CN116019416A - Method for grading correction effect of topographic map after shaping cornea - Google Patents
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Abstract
The invention provides a method for grading correction effect of a topographic map after shaping a cornea, which comprises the steps of collecting the topographic map of the cornea after a user wears a mirror for one month by using a mobile end image collecting system, dividing an effective optical area and pupils in the topographic map of the cornea by a deep learning model in a data dividing system and a data processing system, calculating medical indexes such as eccentricity, defocus amount, effective defocus range and the like, grading according to an evaluation standard, and finally outputting a result to visual hardware. The invention relates to a method for grading correction effect of a topographic map after shaping a cornea, which can accurately segment an effective optical area and a pupil in the topographic map and grade the topographic map according to a calculated medical index by means of the segmentation capability of a deep learning model, so that the interference of subjective factors when an operator performs a test on a wearer is avoided, and the invention can greatly lighten the test pressure of an optometrist as a whole.
Description
Technical Field
The invention belongs to the field of vision detection, and particularly relates to a method for grading correction effect of a topographic map after shaping a cornea.
Background
With the rising and increasingly busy academic tasks of various electronic products in recent years, the eye duration of teenagers is prolonged, which leads to the rising incidence of myopia year by year and gradually decreasing trend. The total myopia rate of the Chinese teenagers in 2020 is 52.7%, which seriously affects the normal learning and life of primary and secondary school students and causes great burden to society, and has become one of the focus problems of global attention. The cornea shaping lens is a specially designed high oxygen permeability hard cornea contact lens, and after sleeping and wearing at night, the cornea is reshaped by positive pressure of the lens and negative pressure formed by the lens and tear under the lens, namely, refractive error of eyes is eliminated by changing geometric form of the cornea, so that the effect of temporarily reducing myopia degree or cornea astigmatism is achieved. Therefore, the cornea shaping lens is widely applied to myopia prevention and control of teenagers.
The cornea topographic map is an essential examination of the cornea shaping lens in the process of fitting, and is intuitively analyzed in morphology, wherein the difference of colors of each point reflects the refractive power of the point. In the inspection process before the cornea shaping lens is matched, the cornea shaping lens can be used for screening applicable people, such as people for filtering irregular corneas such as keratoconus, marginally denatured corneas or corneas which are too flat or steep; the parameters of the shaping lens and the like which are matched with the cornea of the wearer can be selected according to the information of flat k, steep k and the like. The cornea topographic map can also well feed back the shaping area, shaping effect information and the like of the cornea in the process of rechecking the cornea shaping mirror; the cornea shaping lens should be reviewed each year after wear, which requires 4-6 weeks of lens withdrawal to restore the cornea to its original shape. The effects of the corneal topography are helpful to the physician in formulating subsequent treatment regimens for the user. However, in the prior art, only basic information on the morphology of the cornea topography is focused, and no deep analysis of commonly used medical indexes is performed. In fact, when the optometrist performs the cornea shaping lens test on different users, whether the eccentricity of the shaping lens is reasonable or not needs to be comprehensively considered, a plurality of information such as whether a certain defocus amount exists in the pupil and the relative position relationship between the pupil and an effective optical zone exist, and the medical indexes jointly determine the quality of the cornea topographic map correcting effect.
At present, no objective evaluation method for the cornea shaping lens test and matching effect exists, and optometrists often analyze the morphology and curvature of important areas of a cornea topographic map according to clinical experience, but the result has a certain subjectivity. And because clinical experiences of optometrists are different, it is difficult to ensure that optimal test results can be given out according to multiple test indexes and corneal topography information. Furthermore, the eye axis growth varies for the future year for users of statistically different types of corneal topography. Therefore, the cornea topography map of the proper type can be matched for the user to correct vision better, and the increase of the eye axis can be restrained to a certain extent. In conclusion, the method is helpful for improving the cornea shaping lens test and matching effect, and further improving the myopia prevention and control effect.
Disclosure of Invention
In view of the above, the invention aims to provide a method for grading correction effect of a topographic map after shaping a cornea, so as to solve the problems of large influence of personal factors and uncontrollable quality in the process of mirror inspection and matching in the prior art.
