Melanin content analysis method and system based on migration control

Abstract

The invention discloses a melanin content analysis method and system based on migration management and control, and relates to the technical field of quantitative analysis method.The method comprises the steps that a zebra fish embryo is shot along illumination parameters, and multiple images of the zebra fish embryo at different angles are collected; the corresponding three-dimensional data is determined according to the plurality of images, and the time sequence three-dimensional model is generated according to alignment of the three-dimensional data and the time point data, so that the accuracy of the time sequence three-dimensional model is ensured. Therefore, a melanin area is determined based on the identification of the time sequence three-dimensional model, and the three-dimensional volume of melanin is determined according to the area position of the melanin area and the voxel statistics of the melanin area; the migration path of the melanin area is collected, quantitative analysis of the melanin content is triggered according to the migration path of the melanin area, the migration speed of the melanin area and the three-dimensional volume of the melanin, and the accuracy of quantitative analysis of the melanin content is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of quantitative analysis methods, and particularly relates to an analysis method and system for melanin content based on migration control. Background Art

[0002] With the development of technology, zebrafish embryos have become common objects in modern melanin research due to their advantage of being transparent throughout the body and easy to observe. Their melanin refers to a biological pigment present in their skin, eyes, and other tissues. In the prior art, researchers collect and process images of zebrafish to identify melanin regions, and quantify melanin content by calculating parameters such as the area and grayscale value of the melanin regions. However, the prior art only stays at the two-dimensional image level and does not involve the generation and migration path of melanin, which limits the existing quantitative analysis methods for melanin content in deeper melanin research. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art, and the present invention provides an analysis method and system for melanin content based on migration control.

[0004] An embodiment of the present invention provides an analysis method for melanin content based on migration control, including: When the zebrafish is in the online detection state, determine the corresponding lighting parameters according to the thickness of the zebrafish embryo and the environmental conditions where the zebrafish is located; Take pictures of the zebrafish embryo along the lighting parameters, and collect multiple images of different angles of the zebrafish embryo; Determine the corresponding three-dimensional data according to the multiple images, and generate a time-sequential three-dimensional model according to the alignment of the three-dimensional data and the time-point data; Determine the melanin region based on the recognition of the time-sequential three-dimensional model, and determine the three-dimensional volume of melanin according to the regional position of the melanin region and the voxel statistic of the melanin region; Collect the migration path of the melanin region, and trigger the quantitative analysis of melanin content according to the migration path of the melanin region, the migration speed of the melanin region, and the three-dimensional volume of melanin.

[0005] An embodiment of the present invention provides an analysis system for melanin content based on migration control. The analysis system for melanin content based on migration control is applied to the above-mentioned analysis method for melanin content based on migration control. The analysis system for melanin content based on migration control includes: A lighting parameter module, configured to determine the corresponding lighting parameters according to the thickness of the zebrafish embryo and the environmental conditions where the zebrafish is located when the zebrafish is in the online detection state; An image module for photographing zebrafish embryos along the lighting parameters and collecting multiple images of the zebrafish embryos from different angles; A time-sequence three-dimensional model module for determining corresponding three-dimensional data based on multiple images and generating a time-sequence three-dimensional model according to the alignment of the three-dimensional data and time-point data; A melanin three-dimensional volume module for determining the melanin region based on the recognition of the time-sequence three-dimensional model and determining the melanin three-dimensional volume according to the regional position of the melanin region and the voxel statistic of the melanin region; A quantitative analysis module for collecting the migration path of the melanin region and triggering the quantitative analysis of the melanin content according to the migration path of the melanin region, the migration speed of the melanin region, and the melanin three-dimensional volume.

[0006] Compared with the prior art, the beneficial effects of the present invention are: In the embodiment of the present invention, by the method in the embodiment of the present invention, when the zebrafish is in the online detection state, the corresponding lighting parameters are determined according to the thickness of the zebrafish embryo and the environmental conditions where the zebrafish is located; the zebrafish embryo is photographed along the lighting parameters, and multiple images of the zebrafish embryo from different angles are collected; the corresponding three-dimensional data is determined based on the multiple images, and a time-sequence three-dimensional model is generated according to the alignment of the three-dimensional data and the time-point data, ensuring the accuracy of the time-sequence three-dimensional model.

[0007] Therefore, the melanin region is determined based on the recognition of the time-sequence three-dimensional model, and the melanin three-dimensional volume is determined according to the regional position of the melanin region and the voxel statistic of the melanin region; the migration path of the melanin region is collected, and the quantitative analysis of the melanin content is triggered according to the migration path of the melanin region, the migration speed of the melanin region, and the melanin three-dimensional volume, taking into account the migration path of the melanin region, the migration speed of the melanin region, and the melanin three-dimensional volume, ensuring the accuracy of the quantitative analysis of the melanin content. Description of the Drawings

[0008] Figure 1 is a schematic flowchart of the method for analyzing the melanin content based on migration control in the embodiment of the present invention; Figure 2 is a schematic flowchart of step S11 in the method for analyzing the melanin content based on migration control in the embodiment of the present invention; Figure 3 is a schematic flowchart of step S12 in the method for analyzing the melanin content based on migration control in the embodiment of the present invention; Figure 4 is a schematic flowchart of step S13 in the method for analyzing the melanin content based on migration control in the embodiment of the present invention; Figure 5It is a schematic flowchart of step S14 in the method for analyzing melanin content based on migration control in the embodiments of the present invention; Figure 6 It is a schematic flowchart of step S15 in the method for analyzing melanin content based on migration control in the embodiments of the present invention; Figure 7 It is a schematic diagram of the structural composition of the system for analyzing melanin content based on migration control in the embodiments of the present invention. Detailed implementation manners

[0009] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention.

[0010] Please refer to Figures 1 to 7 , a method for analyzing melanin content based on migration control, which is applied to the quantitative analysis scenario of melanin content in zebrafish; the method for analyzing melanin content based on migration control includes: Step S11: When the zebrafish is in the online detection state, determine the corresponding illumination parameters according to the thickness of the zebrafish embryo and the environmental conditions where the zebrafish is located; Step S12: Take pictures of the zebrafish embryo along the illumination parameters, and collect multiple images of different angles of the zebrafish embryo; Step S13: Determine the corresponding three-dimensional data according to the multiple images, and generate a time-sequential three-dimensional model according to the alignment of the three-dimensional data and the time-point data; Step S14: Determine the melanin region based on the recognition of the time-sequential three-dimensional model, and determine the three-dimensional volume of melanin according to the regional position of the melanin region and the voxel statistic of the melanin region; Step S15: Collect the migration path of the melanin region, and trigger the quantitative analysis of the melanin content according to the migration path of the melanin region, the migration speed of the melanin region, and the three-dimensional volume of melanin; Refer to Figure 2 , in step S11, when the zebrafish is in the online detection state, determine the corresponding illumination parameters according to the thickness of the zebrafish embryo and the environmental conditions where the zebrafish is located; In the specific implementation process of the present invention, the specific steps are as follows: S111: Collect the location of the zebrafish, determine the corresponding online detection mode according to the location of the zebrafish and the species of the zebrafish, and trigger the online detection of the zebrafish along the online detection mode; S112: Determine the area where the zebrafish embryo is located based on the on-line detection of the zebrafish, determine the thickness of the zebrafish embryo according to the recognition of the area where the zebrafish embryo is located, collect the environmental conditions where the zebrafish is located, and determine the corresponding illumination parameters according to the thickness of the zebrafish embryo, the environmental conditions where the zebrafish is located, and the illumination mapping relationship.

