A dynamic eye anterior segment image processing method, device, equipment and storage medium

Abstract

The application provides a dynamic anterior segment image processing method, device and equipment and a storage medium, and belongs to the technical field of image processing. The method comprises the following steps: acquiring anterior segment image data of a target eye; the anterior segment image data comprises a static image in a dark room state and a dynamic image sequence under light stimulation conditions; extracting an anterior segment structure boundary based on the static image, and extracting a spatial dynamic change amount of the anterior segment structure based on the dynamic image sequence; and performing quantitative calculation of a preset physiological index based on the anterior segment structure boundary and the spatial dynamic change amount to obtain quantized anterior segment dynamic parameters. The technical scheme can realize quantitative and standardized evaluation of changes in the iris and the anterior chamber structure, and provides a unified reference basis for diagnosis, classification and efficacy research of anterior segment-related diseases.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to a method, apparatus, device and storage medium for processing dynamic anterior segment images. Background Technology

[0002] In various anterior segment diseases or postoperative risk states (such as shallow anterior chamber, age-related degeneration of the ciliary body, lens bulging, eccentricity or tilt of the intraocular lens (IOL), and abnormal fluctuations in the vault of the implantable collamer lens (ICL), the dynamic response of the anterior segment structure often reveals underlying pathological mechanisms more clearly than static measurements. For example, decreased iris muscle contraction ability, abnormal changes in iris curvature, sluggish anterior-posterior displacement of the lens or IOL, insufficient response of ciliary body thickness or volume during accommodation, and unstable fluctuations in the ICL vault under light stimulation may all indicate weakened structural elasticity, abnormal aqueous humor dynamics, angle compression, or increased postoperative risk. Therefore, precise quantification of the dynamic behavior of the anterior segment is of great value for disease screening, preoperative risk assessment, and evaluation of the safety of refractive surgery. However, there are currently no dynamic quantification standards and calculation methods for anterior segment images in related technologies. Summary of the Invention

[0003] The main objective of this application is to propose a method, apparatus, device, and storage medium for processing dynamic anterior segment images, aiming to achieve quantitative and standardized assessment of changes in iris and anterior chamber structure, and to provide a unified reference for the diagnosis, classification, and treatment studies of anterior segment-related diseases.

[0004] To achieve the above objectives, a first aspect of this application proposes a method for processing dynamic anterior segment images, the method comprising: Acquire anterior segment image data of the target eye; the anterior segment image data includes static images in a dark room and dynamic image sequences under light stimulation conditions; The anterior segment structure boundary is extracted based on the static image, and the spatial dynamic changes of the anterior segment structure are extracted based on the dynamic image sequence. Based on the anterior segment structural boundary and the spatial dynamic change, a preset physiological index is quantitatively calculated to obtain the quantified anterior segment dynamic parameters.

[0005] In some embodiments, extracting the spatial dynamic changes of the anterior segment structure based on the dynamic image sequence includes: Perform inter-frame matching and initial trajectory extraction processing on the dynamic image sequence to obtain the inter-frame matching results and initial motion trajectory information of the preceding structure; Based on the inter-frame matching results and the initial motion trajectory information, the landmark structural points and contours are located, and the feature contour positioning information of the front segment structure in each frame is obtained. The feature contour positioning information is subjected to continuous frame consistency tracking processing to generate a multi-dimensional temporal motion trajectory; The spatial dynamic changes of the front segment structure are calculated based on the multidimensional temporal motion trajectory.

[0006] In some embodiments, the step of extracting the spatial dynamic changes of the anterior segment structure based on the dynamic image sequence further includes: In the case where an implanted lens is present in the target eye, the feature contour positioning information of the implanted lens is identified based on the dynamic image sequence, and the feature contour positioning information of the implanted lens is consistently tracked in consecutive frames to generate the spatial dynamic change of the implanted lens.

[0007] In some embodiments, the preset physiological indicators include a cumulative rate ratio and an instantaneous rate ratio, wherein the cumulative rate ratio is used to characterize the cumulative changes in the anterior segment structure, and the instantaneous rate ratio is used to characterize the instantaneous change trend of the anterior segment structure; Based on the aforementioned spatial dynamic changes, a preset physiological index is quantitatively calculated, including: The cumulative rate ratio is calculated based on the changed measurement value in the spatial dynamic change and the original measurement value of the corresponding baseline frame; The instantaneous rate ratio is calculated based on the measured values ​​before and after the change in the spatial dynamic change and the original measured values ​​of the corresponding baseline frame.

[0008] In some embodiments, the extraction of the anterior segment structure boundary based on the static image includes: While performing size correction and proportional standardization on the static image, the anterior segment structural boundaries are extracted; the anterior segment structural boundaries include the upper and lower surfaces of the sclera, the upper and lower surfaces of the iris, the angle boundary of the anterior chamber, the anterior chamber boundary, the ciliary muscle boundary, and the lens boundary.

[0009] In some embodiments, the method further includes: The quantified anterior segment dynamic parameters are compared with the anterior segment dynamic parameters in a preset health database using an interval threshold to obtain the dynamic performance evaluation result of the anterior segment structure; the dynamic performance evaluation result is used at least to characterize whether the dynamic performance of the anterior segment structure deviates from the normal range.

[0010] In some embodiments, the method includes: Obtain the interval threshold comparison results between the quantified anterior segment dynamic parameters and the anterior segment dynamic parameters in the preset health database; The comprehensive risk matching degree of the anterior segment structure is determined based on the interval threshold comparison results; the comprehensive risk matching degree is at least used to characterize the overall functional status of the anterior segment structure in a dynamic process. Based on the dynamic performance evaluation results and / or the comprehensive risk matching degree, a pathological risk feedback report of the anterior segment structure is generated; the pathological risk feedback report includes an abnormal description of the anterior segment structure, the pathological mechanism of the anterior segment, and a correlation indication of potential risk diseases.

[0011] To achieve the above objectives, a second aspect of this application provides a processing apparatus for dynamic anterior segment images, comprising: The acquisition module is used to acquire anterior segment image data of the target eye; the anterior segment image data includes static images in a dark room and dynamic image sequences under light stimulation conditions; The image processing module is used to extract the boundary of the anterior segment structure based on the static image, and to extract the spatial dynamic changes of the anterior segment structure based on the dynamic image sequence. The parameter quantization module is used to perform quantification calculations of preset physiological indicators based on the anterior segment structural boundary and the spatial dynamic change, so as to obtain the quantified anterior segment dynamic parameters.

[0012] To achieve the above objectives, a third aspect of the present application provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method described in the first aspect.

[0013] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0014] To achieve the above objectives, a fifth aspect of the present application provides a computer program product storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0015] This application proposes a method, apparatus, computer device, computer-readable storage medium, and computer program product for processing dynamic anterior segment images. The method involves acquiring anterior segment image data of a target eye; the anterior segment image data includes static images in a dark room and dynamic image sequences under light stimulation; extracting anterior segment structural boundaries based on the static images; and extracting spatial dynamic changes in the anterior segment structure based on the dynamic image sequences; and performing quantitative calculations of preset physiological indicators based on the anterior segment structural boundaries and the spatial dynamic changes to obtain quantified anterior segment dynamic parameters.