In order to achieve the above purpose, the technical scheme of the invention is realized as follows:
a method for grading correction effect of a topographic map after shaping a cornea includes utilizing a mobile end image acquisition system to acquire the topographic map of the cornea after a user wears a mirror for one month, dividing an effective optical area and pupils in the topographic map by a deep learning model in a data dividing system and a data processing system, calculating indexes such as eccentricity, defocus amount and effective defocus contact range, grading the topographic map of the cornea according to specified conditions, and outputting the graded topographic map of the cornea to visual hardware.
Further, the method for acquiring the cornea topographic map by the image acquisition system at the mobile end comprises the following steps:
s1, using an image acquisition device of a mobile terminal to acquire a corneal topography of a wearer according to user requirements, and directly accessing stored images or directly utilizing a corneal topography instrument to perform image shooting.
S2, finishing all the collected corneal topography maps, and cleaning out incomplete topography maps caused by factors such as eye closing of the wearer, and the like, wherein the topography maps are reviewed by the wearer after one month.
And S3, marking the effective optical area and the pupil range under the guidance of an optometrist by adopting a labelme marking method.
Furthermore, the data segmentation system needs to construct a deep learning model, and can be specifically divided into a convolution layer, a pooling layer and an activation layer; wherein, after obtaining the cornea topographic map to be segmented, the information of the image data is input and convolved through multiple channels, and each convolution of the convolution layer generates an output of one channel, so that the outputs of the multiple channels can be obtained, wherein, the convolution is defined as:
where z [ x, y ] is the result of the computation, g [ x, y ] is the input data, f [ x, y ] is the convolution kernel, and x represents the convolution operation.
Further, each value of the input channel needs to be modified by an activation function to obtain an output with the same size, and this calculation process is completed in the activation layer, specifically, a Relu function may be used: f (x) =max (0, x).
Further, the output data needs to be sampled in proportion at the pooling layer, and the sampling method can be expressed as follows: f (X) =max ([ X ]).
As shown in fig. 3, a segmentation map of the effective optical zone and the pupil can be generated after the deep learning model; inputting a cornea topographic map of a user wearing the shaping lens for one month, carrying out repeated convolution and activation on the image, carrying out pooling operation on the image to obtain a multidimensional feature map, carrying out repeated up-sampling on the feature map to obtain a corresponding segmentation map, and optimizing a network structure and parameters according to the actual condition of the image.
Further, the data processing system calculates indexes such as eccentricity, defocus amount, effective defocus contact range and the like, and classifies the corneal topography according to a specified condition, and specifically comprises the following steps:
a1, extracting the outlines of the effective optical area and the pupil according to the appointed color in an hsv space.
A2, calculating the circumscribed rectangle of the effective optical area and the pupil outline, obtaining the positions of the center points of the effective optical area and the pupil outline, and calculating the distance between the center points of the effective optical area and the pupil outline to obtain the eccentricity.
A3, dividing the region corresponding to each color in the pupil in the hsv space.
And A4, taking the intersection of the pupil and the effective optical area, and performing difference operation on the intersection and the area obtained in the step 3 to obtain the range and the area corresponding to each color of the defocused area (the non-optical area in the pupil).
A5, each color in the cornea topographic map corresponds to one diopter, the areas of each color are multiplied by the corresponding diopter respectively and then summed, and the value is the defocus amount.
And A6, binarizing the separated pupils, and acquiring all pupil boundary point coordinates and defocused area boundary point coordinates by adopting a four-neighborhood algorithm.
A7, the overlapping quantity of the pupil boundary point coordinates and the defocusing area boundary point coordinates is marked as n1, the quantity of all coordinate points of the pupil boundary is marked as n2, and n1/n2 is the effective defocusing contact range.
A8, grading the topography of the shaping effect of the cornea shaping mirror according to the three indexes and the conditions specified by the doctor, wherein the conditions are as follows:
stage 1: the eccentricity is less than or equal to 0mm and less than 1mm, the effective defocus contact range is more than 3/4, and the defocus amount is more than 30.
2 stages: the eccentricity is less than or equal to 0mm and less than 1mm, the effective defocusing contact range is between 1/4 and 3/4, and the defocusing amount is more than 30.