[0011] In the embodiment of the present application, the location of the zebrafish is collected, the corresponding on-line detection mode is determined according to the location of the zebrafish and the species of the zebrafish, and the on-line detection of the zebrafish is triggered along the on-line detection mode, introducing the on-line detection of the zebrafish.

[0012] At this time, a camera or an image sensor is used to capture an image of the experimental area, and the image of the experimental area is subjected to image recognition to facilitate the collection of the location of the zebrafish, introducing the location of the zebrafish and the species of the zebrafish.

[0013] Select the most suitable on-line detection mode according to the location of the zebrafish and the species of the zebrafish. At this time, there are multiple on-line detection modes preset in the system, and each on-line detection mode is for a specific zebrafish species and the location of the zebrafish; according to the location information obtained in the first step, determine whether the zebrafish is in the correct position in the detection area; according to the species of the zebrafish (for example, whether it is wild-type or mutant, larva or adult), select the corresponding on-line detection mode; optionally, the corresponding on-line detection mode includes the on-line detection mode for multiple species of zebrafish.

[0014] Once the on-line detection mode is determined, the system automatically triggers the detection process, including steps such as adjusting the camera focus, starting the light-sheet microscopy system, and starting data collection. The system will collect data of the zebrafish in real time or at regular intervals for subsequent analysis and processing.

[0015] Specifically, assume that a study on the melanin distribution of zebrafish is being carried out. There are two types of zebrafish in the experiment: wild-type zebrafish and Albino mutant zebrafish, and there are significant differences in the melanin content and distribution between these two types of zebrafish; at the beginning of the experiment, a high-definition camera was used to capture an image of the entire experimental device, and through an image recognition algorithm, the position of each zebrafish was identified and marked on the image. After discovery, two wild-type zebrafish are located at the center of the detection area, while one Albino mutant zebrafish is located at the edge position.

[0016] According to the position information of the zebrafish, confirm that all zebrafish are in the correct position in the detection area; since two different species of zebrafish are involved, the "on-line detection mode for multiple species of zebrafish" is selected; the "on-line detection mode for multiple species of zebrafish" can process the data of wild-type and mutant zebrafish simultaneously and distinguish them according to their characteristics.

[0017] The system automatically triggers the online detection process. The light-sheet microscopy system starts to work, performs high-resolution imaging on each zebrafish, and the data acquisition system collects image data in real time and stores it at a specified location on the server. It can accurately determine the position and species of the zebrafish and select the most suitable online detection mode, which provides a solid foundation for subsequent data analysis and processing.

[0018] Therefore, based on the online detection of zebrafish, determine the area where the zebrafish embryo is located. According to the identification of the area where the zebrafish embryo is located, measure the thickness of the zebrafish embryo, collect the environmental conditions where the zebrafish is located, and determine the corresponding illumination parameters based on the thickness of the zebrafish embryo, the environmental conditions where the zebrafish is located, and the illumination mapping relationship, ensuring the accuracy of the illumination parameters.

[0019] At this time, based on the online detection, accurately identify the specific position area of the zebrafish embryo in the experimental device. The online detection system has provided the position information of the zebrafish and further determines the area where the zebrafish embryo is located. Optionally, considering the small position changes of the embryo due to movement or growth, the area where the zebrafish embryo is located is updated in real time.

[0020] By identifying the area where the zebrafish embryo is located, indirectly or directly measure its thickness, which is one of the key parameters for adjusting the illumination parameters. At this time, use image processing techniques such as depth estimation or volume measurement to extract the thickness information from the identified area where the zebrafish embryo is located; if the embryo is in a specific posture (such as lying on its side), directly measure its maximum cross-sectional diameter as an approximation of the thickness.

[0021] Obtain the environmental conditions where the zebrafish embryo is located, such as water quality, temperature, light, etc. These conditions affect the imaging quality and melanin distribution; at this time, use sensors to monitor the environmental conditions in the experimental device, such as temperature sensors, water quality monitors, etc., manually record or automatically collect this data and associate it with the image data of the embryo.

[0022] Based on the embryo thickness, environmental conditions, and a preset illumination mapping relationship, determine the optimal illumination parameters to reduce phototoxicity and improve imaging quality. At this time, the illumination mapping relationship is a preset look-up table that provides recommended illumination parameters based on the embryo thickness and environmental conditions. According to the information obtained in step two and step three, query the illumination mapping relationship to determine appropriate parameters such as illumination intensity, wavelength, exposure time, etc., and fine-tune the recommended parameters considering the specific requirements of the experiment (such as high-contrast imaging, long-term monitoring, etc.).

[0023] Specifically, assume that the change in melanin distribution in zebrafish embryos at different temperatures is being studied; in the experiment, the zebrafish embryos are placed in petri dishes at different temperatures and monitored using a light-sheet microscopy system; the online detection system identifies that the zebrafish embryos are roughly located in the central region of the petri dish, and a more refined image recognition algorithm, such as contour detection, is used to determine the precise position and shape of the embryos.

[0024] From the identified embryo region, the maximum cross-sectional diameter is measured using image processing techniques as an approximation of the thickness. Considering the transparency and internal structure of the embryo, the corresponding measurement method is selected, and an estimated thickness of 0.5 mm is obtained.

[0025] The temperature inside the petri dish is monitored using a temperature sensor and recorded as 28 degrees Celsius. At the same time, the water quality conditions are also checked to ensure that no contaminants affect the imaging quality. At this time, based on the embryo thickness (0.5 mm) and the environmental temperature (28 degrees Celsius), the preset illumination mapping relationship is queried. The illumination mapping relationship recommends using lower-intensity blue light illumination for embryos with a thickness of 0.5 mm at 28 degrees Celsius to reduce phototoxicity and improve imaging contrast. The illumination settings of the light-sheet microscopy system are adjusted according to these recommended parameters, and the experimental monitoring is started; through this process, the position, thickness, and environmental conditions of the zebrafish embryos can be accurately determined, and the best illumination parameters can be selected based on this information, providing a reliable basis for subsequent imaging and analysis.

[0026] In an embodiment of the present application, an embryo thickness matching table is preset to associate embryo thickness, environmental conditions, and illumination parameters. The embryo thickness matching table is shown in Table 1: Table 1 Embryo Thickness Matching Table Assume that the detected embryo thickness is 0.4 mm and the environmental temperature is 27 °C. Then, according to the embryo thickness matching table, illumination parameters with a light intensity of 1200 lx, a wavelength of 480 nm, and an exposure time of 50 ms are selected.

[0027] Reference Figure 3 , in step S12, the zebrafish embryo is photographed along the illumination parameters, and multiple images of different angles of the zebrafish embryo are collected; In the specific implementation process of the present invention, the specific steps are as follows: S121: Regulate the illumination system of the zebrafish embryo based on the illumination parameters, and dynamically photograph the zebrafish embryo based on the photographing system; S122: During the dynamic photographing of the zebrafish embryo, collect multiple angular positions of the zebrafish embryo, and collect multiple images of different angles of the zebrafish embryo according to the multiple angular positions, the photographing system, and the illumination system.

