CN118311010A - Exosome automatic scanning image analysis method, medium and system - Google Patents
Exosome automatic scanning image analysis method, medium and system Download PDFInfo
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- 210000001808 exosome Anatomy 0.000 title claims abstract description 180
- 238000003703 image analysis method Methods 0.000 title claims abstract description 15
- 238000011156 evaluation Methods 0.000 claims abstract description 7
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Abstract
An automatic exosome scanning image analysis method, medium and system relates to the technical field of exosomes, and comprises the steps of obtaining a fluorescence microscopic image as a background layer, carrying out scaling identification on the fluorescence microscopic image step by step, eliminating a region which does not contain the exosomes, identifying the parameters of the rest exosomes according to preset identification characteristics, copying and stacking a layer of eliminating layers on the eliminating layers as parameter identification layers, marking the exosomes of the same type by adopting uniform type identification according to parameter identification results, analyzing the parameter states of the exosomes of different types and displaying the parameter states on the parameter identification layers, evaluating the parameter states of the exosomes, giving corresponding evaluation contents on the parameter identification layers according to preset results, receiving external instructions and identifying the instructions, feeding back corresponding results according to the instructions and based on the parameter identification results, and the like.
Description
Technical Field
The invention relates to the technical field of exosomes, in particular to an exosome automatic scanning image analysis method, medium and system.
Background
The exosome is a nano-level vesicle secreted by cells, has rich biological information, and is widely applied to the detection of the exosome as a high-resolution imaging tool by an electron microscope. However, at present, the detection of the exosomes by using an electron microscope still has a certain difficulty, and because the exosomes are small in size and various in form, the exosomes are easily confused with other organelles after the images of the exosomes are acquired, and are difficult to accurately identify, and meanwhile, a great deal of time and effort are required to analyze the states of the exosomes, so that the research of the exosomes is plagued.
Disclosure of Invention
The embodiment of the invention provides an exosome automatic scanning image analysis method, medium and system, which are used for identifying exosome in an image after a fluorescence microscopic image is acquired, so that the effect of reducing the workload and assisting the exosome research process is realized.
An exosome automatic scanning image analysis method comprises the following steps:
acquiring a fluorescence microscopic image, wherein the fluorescence microscopic image is acquired from an electron microscope, and the acquired fluorescence microscopic image contains at least one kind of exosomes;
Taking the fluorescence microscopic image obtained originally as a background layer, and copying and stacking a layer of the fluorescence microscopic image as a rejecting layer on the background layer;
performing scaling identification step by step on the fluorescence microscopic image in the eliminating layer, and eliminating the area which does not contain exosomes;
Identifying parameters of the exosomes in the rest part according to preset identification characteristics;
copying and stacking one layer of the rejecting layer on the rejecting layer to serve as a parameter identification layer;
Marking the same type of exosomes by adopting a uniform type of mark according to a parameter identification result, analyzing parameter states of different types of exosomes, and displaying the parameter states on the parameter identification layer;
The parameter states of the exosomes are evaluated, and corresponding evaluation contents are given out on the parameter identification layer according to preset results;
and receiving an external instruction, identifying the instruction, and feeding back a corresponding result according to the instruction and based on the parameter identification result.
Further, the step-by-step rejection is performed as follows:
Identifying a target in the microscopic image under a fixed scaling factor a, and removing a region which does not contain exosomes at a coarse level;
further identifying targets in the microscopic image under the fixed scaling ratio b, and secondarily eliminating areas which do not contain exosomes;
and (3) at a fixed scaling rate c, further identifying the target in the microscopic image, and removing the area which does not contain exosomes at a high level.
Further, the fixed scale c is a secondary amplification based on the fixed scale b, and the fixed scale b is a secondary amplification based on the fixed scale a.
Further, the non-exosome region is identified by eliminating the fluorescent-based color feature and the non-exosome shape feature at a fixed scale a, the non-exosome region is identified by eliminating the non-exosome shape feature and the exosome shape feature at a fixed scale b, and the non-exosome region is identified by eliminating the exosome shape feature at a fixed scale c.
Further, the instructions include individually labeling specific one or more types of exosomes, sorting or ordering the exosomes according to one or more characteristics, simulating a virtual diffraction animation of the selected exosomes, and simulating a motion profile of the selected exosomes from an initial point to an end point.
