
FIG. 2 is a flow diagram illustrating image cropping according to the present invention;
FIG. 3 is a flow chart of image annotation clipping according to the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
It is noted that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagrams may represent a module, a segment, or a portion of code, which may comprise one or more executable instructions for implementing the logical function specified in the respective embodiment. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Example one
As shown in fig. 1, the present embodiment provides a cropping method for an image and its pixel level segmentation annotation data, and the present embodiment is exemplified by applying the method to a server, it is understood that the method may also be applied to a terminal, and may also be applied to a system including a terminal and a server, and is implemented by interaction between the terminal and the server. The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a network server, cloud communication, middleware service, a domain name service, a security service CDN, a big data and artificial intelligence platform, and the like. The terminal may be, but is not limited to, a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and the like. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the application is not limited herein. In this embodiment, the method includes the steps of:
acquiring an original image, and converting the original image into an image data matrix;
cutting the image data matrix according to the set pixels to obtain a cut image;
acquiring an annotation file corresponding to the original image, and converting the annotation file into an annotation data matrix;
cutting the label data matrix to obtain a cut label data matrix, and cutting in a mode of keeping the same size as the image so as to enable the cut image to correspond to the cut label file one by one;
and determining labeling boundary coordinates one by one according to the labeling types aiming at the cut labeling data matrix, and storing the labeling boundary coordinates into corresponding labeling files.
Specifically, the present embodiment may be implemented by the following steps:
(1) image cropping
For an original image, the read image data is displayed in a matrix form, the matrix is cut according to fixed pixels, the cut matrix is the cut image, then the image matrix is stored in an image format, and the cut image is obtained, wherein a flow chart is shown in fig. 2. The method has a plurality of implementation ways, and a plurality of image-class toolkits can realize the image cropping.
(2) Label cutting
Only the original large image is cut, but a data set which can be used for neural network training cannot be completely manufactured, the embodiment mainly aims at the condition that the scanned image of the pathological section is too large, and the label file is also based on the scanned image, and provides a label cutting scheme which is used for preprocessing the scanned image and manufacturing the data set.
The annotation segmentation is mainly divided into three steps: reading an original label file, and converting the label into a mask matrix; then, the generated mask matrix is cut, and the cutting scheme corresponds to the image cutting, so that the consistency of the label and the image can be ensured; and finally, converting the cut mask matrixes into corresponding labels one by one and storing the labels. The annotations in this embodiment are stored in a data structure of a dictionary to read data information. The flow chart is shown in fig. 3.
Firstly, a reference dictionary between a label and a mask value needs to be established when the label file needs to be converted into the mask matrix, the value of the mask corresponding to the original image background is 0, and each different category has different values so as to distinguish different labels. The shape of the mask matrix is the same as that of the original image, so in the mask matrix, an area with a value of 0 indicates that the position is a background, and if the position is greater than 0, the position is a different category label. For some special cases in pathological scenes, the problem of label coincidence may occur, i.e. a label with a larger range may comprise a smaller label, for example, in the detection of gastric cancer, a poorly differentiated region is often present around a highly differentiated region. For this case, the present embodiment sets a higher priority to a smaller area of the two areas that coincide, so that the smaller area is always above the larger area.
After the original label is converted into the mask matrix, the mask can be cut according to the cutting scheme of the original image, so that the cut mask is ensured to correspond to the original image. The method comprises the following steps of firstly cutting a mask into a plurality of crop _ masks with fixed sizes, sequentially searching label areas existing in the crop _ masks according to a comparison dictionary of labels and masks for each crop _ mask, and generating boundary coordinates according to the label areas, wherein the generated boundary coordinates are new labels, and the corresponding labels are keywords of corresponding values in the comparison dictionary.
The generated boundary coordinates need to be further integrated into a corresponding annotation file (the generated boundary coordinates are written into the annotation file), in addition, each generated cut image generates a corresponding annotation file, and the boundary coordinate information is stored in the annotation file of the corresponding cut image. Input data and corresponding labels are needed to be used for training the deep learning model, but some images without labels are inevitably existed in the cut images, and the images without labels can be removed according to the label file for the images, so that the training effect of the model is ensured.
In order to ensure uniform data size, the embodiment crops the original image by using a pixel value with a fixed size, and all the obtained cropped images have the same pixel number, thereby facilitating subsequent processing. In addition, in order to save the time for labeling and improve the labeling quality, after the original image is labeled, the embodiment can accurately divide the labeled data on the original image to generate a new labeled file, which corresponds to the cut image one by one. In some special cases, the segmentation labels may overlap, and this embodiment performs processing for such special cases, and can perform clipping and boundary extraction on the labels of the overlapped portions, thereby further improving the accuracy of clipping.
Example two
The present embodiment provides a cropping system for an image and its pixel-level segmentation annotation data.