[0028] In an embodiment of the present application, a lighting system for zebrafish embryos is regulated based on the lighting parameters, and dynamic imaging of the zebrafish embryos is achieved based on an imaging system.

[0029] At this time, the lighting parameters are introduced, which generally include light intensity, wavelength (or color), spot size, lighting direction, exposure time, etc., and they are selected according to the characteristics of the zebrafish embryos, experimental requirements, and previous detection results (such as the embryo thickness and environmental conditions determined in step S112); meanwhile, based on the lighting parameters, the lighting system for zebrafish embryos is regulated. Optionally, the lighting parameters are adjusted in real time to respond to the movement or morphological changes of the embryos during imaging, ensuring continuous optimal lighting conditions.

[0030] Specifically, assume that a study on the melanin distribution in zebrafish embryos is being conducted, and the lighting parameters have been determined based on the embryo thickness (0.5 mm) and environmental temperature (28 °C): the light intensity is 1500 lx, the wavelength is 488 nm (blue light, suitable for exciting melanin fluorescence), the spot size is adjusted to cover the entire embryo, and the lighting direction is perpendicular to the embryo plane.

[0031] A programmable LED light source system is used, and these parameters are input through its control software; the light source is automatically adjusted to the specified intensity, and a wavelength of 488 nm is selected through a built-in filter; meanwhile, the position and angle of the light source are ensured so that the spot can evenly cover the embryo; if the system supports real-time adjustment, a feedback mechanism is also set up to finely adjust the light intensity according to the image quality (such as contrast, brightness, etc.) of the captured images to ensure optimal lighting conditions throughout the imaging process.

[0032] Meanwhile, the imaging system includes a high-resolution camera, a microscope (such as a light sheet microscope, a confocal microscope, etc.), an image sensor, and related image acquisition software; dynamic imaging means continuously capturing a series of images over a period of time to record the morphological changes, movements, or other dynamic processes of the embryos, which generally involves setting an appropriate frame rate (the number of images captured per second) and exposure time to ensure clear images without losing important information; meanwhile, in some cases, the imaging system needs to be synchronized with the lighting system to ensure that images are captured under the correct lighting conditions, which is achieved through a hardware trigger signal or a software synchronization mechanism.

[0033] Specifically, a high-resolution CCD camera was used and connected to a light-sheet microscope, which was specifically designed for high-resolution imaging of transparent samples (such as zebrafish embryos); the frame rate of the imaging system was set to 10 frames per second, and the exposure time was matched with the light source of the illumination system (assumed to be 50 ms) to ensure clear images were obtained during each exposure.

[0034] To ensure synchronization between imaging and illumination, a software synchronization mechanism was used; before the start of imaging, a synchronization program was launched, which sent trigger signals to the illumination system and the imaging system to ensure they started working at the same time point; during the entire imaging process, a series of images were continuously captured, recording the morphological changes of the zebrafish embryo at different time points, and these images were used for subsequent analyses, such as quantification of melanin distribution, measurement of morphological parameters, etc.

[0035] Therefore, during the dynamic imaging of zebrafish embryos, multiple angular positions of the zebrafish embryo were collected, and multiple images of different angles of the zebrafish embryo were collected based on the multiple angular positions, the imaging system, and the illumination system, introducing multiple images of different angles of the zebrafish embryo.

[0036] At this time, to comprehensively capture the morphological characteristics of the zebrafish embryo, multiple different angles need to be selected for imaging, and these angles should be able to cover the main characteristic planes of the embryo, such as the front, side, top, and bottom, etc.; optionally, the angle adjustment is achieved by rotating the culture dish, moving the imaging system (such as a camera or microscope), or changing the placement method of the embryo; in some cases, a robotic arm or a rotating device is required to precisely control the angle.

[0037] During the dynamic imaging process, if the embryo moves or undergoes morphological changes, the angle needs to be adjusted in real time to ensure that the required characteristics can always be captured; meanwhile, at each selected angular position, the imaging system and the illumination system are used to capture images of the zebrafish embryo; ensure clear and high-contrast images are obtained at each angle, and the images contain sufficient detail information.

[0038] According to the change in angle, the parameters of the illumination system need to be adjusted to ensure the best illumination effect at different angles, which involves adjusting the light intensity, wavelength, or spot size, etc.; the imaging system and the illumination system should work synchronously to ensure images are captured under the correct lighting conditions; meanwhile, the imaging angle and relevant information of each image should be recorded for subsequent analysis and comparison.

[0039] Specifically, when conducting morphological studies on zebrafish embryos, it was decided to take pictures from four main angles (front, left side, right side, and top); a rotating device was used, which could precisely rotate the culture dish to the specified angle; first, the embryo was placed in the culture dish, and the rotating device was adjusted so that its front faced the camera; then, the imaging system was started, and appropriate frame rate and exposure time were set; during the dynamic imaging process, a series of images at the front angle were captured; next, the culture dish was rotated to the left side, right side, and top in sequence, and the imaging process was repeated, thus obtaining multiple sets of images at these four angles.

[0040] When collecting images at each angle, the synchronization of the imaging system and the lighting system was ensured; a programmable LED light source system was used, which could automatically adjust the light intensity and wavelength according to the imaging angle; for example, when collecting images at the front angle, the light intensity was set to 1500 lx and the wavelength to 488 nm (blue light) to excite the melanin fluorescence in the embryo; when collecting images at the side and top angles, the light intensity and wavelength needed to be adjusted according to the transparency and morphology of the embryo to ensure image quality.

[0041] At each angle, a series of images were continuously captured, and information such as the imaging angle, timestamp, and lighting parameters of each image was recorded. This information was crucial for subsequent 3D reconstruction, morphological analysis, and melanin distribution studies.

[0042] In an embodiment of the present application, the multi-angle imaging matching table for zebrafish embryos is shown in Table II: Table II Multi-angle imaging matching table for zebrafish embryos Suppose a zebrafish embryo is being imaged from multiple angles; according to the multi-angle imaging matching table for zebrafish embryos, first, the imaging system is adjusted to the front imaging mode (focal length 4X, exposure time 50 ms), and the lighting system is set to a light intensity of 1500 lx and a wavelength of 488 nm; then, a series of images at the front angle are captured; next, the imaging system is rotated to the left side angle, and the focal length is adjusted to 2X, and the exposure time is increased to 70 ms to adapt to the transparency change; the lighting system is also adjusted accordingly to a light intensity of 1200 lx and a wavelength of 520 nm; at this setting, images at the left side angle are captured; similarly, the above process is repeated to capture images at the right side and top angles, and the parameters of the imaging system and the lighting system are adjusted according to the matching table before each imaging.

[0043] Reference Figure 4 , in step S13, corresponding stereoscopic data is determined based on multiple images, and a temporal 3D model is generated according to the alignment of the stereoscopic data and the time point data; In the specific implementation process of the present invention, the specific steps are as follows: S131: Generate corresponding three-dimensional data based on the three-dimensional features corresponding to the image segmentation of multiple images and according to the combination of multiple three-dimensional features. S132: Mark corresponding time sequence nodes for multiple three-dimensional data, and determine the time point data according to the matching of multiple time sequence nodes. At this time, align the time point data in the time dimension, and gradually perform three-dimensional construction on multiple three-dimensional features during the alignment process to generate a time sequence three-dimensional model.