Further, the background layer, the eliminating layer and the parameter identification layer can be stacked together for display or can be separately displayed.
Further, assessment of exosomes includes morphology and size, number and concentration.
A computer readable storage medium having stored therein program instructions for implementing the above-described exosome auto-scan image analysis method when executed.
An exosome automatic scanning image analysis system comprises the computer readable storage medium.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
1. the exosome can be identified and analyzed, and the identification and analysis display can be performed through the parameter identification layer, so that the workload is reduced.
2. The corresponding result can be fed back according to the received external instruction and based on the parameter identification result, so that the exosome can be conveniently researched, and the effect of assisting the exosome research process is realized.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
Fig. 1 is a schematic flow chart of an exosome automatic scanning image analysis method according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Fig. 1 shows a flow of an exosome automatic scan image analysis method disclosed in an embodiment of the present invention, which includes the following steps:
s1, acquiring a fluorescence microscopic image, wherein the fluorescence microscopic image is acquired from an electron microscope, and the acquired fluorescence microscopic image contains at least one kind of exosomes;
The steps are as follows:
s11, dyeing by using a specific fluorescent dye according to the type of the collected exosomes, and waiting for stable combination of the fluorescent dye and the lipid bilayer membrane of the exosomes after the dyeing treatment;
and S12, preparing the sample to be observed into a state suitable for fluorescent microscope observation, and then acquiring a fluorescent microscope image.
S2, taking the originally obtained fluorescence microscopic image as a background layer, and copying and stacking a layer of fluorescence microscopic image as a rejecting layer on the background layer;
the background layer is an original fluorescence microscopic image and is provided with an undevelopable modification, the undeveloped fluorescence microscopic image is used for storing original image information, the rejecting layer is a characteristic identification layer, and the characteristic identification layer is used for identifying areas which are not required to be identified and areas which are required to be reserved in the background layer according to the rejecting layer.
S3, performing scaling identification step by step on the fluorescence microscopic image in the eliminating layer, and eliminating the area without exosomes;
Specifically, the step-by-step rejection is performed as follows:
S31, identifying a target in a microscopic image under a fixed scaling factor a, and removing a region which does not contain exosomes at a coarse level;
Wherein, the color characteristic based on fluorescence and the shape characteristic of the non-exosome are removed for identification on the non-exosome area under the fixed scaling ratio a.
S32, under the condition of fixed scaling b, further identifying targets in the microscopic image, and removing areas which do not contain exosomes in a secondary mode;
And (3) removing the non-exosome area under the fixed scaling ratio b, and identifying the non-exosome area based on the shape characteristics of the non-exosome and the shape characteristics of the exosome.
S33, under the fixed scaling factor c, further identifying the target in the microscopic image, and removing the area which does not contain exosomes at a high level;
wherein, the region of the non-exosome is removed and identified based on the shape characteristics of the exosome under the fixed scaling ratio c.
The specific values of the fixed scaling ratios a to c are set according to actual conditions, and three-level scaling is adopted in the embodiment, so that the following benefits are obtained:
1. under the fixed scaling factor a, according to the color characteristics of fluorescence and the shape characteristics of the non-exosomes, the non-exosome areas can be rapidly and largely removed, the computing resources of the non-exosome areas can be saved, and the recognition speed can be improved.
2. Under the fixed scaling factor b, according to the shape characteristics of the non-exosomes and the shape characteristics of the exosomes, the area of the non-exosomes can be removed rapidly and in a large area, and the identification precision of the exosomes can be improved while the area of the non-exosomes can be removed in a large area.
3. And under the condition of fixed scaling c, identifying the rest part according to the shape characteristics of the exosomes, and further improving the identification accuracy of the exosomes.
Through the three-level scaling elimination recognition, the recognition speed can be improved, the recognition accuracy can be kept, and recognition result errors caused by the fact that exosomes are lost in the recognition process are avoided.
The fixed scale c is a secondary amplification based on the fixed scale b, and the fixed scale b is a secondary amplification based on the fixed scale a.
S4, identifying parameters of the remaining exosomes according to preset identification features;
the parameters of exosomes in the fluorescence micrograph include the morphology of exosomes and the number of exosomes.
