CN116421138A

CN116421138A - Method, apparatus, multi-optical path OCT system and medium for determining ocular axis length

Info

Publication number: CN116421138A
Application number: CN202310332121.5A
Authority: CN
Inventors: 袁韬
Original assignee: Hangzhou Weixiao Medical Technology Co ltd
Current assignee: Hangzhou Weixiao Medical Technology Co ltd
Priority date: 2023-03-30
Filing date: 2023-03-30
Publication date: 2023-07-14

Abstract

In accordance with example embodiments of the present disclosure, a method, apparatus, system, and medium for determining an eye axis length are provided. The method comprises the following steps: acquiring a first OCT image and three second OCT images, wherein the first OCT image and the three second OCT images are shot through a multi-light-path OCT system, the first OCT image comprises a front eye section part of the target eye, and one of the three second OCT images comprises a retina of the target eye; determining a corneal vertex position of the target eye based on the first OCT image; for each second OCT image: performing lateral pixel accumulation on the second OCT image to generate a one-dimensional result; carrying out local peak search on the one-dimensional result to obtain the maximum local peak position and a corresponding forward gradient value; determining the maximum local peak position corresponding to the maximum value in the three forward gradient values as the PRE layer position; and determining an axial length of the target eye based on the corneal vertex position, the PRE layer position, and the predetermined calibration parameters. Thus, the eye axis length can be accurately determined.

Description

Method, apparatus, multi-optical path OCT system and medium for determining ocular axis length

Technical Field

Embodiments of the present disclosure relate generally to the field of image processing, and more particularly, to a method, electronic device, multi-optical path OCT system, and computer-readable medium for determining an eye axis length.

Background

Optical coherence tomography (optical cohernce tomography, OCT) was proposed by Huang et al, university of ma, 1991, and has been greatly developed and applied over decades. The technology utilizes the principle of a Michelson interferometer, one of the reflectors is changed into a sample, and the depth information of the sample is obtained through coherent imaging of the reflected light of the sample arm and the reflected light of the reference arm. OCT technology has the characteristics of rapid imaging, deeper imaging depth, non-invasiveness, non-contact, low price and the like, and is widely developed and applied in the medical detection field. OCT imaging techniques fill the gap between confocal microscopy and ultrasound techniques from resolution and imaging depth versus other imaging means.

OCT systems can be classified into time domain OCT (TD-OCT) and frequency domain OCT (FD-OCT) according to specific imaging principles and system designs. The frequency domain OCT can be divided into a spectrum domain OCT (SD-OCT) and a sweep source OCT (SS-OCT) according to the difference of the adopted light source and the adopted receiver. At present, as the depth information of a sample can be directly obtained by the frequency domain OCT, the movement of a reference arm is omitted, the imaging speed is greatly improved, and the frequency domain OCT is basically used as a main device for ophthalmic detection instead of the time domain OCT. At present, the maximum imaging depth of the traditional OCT is about 7mm, and the maximum length of the eye axis can be approximately 30mm, so that the measurement of the length of the eye axis (the distance from the vertex of the cornea to the PRE layer (retinal pigment epithelium, retinal pigment epithelium)) cannot be completed by a single optical path. In current OCT systems, multiple mirrors are added to the reference arm to extend the depth range of OCT. In a practical system, OCT images of different depths can be reconstructed. Although this design solves the problem of the current measurement range of OCT, due to the system design of the spectral domain OCT itself and the influence of noise, especially the characteristic that the reconstructed signal decreases with depth, certain difficulties and difficulties are brought to the actual positioning of the RPE layer. If the traditional signal-to-noise ratio method is adopted, misjudgment can be generated on an actual image, and therefore the wrong eye axis length is obtained.

Disclosure of Invention

Embodiments of the present disclosure provide a method, an electronic device, a multi-optical path OCT system, and a computer-readable medium for determining an eye axis length, whereby accurate calculation of a retinal (RPE layer) position and selection of a correct imaging optical path can be achieved under the condition that a background signal value decreases with axial depth, excluding interference of low signals, noise, and negative frequencies at OCT image reconstruction, thereby more accurately calculating an eye axis length.