[0016] Thus, by dynamically measuring and processing key structural parameters in dynamic anterior segment images, this application embodiment can achieve a quantitative and standardized assessment of changes in iris and anterior chamber structures, thereby providing a unified reference for the diagnosis, classification, and treatment studies of anterior segment-related diseases. Furthermore, by quantitatively and standardizedly assessing changes in iris and anterior chamber structures in dynamic anterior segment images, this application embodiment can also establish a quantitative standard system that objectively reflects the dynamic changes in anterior segment structures under different physiological or illumination conditions. Attached Figure Description

[0017] Figure 1 A flowchart illustrating the steps of a method for processing dynamic anterior segment images provided in this application embodiment; Figure 2a A method for processing dynamic anterior segment images provided in this application provides a schematic diagram of iris and anterior chamber region images in a dark room in some embodiments. Figure 2b A method for processing dynamic anterior segment images provided in this application provides a schematic diagram of iris and anterior chamber region images under light stimulation in some embodiments. Figure 3a A schematic diagram of a dynamic anterior segment image processing method provided in this application embodiment, involving a crystal region image (including ICL) under darkroom conditions in some embodiments; Figure 3b A schematic diagram of a dynamic anterior segment image processing method provided in this application embodiment, involving light-stimulated lens region images (including ICL) in some embodiments; Figure 3c The processing method for dynamic anterior segment images provided in the embodiments of this application includes, in some embodiments, a schematic diagram of a crystal region image (including IOL) under a dark room; Figure 3d A schematic diagram of a dynamic anterior segment image processing method provided in this application, involving light-stimulated lens region images (including IOL) in some embodiments; Figure 4 for Figure 1 A flowchart illustrating step S102; Figure 5 for Figure 1 A flowchart illustrating another step in step S102; Figure 6 The flowchart of the steps for quantifying indicators based on spatial dynamic changes is shown in some embodiments of the method for processing dynamic anterior segment images provided in this application. Figure 7A flowchart illustrating the steps of a method for processing dynamic anterior segment images provided in this application embodiment in other embodiments; Figure 8 A flowchart illustrating the steps of a method for processing dynamic anterior segment images provided in this application embodiment in some other embodiments; Figure 9 A schematic diagram of a quantization standard process based on dynamic anterior segment images in a complete embodiment of a method for processing dynamic anterior segment images provided in this application; Figure 10 A schematic diagram of a processing device for dynamic anterior segment images provided in an embodiment of this application; Figure 11 This is a schematic diagram of the hardware structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0021] First, the overall concept of the embodiments of this application will be explained.

[0022] The anterior segment structures (also known as the anterior segmental structures / key anterior segment structures, including the anterior chamber, iris, lens, and ciliary body) play a central role in maintaining normal visual function and ocular homeostasis. The morphology and depth of the anterior chamber, as well as its dynamic regulatory capacity under different physiological or external stimuli, directly affect aqueous humor circulation, intraocular pressure stability, visual quality, and the occurrence and development of various eye diseases. In particular, the active or passive movement changes of key structures such as the iris, lens, and ciliary body under changes in light intensity, accommodative response, pharmacological stimulation, and age-related factors, and their amplitude, direction, rate, and geometric deformation characteristics, are all important indicators reflecting the stability of anterior chamber function.

[0023] With the widespread adoption of cataract and refractive surgeries, the postoperative dynamic performance of anterior segment implants has become an important clinical concern. The positional stability of the intraocular lens (IOL) during light stimulation or accommodation, as well as the spatial relationship of the implantable lens (ICL) within the anterior chamber and the dynamic changes in the vault, can all affect postoperative visual quality and anterior chamber safety.

[0024] In various anterior segment diseases or postoperative risk states (such as shallow anterior chamber, age-related degeneration of the ciliary body, lens bulging, IOL eccentricity or tilt, and abnormal fluctuations in the ICL Vault), the dynamic response of the anterior segment structure often reveals underlying pathological mechanisms more clearly than static measurements. For example, decreased iris muscle contraction ability, abnormal changes in iris curvature, sluggish anterior-posterior displacement of the lens or IOL, insufficient response of ciliary body thickness or volume during accommodation, and unstable fluctuations in the ICL Vault under light stimulation may all indicate weakened structural elasticity, abnormal aqueous humor dynamics, angle compression, or increased postoperative risk. Therefore, precise quantification of the dynamic behavior of the anterior segment is of great value for disease screening, preoperative risk assessment, and evaluation of the safety of refractive surgery.

[0025] Currently, clinical imaging assessment of anterior segment anatomy mainly relies on techniques such as anterior segment optical coherence tomography (AS-OCT) or ultrasound biomicroscopy (UBM). In traditional assessments, these methods utilize static imaging measurements to measure fixed anterior chamber parameters (such as anterior chamber depth, iris thickness, and angle-opening distance), primarily reflecting the static anterior chamber structure. They are commonly used to differentiate between open-angle and closed-angle glaucoma, for pre- and post-operative measurements in cataract surgery, and for pre- and post-operative examinations in refractive surgery.

[0026] However, there is currently no dynamic quantification standard or calculation method for anterior segment images.

[0027] While geometric measurements of the anterior segment structure have matured with advancements in imaging technology, significant shortcomings remain in the quantitative analysis of dynamic changes. Current analysis of anterior segment images primarily relies on static parameter measurements, such as anterior chamber depth, iris thickness, and angle-opening distance, which only reflect the structural state at a specific moment and fail to describe its dynamic changes. The iris and angle undergo significant deformation under illumination, accommodation, or pharmacological effects, but inconsistent parameter definitions, measurement methods, and calculation standards in related studies lead to a lack of comparability between results. The absence of a unified quantitative system hinders data standardization and replication, limiting the application value of dynamic imaging in glaucoma diagnosis and research.

[0028] In practical dynamic image analysis, accurately tracking subtle changes in the iris or anterior chamber angle is crucial for achieving dynamic quantification. Current dynamic image analysis techniques often employ image registration, edge detection, or region segmentation, which struggle to achieve high-precision continuous tracking of any point on the iris or anterior chamber angle. Under conditions of changing illumination, subtle eye movements, or noise interference, tracking points are prone to drift or loss, resulting in unstable motion trajectories. Some methods also rely on manual annotation, leading to strong subjectivity and poor repeatability. Consequently, these techniques fail to accurately reflect the true dynamic characteristics of the anterior segment structure, especially exhibiting significant errors in the analysis of subtle structural changes.

[0029] Most of the previous image analysis methods in related technologies can only compare the changes of two frames of images at different time points or under different lighting conditions, and cannot achieve continuous tracking and quantitative analysis of the entire dynamic process. The process of iris contraction and iris angle opening and closing is a complex nonlinear dynamic change involving multidimensional dynamic characteristics such as displacement degree, instantaneous velocity and elastic extension amplitude. Due to the lack of stable time series modeling and trajectory tracking algorithms, key indicators such as deformation amplitude and motion rate in the dynamic process are difficult to extract accurately, and the analysis often remains at the qualitative description stage.

[0030] To address this, embodiments of this application propose a method, apparatus, computer device, computer-readable storage medium, and computer program product for processing dynamic anterior segment images. This involves acquiring anterior segment image data of the target eye; the anterior segment image data includes static images in a dark room and dynamic image sequences under light stimulation conditions; extracting anterior segment structural boundaries based on the static images; and extracting spatial dynamic changes in the anterior segment structure based on the dynamic image sequences; and performing quantitative calculations of preset physiological indicators based on the anterior segment structural boundaries and the spatial dynamic changes to obtain quantified anterior segment dynamic parameters.

[0031] Thus, by dynamically measuring and processing key structural parameters in dynamic anterior segment images, this application embodiment can achieve a quantitative and standardized assessment of changes in iris and anterior chamber structures, thereby providing a unified reference for the diagnosis, classification, and treatment studies of anterior segment-related diseases. Furthermore, by quantitatively and standardizedly assessing changes in iris and anterior chamber structures in dynamic anterior segment images, this application embodiment can also establish a quantitative standard system that objectively reflects the dynamic changes in anterior segment structures under different physiological or illumination conditions.

