METHOD FOR PROCUREMENT OF TOMOGRAPHIC EYE IMAGES

Abstract

To provide a method for generating an ocular tomographic image that makes it possible to acquire an ocular tomographic image with a simple configuration of an optical system, even when the distance between an eye being examined and an ophthalmic analysis device is large.A method for generating an ocular tomographic image comprising: a first step to acquire depth information of an intraocular structure of the eye under investigation using a rangefinder; a second step to acquire a variety of depth information while changing the relative positions of the eye under investigation and the rangefinder; a third step to establish a three-dimensional position of the depth information in the eyeball; and a fourth step to generate an ocular tomographic image by assembling the variety of depth information based on the position information.

Description

REFERENCES TO RELATED REGISTRATIONS

[0001] The present invention is based on the Japanese patent application JP 2023-121603, which was filed on July 26, 2023, and the contents of which are hereby incorporated by reference. Technical field

[0002] The present invention relates to a method for generating an ocular tomographic image. State of the art

[0003] Generating an ocular tomographic image is crucial for diagnosing fundus diseases and assessing the risk of an acute glaucoma attack. One typical method for generating an ocular tomographic image involves the use of optical coherence tomography (OCT). OCT can acquire an ocular tomographic image by emitting near-infrared light onto the eye being examined and analyzing the reflection from an intraocular structure.

[0004] In eye diseases such as fundus macular degeneration or glaucoma, long-term follow-up is often necessary. Therefore, various methods for generating ocular tomographic images are being developed to achieve efficiency and preferably to perform follow-up examinations of the eye under investigation. For example, patent document 1 describes a known ophthalmological analysis device for obtaining analysis results from tomographic images of an eye under investigation, acquired on different days using an ophthalmological optical coherence tomography device, and for outputting statistical information derived from time-series data of the analysis results.

[0005] Patent document 1: Japanese patent application disclosure JP 2014-83266 Summary of the invention

[0006] Generating an ocular tomographic image using such an ophthalmic analysis device requires intraocular observation to be performed in a state where the distance between the eye being examined and the device is very small, with the patient's chin resting on a chin rest of a device mounted on a stand. Furthermore, performing intraocular observation requires an ophthalmic examination device installed in a hospital setting, thus precluding telemedicine and similar methods.

[0007] The present invention was therefore developed taking into account the problems mentioned above and aims to provide a method for generating an ocular tomography image which makes it possible to acquire an ocular tomography image with a simple configuration of an optical system, even if the distance between the eye to be examined and the ophthalmological analysis device is large.

[0008] To solve the aforementioned problems, the inventors conducted active investigations. As a result, the inventors determined that the problems could be solved by the following method for generating an ocular tomographic image.

[0009] In other words, the method for generating an ocular tomographic image according to one aspect of the present invention includes a first step for acquiring depth information of an intraocular structure of the eye under investigation using a rangefinder, a second step for acquiring a plurality of parts of the depth information while changing the relative positions of the eye under investigation and the distance sensor, a third step for determining a three-dimensional position of the depth information in the eyeball, and a fourth step for generating an ocular tomographic image by assembling the plurality of parts of the depth information based on the position information.

[0010] In the third step, the three-dimensional position of the depth information can be determined based on an angle of incidence of the light entering from the rangefinder in relation to the corneal vertex of the eye being examined and / or the thickness of the retina.

[0011] In the fourth step, the ocular tomography image can be generated by a mathematical model that has been trained through machine learning or deep learning.

[0012] In the fourth step, the ocular tomographic image can be supplemented by using a standard tomographic image.

[0013] In the fourth step, the numerous in-depth details collected in the second step can be used as training data for the mathematical model.

[0014] In the first step, resolution processing can be performed using adaptive optics.

[0015] The procedure for generating an ocular tomographic image may further include a step for capturing information about disease risk, in which information about the presence / absence of a disease risk is captured by inputting the depth information into a mathematical model that has been trained by machine learning or deep learning using ocular tomographic image data relating to an eye disease as training data.