In a first aspect of the present disclosure, a method for determining an ocular axis length is provided. The method comprises the following steps: acquiring a first OCT image and three second OCT images, wherein the first OCT image and the three second OCT images are shot through a multi-light-path OCT system, the first OCT image comprises a front eye section part of a target eye, and one of the three second OCT images comprises a retina of the target eye; determining a corneal vertex position of the target eye based on the first OCT image; for each of the three second OCT images, the following steps are performed: performing lateral pixel accumulation on the second OCT image to generate a one-dimensional result; carrying out local peak search on the one-dimensional result to obtain the maximum local peak position and a corresponding forward gradient value; determining the maximum local peak position corresponding to the maximum value in the three forward gradient values corresponding to the three second OCT images as the PRE layer position; and determining an axial length of the target eye based on the corneal vertex position, the PRE layer position, and the predetermined calibration parameters.

In a second aspect of the present disclosure, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to the first aspect of the present disclosure.

In a third aspect of the present disclosure, there is provided a multi-optical path OCT system comprising the electronic device according to the second aspect.

In a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method according to the first aspect of the present disclosure.

The summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the disclosure, nor is it intended to be used to limit the scope of the disclosure.

Drawings

The foregoing and other objects, features and advantages of the disclosure will be apparent from the following more particular descriptions of exemplary embodiments of the disclosure as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the disclosure.

FIG. 1 illustrates a schematic diagram of an example environment 100, according to an embodiment of the disclosure;

FIG. 2 illustrates a schematic diagram of a method 200 for determining an eye axis length according to an embodiment of the disclosure;

FIG. 3 shows a schematic diagram of a target eye imaging result 300 according to an embodiment of the present disclosure;

FIG. 4 shows a schematic diagram of a etalon imaging result 400 according to embodiments of the present disclosure;

FIG. 5 shows a schematic diagram of a method 500 for local area peak searching of one-dimensional results, according to an embodiment of the disclosure;

FIG. 6 illustrates a schematic diagram of an example of a method 600 for determining a maximum local peak position corresponding to a maximum of three forward gradient values corresponding to three second OCT images as a PRE layer position, in accordance with an embodiment of the present disclosure;

fig. 7 illustrates a schematic diagram of an example of a method 700 for determining an eye axis length of a target eye in accordance with an embodiment of the present disclosure;

FIG. 8 schematically illustrates a block diagram of an electronic device 800 suitable for use in implementing embodiments of the present disclosure;

FIG. 9 shows a schematic representation of the position of the corneal vertex a, the position of the PRE layer b, the image longitudinal length f and the spacing S12 between images img_0 and img_1;

fig. 10 shows a schematic of the front and rear surfaces of the etalon imaged in different images.

Like or corresponding reference characters indicate like or corresponding parts throughout the several views.

Detailed Description

Preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present disclosure are illustrated in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

As described above, due to the system design of the spectral domain OCT itself and the influence of noise, especially the characteristic that the reconstructed signal decreases with depth, certain difficulties and difficulties are brought to the actual positioning of the RPE layer. If the traditional signal-to-noise ratio method is adopted, misjudgment can be generated on an actual image, and therefore the wrong eye axis length is obtained.

To solve the above-described problems or other problems not described, the present disclosure provides a solution for determining an eye axis length. In this approach, the computing device acquires a first OCT image of the target eye taken via the multi-path OCT system, the first OCT image including an anterior segment region of the target eye, and three second OCT images, one of the three second OCT images including a retina of the target eye. Subsequently, the computing device determines a corneal vertex position of the target eye based on the first OCT image. For each of the three second OCT images, the computing device performs the steps of: performing lateral pixel accumulation on the second OCT image to generate a one-dimensional result; and carrying out local peak search on the one-dimensional result to obtain the maximum local peak position and the corresponding forward gradient value. Next, the computing device determines, as the PRE layer position, a maximum local peak position corresponding to a maximum of three forward gradient values corresponding to the three second OCT images. Finally, the computing device determines an eye axis length of the target eye based on the corneal vertex position, the PRE layer position, and the predetermined calibration parameters.

Therefore, under the condition that the background signal value is reduced along with the axial depth, the accurate calculation of the position of the retina (RPE layer) and the selection of a correct imaging light path are realized, and the interference of low signals, noise and negative frequency during OCT image reconstruction is eliminated, so that the length of the eye axis is calculated more accurately.

The following detailed description is provided with reference to the accompanying drawings.

Fig. 1 illustrates a schematic diagram of an example environment 100, according to an embodiment of the disclosure. As shown in fig. 1, the example environment 100 includes a computing device 110, a first OCT image 120, three second OCT images 130-1 through 130-3 (hereinafter collectively 130), and an eye axis length 140.