[0044] In the embodiment of the present application, generating corresponding three-dimensional data based on the three-dimensional features corresponding to the image segmentation of multiple images and according to the combination of multiple three-dimensional features ensures the accuracy of the synthesis of three-dimensional data.

[0045] At this time, preprocess the zebrafish embryo images taken from multiple angles, including denoising, enhancing contrast, correcting distortion, etc., to improve the image quality; use a preset edge detection model, such as an edge detection model, to identify the feature regions in the images, and these feature regions are usually related to the morphology of the zebrafish embryo; apply an image segmentation algorithm (such as the graph cut algorithm) to separate the feature regions from the background to form the segmented images.

[0046] Specifically, assume there are three zebrafish embryo images taken from different angles: the front view, the left side view, and the right side view; first, preprocess these images, including removing noise and enhancing contrast; then, use an edge detection algorithm to identify the edge features in the images, and these edge features correspond to the contours of the zebrafish embryo; finally, apply a region growing algorithm to gradually grow the complete embryo region starting from the identified edge features to form the segmented images, and these segmented images will be used to extract three-dimensional features in the subsequent steps.

[0047] Meanwhile, in the segmented images, use a feature point detection model to identify key feature points; describe the detected feature points to generate feature descriptors, and the feature descriptors can uniquely represent the local morphological information of the feature points, and determine the corresponding feature points in images at different angles, thereby establishing the corresponding relationship between the feature points; according to the corresponding relationship of the feature points, use the principle of triangulation to estimate the depth information of each feature point.

[0048] Optionally, in the segmented front, left side, and right side images, the SIFT algorithm is used to detect feature points, and a feature descriptor is generated for each feature point; then, using the stereo matching algorithm, corresponding feature points are found between the front image and the left side image, and the corresponding relationship between them is established; similarly, the corresponding relationship of feature points is also established between the front image and the right side image; finally, based on the principle of triangulation, the depth information of each feature point is estimated according to the corresponding relationship of the feature points, and this depth information will be used to generate stereo data in the subsequent steps.

[0049] Combine the extracted feature points and the corresponding depth information to generate point cloud data; each point in the point cloud data contains three-dimensional coordinate information (X, Y, Z), so as to convert the point cloud data into continuous stereo data; perform optimization processing on the generated stereo data type to improve the accuracy and visualization effect of the model.

[0050] Specifically, combine the extracted feature points and the corresponding depth information to generate a point cloud data containing the three-dimensional morphology of the zebrafish embryo; then, use the Poisson surface reconstruction algorithm to convert the point cloud data into continuous stereo data, and this stereo data shows the three-dimensional morphology and surface details of the zebrafish embryo; finally, perform optimization processing on the stereo data, including steps such as smoothing the surface and filling holes, to improve the accuracy and visualization effect of the model.

[0051] Therefore, mark the corresponding time sequence nodes for multiple stereo data, and determine the time point data according to the matching of multiple time sequence nodes. At this time, align the time point data in the time dimension, and gradually perform stereo construction on multiple stereo features during the alignment process to generate a time sequence three-dimensional model, ensuring the accuracy of the time sequence three-dimensional model.

[0052] At this time, ensure that a series of stereo data arranged in chronological order have been obtained; the stereo data are from the shooting of zebrafish embryos at different time points; assign a unique time sequence node label to each stereo data, and this time sequence node label is usually a timestamp or serial number, used to identify the position of the data in the time sequence; in addition to the stereo data itself, metadata related to each data point, such as shooting time, environmental conditions, embryo development stage, etc., also need to be recorded.

[0053] Use feature matching algorithms (such as ICP, NDT, etc.) to find corresponding feature points or regions between stereo data at different time points; based on the results of feature matching, establish the corresponding relationship between stereo data at different time points, which usually involves matching the three-dimensional coordinates and / or descriptors of feature points; according to the established corresponding relationship, generate time point data, and these data describe the morphological changes of the embryo between different time points.

[0054] Meanwhile, it is necessary to adjust the timestamps of the data or perform time interpolation to fill in the missing time points, ensuring that all time point data are synchronized in time. Sort the time point data in the order of the time series to ensure that they can be processed subsequently in chronological order.

[0055] On the aligned time point data, gradually construct the three-dimensional features of each time point, which connects the three-dimensional features of different time points to form a continuous three-dimensional model of the time series. This usually involves interpolation processing to smooth the morphological changes between adjacent time points; perform optimization processing on the generated three-dimensional model of the time series to improve the accuracy and visualization effect of the model.

[0056] Specifically, assume there is a three-dimensional dataset of zebrafish embryos containing 10 time points; each time point has a corresponding three-dimensional data representing the three-dimensional morphology of the embryo at that moment; assign a unique serial number as the time series node marker to each three-dimensional data, from T1 to T10; meanwhile, record the shooting time (such as hour: minute) and the embryo development stage (such as somite stage, pharyngeal pouch stage, etc.) of each time point.

[0057] Use the ICP algorithm to find the corresponding feature points between the three-dimensional data at time points T1 and T2. These feature points are mainly located at significant positions such as the head, tail, and midline of the body of the embryo; establish the correspondence between time points T1 and T2 by comparing the three-dimensional coordinate changes of these feature points; similarly, perform feature matching and establish correspondence for other adjacent time points; finally, generate a series of time point data describing the morphological changes of the embryo between time points T1 and T10.

[0058] Checked the timestamps of all time point data and found that they were already arranged in the order of the shooting time; therefore, no time adjustment or interpolation was required; confirmed that the time point data was already arranged in the order of T1 to T10 and was ready for the subsequent alignment and three-dimensional construction steps.

[0059] Use three-dimensional modeling software to gradually construct the three-dimensional data of each time point; at time point T1, construct the preliminary morphology of the embryo, including basic structures such as the head, body, and tail; as time goes by, gradually add more details in subsequent time points, such as the differentiation of somites, the formation of fins, etc.; meanwhile, use interpolation processing to smooth the morphological changes between adjacent time points; finally, generate a continuous three-dimensional model of the time series, showing the complete morphological change process of the embryo between time points T1 and T10; perform optimization processing on the model to improve its accuracy and visualization effect.

[0060] Reference Figure 5, in step S14, a melanin region is determined based on the recognition of the temporal three-dimensional model, and the three-dimensional melanin volume is determined according to the regional position of the melanin region and the voxel statistics of the melanin region; In the specific implementation process of the present invention, the specific steps are as follows: S141: Divide the temporal three-dimensional model, and generate multiple regions to be recognized according to the division of the temporal three-dimensional model. The multiple regions to be recognized are divided based on each temporal node in the temporal three-dimensional model, and the multiple regions to be recognized respectively correspond to different parts of the temporal three-dimensional model; S142: Determine multiple melanin features according to the recognition of the multiple regions to be recognized, determine the melanin region according to the location of the multiple melanin features and the convergence range of the melanin features, mark the regional position of the melanin region, determine the voxel statistics of the melanin region based on the real-time monitoring of the melanin region, and determine the three-dimensional melanin volume based on the synthesis of the regional position of the melanin region and the voxel statistics of the melanin region.

[0061] In the embodiment of the present application, the temporal three-dimensional model is divided, and multiple regions to be recognized are generated according to the division of the temporal three-dimensional model. The multiple regions to be recognized are divided based on each temporal node in the temporal three-dimensional model, and the multiple regions to be recognized respectively correspond to different parts of the temporal three-dimensional model, ensuring the accuracy of the multiple regions to be recognized.