A cropping system for images and their pixel-level segmentation annotation data, comprising:
a first conversion module configured to: acquiring an original image, and converting the original image into an image data matrix;
a first cropping module configured to: cutting the image data matrix according to the set pixels to obtain a cut image;
a second conversion module configured to: acquiring an annotation file corresponding to the original image, and converting the annotation file into an annotation data matrix;
a second cropping module configured to: cutting the label data matrix to obtain a cut label data matrix, and cutting in a mode of keeping the same size as the image so as to enable the cut image to correspond to the cut label file one by one;
an annotation module configured to: and determining labeling boundary coordinates one by one according to the labeling types aiming at the cut labeling data matrix, and storing the labeling boundary coordinates into corresponding labeling files.
It should be noted here that the first conversion module, the first cropping module, the second conversion module, the second cropping module, and the labeling module are the same as the example and the application scenario realized by the steps in the first embodiment, but are not limited to the disclosure in the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
EXAMPLE III
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in the cropping method for image and its pixel-level segmentation annotation data as described in the first embodiment above.
Example four
The present embodiment provides a computer device, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the cropping method for segmenting annotation data of an image and the pixel level thereof as described in the first embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A cropping method for image and its pixel level segmentation labeling data is characterized by comprising the following steps:
acquiring an original image, and converting the original image into an image data matrix;
cutting the image data matrix according to the set pixels to obtain a cut image;
acquiring an annotation file corresponding to the original image, and converting the annotation file into an annotation data matrix;
cutting the label data matrix to obtain a cut label data matrix, and cutting in a mode of keeping the same size as the image so as to enable the cut image to correspond to the cut label file one by one;
and determining labeling boundary coordinates one by one according to the labeling types aiming at the cut labeling data matrix, and storing the labeling boundary coordinates into corresponding labeling files.
2. The cropping method for the image and the pixel-level segmentation annotation data thereof according to claim 1, wherein the cropping the image data matrix according to the set pixels to obtain the cropped image specifically comprises: and cutting the image data matrix according to the set pixels to obtain a cut image data matrix, and storing the cut image data matrix in an image format to obtain a cut image.
3. The cropping method for image and pixel level segmentation annotation data thereof according to claim 1, wherein the obtaining of the annotation file corresponding to the original image and the conversion of the annotation file into an annotation data matrix specifically comprises: and converting the annotation file into a mask matrix.
4. The clipping method for image and its pixel-level segmentation annotation data according to claim 3, wherein said converting the annotation file into a mask matrix specifically comprises:
a dictionary of references between annotations and mask values is established,
different values are set in the mask corresponding to different types of the original image so as to distinguish different labels.
5. A cropping method for image and its pixel level segmentation annotation data according to claim 4, characterized in that, of the two regions that coincide, the smaller region has a higher priority than the larger region.
6. The cropping method for image and its pixel level segmentation annotation data of claim 4, wherein said cropping the annotation data matrix specifically comprises:
cutting the mask matrix into a plurality of crop _ mask matrixes with fixed sizes, sequentially searching label areas existing in the crop _ masks for each crop _ mask matrix according to a comparison dictionary of labels and mask values, and generating boundary coordinates according to the label areas, wherein the generated boundary coordinates are new labels, and the corresponding labels are keywords of corresponding values in the comparison dictionary; and integrating the generated boundary coordinates into the cut labeling file.
7. The cropping method for image and its pixel level segmentation annotation data as claimed in claim 1, wherein said cropping the image data matrix according to the set pixels specifically comprises: and (4) clipping the original image by using a pixel value with a fixed size, wherein all the obtained clipped images have the same pixel number.