[0032] Based on the overall concept of the embodiments of this application described above, specific embodiments of a method, apparatus, computer device, computer-readable storage medium, and computer program product for processing dynamic anterior segment images are proposed in this application. First, various specific embodiments of a method for processing dynamic anterior segment images according to the embodiments of this application will be described in detail.

[0033] It should be noted that the embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0034] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0035] Furthermore, in various specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Moreover, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. Additionally, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirects to confirmation pages. Only after explicitly obtaining the user's separate permission or consent is the necessary user-related data for the proper functioning of these embodiments acquired.

[0036] Furthermore, the dynamic anterior segment image processing method provided in this application embodiment can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be an anterior segment structure inspection-related device, a control and management device for that device, a smartphone, tablet computer, laptop computer, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements a dynamic anterior segment image processing method, etc., but is not limited to the above forms.

[0037] Alternatively, the method for processing dynamic anterior segment images provided in this application embodiment can also be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer computer devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can reside in local and remote computer storage media, including storage devices.

[0038] For ease of understanding and explanation, the following description will use a method for processing dynamic anterior segment images provided in the embodiments of this application on a terminal device as an example. The implementation of any of the above-described subject matter using the method for processing dynamic anterior segment images provided in the embodiments of this application can refer to the process described below for applying the method on a terminal device.

[0039] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating the steps of a dynamic anterior segment image processing method provided in some embodiments of this application. It should be understood that, although... Figure 1 The figure shows the execution order of some method steps, but based on different design needs of actual applications, the dynamic anterior segment image processing method provided in this application embodiment can of course adopt a different execution order of method steps than that shown in the figure. That is, Figure 1 The order of the method steps shown does not constitute a limitation on the execution logic order of the dynamic anterior segment image processing method provided in this application embodiment. Any other method based on... Figure 1 Reasonable variations in the order of the steps shown should be included within the protection scope of the dynamic anterior segment image processing method provided in this application embodiment.

[0040] like Figure 1 As shown, in some embodiments, the method for processing dynamic anterior segment images provided in this application may include, but is not limited to, steps S101 to S103.

[0041] Step S101: Acquire anterior segment image data of the target eye; the anterior segment image data includes static images in a dark room and dynamic image sequences under light stimulation conditions.

[0042] When performing tasks such as anterior segment structure examination, the terminal device can use the anterior segment of the subject as the research object and determine the stimulation pattern and analysis type according to the experimental conditions. In this way, the terminal device can acquire static images of the subject's target eye (left eye and / or right eye) in a dark room and dynamic image sequences of the target eye under light stimulation conditions. The static images and dynamic image sequences can then be used as anterior segment image data to jointly establish dynamic quantitative standards for anterior segment structure.

[0043] In some embodiments, the terminal device can acquire AS-OCT data of the target eye (such as...). Figure 2a and Figure 2b (As shown) is used as the image data of the previous segment.

[0044] In other embodiments, the terminal device can acquire UBM data of the target eye as anterior segment image data.

[0045] Step S102: Extract the anterior segment structure boundary based on the static image, and extract the spatial dynamic change of the anterior segment structure based on the dynamic image sequence.

[0046] After acquiring the anterior segment image data of the target eye, the terminal device further processes the anterior segment image data to determine the parameters related to the anterior segment structure of the target eye. Specifically, for static images, the terminal device can extract the anterior segment structure boundaries, while for dynamic image sequences, it extracts the spatial dynamic changes of the anterior segment structure through dynamic tracking.

[0047] In some embodiments, step S102 above, the step of "extracting the anterior segment structure boundary based on the static image" may include the following steps: While performing size correction and proportional standardization on the static image, the anterior segment structural boundaries are extracted; the anterior segment structural boundaries include the upper and lower surfaces of the sclera, the upper and lower surfaces of the iris, the angle boundary of the anterior chamber, the anterior chamber boundary, the ciliary muscle boundary, and the lens boundary.

[0048] For static images in the anterior segment imaging data, the terminal device can perform size correction and scale standardization on the image data to ensure the consistency of data collected by different devices. At the same time, the terminal device extracts the boundaries of key anterior segment structures, including the upper and lower surfaces of the sclera, the upper and lower surfaces of the iris, the boundary of the anterior chamber angle, the boundary of the anterior chamber, the boundary of the ciliary muscle, and the boundary of the lens, providing a basis for subsequent parameter measurements.

[0049] In some embodiments, for a dynamic image sequence, the terminal device can select a baseline frame in a dark room and pair it with iris and pupil image frames after stable light stimulation to ensure that the image orientation is consistent with the scanning position. The image quality is then evaluated and filtered, removing frames with low signal-to-noise ratios or positional offsets, thereby obtaining a preprocessed anterior segment dynamic image sequence. Then, the terminal device can use this preprocessed anterior segment dynamic image sequence as input in the dynamic tracking section.

[0050] Step S103: Based on the anterior segment structural boundary and the spatial dynamic change, perform quantitative calculation of preset physiological indicators to obtain the quantified anterior segment dynamic parameters.

[0051] After extracting the anterior segment structural boundary based on static images and extracting the spatial dynamic changes of the anterior segment structure based on dynamic image sequences, the terminal device further performs quantitative calculations of preset physiological indicators based on the anterior segment structural boundary and the spatial dynamic changes, thereby obtaining the quantified anterior segment dynamic parameters. These anterior segment dynamic parameters can provide a reliable quantitative basis for the assessment of anterior segment diseases and postoperative risks.

[0052] It should be noted that the preset physiological indicators can be selected in advance. In the indicator selection section, the terminal device can perform quantitative analysis on geometric features in the anterior segment imaging data that reflect the dynamic changes of the iris, lens, ciliary body, and anterior chamber structures, selecting indicators such as… Figure 2a , Figure 2b and Figures 3a to 3d As shown. The measurement range covers the iris, anterior chamber, anterior chamber angle, ciliary body, pupil, and the area where the lens or implanted lens is located. The structural indicators together constitute the basic dataset for dynamic assessment of the anterior segment.

[0053] Among them, such as Figure 2a and Figure 2bAs shown, in the iris and anterior chamber regions, the main indicators for the iris include iris length, iris thickness (IT), iris volume (IV), iris curvature (IR), and pupil diameter (PD), used to describe the contraction morphology, boundary displacement, and structural elasticity changes of the iris under light stimulation. For the anterior chamber and iridocorneal structures, parameters such as anterior chamber opening distance (AOD), anterior chamber recess area (ARA), trabecular-iris space area (TIA), trabecular-iris angle (TIA), anterior chamber depth (ACD), and anterior chamber width (ACW) are selected to reflect changes in anterior chamber volume and the dynamic adjustment of the iridocorneal structure at different stimulation stages.

[0054] In addition, such as Figures 3a to 3d As shown, for lens-related structures, the terminal device can select indicators such as lens vault (LV), lens curvature, lens thickness (LT), and key point positions of the anterior and posterior capsules to describe the morphological characteristics and spatial relationships of the lens under different stimulation conditions. In the presence of an IOL or ICL, corresponding structural parameters related to the implant are selected, including the anterior-posterior position, edge orientation, and relative relationship with surrounding tissues of the IOL, as well as the Vault of the ICL, its distance from the cornea or iris, and the key point positions of the anterior surface or edge of the implant, to fully reflect the basic geometric characteristics of the implanted lens in the anterior segment. Furthermore, for the ciliary body, ciliary muscle thickness, ciliary muscle length, ciliary body outline, and the positions of related structures involved during accommodation are selected to record the basic morphological indicators of the ciliary body as an accommodation device.