[0062] At this time, the temporal three-dimensional model is a set containing three-dimensional data of multiple time points. Each time point corresponds to a three-dimensional model, and these three-dimensional models jointly describe the dynamic changes of a certain object or phenomenon over time; when processing the temporal three-dimensional model, it is necessary to first understand its data structure, including the number of time points, the resolution of the three-dimensional model at each time point, the features included in the model, etc.

[0063] According to the research purpose and specific requirements, determine the division rule of the temporal three-dimensional model; the division rule is based on factors such as the interval of time points, the morphological characteristics of the three-dimensional model, and the spatial position relationship; the purpose of the division is to divide the model into multiple regions to be recognized with specific meanings or characteristics for subsequent analysis and processing.

[0064] According to the determined division rule, perform a division operation on the temporal three-dimensional model to extract regions with specific features or located in specific positions; the result of the division is a set of regions to be recognized, and each region corresponds to a specific part or feature in the temporal three-dimensional model. At the same time, mark each region to be recognized so that they can be easily identified and processed in subsequent analysis; the marking includes assigning a unique identifier to each region, recording the position information of the region (such as coordinate range), and describing the features of the region.

[0065] Therefore, multiple melanin features are determined based on the recognition of multiple regions to be recognized, and a melanin region is determined according to the locations of the multiple melanin features and the convergence range of the melanin features. The regional position of the melanin region is marked, the voxel statistic of the melanin region is determined based on the real-time monitoring of the melanin region, and the three-dimensional volume of melanin is determined based on the synthesis of the regional position of the melanin region and the voxel statistic of the melanin region, ensuring the accuracy of the three-dimensional volume of melanin.

[0066] At this time, within each region to be recognized, image processing or machine learning algorithms are used to recognize melanin features; melanin features are manifested as specific colors (such as dark brown or black), textures (such as spots or stripes), shapes (such as circular or oval), etc.; the recognition algorithms include color threshold segmentation, morphological operations, machine learning classifiers, etc.

[0067] According to the recognized melanin features, determine their specific positions in three-dimensional space; analyze the convergence of melanin features, that is, their distribution and aggregation degree in space; the convergence range is evaluated by calculating indicators such as the spatial density and connectivity of melanin features.

[0068] Based on the locations and convergence range of melanin features, determine the melanin region; the melanin region is a continuous or nearly continuous region composed of multiple melanin features; each melanin region is marked, and its position information (such as coordinate range, center point coordinates, etc.) is recorded.

[0069] Perform real-time monitoring on the melanin region, record its changes at different time points; calculate the voxel statistics of the melanin region, including the number of voxels (i.e., the number of voxels contained in the melanin region), volume (i.e., the spatial size occupied by the melanin region), etc.; the calculation of voxel statistics involves image processing technologies such as three-dimensional reconstruction and volume measurement. At the same time, the melanin regions at different time points are registered and fused to form a continuous three-dimensional volume representation; based on the registered melanin region and voxel statistics, calculate the three-dimensional volume of melanin.

[0070] Specifically, assume there is a temporal three-dimensional model of the change of zebrafish melanin patches, which records the three-dimensional morphology of zebrafish at different time points; the goal is to identify melanin patches and calculate their three-dimensional volumes.

[0071] Within each region to be recognized (i.e., different parts of the zebrafish), the color threshold segmentation algorithm is used to identify dark brown or black pixel points as melanin features; then, morphological operations (such as dilation and erosion) are used to smooth the boundaries of melanin features and remove noise; calculate the spatial coordinates of each melanin feature and analyze their distribution and aggregation on the zebrafish surface; by calculating the spatial density and connectivity of melanin features, the convergence range of melanin patches is determined.

[0072] Based on the convergence range of melanin features, the specific positions of melanin patches were determined and marked as continuous regions; the position information such as the coordinate range and the central point coordinates of each melanin patch was recorded; the zebrafish was continuously monitored, and the changes of melanin patches at different time points were recorded; using three-dimensional reconstruction technology, the voxel number and volume of each melanin patch were calculated.

[0073] The melanin patches at different time points were registered and fused to form a continuous three-dimensional volume representation; based on the registered melanin patches and voxel statistics, the three-dimensional volume of melanin was calculated, and a three-dimensional visualization model was generated to show the distribution and volume changes of melanin patches on the surface of the zebrafish.

[0074] Reference Figure 6 , in step S15, the migration path of the melanin region is collected, and the quantitative analysis of melanin content is triggered according to the migration path of the melanin region, the migration speed of the melanin region, and the three-dimensional volume of melanin; In the specific implementation process of the present invention, the specific steps are as follows: S151: Mark the melanin region, and determine the migration state of the melanin region according to the movement of the mark of the melanin region. In the migration state of the melanin region, determine the migration path of the melanin region according to the movement route of the mark of the melanin region and the regional change amount of the melanin region; S152: Determine multiple migration nodes according to the division of the migration path of the melanin region, and determine the corresponding migration speed of the melanin region according to the matching of the multiple migration nodes; determine multiple parameter combinations based on the migration path of the melanin region, the migration speed of the melanin region, and the three-dimensional volume of melanin, determine the corresponding analysis features according to the multiple parameter combinations, construct the quantitative analysis logic of melanin content according to the multiple analysis features, and trigger the quantitative analysis of melanin content.

[0075] In the embodiment of the present application, the melanin region is marked, and the migration state of the melanin region is determined according to the movement of the mark of the melanin region. In the migration state of the melanin region, the migration path of the melanin region is determined according to the movement route of the mark of the melanin region and the regional change amount of the melanin region, ensuring the accuracy of the migration path of the melanin region.

[0076] At this time, the melanin regions are marked to determine their migration status and further determine the migration paths. At each time point or image frame, image processing or machine learning algorithms are used to identify and mark the melanin regions; the mark is a unique identifier (such as an ID number) used to distinguish different melanin regions; at the same time, the position information of each melanin region is recorded, such as the coordinate range, the coordinates of the center point, or the boundary contour, etc.

[0077] Compare the position information of the melanin regions at consecutive time points or image frames; if the position of a certain melanin region has changed significantly (such as the moving distance of the center point coordinates exceeds a certain threshold), then this region is considered to be in a migration state; the migration state is further subdivided into stages such as starting migration, continuous migration, and stopping migration. Optionally, compare the position information of "Region 1" in two consecutive images; if it is found that the center point coordinates of "Region 1" have moved from (x1, y1) to (x2, y2), and the moving distance exceeds the preset threshold (such as 5 pixel units), then it is determined that "Region 1" is in a migration state.

[0078] After determining that the melanin region is in a migration state, track and record the position information of this region at different time points or image frames; connect these position information to form a continuous path, that is, the migration path of the melanin region; the migration path is represented as a series of points (such as the center point coordinates) or line segments (such as the line segments connecting adjacent time points). Optionally, track and record the position information of "Region 1" in subsequent image frames; connect these position information to form a continuous path; for example, if the center point coordinates of "Region 1" in three consecutive images are represented as (x1, y1), (x2, y2), and (x3, y3) respectively, then the migration path is represented as a line segment from (x1, y1) to (x2, y2) and then to (x3, y3).