[0016] The procedure for generating an ocular tomographic image may also include a display step.

[0017] The rangefinder can be a distance measuring unit of a mobile device.

[0018] The rangefinder can be a distance measuring unit of a stationary device.

[0019] The rangefinder can be a distance measuring unit of a device attached to the head.

[0020] The present invention can provide a method for generating an ocular tomography image that makes it possible to acquire an ocular tomography image with a simple configuration of an optical system, even when the distance between an eye to be examined and an ophthalmological analysis device is large. Brief description of the drawings Fig. Figure 1 is an example of a diagram illustrating one aspect of capturing depth information of an intraocular structure of an eye under investigation using a rangefinder. Fig. Figure 2 is an example of a diagram illustrating an example of depth information in relation to the cornea, iris, and retina. Fig. Figure 3 is an example of a diagram illustrating one aspect of capturing a variety of depth information of the intraocular structure by changing the relative positions of the eye under investigation and the rangefinder. Fig. Figure 4 is an example of a diagram illustrating the change in the landscape reflected in the eye under investigation, the change in the corneal margin of the eye under investigation, the movement of the pupil center in the corneal margin, or the change in the pupil margin according to an embodiment of the present invention. Fig. Figure 5 is an example of a diagram illustrating an area of ​​an ocular tomography image that includes the cornea and the iris and represents the ocular tomography images obtained by a method for generating ocular tomography images according to the present embodiment. Fig. Figure 6 is an example of a diagram illustrating an area of ​​an ocular tomography image that includes the retina, among the ocular tomography images obtained by the method for generating ocular tomography images according to the present embodiment. Fig. Figure 7 is a schematic diagram illustrating an example of the use of a mobile device according to an embodiment of the present invention as a device for generating ocular tomography images. Fig. Figure 8 is a schematic diagram illustrating an example of the use of a stationary device according to an embodiment of the present invention as a device for generating ocular tomography images. Fig. Figure 9 is a schematic diagram illustrating an example of the use of a head-mounted device according to an embodiment of the present invention as a device for generating ocular tomography images. Fig. Figure 10 is a flowchart relating to the implementation of the device for generating ocular tomography images according to an embodiment of the present invention. Fig. 11 is a hardware configuration of the device for generating ocular tomography images according to an embodiment of the present invention. Fig. 12 is a functional block configuration of the device for generating ocular tomography images according to an embodiment of the present invention. Description of embodiments

[0021] One embodiment of the present invention is described below with reference to the accompanying drawings. The following embodiment serves to improve understanding of the present invention and is not to be understood as limiting it. The present invention can be modified in various ways without departing from the essential nature of the invention. Furthermore, embodiments obtained by replacing the elements described below with equivalents may also fall within the scope of the present invention. 1. Device for generating ocular tomography images

[0022] Fig. Figure 1 is a diagram illustrating an example of a method for acquiring depth information of an intraocular structure of an eye under examination by a device for generating ocular tomography images 4. The device for generating ocular tomography images 4 according to the present embodiment is a device that acquires depth information of an intraocular structure of an eye 20 under examination by directing incident light 31 onto the eye 20 under examination using a light source 41 and a beam splitter 42 of a rangefinder 40, and by using a detector 43 to acquire different types of reflected light originating from the intraocular structure, such as reflected light 32 from the cornea 21, reflected light 33 from the iris 22, and reflected light 34 reflected from the retina 24. <hardwarekonfiguration>

[0023] Fig. Figure 11 is a diagram showing an example of a hardware configuration for the device for generating ocular tomography images 4. The device for generating ocular tomography images 4 includes a processor 11, such as a central processing unit (CPU) and a graphics processing unit (GPU), a storage device 12, such as memory, a hard disk drive (HDD), and / or a solid-state drive (SSD), a communication interface (IF) 13 that performs wired or wireless communication, an input device 14 that receives an input operation, and an output device 15 that outputs information. The input device 14 is, for example, a keyboard, a touch panel, a mouse, and / or a microphone. The output device 15 is, for example, a display, a touch panel, and / or a speaker.