[0055] In this embodiment, when performing tasks such as anterior segment structure examination, the terminal device acquires static images of the target eye in a dark room and dynamic image sequences of the target eye under light stimulation. These static and dynamic image sequences are then used as anterior segment image data to establish dynamic quantitative standards for the anterior segment structure. Subsequently, the terminal device further processes the anterior segment image data to determine parameters related to the target anterior segment structure. Specifically, for static images, the terminal device can extract the anterior segment structure boundaries, while for dynamic image sequences, it extracts the spatial dynamic changes of the anterior segment structure through dynamic tracking. Then, based on the anterior segment structure boundaries and the spatial dynamic changes, the terminal device performs quantitative calculations of preset physiological indicators to obtain quantified anterior segment dynamic parameters. These parameters provide reliable quantitative evidence for assessing anterior segment diseases and postoperative risks.

[0056] Thus, by dynamically measuring and processing key structural parameters in dynamic anterior segment images, this application embodiment can achieve a quantitative and standardized assessment of changes in iris and anterior chamber structures, thereby providing a unified reference for the diagnosis, classification, and treatment studies of anterior segment-related diseases. Furthermore, by quantitatively and standardizedly assessing changes in iris and anterior chamber structures in dynamic anterior segment images, this application embodiment can also establish a quantitative standard system that objectively reflects the dynamic changes in anterior segment structures under different physiological or illumination conditions.

[0057] Please refer to Figure 4 , Figure 4 for Figure 1 A flowchart illustrating step S102.

[0058] like Figure 4 As shown, in some embodiments, the step of "extracting the spatial dynamic change of the anterior segment structure based on the dynamic image sequence" in step S102 above may include steps S401 to S404 as shown below.

[0059] Step S401: Perform inter-frame matching and initial trajectory extraction processing on the dynamic image sequence to obtain the inter-frame matching result and initial motion trajectory information of the preceding structure.

[0060] When extracting the spatial dynamic changes of the anterior segment structure from a dynamic image sequence, the terminal device can use the pre-processed anterior segment dynamic image sequence (which has undergone denoising, enhancement, frame alignment, and other pre-processing operations) as input. It can then use computer vision algorithms to perform inter-frame feature matching on key physiological structures such as the iris, lens, and anterior chamber in consecutive frames of the sequence, simultaneously extract the initial motion trajectory of each structure, and capture the continuous motion signal of the structure during the light stimulus response process, thereby obtaining the inter-frame matching results and initial motion trajectory information of the anterior segment structure.

[0061] Step S402: Based on the inter-frame matching results and the initial motion trajectory information, locate the landmark structure points and contours to obtain the feature contour positioning information of the front structure in each frame.

[0062] After obtaining the inter-frame matching results and initial motion trajectory information of the anterior segment structure, the terminal device further integrates multiple algorithms to perform localization processing of landmark structural points and contours. That is, based on the inter-frame matching results and initial motion trajectory, combined with structural segmentation, boundary detection, and temporal point tracking algorithms, the anterior segment structure is further accurately located based on the initial trajectory: landmark structures such as the iris muscle region, pupillary margin, anterior / posterior lens capsule contour, anterior chamber angle structural points, and ciliary body sites, thereby obtaining a set of accurate feature points / contour localizations of the anterior segment structure in each frame (feature contour localization information).

[0063] Step S403: Perform continuous frame consistency tracking processing on the feature contour positioning information to generate a multi-dimensional temporal motion trajectory.

[0064] The terminal device can further perform continuous frame consistency tracking processing on the feature contour positioning information. That is, in the continuous frames of the dynamic image sequence, all the located feature points are tracked consistently to ensure the correlation and continuity of feature points of the same structure in the temporal dimension. As the light stimulation process progresses, the structural positions of each time node are automatically associated, thereby generating a multi-dimensional temporal motion trajectory of the anterior segment structure.

[0065] In some embodiments, the multidimensional temporal motion trajectory may include the iris muscle contraction / relaxation trajectory, the lens anterior-posterior movement and curvature change trajectory, the ciliary body adjustment trajectory, and the overall deformation trajectory of the anterior chamber, etc.

[0066] Step S404: Calculate the spatial dynamic change of the front structure based on the multidimensional temporal motion trajectory.

[0067] After generating a multidimensional temporal motion trajectory through continuous frame consistency tracking, the terminal device can further quantify and calculate the spatial dynamic changes of the anterior segment structure based on the motion coordinates of each feature point on the time axis in the multidimensional temporal motion trajectory, such as: iris muscle motion vector and elastic threshold, rate curve of anterior chamber depth change over time, lens curvature change trend, etc.

[0068] In some embodiments, the terminal device can also combine structural priors to further infer the three-dimensional spatial dynamic change characteristics of the anterior segment structure. That is, based on the spatial dynamic change of the anterior segment structure and prior knowledge of eye structures (such as anterior segment anatomical parameters, tissue mechanical properties, etc.), three-dimensional reconstruction and inference are performed by combining structural prior information, thereby compensating for the lack of spatial information in two-dimensional images and restoring the dynamic change law of the structure in three-dimensional space. In this way, the terminal device can obtain the three-dimensional spatial dynamic change characteristics of the anterior segment structure.

[0069] Please refer to Figure 5 , Figure 5 for Figure 1 Another step flow diagram for step S102.

[0070] like Figure 5 As shown, in some embodiments, the step of "extracting the spatial dynamic change of the anterior segment structure based on the dynamic image sequence" in step S102 above may also include step S501 as shown below.

[0071] Step S501: In the case where an implanted lens is present in the target eye, the feature contour positioning information of the implanted lens is identified based on the dynamic image sequence, and the feature contour positioning information of the implanted lens is consistently tracked in consecutive frames to generate the spatial dynamic change amount of the implanted lens.

[0072] When the terminal device extracts the spatial dynamic changes of the anterior segment structure from the dynamic image sequence, if the target eye has an implanted lens (artificial lens IOL or implantable lens ICL), in this case, the terminal device can, based on the same operation process described in steps S401 to S404 above, identify the feature contour positioning information of the implanted lens based on the dynamic image sequence, and perform consistent tracking of the feature contour positioning information of the implanted lens in consecutive frames, thereby generating the spatial dynamic changes of the implanted lens.

[0073] For example, in the dynamic tracking section, the terminal device takes the preprocessed anterior segment dynamic image sequence as input and uses computer vision methods to perform inter-frame matching and trajectory extraction on key structures of the iris, lens, and anterior chamber to obtain continuous motion information of physiological structures during photostimulation response. By combining algorithms such as structural segmentation, boundary detection, and temporal point tracking, it locates the iris muscle region, pupillary margin, anterior and posterior lens capsule contours, anterior chamber angle structures, ciliary body sites, and other landmark structures reflecting changes in the anterior chamber. When an intraocular lens (IOL) or implantable intraocular lens (ICL) is present, the terminal device also simultaneously identifies its anterior surface position, edge features, and Vault-related points, and performs consistent tracking of these feature points across consecutive frames.

[0074] As the stimulation process progresses, the terminal device can automatically generate temporal information such as the iris muscle contraction and relaxation trajectory, the lens's anterior-posterior movement and curvature change trajectory, the ciliary body's movement trajectory during accommodation, the IOL or ICL's positional change trajectory, and the overall deformation trajectory of the anterior chamber. Subsequently, based on the movement of key points on the time axis, the terminal device calculates the iris muscle motion vector and elastic threshold, the rate curve of anterior chamber depth change over time, the trend of lens curvature change, and the dynamic stability performance of the implant, and, when necessary, combines structural priors to infer the corresponding three-dimensional change characteristics.