[0079] Specifically, assume there is a series of zebrafish images that record the changes of a certain melanin region over a period of time; use image processing software to process these images, identify and mark the melanin regions; by comparing the position information of the melanin regions in consecutive images, it is found that this region starts to move from the initial position (x1, y1), undergoes a series of position changes, and finally reaches the position (xn, yn); connect these position information to form a clear migration path, which not only shows the moving trajectory of the melanin region but also provides important information for subsequent analysis of migration speed, migration direction, etc.

[0080] Furthermore, multiple migration nodes are determined according to the division of the migration path of the melanin region, and the migration speed of the corresponding melanin region is determined according to the matching of the multiple migration nodes; multiple parameter combinations are determined based on the migration path of the melanin region, the migration speed of the melanin region, and the three-dimensional volume of the melanin, and the corresponding analysis features are determined according to the multiple parameter combinations. A quantitative analysis logic for the melanin content is constructed based on the multiple analysis features, and a quantitative analysis of the melanin content is triggered, taking into account the migration path of the melanin region, the migration speed of the melanin region, and the three-dimensional volume of the melanin, ensuring the accuracy of the quantitative analysis of the melanin content.

[0081] At this time, the migration nodes and migration speed are determined according to the migration path of the melanin region. How to determine the analysis features based on these parameters and the three-dimensional volume of the melanin, and finally construct a quantitative analysis logic for the melanin content; at this time, on the migration path of the melanin region, multiple migration nodes are divided according to the morphological changes of the path or specific events (such as speed changes, direction turns, etc.); the migration nodes are key points on the path, such as the starting point, the ending point, the speed change point, or the direction turning point, etc.; each migration node records the corresponding time point and position information.

[0082] Optionally, assume there is a migration path of the melanin region that starts from point A(x1, y1, t1), undergoes a series of changes, and finally ends at point B(xn, yn, tn); on this path, it is observed that the speed significantly increases or decreases, or the direction changes significantly at a certain position; according to these changes, several migration nodes are divided on the path, such as node 1(x2, y2, t2), node 2(x3, y3, t3), etc.

[0083] At the same time, for each migration node, calculate the time interval and distance change between it and the adjacent node to determine the migration speed of this section; the migration speed is the average speed and also the instantaneous speed, specifically depending on the accuracy requirements of the analysis; record the migration speed of each section and perform smoothing processing to reduce the influence of noise; optionally, for the section between node 1 and node 2, calculate the time interval Δt = t2 - t1 and the distance change Δd (using the Euclidean distance or other appropriate distance metrics); then, calculate the migration speed v = Δd / Δt of this section; repeat this process for all sections on the path to obtain a series of migration speed values.

[0084] Determine multiple analysis features based on the migration path, migration speed, and three-dimensional volume of the melanin region; the analysis features include the total length of the migration path, average migration speed, maximum migration speed, minimum migration speed, stability of the migration direction, change in melanin volume, etc. These features provide a comprehensive description of the migration behavior of the melanin region; Optionally, calculate the total length of the migration path, which is the sum of the distances of all segments; calculate the average migration speed, which is the average of the migration speeds of all segments; find the maximum and minimum migration speeds, as well as their corresponding positions and time points; analyze the stability of the migration direction, which is done by calculating the angle of direction change or using other direction stability metrics; record the value of the melanin volume at each time point and calculate the total change in volume.

[0085] Further, based on the multiple analysis features, construct a quantitative analysis logic for melanin content, which involves comparing the analysis features with known melanin content standards or reference ranges to determine the normal, abnormal, or changing trend of melanin content; the quantitative analysis logic is a simple threshold judgment and also a complex machine learning model.

[0086] Optionally, set a series of thresholds, such as thresholds for average migration speed, change in volume, etc.; compare the calculated analysis features with these thresholds to determine the status of melanin content; if a certain feature exceeds the corresponding threshold, trigger an alarm or mark the area as abnormal; additionally, also use machine learning algorithms (such as support vector machines, random forests, etc.) to train a classification model that can automatically determine the status of melanin content based on the analysis features.

[0087] Apply the constructed quantitative analysis logic to quantitatively analyze the melanin content and output the quantitative analysis results, which include numerical representation of melanin content, trend graph, abnormal alarm, etc.; at this time, input the analysis features into the constructed quantitative analysis logic to obtain the quantitative analysis results of melanin content; if the results show abnormality, trigger an alarm and send the relevant information to the technical staff.

[0088] In an embodiment of the present application, the present invention proposes an efficient and accurate zebrafish embryo melanin migration monitoring and analysis system, which integrates advanced light sheet fluorescence imaging technology (LS-FIS), artificial intelligence algorithms, and a dynamic correction module, aiming to achieve comprehensive and dynamic monitoring and quantitative analysis of the melanin migration process in zebrafish. The following will elaborate on the technical process, advantages, and key technical points of the present invention.

[0089] In the image acquisition stage, the present invention adopts an adaptive light sheet adjustment technology, which can dynamically adjust illumination parameters such as light sheet thickness, intensity and position according to the thickness of the zebrafish embryo, so as to minimize the impact of phototoxicity on living samples while ensuring imaging quality; in addition, by integrating a high-speed camera (frame rate ≥ 100 fps), the system can capture the dynamic migration process of melanocytes and provide high-quality time-series image data for subsequent quantitative analysis.

[0090] Based on the improved U-Net network, the system can process multi-angle light sheet images and output high-precision three-dimensional volume data. In this step, a graph optimization algorithm (such as SLAM technology) is introduced, which can automatically align the data collected at multiple time points, thus generating a temporally consistent three-dimensional model. This innovation not only improves the accuracy of three-dimensional reconstruction, but also ensures the coherence of temporal data, providing a solid foundation for subsequent dynamic analysis.

[0091] At this time, in the zebrafish embryo melanocyte migration monitoring and analysis system, three-dimensional reconstruction is a crucial step; in order to achieve the construction of a high-precision and temporally consistent three-dimensional model, an innovative method based on the improved U-Net network and graph optimization algorithm (such as SLAM technology) is adopted; the following will elaborate on this process in detail and provide implementation guidance for those skilled in the art.

[0092] The improved U-Net network is optimized on the basis of the original U-Net network to adapt to the characteristics of light sheet images; the network adopts strategies such as deeper convolutional layers, larger receptive fields and skip connections to improve the accuracy and efficiency of image segmentation; at the same time, aiming at the multi-angle characteristics of light sheet images, the network designs multiple input channels to process image data from different angles simultaneously; in the training stage, a large number of labeled zebrafish light sheet image datasets are used to train the improved U-Net network, and these image data cover zebrafish embryos at different developmental stages, different angles and different illumination conditions; through data augmentation techniques (such as rotation, scaling, flipping, etc.), the training samples are further enriched and the generalization ability of the model is improved; in the validation stage, an independent test dataset is used to evaluate the model to ensure its segmentation accuracy and robustness; the trained improved U-Net network can accurately segment the input multi-angle light sheet images; the network can distinguish the melanin region from the background noise, thus extracting the melanocytes in the zebrafish embryo, and the output of this step is multi-angle two-dimensional segmentation images.