[0024] In the present embodiment, the rangefinder 40 can be configured to be included in the input device 14. Alternatively, the rangefinder 40 can be configured to be included in an external device of the ocular tomography image generation device 4, so that depth information acquired by the rangefinder 40 via a communication line and the like is entered via the input device 14. <funktionsblockkonfiguration>

[0025] Fig. Figure 12 is a diagram showing an example of a functional block configuration of the ocular tomography image generation device 4. The ocular tomography image generation device 4 includes a storage unit 110 and a control unit 120. The storage unit 110 can be implemented using the storage device 12 included in the ocular tomography image generation device 4. The control unit 120 can be implemented by the processor 11 of the ocular tomography image generation device 4 executing a program stored in the storage device 12. The program can be stored on a storage medium. The storage medium on which the program is stored can be a non-volatile, computer-readable medium. The non-volatile storage medium is not particularly restricted, but could, for example, be a storage medium such as a USB flash drive or a CD-ROM.

[0026] Fig. Figure 7 is a schematic diagram illustrating an example of using a mobile device 50 as an ocular tomography imaging device 4. By using the mobile device 50 as an ocular tomography imaging device 4, an ocular tomography image can be generated with a simpler optical system configuration. For simplicity, the mobile device 50 is shown in Fig. Figure 7 is shown enlarged compared to its actual size. The rangefinder 40 is not particularly limited; however, for example, a rangefinder of the mobile device 50 can be used. The mobile device 50 is not particularly limited, but examples include information processing devices such as a smartphone, a tablet computer, a notebook computer, or a workstation computer. Here, the device refers to a device that can play a key role in communicating with other devices by being connected to a circuit or a network.

[0027] Fig. Figure 8 is a schematic diagram showing an example of using a stationary device 51 as a device for generating ocular tomography images 4. By using the stationary device 51 as a device for generating ocular tomography images 4, an ocular tomography image can be generated with a simpler optical system configuration. The rangefinder 40 is not particularly restricted, but, for example, a rangefinder of the stationary device 51 is used. When using the stationary device 51 as a device for generating ocular tomography images, the relative positions of the eye under investigation 20 and the rangefinder 40 can be easily changed if a person moves while facing the stationary device 51. In addition, the second step S102 (described later) can be performed for a certain period of time without the person undergoing any surgery.

[0028] The stationary device 51 is not particularly limited, but could, for example, be a smart mirror. Using the smart mirror, the test subject can be photographed naturally and without their knowledge in everyday situations. Here, the smart mirror refers to a mirror-like device that can be connected to the internet and includes a mirror that can reflect the test subject's shape, as well as a rangefinder.

[0029] Fig. Figure 9 is a schematic diagram showing an example of using a head-mounted device 60 as a device for generating ocular tomography images 4. By using a head-mounted device 60 as a device for generating ocular tomography images 4, an ocular tomography image can be generated with a simpler configuration of an optical system. The rangefinder 40 is not particularly limited, but, for example, a rangefinder integrated into the head-mounted device 60 is used. When using the head-mounted device 60 as a device 4 for generating ocular tomography images, as shown in Figure 9, the following can be achieved: Fig. As shown in Figure 9, the first step S101 and the second step S102 (described later) can be easily carried out independently of any action by the subject.