The method comprises the following specific steps:
S41, processing the exosome image by using a morphological algorithm, wherein the exosome image comprises expansion and corrosion operations so as to highlight morphological characteristics of the exosome;
s42, classifying and identifying exosomes by adopting a decision tree according to morphological characteristics of the exosomes;
S43, outputting the form, the type and the number of the exosomes.
S5, copying and stacking a reject layer on the reject layer to serve as a parameter identification layer;
The parameter identification layer is a transparent layer, and is not provided with image information, and is only used for displaying characters, symbols, patterns and color information.
S6, marking the same type of exosomes by adopting a uniform type of mark according to a parameter identification result, analyzing parameter states of different types of exosomes and displaying the parameter states on a parameter identification layer;
And S7, evaluating the parameter states of the exosomes, and giving corresponding evaluation contents on the parameter identification layer according to a preset result, wherein, for a single exosome, the morphological characteristics of the exosomes, such as size, shape, texture and the like, are extracted by utilizing an image processing technology. By comparing these characteristics with known normal exosome characteristics, the degree of exosome health can be initially determined. For example, if the exosomes are of abnormal size, irregular shape or blurred surface texture, this may indicate poor health, resulting in a population of exosomes; quantitative analysis of parameters such as the number, distribution, density and the like of exosomes is also performed. The health of the exosome population can be assessed by comparison with the parameters of the normal exosome population. For example, if the number of exosomes is reduced, unevenly distributed or abnormally dense, it may indicate poor health;
And (3) analyzing according to the identification and evaluation results (form, type and quantity) of the exosomes in the steps S6-S67 to obtain parameter state data of the exosomes, and displaying the parameter state data on a parameter identification layer.
The parameter status is used for displaying the form, the type and the quantity of the exosomes as well as the health degree of the exosomes and the health degree of the exosomes.
In one example, coordinates are assigned to each exosome, a hidden table is set according to the coordinates of the exosome, and parameter states of the individual exosomes are displayed in the table.
In another example, a separate, concealable form is provided to show the parameter status of the exosome population.
S8, receiving an external instruction, identifying the instruction, and feeding back a corresponding result according to the instruction and based on the parameter identification result;
The instructions include individually marking a particular type or types of exosomes, sorting or ordering the exosomes according to one or more characteristics, simulating a virtual diffraction animation of the selected exosomes, and simulating a motion profile of the selected exosomes from an initial point to an end point.
In one example of the above embodiment, when performing single labeling of one type of exosomes, if the fibrous cell exosomes need to be labeled, according to the identification result of the exosomes, wrapping the external contour of the fibrous cell exosomes with a closed pattern to achieve the effect of labeling the fibrous cell exosomes;
In another example, when the plurality of types of exosomes are individually labeled, if the fibrous exosomes, the stem exosomes, and the tumor exosomes need to be labeled, the external contours of the fibrous exosomes, the stem exosomes, and the tumor exosomes are respectively wrapped by using distinguishable (different colors/shapes) closed patterns according to the identification result of the exosomes, so as to achieve the effect of respectively labeling the fibrous exosomes, the stem exosomes, and the tumor exosomes.
In another example of the above embodiment, the exosomes are classified according to the species characteristics.
In another example of the above embodiment, the exosomes are ordered by wellness.
In another example of the above embodiment, the simulated construction of the virtual diffraction animation of the selected exosomes specifically comprises the steps of:
Analyzing parameters and states of the current exosomes;
constructing a simulated cell according to parameters of exosomes;
simulating and controlling budding and fusion of the membrane vesicles to simulate the formation of exosomes and the current state process;
The virtual diffraction animation is derived.
In another example of the above embodiment, the step of modeling the motion trajectory from the initial point to the end point of the selected exosome is as follows:
constructing an environment around the exosomes;
Adding parameters such as fluid resistance, gravity, electric field and the like consistent with the current environment;
And simulating the movement track of the exosome by adopting molecular dynamics simulation.
It should be noted that, the initial point to the end point of the exosome is set according to the time axis, for example, the initial point is set to 3 hours before the current time, the end point is set to 2 hours after the current time, and the motion track of the exosome is simulated according to the set initial point to the end point.