3 stages: eccentricity is more than 1mm, defocus is more than 30.
4 stages: the eccentricity is less than 0.5mm, the effective defocus contact range is less than 1/4, and the defocus amount is less than 30.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
fig. 1 is a schematic diagram of an application unit according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a deep learning grid structure according to an embodiment of the present invention.
Fig. 3 is a schematic view of a segmented image according to an embodiment of the present invention.
Fig. 4 is a flowchart of eccentricity calculation according to an embodiment of the present invention.
Fig. 5 is a schematic view of the extraction of colors from an out-of-focus area according to an embodiment of the present invention.
FIG. 6 is a flow chart of effective defocus contact range calculation according to an embodiment of the present invention.
Fig. 7 is a graphical illustration of various levels of corneal topography, in accordance with an embodiment of the present invention.
Detailed Description
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first", "a second", etc. may explicitly or implicitly include one or more such feature. In the description of the present invention, unless otherwise indicated, the meaning of "a plurality" is two or more.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art in a specific case.
The invention will be described in detail below with reference to the drawings in connection with embodiments.
As shown in fig. 1, in this embodiment, a method for grading correction effects on a topographic map after shaping a cornea includes a mobile terminal image acquisition system, a data segmentation system, a data processing system and a personalized user use system, wherein the mobile terminal image acquisition system is used for acquiring the topographic map of the cornea after wearing for one month, indexes such as eccentricity, defocus amount, effective defocus contact range and the like are further calculated after identifying an effective optical area and a pupil in the topographic map of the cornea through a deep learning model, and grading is carried out on the topographic map of the cornea according to specified conditions, so that operators can diagnose the wearer more quickly and effectively;
and the mobile end image acquisition system is used for directly acquiring corneal topography information of a wearer through a corneal topography instrument.
And the data segmentation system is used for marking the effective optical area and the pupil under the guidance of a professional optometrist by adopting a labelme marking method by acquiring a large number of cornea topographic maps of the wearer in the early stage, and inputting the marking file and the cornea topographic map corresponding to the marking file into the U-net neural network together for training, so that the effective optical area and the pupil in the cornea topographic map are segmented.
And the data processing system is used for identifying the effective optical area and the pupil in the corneal topography, calculating indexes such as eccentricity, defocus amount, effective defocus contact range and the like, and grading the corneal topography according to the specified conditions.
The personalized user using system returns information such as the effective optical area and the pupil to the operation interface according to the requirements and the using habits of the operator, thereby helping the operator to make more effective diagnosis on the wearer.
According to the mobile terminal image acquisition system, when a user installs cornea shaping vision correction effect rating software on a mobile client, certain operation authority is required to be given to the method, namely the method is required to acquire the image use authority of the mobile terminal, and the specific steps are as follows:
s1, using an image acquisition device of a mobile terminal to acquire a corneal topography of a wearer according to user requirements, and directly accessing stored images or directly shooting images by using a corneal topography instrument;
s2, finishing all the collected corneal topography maps, and cleaning out incomplete topography maps caused by factors such as eye closing of the wearer, and the like, wherein the topography maps are reviewed by the wearer after one month.
And S3, marking the effective optical area and the pupil range under the guidance of an optometrist by adopting a labelme marking method.
The data segmentation system needs to construct a deep learning model. The method can be specifically divided into a convolution layer, a pooling layer and an activation layer as shown in fig. 2; wherein, after obtaining the cornea topographic map to be segmented, the information of the image data is input and convolved through multiple channels, and each convolution of the convolution layer generates an output of one channel, so that the outputs of the multiple channels can be obtained, wherein, the convolution is defined as:
where z [ x, y ] is the result of the computation, g [ x, y ] is the input data, f [ x, y ] is the convolution kernel, and x represents the convolution operation.
Further, each value of the input channel needs to be modified by an activation function to obtain an output with the same size, and this calculation process is completed in the activation layer, specifically, a Relu function may be used: f (x) =max (0, x).
Further, the output data needs to be sampled in proportion at the pooling layer, and the sampling method can be expressed as follows: f (X) =max ([ X ]).