8. A cropping system for an image and its pixel-level segmentation annotation data, comprising:
a first conversion module configured to: acquiring an original image, and converting the original image into an image data matrix;
a first cropping module configured to: cutting the image data matrix according to the set pixels to obtain a cut image;
a second conversion module configured to: acquiring an annotation file corresponding to the original image, and converting the annotation file into an annotation data matrix;
a second cropping module configured to: cutting the label data matrix to obtain a cut label data matrix, and cutting in a mode of keeping the same size as the image so as to enable the cut image to correspond to the cut label file one by one;
an annotation module configured to: and determining labeling boundary coordinates one by one according to the labeling types aiming at the cut labeling data matrix, and storing the labeling boundary coordinates into corresponding labeling files.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for cropping segmentation annotation data for the image and its pixel level as claimed in any one of claims 1 to 7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps in the clipping method for segmentation annotation data for an image and its pixel level as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111642603.8A CN114463336A (en) | 2021-12-29 | 2021-12-29 | Cutting method and system for image and pixel level segmentation marking data thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111642603.8A CN114463336A (en) | 2021-12-29 | 2021-12-29 | Cutting method and system for image and pixel level segmentation marking data thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114463336A true CN114463336A (en) | 2022-05-10 |
Family
ID=81407431
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111642603.8A Pending CN114463336A (en) | 2021-12-29 | 2021-12-29 | Cutting method and system for image and pixel level segmentation marking data thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114463336A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114973244A (en) * | 2022-06-12 | 2022-08-30 | 桂林电子科技大学 | System and method for automatically identifying mitosis of H & E staining pathological image of breast cancer |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112017191A (en) * | 2020-08-12 | 2020-12-01 | 西北大学 | Method for establishing and segmenting liver pathology image segmentation model based on attention mechanism |
| WO2020238044A1 (en) * | 2019-05-31 | 2020-12-03 | 深圳市前海安测信息技术有限公司 | Method and device for constructing 3d unet network model for tumor detection, and storage medium |
| CN112733860A (en) * | 2021-01-27 | 2021-04-30 | 上海微亿智造科技有限公司 | Method and system for improving accuracy and recall rate of two-classification segmentation network |
| CN112784844A (en) * | 2020-12-31 | 2021-05-11 | 上海微亿智造科技有限公司 | Method, system and medium for making semantic segmentation net training sample |
| CN113221773A (en) * | 2021-05-19 | 2021-08-06 | 中国电子科技集团公司第二十八研究所 | Method for quickly constructing airplane classification data set based on remote sensing image |
-
2021
- 2021-12-29 CN CN202111642603.8A patent/CN114463336A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020238044A1 (en) * | 2019-05-31 | 2020-12-03 | 深圳市前海安测信息技术有限公司 | Method and device for constructing 3d unet network model for tumor detection, and storage medium |
| CN112017191A (en) * | 2020-08-12 | 2020-12-01 | 西北大学 | Method for establishing and segmenting liver pathology image segmentation model based on attention mechanism |
| CN112784844A (en) * | 2020-12-31 | 2021-05-11 | 上海微亿智造科技有限公司 | Method, system and medium for making semantic segmentation net training sample |
| CN112733860A (en) * | 2021-01-27 | 2021-04-30 | 上海微亿智造科技有限公司 | Method and system for improving accuracy and recall rate of two-classification segmentation network |
| CN113221773A (en) * | 2021-05-19 | 2021-08-06 | 中国电子科技集团公司第二十八研究所 | Method for quickly constructing airplane classification data set based on remote sensing image |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114973244A (en) * | 2022-06-12 | 2022-08-30 | 桂林电子科技大学 | System and method for automatically identifying mitosis of H & E staining pathological image of breast cancer |
| US12002206B2 (en) | 2022-06-12 | 2024-06-04 | Guilin University Of Electronic Technology | System and method for automatically identifying mitosis in H and E stained breast cancer pathological images |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11373305B2 (en) | Image processing method and device, computer apparatus, and storage medium | |
| CN110148121B (en) | Skin image processing method and device, electronic equipment and medium | |
| CN110705583A (en) | Cell detection model training method and device, computer equipment and storage medium | |
| Bussa et al. | Smart attendance system using OPENCV based on facial recognition | |
| CN111899247B (en) | Method, device, equipment and medium for identifying lumen area of choroidal blood vessel | |
| CN112101386B (en) | Text detection method, device, computer equipment and storage medium | |
| CN113705733A (en) | Medical bill image processing method and device, electronic device and storage medium | |
| CN112132827A (en) | Pathological image processing method and device, electronic equipment and readable storage medium | |
| CN111553923A (en) | Image processing method, electronic equipment and computer readable storage medium | |
| US20220319141A1 (en) | Method for processing image, device and storage medium | |
| CN114882047B (en) | Medical image segmentation method and system based on semi-supervision and Transformers | |
| CN113177957B (en) | Cell image segmentation method and device, electronic equipment and storage medium | |
| CN114463336A (en) | Cutting method and system for image and pixel level segmentation marking data thereof | |
| CN110110622B (en) | Medical text detection method, system and storage medium based on image processing | |
| CN114782355A (en) | Gastric cancer digital pathological section detection method based on improved VGG16 network | |
| WO2021159778A1 (en) | Image processing method and apparatus, smart microscope, readable storage medium and device | |
| CN113450355A (en) | Method for extracting image features based on multi-membrane CT image and 3DCNN network | |
| CN115908363B (en) | Tumor cell statistics method, device, equipment and storage medium | |
| Panchal et al. | An investigation on feature and text extraction from images using image recognition in Android | |
| CN113707280B (en) | Method, device, medium and computing equipment for expanding labeled data set | |
| CN115760886A (en) | Plot partitioning method and device based on aerial view of unmanned aerial vehicle and related equipment | |
| CN113610161A (en) | Target detection data labeling method based on image classification technology | |
| CN114399782A (en) | Text image processing method, device, equipment, storage medium and program product | |
| CN113537229A (en) | Bill image generation method and device, computer equipment and storage medium | |
| CN114663414B (en) | Rock and ore recognition and extraction system and method based on UNET convolutional neural network |
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 |