[0030] Fig. Figure 10 is a flowchart illustrating steps related to the implementation of the device 4 for generating ocular tomography images according to an embodiment of the present invention, wherein each step is performed by the control unit 120 of the device 4 for generating ocular tomography images. The individual steps are described below with reference to Fig. 10 described in detail. 2. Method for generating an ocular tomographic image

[0031] In Fig. 10. The device 4 for generating ocular tomography images comprises a first step S101 for acquiring the depth information of the intraocular structure of the eye under investigation, a second step S102 for acquiring a multitude of parts of the depth information while the relative positions of the eye under investigation and the distance sensor are changed, a third step S103 for establishing a three-dimensional position of the depth information in the eyeball, and a fourth step S104 for generating an ocular tomography image by assembling the multitude of parts of the depth information. By performing these steps, the device 4 generates an ocular tomography image of the eye under investigation. 20. The device 4 for generating ocular tomography images may also include a display step S105. Furthermore, additional steps may be provided as required. 2.1. First step S101

[0032] As in Fig. As shown in Figure 1, the first step S101 is a step to acquire the depth information of the intraocular structure of the eye 20 to be examined using the rangefinder 40. In the first step S101, the incident light 31 is directed onto the eye 20 to be examined in the device 4 for generating ocular tomography images using the light source 41 and the beam splitter 42 of the depth sensor 40, and the light reflected from the intraocular structure is acquired by the detector 43. Fig. Figure 1 illustrates that the light 32 reflected by the cornea 21, the light 33 reflected by the iris 22 and the light 34 reflected by the retina 24 can be detected.

[0033] Here, in Fig. d1 is the depth of the anterior chamber and d2 is the ocular axis. d1 is information useful for the diagnosis of glaucoma and similar conditions, and d2 is information useful for the diagnosis of myopia and similar conditions.

[0034] Here, a rangefinder refers to a sensor that measures distance. The term "rangefinder" is not particularly limited; it can be an optical rangefinder (LiDAR), a rangefinder that uses radio waves, or a rangefinder that uses ultrasonic waves. Of these, LiDAR is preferable because it can easily acquire depth information. While not strictly limited, LiDAR can be, for example, a Time-of-Flight (ToF) type LiDAR or a Full-Motion Warning Caught (FMCW) type LiDAR.

[0035] Among these, it is advantageous to use an FMCW-type LiDAR in the present embodiment. The FMCW-type LiDAR emits a laser beam that continuously switches between different frequencies, so that when a detector collects reflected light and measures a reflection time, a specific frequency pattern and other light sources can be distinguished from one another, enabling fast and accurate operation under all illumination conditions. This results in a more accurate acquisition of depth information, even in cases where light from a surrounding light source and its reflected light can easily penetrate the detector due to a large distance between the eye being examined and the rangefinder.Even in cases with a very weak reflection intensity of the reflected light derived from the intraocular structure, as in the present embodiment, the depth information tends to be captured more accurately.

[0036] The intraocular structure refers to the entire structure within the eyeball and is not particularly limited. For example, in addition to the in Fig. In addition to the cornea 21, iris 22 and retina 24 shown in Figure 1, the ciliary body, zinnia zonula, choroid and sclera should also be mentioned.

[0037] Depth information refers to information about the distance between the rangefinder, located at a predetermined relative position to the eye being examined, and the intraocular structure. This depth information can be derived from reflected light, which is generated when light emitted by the rangefinder is reflected and bounced back from the intraocular structure.

[0038] In Fig. 2 denote P1, P2, P3, P4, P5 and P6 examples of depth information relating to the cornea, Q1, Q2, Q3 and Q4 examples of depth information relating to the iris and R1, R2, R3, R4, R5 and R6 examples of depth information relating to the retina.

[0039] Typically, when detecting reflected light within a substance, the intensity of the light reflected from a structure near the surface of the substance tends to be greatest, while the intensity of the light reflected from the inner structure tends to decrease with increasing distance from the interior of the substance. Fig. 1 denotes i1 the intensity of the light reflected by the cornea 21 32, i2 the intensity of the light reflected by the iris 22 33 and i3 the intensity of the light reflected by the retina 24 34. As the incident light moves from the surface of the eye being examined towards the interior, the intensity of the light reflected by the intraocular structure tends to decrease.