As shown in fig. 3, a segmentation map of the effective optical zone and pupil can be generated after the deep learning model. Inputting a cornea topographic map of a user wearing the shaping lens for one month, carrying out repeated convolution and activation on the image, carrying out pooling operation on the image to obtain a multidimensional feature map, carrying out repeated up-sampling on the feature map to obtain a corresponding segmentation map, and optimizing a network structure and parameters according to the actual condition of the image.
The data processing system calculates indexes such as eccentricity, defocus amount, effective defocus contact range and the like, classifies cornea topographic maps according to specified conditions, and comprises the following specific steps:
a1, extracting the outlines of the effective optical area and the pupil according to the appointed color in an hsv space.
A2, as shown in fig. 4, calculating the circumscribed rectangle of the effective optical zone and the pupil outline, acquiring the positions of the center points of the effective optical zone and the pupil outline, and calculating the distance between the center points of the effective optical zone and the pupil outline to obtain the eccentricity.
A3, as shown in FIG. 5, the regions corresponding to the colors inside the pupils are divided in the hsv space.
And A4, taking the intersection of the pupil and the effective optical area, and performing difference operation on the intersection and the area obtained in the step 3 to obtain the range and the area corresponding to each color of the defocused area (the non-optical area in the pupil).
A5, each color in the cornea topographic map corresponds to one diopter, the areas of each color are multiplied by the corresponding diopter respectively and then summed, and the value is the defocus amount.
And A6, binarizing the separated pupils, and acquiring all pupil boundary point coordinates and defocused area boundary point coordinates by adopting a four-neighborhood algorithm.
A7, as shown in FIG. 6, the overlapping number of the pupil boundary point coordinates and the defocus region boundary point coordinates is marked as n1, the number of all coordinate points of the pupil boundary is marked as n2, and n1/n2 is the effective defocus contact range.
A8, as shown in fig. 7, the three indexes are rated for the effect of correcting the cornea topography vision according to the condition specified by the optometrist, and the condition is specifically as follows:
stage 1: the eccentricity is less than or equal to 0mm and less than 1mm, the effective defocus contact range is more than 3/4, and the defocus amount is more than 30.
2 stages: the eccentricity is less than or equal to 0mm and less than 1mm, the effective defocusing contact range is between 1/4 and 3/4, and the defocusing amount is more than 30.
3 stages: eccentricity is more than 1mm, defocus is more than 30.
4 stages: the eccentricity is less than 0.5mm, the effective defocus contact range is less than 1/4, and the defocus amount is less than 30.
The personalized user can directly face an optometrist using the system, and the optometrist can directly observe the cornea topography vision correction effect rating. Meanwhile, if the optometrist feels that the calculation of the cornea topography vision correction effect rating has obvious problems, if the segmentation of the effective optical area or the pupil is inaccurate, the cornea topography vision correction effect rating is deviated, and the labeling file of the effective optical area or the pupil can be corrected. After the second discussion, if the optometrist corrects correctly, the corrected topographic map labeling file can be used as a training set to perform secondary training on the model so as to optimize the model, and the effective optical area and the pupils are more accurately segmented, so that the corneal topographic map vision correction effect rating can be more accurately calculated.
By means of the segmentation capability of the deep learning model, an effective optical area and pupils in the corneal topography can be accurately segmented, and the vision correction effect rating of the corneal topography can be calculated, so that the verification effect is evaluated, and the interference of subjective factors received by a doctor when a user is verified is avoided; the invention can greatly lighten the test and distribution pressure of optometrists on the whole, and better and faster help optometrists to carry out the most effective test and distribution on users.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (6)
1. A method for grading correction effect of a topographic map after shaping a cornea is characterized by comprising the following steps of: and acquiring a cornea topographic map of a user after wearing the lens for one month by using the mobile terminal image acquisition system, dividing an effective optical area and a pupil in the cornea topographic map by using a deep learning model in the data dividing system and the data processing system, calculating the eccentricity, the defocus amount and the effective defocus range medical index, grading according to an evaluation standard, and finally outputting a calculation result to visual hardware.