Through the analysis, evaluation and instruction operation of the exosomes, parameters, states and evaluation results of the exosomes can be obtained, and the movement and evolution process of the exosomes can be obtained in an animation mode in the research process, so that the exosomes have an auxiliary role in the research.
A computer readable storage medium, in which program instructions are stored, the program instructions being operative to implement the exosome auto-scan image analysis method described above.
An exosome automatic scanning image analysis system comprises the computer readable storage medium.
According to the exosome automatic scanning image analysis method, medium and system provided by the invention, exosome in the image is identified after fluorescent microscopic image is obtained, exosome can be identified and analyzed, and is identified and analyzed through the parameter identification layer, meanwhile, corresponding results can be fed back according to received external instructions and based on the parameter identification result, so that exosome research is facilitated, and the effect of assisting exosome research process is realized.
It should be understood that the specific order or hierarchy of steps in the processes disclosed are examples of exemplary approaches. Based on design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not meant to be limited to the specific order or hierarchy.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate preferred embodiment of this invention.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. The processor and the storage medium may reside as discrete components in a user terminal.
For a software implementation, the techniques described in this disclosure may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. These software codes may be stored in memory units and executed by processors. The memory unit may be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.
The foregoing description includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, as used in the specification or claims, the term "comprising" is intended to be inclusive in a manner similar to the term "comprising," as interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean "non-exclusive or".
Claims (9)
1. An exosome automatic scanning image analysis method is characterized by comprising the following steps:
acquiring a fluorescence microscopic image, wherein the fluorescence microscopic image is acquired from an electron microscope, and the acquired fluorescence microscopic image contains at least one kind of exosomes;
Taking the fluorescence microscopic image obtained originally as a background layer, and copying and stacking a layer of the fluorescence microscopic image as a rejecting layer on the background layer;
performing scaling identification step by step on the fluorescence microscopic image in the eliminating layer, and eliminating the area which does not contain exosomes;
Identifying parameters of the exosomes in the rest part according to preset identification characteristics;
copying and stacking one layer of the rejecting layer on the rejecting layer to serve as a parameter identification layer;
Marking the same type of exosomes by adopting a uniform type of mark according to a parameter identification result, analyzing parameter states of different types of exosomes, and displaying the parameter states on the parameter identification layer;
The parameter states of the exosomes are evaluated, and corresponding evaluation contents are given out on the parameter identification layer according to preset results;
and receiving an external instruction, identifying the instruction, and feeding back a corresponding result according to the instruction and based on the parameter identification result.
2. The method for analyzing an exosome auto-scan image according to claim 1, wherein the step of performing progressive culling comprises:
Identifying a target in the microscopic image under a fixed scaling factor a, and removing a region which does not contain exosomes at a coarse level;
further identifying targets in the microscopic image under the fixed scaling ratio b, and secondarily eliminating areas which do not contain exosomes;
and (3) at a fixed scaling rate c, further identifying the target in the microscopic image, and removing the area which does not contain exosomes at a high level.
3. The method of claim 2, wherein the fixed scale c is a quadratic magnification based on a fixed scale b, and the fixed scale b is a quadratic magnification based on a fixed scale a.
4. An automatic exosome scan image analysis method according to claim 2, wherein the fluorescent-based color features and the shape features of the non-exosome are identified for the non-exosome region at a fixed scale a, the non-exosome region is identified for the non-exosome region at a fixed scale b based on the non-exosome shape features and the exosome shape features, and the non-exosome region is identified for the non-exosome region at a fixed scale c based on the exosome shape features.
5. The method of claim 1, wherein the instructions include individually marking specific one or more types of exosomes, sorting or ordering the exosomes according to one or more characteristics, simulating construction of a virtual diffraction animation of the selected exosomes, simulating construction of a motion profile of the selected exosomes from an initial point to an end point.
6. The method of claim 1, wherein the background layer, the culling layer, and the parameter identification layer are displayed in a stacked manner or in a single display.
7. An exosome auto-scan image analysis method according to claim 1 wherein the assessment of exosomes comprises morphology and size, number and concentration.
8. A computer readable storage medium, wherein program instructions are stored in the computer readable storage medium, the program instructions being operable to implement the exosome auto-scan image analysis method according to any one of claims 1 to 7.
9. An exosome auto-scan image analysis system comprising the computer-readable storage medium of claim 8.
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