[0040] In the first step S101, an ocular tomographic image can be generated even if the rangefinder 40 of the device 4 for generating ocular tomographic images is located away from the eye 20 being examined. The distance between the rangefinder 40 and the eye 20 being examined is not particularly limited, but should preferably be 3 cm or more, more preferably 5 cm or more, more preferably 10 cm or more, more preferably equal to or greater than 15 cm, more preferably equal to or greater than 20 cm, and most preferably equal to or greater than 30 cm, in order to easily generate an ocular tomographic image.

[0041] The lower limit for the distance between the rangefinder 40 and the eye 20 to be examined is not particularly limited, but may be, for example, 1 mm, 3 mm, 5 mm or 1 cm.

[0042] The upper limit for the distance between the rangefinder 40 and the eye 20 to be examined is not particularly limited, but may be, for example, 10 m, 5 m, 3 m, 1 m or 50 cm.

[0043] In the first step, S101, resolution processing can be performed using adaptive optics. Specifically, a rangefinder can be used, for example, to which adaptive optics are applied. Here, adaptive optics refers to a technique for obtaining a high-resolution image by removing wavefront aberrations. 2.2. Second step S102

[0044] As in Fig. As shown in Figure 3, the second step S102 is a step to acquire a variety of depth information while changing the relative positions (1, θi, θr) of the eye under investigation 20 and the rangefinder 40.

[0045] The details are described below.

[0046] A conventional, known method can be used to change the relative positions (1, θi, θr) of the eye 20 under investigation and the rangefinder 40. Such a method is not particularly restricted. For example, the orientation of the eye 20 under investigation can be changed without changing the position of the rangefinder 40, or the position of the rangefinder 40 can be changed without changing the orientation of the eye 20 under investigation.

[0047] There are no particular restrictions on the means used to change the orientation of the eye under investigation. For example, a fixation target projection device can be used to display a variety of fixation targets. The fixation target projection device includes a fixation lamp that emits visible light and can display the variety of fixation targets to the subject by switching on the fixation lamp, thus instructing the eye under investigation to look in any desired direction.

[0048] The number of depth information points to be acquired is used for image generation step S104 (described later), so it is not particularly limited. For example, the number of depth information points is preferably equal to or greater than 30, more preferably equal to or greater than 100, and even more preferably equal to or greater than 300. Here, the number of depth information points for each of the relative positions (1, θi, θr) of the eye under investigation 20 and the rangefinder 40 is counted as one. 2.3. Third step S103

[0049] The third step, S103, is a step to establish a three-dimensional position of the depth information in the eyeball.

[0050] The third step, S103, can be performed based on the portions of depth information obtained by changing the relative positions (1, θi, θr) of the eye under investigation 20 and the rangefinder 40. A conventional, well-known method can be used to determine the three-dimensional position. The method is not particularly limited, but Structure from Motion (SfM) can be cited as an example.

[0051] In the third step S103, the three-dimensional position of the depth information can be determined on the basis of an angle of incidence of the incident light 31 from the rangefinder 40 in relation to the corneal vertex of the eye to be examined 20 and / or a thickness of the retina 24.

[0052] In the third step S103, it is as in Fig. As shown in Figure 4, it is possible to use information in combination, wherein the information includes a change in the landscape 54 reflected in the eye 20 under investigation, such as a movement of the environment reflected in a pupil of the eye 20 under investigation, a change in the corneal margin 55 of the eye 20 under investigation, movement of the pupil center in the corneal margin 55, or a change in the pupil margin. In particular, the accuracy in specifying the three-dimensional position of the depth information can be improved by a method used to specify the three-dimensional position of the depth information by detecting a change in the landscape 54 reflected in the eye 20 under investigation caused by a rotational movement 59 of the eyeball of the eye 20 under investigation.a method for determining the three-dimensional position of depth information by detecting a change 56 in the corneal margin caused by the rotational movement 59 of the eyeball, such as a deformation of the corneal margin 55 from a circular to an essentially elliptical shape corresponding to the rotational movement 59 of the eyeball; a method for determining the three-dimensional position of depth information by detecting a movement 58 of the pupil center in the corneal margin caused by the rotational movement 59 of the eyeball; or by detecting a change in the pupil margin 57 caused by the rotational movement 59 of the eyeball. 2.4. Fourth step S104

[0053] The fourth step S104 is a step to assemble the multitude of parts of the depth information in order to generate an ocular tomographic image based on the parts of the depth information obtained in the second step S102 and the three-dimensional position in the eyeball determined in step S103 to establish the three-dimensional position.