2. A method of grading corrective effects on a post-corneal shaping topography as set forth in claim 1, wherein: the method for acquiring the cornea topographic map by the image acquisition system at the mobile end comprises the following steps:
s1, using an image acquisition device of a mobile terminal to acquire a corneal topography of a wearer according to user requirements, and directly accessing stored images or directly shooting images by using a corneal topography instrument;
s2, finishing all the collected corneal topography maps, wherein the topography maps are reviewed by the wearer after one month, and the incomplete topography maps caused by the eye closing factors of the wearer are cleaned;
s3, marking the effective optical area and the pupil range under the guidance of an optometrist by adopting a labelme marking method.
3. A method of grading corrective effects on a post-corneal shaping topography as set forth in claim 1, wherein: the data segmentation system needs to construct a deep learning model and can be specifically divided into a convolution layer, a pooling layer and an activation layer; wherein, after obtaining the cornea topographic map to be segmented, the information of the image data is input and convolved through multiple channels, and each convolution of the convolution layer generates an output of one channel, so that the outputs of the multiple channels can be obtained, wherein, the convolution is defined as:
where z [ x, y ] is the result of the computation, g [ x, y ] is the input data, f [ x, y ] is the convolution kernel, and x represents the convolution operation.
4. A method of grading corrective effects on a post-corneal shaping topography as set forth in claim 3, wherein: each value of the input channel needs to be modified numerically by an activation function to obtain an output of the same size, and this calculation process is performed in the activation layer, specifically, the Relu function may be used: f (x) =max (0, x).
5. A method of grading corrective effects on a post-corneal shaping topography as set forth in claim 3, wherein: the output data is required to be sampled in proportion at the pooling layer, and the sampling method can be expressed as follows: f (X) =max ([ X ]);
generating a segmentation map of the effective optical zone and the pupil after the deep learning model; inputting a cornea topographic map of a user wearing the shaping lens for one month, carrying out repeated convolution and activation on the image, carrying out pooling operation on the image to obtain a multidimensional feature map, carrying out repeated up-sampling on the feature map to obtain a corresponding segmentation map, and optimizing a network structure and parameters according to the actual condition of the image.
6. A method of grading corrective effects on a post-corneal shaping topography as set forth in claim 1, wherein: the data processing system calculates the index of the eccentricity, defocus amount and effective defocus contact range, and classifies the cornea topographic map according to the specified conditions, and the specific steps are as follows:
a1, extracting the outlines of an effective optical zone and a pupil according to a specified color in an hsv space;
a2, calculating the circumscribed rectangle of the effective optical area and the pupil outline, acquiring the positions of the center points of the effective optical area and the pupil outline, and calculating the distance between the center points of the effective optical area and the pupil outline to obtain the eccentricity;
a3, dividing regions corresponding to colors in the pupil in an hsv space;
a4, taking the intersection of the pupil and the effective optical zone, and performing difference operation on the area obtained in the step 3 and the intersection to obtain the range and the area corresponding to each color of the defocused area (the non-optical zone in the pupil);
a5, multiplying the areas of the colors by the corresponding diopters respectively and summing to obtain defocus;
a6, binarizing the separated pupils, and acquiring all pupil boundary point coordinates and defocused area boundary point coordinates by adopting a four-neighborhood algorithm;
a7, marking the superposition quantity of the pupil boundary point coordinates and the defocusing area boundary point coordinates as n1, marking the quantity of all coordinate points of the pupil boundary as n2, and obtaining n1/n2 as an effective defocusing contact range;
a8, grading the cornea topography vision correction effect according to the three indexes in the step 7 according to the condition specified by an optometrist, wherein the condition is specifically as follows:
stage 1: the eccentricity is less than or equal to 0mm and less than 1mm, the effective defocus contact range is more than 3/4, and the defocus amount is more than 30.
2 stages: the eccentricity is less than or equal to 0mm and less than 1mm, the effective defocusing contact range is between 1/4 and 3/4, and the defocusing amount is more than 30.
3 stages: eccentricity is more than 1mm, defocus is more than 30.
4 stages: the eccentricity is less than 0.5mm, the effective defocus contact range is less than 1/4, and the defocus amount is less than 30.
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| CN116269187A (en) * | 2023-05-18 | 2023-06-23 | 汕头大学·香港中文大学联合汕头国际眼科中心 | Automatic measuring method and system for effective optical area of cornea after refractive surgery |
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