[0093] For the moving-aligned multi-timepoint data, taking the two-dimensional segmentation images from multiple angles as the input, the SLAM algorithm first constructs an initial three-dimensional point cloud model; then, through an iterative optimization process, the algorithm continuously adjusts the positions of the points in the point cloud model to minimize the reprojection error between the images at different time points and different angles. This process realizes the automatic alignment of multi-timepoint data and generates a temporally consistent three-dimensional model; to improve the alignment accuracy and efficiency, a variety of optimization strategies are adopted; for example, sparse representation is used to reduce the computational amount; prior knowledge (such as the anatomical structure of zebrafish) is introduced to constrain the optimization process; and parallel computing technology is used to accelerate the iterative process.

[0094] For those skilled in the art, to achieve the three-dimensional reconstruction of light sheet images based on the improved U-Net network and graph optimization algorithm, the following steps are required: collect and annotate a large amount of zebrafish light sheet image data, including images at different angles, different time points, and different lighting conditions; design the improved U-Net network structure and use the prepared dataset for training; during the training process, continuously adjust the network parameters to optimize the segmentation accuracy; use the trained network to segment the input multi-angle light sheet images and extract the melanin region; take the segmented two-dimensional images as the input and use the SLAM algorithm for the automatic alignment of multi-timepoint data; during the alignment process, adjust the optimization strategy according to actual needs to improve the accuracy and efficiency; construct a three-dimensional point cloud model based on the aligned data and perform post-processing (such as smoothing, denoising, etc.) to improve the model quality.

[0095] Furthermore, to achieve the precise segmentation and quantitative analysis of the melanin region, a multi-task deep learning model (an improved version based on Mask R-CNN) is constructed; this model can simultaneously achieve semantic segmentation (distinguish the melanin region from background noise), volume calculation (quantify the three-dimensional volume of melanin based on voxel statistics), and dynamic tracking (analyze the migration path and rate of melanin through a temporal model); to improve the generalization ability of the model, a transfer learning strategy is adopted, first pre-train on a public biological image dataset (such as ImageNet-Zebrafish), and then fine-tune on a small number of labeled samples.

[0096] To reduce the artifacts generated by the movement of living samples during long-term imaging, an optical flow-based motion compensation algorithm is designed; this algorithm can correct the displacement of the sample in real time, thus ensuring the accuracy of the imaging data; in addition, a reinforcement learning algorithm is combined to optimize the imaging parameters (such as exposure time, light sheet intensity) to further improve the signal-to-noise ratio and ensure the imaging quality.

[0097] Specifically, collect imaging data of live samples such as zebrafish, including image sequences of consecutive frames and corresponding imaging parameters; select a suitable optical flow algorithm, implement and optimize it; at the same time, design a displacement correction algorithm and post - processing steps; define the state space, action space, and reward function, design the structure of the policy network, and use the collected data for training; during the training process, continuously adjust the model parameters and optimize the policy; integrate the optical flow motion compensation algorithm and the reinforcement learning model into the imaging system and conduct test verification; evaluate the effectiveness and performance of the algorithm through comparative experiments.

[0098] At this time, optical flow is a method for describing the velocity vector of pixel motion in an image; in live imaging, the optical flow algorithm is used to estimate the displacement of the sample between consecutive frames; the optical flow algorithm calculates the motion vector of each pixel by analyzing the brightness changes of pixels in the image sequence, thereby realizing real - time tracking of the sample motion; the motion compensation algorithm based on optical flow is implemented through the following steps: pre - process the imaging data of consecutive frames, including denoising, enhancing contrast, etc., to improve the accuracy of optical flow calculation; use the optical flow algorithm to calculate the pixel motion vector between consecutive frames; common optical flow algorithms include the Lucas - Kanade algorithm, the Farneback algorithm, etc.; according to the calculated motion vector, correct the displacement of each frame of the image to eliminate artifacts caused by sample motion; post - process the corrected image, such as smoothing filtering, denoising, etc., to further improve the image quality.

[0099] A reinforcement learning model is designed, including a state space, an action space, a reward function, and a policy network; State space: includes information such as the image features of the current frame and historical imaging parameters; Action space: is the value range of imaging parameters, such as the adjustment step of the exposure time, the increase or decrease of the light sheet intensity, etc.; Reward function: define the reward value according to the imaging quality (such as signal - to - noise ratio); the higher the signal - to - noise ratio, the greater the reward value; Policy network: use a deep learning model (such as a convolutional neural network) to approximate the optimal policy, that is, select the optimal action according to the current state; through interacting with the environment, the reinforcement learning model continuously learns and optimizes the imaging parameters; during the training process, use the policy gradient algorithm to update the parameters of the policy network to maximize the cumulative reward value; at the same time, in order to accelerate the convergence speed and improve the optimization effect, common techniques in reinforcement learning such as experience replay and target network are adopted.

[0100] Please refer to Figure 7 , Figure 7 which is a schematic diagram of the structural composition of the melanin content analysis system based on migration control in the embodiments of the present invention; the melanin content analysis system based on migration control includes: The lighting parameter module 21 is configured to determine corresponding lighting parameters according to the thickness of the zebrafish embryo and the environmental conditions where the zebrafish is located when the zebrafish is in the online detection state; The image module 22 is configured to photograph the zebrafish embryo along the lighting parameters and collect multiple images of different angles of the zebrafish embryo; The time-sequential three-dimensional model module 23 is configured to determine corresponding three-dimensional data according to the multiple images and generate a time-sequential three-dimensional model based on the alignment of the three-dimensional data and the time point data; The melanin three-dimensional volume module 24 is configured to determine the melanin region based on the recognition of the time-sequential three-dimensional model and determine the melanin three-dimensional volume according to the regional position of the melanin region and the voxel statistic of the melanin region; The quantitative analysis module 25 is configured to collect the migration path of the melanin region and trigger the quantitative analysis of the melanin content according to the migration path of the melanin region, the migration speed of the melanin region, and the melanin three-dimensional volume.

[0101] For any combination of the technical features of the above embodiments, for the sake of brevity of description, not all combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

Claims

1. An analysis method for melanin content based on migration control, characterized in that, Including: When the zebrafish is in the online detection state, determine the corresponding illumination parameters according to the thickness of the zebrafish embryo and the environmental conditions where the zebrafish is located; Take pictures of the zebrafish embryo along the illumination parameters, and collect multiple images of different angles of the zebrafish embryo; Determine the corresponding three-dimensional data according to the multiple images, and generate a time-series three-dimensional model according to the alignment of the three-dimensional data and the time-point data; Determine the melanin region based on the recognition of the time-series three-dimensional model, and determine the three-dimensional volume of melanin according to the regional position of the melanin region and the voxel statistic of the melanin region; Collect the migration path of the melanin region, and trigger the quantitative analysis of the melanin content according to the migration path of the melanin region, the migration speed of the melanin region, and the three-dimensional volume of melanin.

2. The analysis method of melanin content based on migration control according to claim 1, characterized in that, When the zebrafish is in the online detection state, determining the corresponding illumination parameters according to the thickness of the zebrafish embryo and the environmental conditions where the zebrafish is located includes: Collect the location of the zebrafish, determine the corresponding online detection mode according to the location of the zebrafish and the species of the zebrafish, and trigger the online detection of the zebrafish along the online detection mode; At this time, according to the position information of the zebrafish, confirm that all zebrafish are in the correct positions in the detection area; Since two different species of zebrafish are involved, the "online detection mode for multiple species of zebrafish" is selected; Determine the area where the zebrafish embryo is located based on the online detection of the zebrafish, determine the thickness of the zebrafish embryo according to the recognition of the area where the zebrafish embryo is located, collect the environmental conditions where the zebrafish is located, and determine the corresponding illumination parameters according to the thickness of the zebrafish embryo, the environmental conditions where the zebrafish is located, and the illumination mapping relationship; The illumination mapping relationship is a preset look-up table that provides recommended illumination parameters according to the embryo thickness and environmental conditions; Query the illumination mapping relationship to determine the appropriate illumination intensity, wavelength, and exposure time.