[0075] In some embodiments, when the terminal device performs quantitative calculations of preset physiological indicators based on the anterior segment structural boundaries and spatial dynamic changes extracted from anterior segment image data, since static anterior segment images (such as a darkroom baseline frame or a single scan under no-stimulation conditions) use the extracted structural boundaries of the iris, anterior chamber angle, lens, and ciliary body, the terminal device can directly calculate the corresponding geometric parameters based on these anterior segment structural boundaries. These parameters include iris length, iris thickness, iris volume, iris curvature, pupil diameter, anterior chamber depth, lens thickness, curvature of the anterior and posterior lens capsules, anterior chamber angle opening distance, and ciliary body thickness or length. Furthermore, if the image contains an IOL or ICL, the terminal device can also simultaneously record the relative position of the implanted lens in the anterior chamber, including its anterior and posterior placement points, distance from adjacent tissues, and static Vault measurements.

[0076] In some embodiments, to ensure comparability between devices from different sources, the terminal device can perform pixel-scale correction and scaling before calculating all static parameters, and can perform measurements according to predetermined anatomical positioning points (IR, PM, SS, AR, EP, etc.) to obtain a set of structural baseline parameters under the same reference system.

[0077] In some embodiments, the preset physiological indicators include a cumulative rate ratio and an instantaneous rate ratio, wherein the cumulative rate ratio is used to characterize the cumulative changes in the anterior segment structure, and the instantaneous rate ratio is used to characterize the instantaneous change trend of the anterior segment structure.

[0078] When the terminal device performs quantitative calculations of preset physiological indicators based on the anterior segment structural boundaries and spatial dynamic changes extracted from the anterior segment image data, it has already compared the images at different stimulation time points frame by frame for the dynamic image sequence, starting from the darkroom baseline frame, and extracted the changes in the iris, lens, ciliary body and anterior chamber structures (if there is an IOL or ICL in the image, its position and Vault changes are also obtained). Thus, in the quantification process, the terminal device can adopt a dual indicator system that considers both "cumulative changes" and "instantaneous changes" for the above-mentioned parameters, so that it can reflect the overall response and temporal details of the structure throughout the entire stimulation cycle.

[0079] Please refer to Figure 6 , Figure 6 The flowchart illustrates the steps involved in quantifying indicators based on spatial dynamic changes in a method for processing dynamic anterior segment images provided in this application embodiment.

[0080] like Figure 6 As shown, in some embodiments, the quantitative calculation of preset physiological indicators based on the spatial dynamic change amount may include steps S601 and S602 as shown below.

[0081] Step S601: Calculate the cumulative rate ratio based on the changed measurement value in the spatial dynamic change amount and the original measurement value of the corresponding baseline frame.

[0082] When the terminal device performs quantitative calculation of the cumulative rate ratio based on the spatial dynamic changes of the front section structure, it can use the following formula to calculate the cumulative rate ratio based on the changed measurement value in the spatial dynamic changes and the original measurement value of the corresponding baseline frame.

[0083] .

[0084] It can be used to describe the overall response amplitude of the anterior segment structure during stimulation, such as significant iris retraction, pupillary constriction amplitude, and increased lens curvature.

[0085] Step S602: Calculate the instantaneous rate ratio based on the measured values ​​before and after the change in the spatial dynamic change and the original measured values ​​of the corresponding baseline frame.

[0086] When a terminal device performs a quantitative calculation of the instantaneous rate ratio based on the spatial dynamic changes of the front-side structure, it can use the formula shown below to calculate the instantaneous rate ratio based on the measured values ​​before the change, the measured values ​​after the change, and the original measured values ​​of the corresponding baseline frame in the spatial dynamic changes.

[0087] .

[0088] It can reflect short-term dynamics during the stimulation process, such as the rapid contraction phase of the pupil at the beginning of stimulation, the change in the speed of lens anterior movement, and the starting point of changes in the iridocorneal angle.

[0089] In this embodiment, the method used by the terminal device to calculate the cumulative rate ratio and instantaneous rate ratio is applicable to the quantification process of dynamic structural parameters such as iris length, iris thickness, iris volume, iris curvature, pupil diameter, lens curvature, lens thickness, anterior chamber angle opening angle, anterior chamber angle opening distance, anterior chamber crypt area, trabecular meshwork interiris area, and anterior chamber depth. If an IOL or ICL exists, it can also be used for the quantitative analysis of its positional changes and Vault fluctuations. By processing the curves of these parameters on the time axis, the response start time, peak value changes, recovery stage characteristics, and overall stability of the structure can be further obtained, which can be used to establish a dynamic quantitative standard system for the anterior chamber structure.

[0090] Please refer to Figure 7 , Figure 7 This is a flowchart illustrating the steps of a method for processing dynamic anterior segment images provided in this application in other embodiments.

[0091] like Figure 7 As shown, in some embodiments, the method for processing dynamic anterior segment images provided in this application may further include the following step S701.

[0092] Step S701: Compare the quantified anterior segment dynamic parameters with the anterior segment dynamic parameters in the preset health database using an interval threshold to obtain the dynamic performance evaluation result of the anterior segment structure; the dynamic performance evaluation result is used at least to characterize whether the dynamic performance of the anterior segment structure deviates from the normal range.

[0093] It should be noted that the preset health database contains the dynamic distribution of anterior chamber depth, iris thickness, iris volume, pupil diameter, lens curvature, ciliary body thickness, and angle-related parameters of normal individuals under light stimulation and baseline conditions. When it is necessary to evaluate implanted lenses, it also includes the normal fluctuation range of IOL position stability and ICL Vault.

[0094] After obtaining the quantified anterior segment dynamic parameters, the terminal device can further perform disease assessment on the anterior segment structure of the target eye based on these quantified parameters. Specifically, the terminal device compares the quantified anterior segment dynamic parameters with anterior segment dynamic parameters in a preset health database using interval thresholds to obtain a dynamic performance assessment result for the anterior segment structure. This assessment result is then used to characterize whether the dynamic performance of the anterior segment structure deviates from the normal range.

[0095] In some embodiments, during the disease assessment phase, the terminal device can use quantified anterior segment dynamic parameters as input to compare the structural change values, cumulative rate ratios, instantaneous rate ratios, and temporal characteristics obtained by the subject under different stimulation conditions with interval thresholds in a health database constructed according to age group, refractive state, and anatomical features. By matching the subject's parameters with each anterior segment dynamic parameter interval contained in the aforementioned health database, it is possible to determine whether the dynamic performance of the corresponding anterior segment structure deviates from the normal range, thereby identifying conditions such as sluggish iris response, insufficient lens or ciliary body deformation, restricted angle structure movement, decreased anterior chamber cavity stability, or abnormal fluctuations in implanted lens position.

[0096] Please refer to Figure 8 , Figure 8 This is a flowchart illustrating the steps of a method for processing dynamic anterior segment images provided in this application.

[0097] like Figure 8 As shown, in some embodiments, the method for processing dynamic anterior segment images provided in this application may further include steps S801 to S803 as shown below.

[0098] Step S801: Obtain the interval threshold comparison result between the quantified anterior segment dynamic parameters and the anterior segment dynamic parameters in the preset health database.

[0099] After comparing the quantified anterior segment dynamic parameters with the anterior segment dynamic parameters in the preset health database using interval thresholds, the terminal device can also obtain the interval threshold comparison result between the two anterior segment dynamic parameters (the result of matching the subject's parameters with each anterior segment dynamic parameter interval contained in the aforementioned health database).

[0100] Step S802: Determine the comprehensive risk matching degree of the anterior segment structure based on the interval threshold comparison results; the comprehensive risk matching degree is at least used to characterize the overall functional status of the anterior segment structure in the dynamic process.