[0054] The Fig. 5 and Fig. Six are examples of the ocular tomography image obtained in the fourth step. Fig. Figure 5 shows an area of ​​the ocular tomography image that includes the cornea 21 and the iris 22, and Fig. Figure 6 shows an area of ​​the ocular tomography image that includes the retina.

[0055] In the fourth step, the ocular tomography image can be generated by a mathematical model that has been trained through machine learning or deep learning.

[0056] In the fourth step, the ocular tomography image can be supplemented by using a standard tomography image. A conventional, well-known method can be used to acquire the standard tomography image. The method for acquiring the standard tomography image is not particularly limited; for example, optical coherence tomography (OCT) can be used.

[0057] The training data for the mathematical model are not particularly limited, but it is possible, for example, to use the wealth of depth information acquired in the second step described above. This allows the depth information corresponding to an uncaptured area to be generated and supplemented in the second step S102 without using the standard tomography image. Accordingly, the number of calculations performed by the computer increases, but there is no need to prepare a standard tomography image for the eye under examination 20, so the ocular tomography image can be easily generated.

[0058] Here, a machine learning-trained model (hereinafter referred to as the machine learning model) is an example of a mathematical model. The machine learning model comprises a model with a predefined structure and parameters that change as a result of the learning process. The model exhibits improved discrimination accuracy when its processing parameters are optimized based on experience gained from the training data. Thus, the machine learning model is a model that learns optimal processing parameters through the learning process. Examples of algorithms that can be used for the machine learning model include a support vector machine, logistic regression, random forest, a neural network, and similar algorithms, and their types are not particularly restricted.Regarding the generation of the ocular tomography image, even with a small amount of training data, a neural network is preferably used. The machine learning model that performs the learning includes a model that has already undergone some training with the training data and a model prior to the training. 2.5. Display step S105

[0059] As in Fig. As shown in Figure 10, display step S105 is a step for displaying the ocular tomography image, and the device for generating ocular tomography images 4 in the present embodiment preferably includes display step S105. This makes it easier to check the subject's intraocular information. 2.6. Next steps

[0060] The device for generating ocular tomography images 4 in the present embodiment may include further steps in addition to those described above, as required. These further steps are not particularly limited, but as an example, a step for acquiring disease risk information may be mentioned. This step involves acquiring information about the presence or absence of a disease risk by inputting depth information into a mathematical model that has been trained by machine learning or deep learning using the ocular tomographic image data relating to an eye disease as training data. This step can be used to easily detect an eye disease such as glaucoma or myopia at an early stage. Industrial applicability

[0061] According to the present invention, it is possible, without any particular limitation, to easily capture an ocular tomography image used for eye examinations, for example, with a mobile device such as a smartphone. Therefore, it is expected that the method for capturing the ocular tomography image according to the present invention will spread worldwide.

[0062] It is also of great importance for clinical applications. In particular, during a typical eye examination by an ophthalmologist, the observation and diagnosis of the eye under examination are carried out using a device mounted on a stand. However, the medical examination of an infant or a bedridden elderly person is difficult and requires a certain degree of dexterity. With the present invention, however, the ocular tomography image can be generated very easily using a mobile device or similar, regardless of the subject's posture, and used accordingly for observation and diagnosis. Therefore, it is expected that the present invention can be used for telemedicine in local areas or to support developing countries.