3. The analysis method of melanin content based on migration control according to claim 1, wherein Taking pictures of the zebrafish embryo along the illumination parameters and collecting multiple images of different angles of the zebrafish embryo includes: Regulate the illumination system of the zebrafish embryo based on the illumination parameters, and dynamically take pictures of the zebrafish embryo based on the imaging system; The illumination parameters are adjusted in real time to respond to the movement or morphological changes of the embryo during the shooting process, ensuring continuous optimal illumination conditions; During the dynamic shooting of the zebrafish embryo, collect multiple angular positions of the zebrafish embryo, and collect multiple images of different angles of the zebrafish embryo according to the multiple angular positions, the imaging system, and the illumination system; The imaging system and the illumination system should work synchronously to ensure that images are captured under the correct lighting conditions.

4. The analysis method of melanin content based on migration control according to claim 1, wherein Determining the corresponding three-dimensional data according to the multiple images and generating a time-series three-dimensional model according to the alignment of the three-dimensional data and the time-point data includes: Stereo features corresponding to image segmentation based on multiple images, and generating corresponding stereo data according to the combination of multiple stereo features: In the segmented image, a feature point detection model is used to identify key feature points; the detected feature points are described to generate feature descriptors, which can uniquely represent the local morphological information of the feature points, and corresponding feature points are determined in images at different angles, and the extracted feature points and corresponding depth information are combined to generate point cloud data; each point in the point cloud data contains three-dimensional coordinate information to convert the point cloud data into continuous stereo data.

5. The analysis method of melanin content based on migration control according to claim 4, wherein Determining corresponding stereo data according to multiple images, and generating a temporal three-dimensional model according to the alignment of the stereo data and time point data, further including: Marking corresponding temporal nodes for multiple stereo data, and determining time point data according to the matching of multiple temporal nodes. At this time, the time point data is aligned in the time dimension, and multiple stereo features are gradually stereoscopically constructed during the alignment process to generate a temporal three-dimensional model: The stereo data at each time point is gradually constructed; at time point T1, the preliminary morphology of the embryo is constructed, including basic structures such as the head, body, and tail; as time goes by, more details are gradually added in subsequent time points, such as the differentiation of somites, the formation of fins, etc.; at the same time, interpolation processing is used to smooth the morphological changes between adjacent time points; finally, a continuous temporal three-dimensional model is generated.

6. The analysis method of melanin content based on migration control according to claim 1, wherein Determining the melanin region based on the recognition of the temporal three-dimensional model, and determining the three-dimensional volume of melanin according to the regional position of the melanin region and the voxel statistics of the melanin region, including: Dividing the temporal three-dimensional model, and generating multiple regions to be recognized according to the division of the temporal three-dimensional model. The multiple regions to be recognized are divided based on each temporal node in the temporal three-dimensional model, and the multiple regions to be recognized respectively correspond to different parts of the temporal three-dimensional model; the temporal three-dimensional model is a set containing three-dimensional data at multiple time points, and each time point corresponds to a three-dimensional model, and these three-dimensional models jointly describe the dynamic changes of a certain object or phenomenon over time; each region to be recognized is marked, and the marking includes assigning a unique identifier to each region, recording the position information of the region, and describing the characteristics of the region.

7. The analysis method of melanin content based on migration control according to claim 6, wherein Determining the melanin region based on the recognition of the temporal three-dimensional model, and determining the three-dimensional volume of melanin according to the regional position of the melanin region and the voxel statistics of the melanin region, further including: Determine multiple melanin features based on the recognition of multiple regions to be recognized, determine the melanin region according to the locations of the multiple melanin features and the convergence range of the melanin features, mark the regional position of the melanin region, determine the voxel statistic of the melanin region based on the real-time monitoring of the melanin region, and determine the three-dimensional volume of melanin based on the synthesis of the regional position of the melanin region and the voxel statistic of the melanin region; the melanin region is a continuous or nearly continuous region composed of multiple melanin features; mark each melanin region and record its position information; at the same time, register and fuse the melanin regions at different time points to form a continuous three-dimensional volume representation; calculate the three-dimensional volume of melanin based on the registered melanin region and the voxel statistic.

8. The method for analyzing melanin content based on migration control according to claim 1, wherein Collect the migration path of the melanin region, and trigger the quantitative analysis of the melanin content according to the migration path of the melanin region, the migration speed of the melanin region, and the three-dimensional volume of melanin, including: Mark the melanin region, and determine the migration state of the melanin region according to the movement of the mark of the melanin region. In the migration state of the melanin region, determine the migration path of the melanin region according to the movement route of the mark of the melanin region and the regional change amount of the melanin region; after determining that the melanin region is in the migration state, track and record the position information of the region at different time points or image frames; connect these position information to form a continuous path, that is, the migration path of the melanin region.

9. The analysis method of melanin content based on migration control according to claim 8, wherein Collect the migration path of the melanin region, and trigger the quantitative analysis of the melanin content according to the migration path of the melanin region, the migration speed of the melanin region, and the three-dimensional volume of melanin, further including: Determine multiple migration nodes according to the division of the migration path of the melanin region, and determine the corresponding migration speed of the melanin region according to the matching of the multiple migration nodes; determine multiple parameter combinations based on the migration path of the melanin region, the migration speed of the melanin region, and the three-dimensional volume of melanin, and determine the corresponding analysis features according to the multiple parameter combinations, construct the quantitative analysis logic of the melanin content according to the multiple analysis features, and trigger the quantitative analysis of the melanin content; the migration node is a key point on the path, such as the starting point, the ending point, the speed change point, or the direction turning point, etc.; each migration node records the corresponding time point and position information; apply the constructed quantitative analysis logic to quantitatively analyze the melanin content and output the quantitative analysis result; the quantitative analysis result includes the numerical representation of the melanin content, the trend chart, and the abnormal alarm.

10. An analysis system for melanin content based on migration control, characterized in that, The analysis system for melanin content based on migration control is applied to the analysis method for melanin content based on migration control as described in any one of claims 1-9. The analysis system for melanin content based on migration control includes: An illumination parameter module, which is used to determine the corresponding illumination parameters according to the thickness of the zebrafish embryo and the environmental conditions where the zebrafish is located when the zebrafish is in the online detection state; An image module, which is used to photograph the zebrafish embryo along the illumination parameters and collect multiple images of different angles of the zebrafish embryo; A temporal three-dimensional model module, which is used to determine corresponding three-dimensional data according to multiple images, and generate a temporal three-dimensional model based on the alignment of the three-dimensional data and time point data; A melanin three-dimensional volume module, which is used to determine the melanin region based on the recognition of the temporal three-dimensional model, and determine the melanin three-dimensional volume according to the regional position of the melanin region and the voxel statistic of the melanin region; A quantitative analysis module, which is used to collect the migration path of the melanin region, and trigger the quantitative analysis of the melanin content according to the migration path of the melanin region, the migration speed of the melanin region and the melanin three-dimensional volume.

Images