[0101] After obtaining the interval threshold comparison result, the terminal device further determines the comprehensive risk matching degree of the front segment structure based on the interval threshold comparison result, and thus characterizes the overall functional status of the front segment structure in the dynamic process based on the comprehensive risk matching degree.

[0102] For example, after comparing the parameters across different intervals, the terminal device can further construct multiple risk factor models to analyze the correlation and consistency between different structural parameters. These risk factors include, but are not limited to, abnormal iris dynamic feedback in the blue spectrum, consistency of dynamic rates between the left and right irises, stability of key angle parameters during the stimulation cycle, health of iris muscle deformation, coordination of lens displacement and curvature changes, positional stability of the IOL or ICL, dynamic fluctuations in the Vault, and the recoverability of anterior chamber depth changes. Based on these risk factors, the terminal device calculates a comprehensive risk matching score to reflect the overall functional status of the subject's anterior chamber structure during the dynamic process. When the risk matching score is lower than a preset threshold, the system automatically identifies the corresponding abnormal structural region and the time period in which the abnormality occurred. For example, abnormal iris retraction at a certain point after stimulation begins, unstable lens or IOL displacement, or anterior chamber depth oscillations during the recovery phase, thus providing clear abnormal location information for clinical practice.

[0103] Step S803: Based on the dynamic performance evaluation results and / or the comprehensive risk matching degree, generate a pathological risk feedback report for the anterior segment structure; the pathological risk feedback report includes an abnormal description of the anterior segment structure, the pathological mechanism of the anterior segment, and the correlation indication of potential risk diseases.

[0104] The terminal device can further generate a pathological risk feedback report for the anterior segment structure based on the dynamic performance evaluation results and / or comprehensive risk matching degree of the anterior segment structure. The pathological risk feedback report can then describe the abnormalities of the anterior segment structure, the pathological mechanisms of the anterior segment, and the association with potential risk diseases.

[0105] In some embodiments, to enhance the readability and clinical interpretability of the assessment results, the terminal device can integrate a specifically optimized large language model based on the risk assessment (inputting the dynamic performance assessment results of the anterior segment structure and / or the comprehensive risk matching degree into the large language model) to comprehensively interpret structural change characteristics, abnormal parameters, and timeline anomalies, generating a pathological risk feedback report for physicians. The report includes a detailed description of the abnormal structure, the possible anterior segment pathological mechanisms involved, correlations with potential risk diseases, and auxiliary risk guidance for surgical procedures (such as goniosynostosis surgery, lens-related surgery) or further examinations.

[0106] This embodiment obtains corresponding comparison results by comparing the quantified anterior segment dynamic parameters with the anterior segment dynamic parameters in a preset health database using interval thresholds. Then, based on the interval threshold comparison results, the comprehensive risk matching degree of the anterior segment structure is determined. This comprehensive risk matching degree characterizes the overall functional status of the anterior segment structure in the dynamic process. Based on the dynamic performance evaluation results and / or the comprehensive risk matching degree, a pathological risk feedback report of the anterior segment structure is generated. This can realize a complete closed loop from quantitative measurement to pathological interpretation of the dynamic behavior of the anterior chamber structure, thereby providing a reliable basis for the early screening, risk assessment and clinical decision-making of anterior segment diseases.

[0107] Next, a complete embodiment of a method for processing dynamic anterior segment images provided in this application will be presented.

[0108] Please refer to Figure 9 , Figure 9 This is a schematic diagram of a quantization standard process based on dynamic anterior segment images, provided in a complete embodiment of a method for processing dynamic anterior segment images according to an embodiment of this application.

[0109] like Figure 9 As shown, in the data processing section, the terminal device uses the anterior segment images of the subject as the research object, reads the anterior segment image data of the target eye (such as the AS-OCT data shown in Figure 2, or UBM and other anterior segment image data), and determines the stimulation mode and analysis type according to the experimental conditions. The anterior segment image data used includes static images in a dark room and dynamic image sequences under light stimulation conditions, both of which are used together to establish dynamic quantitative standards for anterior segment structure.

[0110] For static image data, size correction and scaling were performed to ensure consistency of data acquired from different devices. Simultaneously, key anterior segment structural boundaries were extracted, including the upper and lower surfaces of the sclera and iris, the anterior chamber angle, the anterior chamber, the ciliary muscle, and the lens, providing a basis for subsequent parameter measurements. For dynamic image sequences, baseline frames in a dark room were paired with iris and pupil images after stable light stimulation to ensure image orientation and scanning position consistency. Image quality was also evaluated and filtered, removing frames with low signal-to-noise ratios or positional shifts.

[0111] In the dynamic tracking section, the terminal device uses a pre-processed sequence of anterior segment dynamic images as input and employs computer vision methods to perform inter-frame matching and trajectory extraction on key structures of the iris, lens, and anterior chamber to obtain continuous motion information of physiological structures during photostimulation response. By combining algorithms such as structural segmentation, boundary detection, and temporal point tracking, the system locates the iris muscle region, pupillary margin, anterior and posterior lens capsule contours, angle structures, ciliary body sites, and other landmark structures that reflect changes in the anterior chamber. When an intraocular lens (IOL) or implantable lens (ICL) is present, the system also simultaneously identifies its anterior surface position, edge features, and Vault-related points, and performs consistent tracking of these feature points across consecutive frames.

[0112] As the stimulation process progresses, the system automatically generates temporal information such as the trajectories of iris muscle contraction and relaxation, lens anterior-posterior movement and curvature changes, ciliary body movement during accommodation, IOL or ICL positional changes, and overall anterior chamber deformation. Subsequently, based on the movement of key points along the time axis, the system calculates the iris muscle motion vector and elastic threshold, the rate curve of anterior chamber depth change over time, the trend of lens curvature changes, and the dynamic stability of the implant. When necessary, it also incorporates structural priors to extrapolate corresponding three-dimensional change characteristics.

[0113] In the indicator selection section, the terminal device performs quantitative analysis on geometric features in the anterior segment images that can reflect the dynamic changes of the iris, lens, ciliary body, and anterior chamber structures. The measurement range covers the iris, anterior chamber, anterior chamber angle, ciliary body, pupil, and the area where the lens or implanted lens is located. These structural indicators together constitute the basic dataset for the dynamic assessment of the anterior segment.

[0114] In the iris and anterior chamber regions, key indicators for the iris include iris length, iris thickness (IT), iris volume (IV), iris curvature (IR), and pupil diameter (PD), used to describe the iris's contraction morphology, boundary displacement, and structural elasticity changes under light stimulation. For the anterior chamber and angle structures, parameters such as anterior chamber opening distance (AOD), anterior chamber recess area (ARA), trabecular-iris space area (TIA), trabecular-iris angle (TIA), anterior chamber depth (ACD), and anterior chamber width (ACW) are selected to reflect changes in anterior chamber volume and the dynamic adjustments of the angle structure at different stages of stimulation.

[0115] For lens-related structures, the terminal device selects indicators such as lens vault (LV), lens curvature, lens thickness (LT), and key point positions of the anterior and posterior capsules to describe the morphological characteristics and spatial relationships of the lens under different stimulation conditions. In the presence of an IOL or ICL, corresponding structural parameters related to the implant are selected, including the anterior-posterior position, edge orientation, and relative relationship with surrounding tissues of the IOL, as well as the Vault of the ICL, its distance from the cornea or iris, and the key point positions of the anterior surface or edge of the implant, to fully reflect the basic geometric characteristics of the implanted lens in the anterior segment. Furthermore, for the ciliary body, ciliary muscle thickness, ciliary muscle length, ciliary body outline, and the positions of related structures involved during accommodation are selected to record the basic morphological indicators of the ciliary body as an accommodation device.