[0063] Furthermore, the present invention can be used similarly for observing and photographing the eyeball of an animal. In particular, it is possible to acquire an ocular tomography image of a pet or a large animal in a zoo, whose eye to be examined is particularly difficult to bring near an ophthalmological diagnostic device, and to record the ophthalmological findings of the animal described above.

[0064] Furthermore, it is expected that the data will be analyzed using AI as big data, improving the diagnostic accuracy of ophthalmologists. Finally, it can be used as a self-diagnosis tool for test subjects, and ophthalmic care itself can be further developed. Reference symbol list 4 Device for generating ocular tomography images 20 Eyes to be examined 21 Cornea 22 Iris 23 Crystal lens 24 Retina 31 incident light 32 light reflected from the cornea 21 33 light reflected from the iris 22 34 light reflected from the retina 40 rangefinders 41 Light source 42 beam splitters 43 Detector 50 mobile devices 51 stationary device 60 head-mounted device i1 Reflectance intensity of the light reflected by the cornea 21 i2 Reflectance intensity of the light reflected by the iris 22 i3 Reflectance intensity of the light reflected by the retina 24 P1, P2, P3, P4, P5, P6, Q1, Q2, Q3, Q4, R1, R2, R3, R4, R5, R6, R6 depth information ▪ Distance from the corneal vertex to the distance sensor qi, qr Azimuth angle of the distance sensor relative to the corneal vertex 54 landscape reflected in the eye under examination 55 Corneal margin 56 Changes in the corneal margin 57 Pupil margin 58 Movement of the pupil center in the corneal margin 59 Rotational movement of the eyeball 11 processor 12 storage device 13 Communication IF 14 Input device 15 Output device 110 storage units 120 control unit S101 first step S102 second step S103 third step S104 fourth step S105 Display step QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] JP 2023-121603

[0001] JP 2014-83266

[0005] < / funktionsblockkonfiguration> < / hardwarekonfiguration>

Claims

[1] Method for generating an ocular tomographic image, comprising: a first step towards capturing depth information of an intraocular structure of an eye under investigation using a rangefinder; a second step to capture a variety of parts of the depth information while changing the relative positions of the eye being examined and the rangefinder; a third step in determining a three-dimensional position of depth information in an eyeball; and a fourth step to generate an ocular tomographic image by assembling the multitude of parts of the depth information based on the position information. [2] Method for generating an ocular tomographic image according to claim 1, wherein the three-dimensional position of the depth information is determined in the third step on the basis of the angle of incidence of the light incident from the rangefinder in relation to the corneal vertex of the eye to be examined and / or the thickness of the retina. [3] Method for generating an ocular tomographic image according to claim 1, wherein in the fourth step the ocular tomographic image is generated by a mathematical model trained by machine learning or deep learning. [4] Method for generating an ocular tomographic image according to claim 1, wherein the ocular tomographic image is supplemented in the fourth step by using a standard tomography image. [5] Method for generating an ocular tomographic image according to claim 3 or 4, wherein the plurality of depth information acquired in the second step is used as training data for the mathematical model. [6] Method for generating an ocular tomographic image according to claim 1, wherein the resolution processing in the first step is carried out using adaptive optics. [7] Method for generating an ocular tomographic image according to claim 1, further comprising a step for acquiring information about the presence / absence of a disease risk by inputting the depth information into a mathematical model which is trained by machine learning or deep learning using ocular tomographic image data relating to an eye disease as training data. [8] Method for generating an ocular tomographic image according to claim 1, which further includes a display step for displaying the depth information. [9] Method for generating an ocular tomographic image according to claim 1, wherein the rangefinder is a distance measuring unit of a mobile terminal device. [10] Method for generating an ocular tomographic image according to claim 1, wherein the rangefinder is a distance measuring unit of a stationary device. [11] Method for producing an ocular tomographic image according to claim 1, wherein the rangefinder is a distance measuring unit of a device attached to the head.

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