[0116] In the parameter quantification and calculation section, the terminal device can perform quantitative calculations of various physiological indicators based on the results of previous dynamic tracking and indicator selection, providing a reliable quantitative basis for the subsequent assessment of anterior segment diseases and postoperative risks.

[0117] For static anterior segment images (such as baseline frames in a dark room or single scans under no-stimulation conditions), the terminal device calculates corresponding geometric parameters based on the extracted boundaries of structures such as the iris, anterior chamber angle, lens, and ciliary body. These parameters include iris length, iris thickness, iris volume, iris curvature, pupil diameter, anterior chamber depth, lens thickness, curvature of the anterior and posterior lens capsules, anterior chamber angle opening distance, and ciliary body thickness or length. If the image contains an IOL or ICL, the terminal device simultaneously records the relative position of the implanted lens in the anterior chamber, including its anterior and posterior placement points, distance from adjacent tissues, and static measurements of the Vault.

[0118] To ensure comparability between devices from different sources, all static parameters were pixel-scale calibrated and scaled before calculation, and measurements were performed according to predetermined anatomical positioning points (IR, PM, SS, AR, EP, etc.) to obtain a set of structural baseline parameters under the same reference system.

[0119] For dynamic image sequences, the terminal device starts with the darkroom baseline frame and compares images frame by frame at different stimulation time points to extract changes in the iris, lens, ciliary body, and anterior chamber structures. If an IOL or ICL is present in the image, its location and Vault changes are also acquired. During quantization, a dual-indicator system considering both "cumulative change" and "instantaneous change" is used for all parameters to reflect the overall structural response and temporal details throughout the stimulation cycle. (Cumulative rate ratio and...) The calculations are as described above, and will not be repeated here.

[0120] During the disease assessment phase, the terminal device uses quantified anterior segment dynamic parameters as input. It compares the structural change values, cumulative rate ratios, instantaneous rate ratios, and temporal characteristics obtained by the subject under different stimulation conditions with interval thresholds from a health database constructed according to age group, refractive status, and anatomical features. The health database contains the dynamic distribution of anterior chamber depth, iris thickness, iris volume, pupil diameter, lens curvature, ciliary body thickness, and angle-related parameters of normal individuals under light stimulation and baseline conditions. For those requiring assessment of implanted lenses, it additionally includes the normal fluctuation range of IOL position stability and ICL Vault. By matching the subject's parameters item by item with the aforementioned intervals, it can determine whether the dynamic performance of the corresponding structures deviates from the normal range, thereby identifying conditions such as sluggish iris response, insufficient lens or ciliary body deformation, restricted angle structure movement, decreased anterior chamber cavity stability, or abnormal fluctuations in implanted lens position.

[0121] After comparing the parameters across different intervals, the terminal device further constructs multiple risk factor models to analyze the correlation and consistency between different structural parameters. Risk factors include, but are not limited to, abnormal iris dynamic feedback in the blue spectrum, consistency of dynamic rates between the left and right irises, stability of key angle parameters during the stimulation cycle, health of iris muscle deformation, coordination of lens displacement and curvature changes, positional stability of the IOL or ICL, dynamic fluctuations in the Vault, and the recoverability of anterior chamber depth changes. Based on these factors, the system calculates a comprehensive risk matching score to reflect the overall functional status of the subject's anterior chamber structure during the dynamic process. When the risk matching score is lower than a preset threshold, the system automatically identifies the corresponding abnormal structural region and the time period in which the abnormality occurs. For example, abnormal iris retraction at a certain point after stimulation begins, unstable lens or IOL displacement, or anterior chamber depth oscillations during the recovery phase, thus providing clear abnormal location information for clinical practice.

[0122] To ensure the readability and clinical interpretability of the assessment results, the terminal device integrates a specially optimized large language model based on risk assessment. This model comprehensively interprets structural change characteristics, abnormal parameters, and timeline anomalies, generating a pathological risk feedback report for physicians. The report includes a detailed description of the abnormal structure, the possible anterior segment pathological mechanisms involved, correlations with potential risk diseases, and auxiliary risk guidance for surgical procedures (such as goniosynostosis surgery or lens-related surgery) or further examinations. Through this process, the terminal device achieves a complete closed loop from quantitative measurement to pathological interpretation of the dynamic behavior of anterior chamber structures, providing a reliable basis for early screening, risk assessment, and clinical decision-making for anterior segment diseases.

[0123] In summary, compared with traditional quantization methods, the arbitrary point tracking technology proposed in this embodiment can track any point in the anterior segment image, whether it is the iris, pupil, or other key points in the anterior chamber region, achieving precise localization and dynamic tracking. Through adaptive image processing and algorithm optimization, this technology can stably track the positional changes of target points under complex lighting and motion interference conditions, without relying on specific calibration points or initial frames, greatly improving the flexibility and accuracy of tracking.

[0124] Furthermore, the trajectory quantization technology of the terminal device can not only monitor the dynamic changes of the iris and pupil, but also quantify dynamic parameters such as their motion trajectory, speed, and amplitude of change. Through this technology, the system can calculate in real time the reaction rate, motion trajectory, and amplitude of change of the iris and pupil under different light stimuli, generating intuitive dynamic change curves to help doctors understand the physiological responses and changes of the eye structure. The trajectory quantization employs a stable algorithm, reducing the interference of motion artifacts and improving the accuracy and stability of the quantification results.

[0125] Furthermore, based on relevant technologies, the terminal device proposes a novel quantification standard and calculation method, particularly providing a more detailed and precise calculation framework for the quantification of the iris and anterior chamber structure. By calculating multiple dynamic parameters such as the length, thickness, volume of the iris, and pupil diameter, it not only achieves efficient quantification of the anterior segment image but also enhances the spatial accuracy of the parameters by introducing 3D reconstruction and multidimensional image data.

[0126] Please see Figure 10 This application also provides a processing apparatus for dynamic anterior segment images, which can implement the above-described method for processing dynamic anterior segment images.

[0127] like Figure 10 As shown in the embodiment of this application, a processing apparatus for dynamic anterior segment images includes: The acquisition module is used to acquire anterior segment image data of the target eye; the anterior segment image data includes static images in a dark room and dynamic image sequences under light stimulation conditions; The image processing module is used to extract the boundary of the anterior segment structure based on the static image, and to extract the spatial dynamic changes of the anterior segment structure based on the dynamic image sequence. The parameter quantization module is used to perform quantification calculations of preset physiological indicators based on the anterior segment structural boundary and the spatial dynamic change, so as to obtain the quantified anterior segment dynamic parameters.

[0128] In some embodiments, the image processing module is further configured to perform inter-frame matching and initial trajectory extraction processing on the dynamic image sequence to obtain inter-frame matching results and initial motion trajectory information of the anterior segment structure; locate the landmark structure points and contours based on the inter-frame matching results and the initial motion trajectory information to obtain the feature contour positioning information of the anterior segment structure in each frame; perform continuous frame consistency tracking processing on the feature contour positioning information to generate a multi-dimensional temporal motion trajectory; and calculate the spatial dynamic change of the anterior segment structure based on the multi-dimensional temporal motion trajectory.

[0129] In some embodiments, the image processing module is further configured to, when an implanted lens is present in the target eye, identify the feature contour positioning information of the implanted lens based on the dynamic image sequence, and perform consistency tracking of the feature contour positioning information of the implanted lens in consecutive frames to generate the spatial dynamic change amount of the implanted lens.

[0130] In some embodiments, the preset physiological indicators include a cumulative rate ratio and an instantaneous rate ratio. The cumulative rate ratio is used to characterize the cumulative changes in the anterior segment structure, and the instantaneous rate ratio is used to characterize the instantaneous change trend of the anterior segment structure. The parameter quantization module is further used to calculate the cumulative rate ratio based on the post-change measurement value in the spatial dynamic change quantity and the original measurement value of the corresponding baseline frame; and to calculate the instantaneous rate ratio based on the pre-change measurement value, the post-change measurement value in the spatial dynamic change quantity, and the original measurement value of the corresponding baseline frame.

[0131] In some embodiments, the image processing module is further configured to extract the anterior segment structural boundaries while performing size correction and proportional standardization processing on the static image; the anterior segment structural boundaries include the upper and lower surfaces of the sclera, the upper and lower surfaces of the iris, the angle boundary of the anterior chamber, the anterior chamber boundary, the ciliary muscle boundary, and the lens boundary.

[0132] In some embodiments, the processing apparatus for dynamic anterior segment images provided in this application further includes: The disease assessment module is used to compare the quantified anterior segment dynamic parameters with the anterior segment dynamic parameters in a preset health database using interval thresholds to obtain the dynamic performance assessment result of the anterior segment structure; the dynamic performance assessment result is used at least to characterize whether the dynamic performance of the anterior segment structure deviates from the normal range.

[0133] In some embodiments, the processing apparatus for dynamic anterior segment images provided in this application further includes: The report feedback module is used to obtain the interval threshold comparison results between the quantified anterior segment dynamic parameters and the anterior segment dynamic parameters in the preset health database; determine the comprehensive risk matching degree of the anterior segment structure based on the interval threshold comparison results; the comprehensive risk matching degree is used at least to characterize the overall functional status of the anterior segment structure in the dynamic process; and generate a pathological risk feedback report of the anterior segment structure based on the dynamic performance evaluation results and / or the comprehensive risk matching degree; the pathological risk feedback report includes an abnormal description of the anterior segment structure, the pathological mechanism of the anterior segment, and the correlation prompts of potential risk diseases.

[0134] It should be noted that the specific implementation of the dynamic anterior segment image processing device provided in this application embodiment is basically the same as the specific implementation of the dynamic anterior segment image processing method described above, and will not be repeated here.

[0135] This application also provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned method for processing dynamic anterior segment images. This computer device can be an anterior segment structure inspection device, a control and management device for that device, a smartphone, tablet computer, laptop computer, desktop computer, etc.

[0136] Please see Figure 11 , Figure 11 This illustration shows the hardware structure of a computer device according to one embodiment. The computer device includes: The processor 1101 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 1102 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1102 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1102 and is called and executed by the processor 1101 to execute a method for processing dynamic anterior segment images according to an embodiment of this application. Input / output interface 1103 is used to implement information input and output; The communication interface 1104 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1105 transmits information between various components of the device (e.g., processor 1101, memory 1102, input / output interface 1103, and communication interface 1104); The processor 1101, memory 1102, input / output interface 1103 and communication interface 1104 are connected to each other within the device via bus 1105.

[0137] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for processing dynamic anterior segment images.

[0138] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0139] This application also provides a computer program product that stores a computer program that, when executed by a processor, implements the above-described method for processing dynamic anterior segment images.

[0140] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of this application, and do not constitute a limitation on the technical solutions provided in this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in this application are also applicable to similar technical problems.

[0141] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0142] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0143] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0144] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0145] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0146] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0147] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0148] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0149] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to related technologies, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0150] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for processing dynamic anterior segment images, characterized in that, The method includes: Acquire anterior segment image data of the target eye; the anterior segment image data includes static images in a dark room and dynamic image sequences under light stimulation conditions; The anterior segment structure boundary is extracted based on the static image, and the spatial dynamic changes of the anterior segment structure are extracted based on the dynamic image sequence. Based on the anterior segment structural boundary and the spatial dynamic change, a preset physiological index is quantitatively calculated to obtain the quantified anterior segment dynamic parameters.

2. The method according to claim 1, characterized in that, The extraction of spatial dynamic changes in the anterior segment structure based on the dynamic image sequence includes: Perform inter-frame matching and initial trajectory extraction processing on the dynamic image sequence to obtain the inter-frame matching results and initial motion trajectory information of the preceding structure; Based on the inter-frame matching results and the initial motion trajectory information, the landmark structural points and contours are located, and the feature contour positioning information of the front segment structure in each frame is obtained. The feature contour positioning information is subjected to continuous frame consistency tracking processing to generate a multi-dimensional temporal motion trajectory; The spatial dynamic changes of the front segment structure are calculated based on the multidimensional temporal motion trajectory.

3. The method according to claim 2, characterized in that, The extraction of spatial dynamic changes in the anterior segment structure based on the dynamic image sequence further includes: In the case where an implanted lens is present in the target eye, the feature contour positioning information of the implanted lens is identified based on the dynamic image sequence, and the feature contour positioning information of the implanted lens is consistently tracked in consecutive frames to generate the spatial dynamic change of the implanted lens.

4. The method according to claim 1, characterized in that, The preset physiological indicators include the cumulative rate ratio and the instantaneous rate ratio. The cumulative rate ratio is used to characterize the cumulative changes in the anterior segment structure, and the instantaneous rate ratio is used to characterize the instantaneous change trend of the anterior segment structure. Based on the aforementioned spatial dynamic changes, a preset physiological index is quantitatively calculated, including: The cumulative rate ratio is calculated based on the changed measurement value in the spatial dynamic change and the original measurement value of the corresponding baseline frame; The instantaneous rate ratio is calculated based on the measured values ​​before and after the change in the spatial dynamic change and the original measured values ​​of the corresponding baseline frame.

5. The method according to claim 1, characterized in that, The extraction of the anterior segment structure boundary based on the static image includes: While performing size correction and proportional standardization on the static image, the anterior segment structural boundaries are extracted; the anterior segment structural boundaries include the upper and lower surfaces of the sclera, the upper and lower surfaces of the iris, the angle boundary of the anterior chamber, the anterior chamber boundary, the ciliary muscle boundary, and the lens boundary.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: The quantified anterior segment dynamic parameters are compared with the anterior segment dynamic parameters in a preset health database using an interval threshold to obtain the dynamic performance evaluation result of the anterior segment structure; the dynamic performance evaluation result is used at least to characterize whether the dynamic performance of the anterior segment structure deviates from the normal range.

7. The method according to claim 6, characterized in that, The method includes: Obtain the interval threshold comparison results between the quantified anterior segment dynamic parameters and the anterior segment dynamic parameters in the preset health database; The comprehensive risk matching degree of the anterior segment structure is determined based on the interval threshold comparison results; the comprehensive risk matching degree is at least used to characterize the overall functional status of the anterior segment structure in a dynamic process. Based on the dynamic performance evaluation results and / or the comprehensive risk matching degree, a pathological risk feedback report of the anterior segment structure is generated; the pathological risk feedback report includes an abnormal description of the anterior segment structure, the pathological mechanism of the anterior segment, and a correlation indication of potential risk diseases.

8. A processing apparatus for dynamic anterior segment images, characterized in that, The device includes: The acquisition module is used to acquire anterior segment image data of the target eye; the anterior segment image data includes static images in a dark room and dynamic image sequences under light stimulation conditions; The image processing module is used to extract the boundary of the anterior segment structure based on the static image, and to extract the spatial dynamic changes of the anterior segment structure based on the dynamic image sequence. The parameter quantization module is used to perform quantification calculations of preset physiological indicators based on the anterior segment structural boundary and the spatial dynamic change, so as to obtain the quantified anterior segment dynamic parameters.

9. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a method for processing dynamic anterior segment images as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a method for processing dynamic anterior segment images according to any one of claims 1 to 7.

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