Computing device 110 may include, but is not limited to, a multi-path OCT system, a personal computer, a personal digital assistant, a wearable device, a tablet computer, a smartphone, and the like. In some embodiments, the computing device 110 may have or be coupled to an image acquisition apparatus for acquiring human eye images.

The computing device 110 may be used to obtain a first OCT image 120, taken via a multi-path OCT system, about the target eye, the first OCT image including the anterior segment region of the target eye, and three second OCT images 130, one of which includes the retina of the target eye. The computing device 110 may determine the corneal vertex position of the target eye based on the first OCT image 120. The computing device 110 may also perform the following steps for each of the three second OCT images 130: performing lateral pixel accumulation on the second OCT image 130 to generate a one-dimensional result; and carrying out local peak search on the one-dimensional result to obtain the maximum local peak position and the corresponding forward gradient value. Subsequently, the computing device 110 can determine a maximum local peak position corresponding to a maximum of the three forward gradient values corresponding to the three second OCT images 130 as the PRE layer position. Finally, computing device 110 may determine an axial length 140 of the target eye based on the corneal vertex position, the PRE layer position, and predetermined calibration parameters.

Therefore, under the condition that the background signal value is reduced along with the axial depth, the method and the device can realize accurate calculation of the position of the retina (RPE layer) and selection of a correct imaging light path, and eliminate interference of low signals, noise and negative frequency during OCT image reconstruction, so that the length of the eye axis is calculated more accurately.

Fig. 2 shows a schematic diagram of an example of a method 200 for determining an eye axis length according to an embodiment of the disclosure. In fig. 2, various actions may be performed, for example, by the computing device shown in fig. 1. It should be understood that method 200 may also include additional acts not shown and/or may omit acts shown, the scope of the present disclosure being not limited in this respect.

At block 202, computing device 110 acquires a first OCT image of the target eye, taken via a multi-path OCT system, the first OCT image including an anterior segment region of the target eye, and three second OCT images, one of which includes a retina (also referred to as a PRE layer) of the target eye.

Referring to fig. 3, the multi-path OCT system can capture a first OCT image img01 and three second OCT images img02, img03, and img04, which correspond to three channel modes (also referred to as optical paths), for a target eye. The anterior ocular segment region (anterior surface) appears inside the first OCT image img 01. Depending on the eye axis length of the different subjects or the specification of the etalon, the retina (back surface) may appear in the three second OCT images img02, img03, or img 04. One of the three second OCT images is a true reconstructed image of the retina, at least one is an empty image, and there may be an inverse artifact (negative frequency image, such as img03 in fig. 3).

Returning to fig. 2, at block 204, computing device 110 determines a corneal vertex position of the target eye based on the first OCT image.

In some embodiments, computing device 110 may obtain a central predetermined number of points from the first OCT image to generate a first matrix. For example, the resolution of the first OCT image is 2048×1024 (M1×n), the central 600 points can be truncated, and 2048×600 (M2×n, M2< M1) after the truncation is recorded as the first matrix M1. In some examples, the first OCT image may be filtered to remove noise prior to generating the first matrix.

After generating the first matrix, computing device 110 may determine, for any mth row in the first matrix, a difference between the value of the mth row and the value of the mth column in the first matrix and the value of the mth column in the m-1 row as the value of the mth column in the second matrix. For example, taking the second matrix M2, the same size as the first matrix M1, all initial pixel values are 0. The value of M2 is calculated starting from lines 1+10 (1+num) to 2024-10 (2024-num), M2[ M, n ] =m1 [ M, n ] -M1[ M-1, n ]. The value of num may be preset, e.g., 0, 5, 10, 15, etc.

After obtaining the second matrix, computing device 110 may transversely accumulate the second matrix to obtain a longitudinally accumulated result. Finally, computing device 110 may determine the location corresponding to the maximum value in the longitudinal accumulation results as the corneal vertex location.

Therefore, under the condition of low signal-to-noise ratio and strong interference, the corneal vertex position can be accurately calculated.

Returning to fig. 2, for each of the three second OCT images, computing device 110 may perform lateral pixel accumulation on the second OCT image at block 206 to generate a one-dimensional result. The one-dimensional result may for example be represented as a one-dimensional curve, e.g. with the longitudinal position of the second OCT image as an independent variable and the accumulated pixel value as a dependent variable. The computing device 110 may also perform a local peak search on the one-dimensional results to obtain a maximum local peak location and a corresponding forward gradient value. In some examples, the second OCT image may also be median filtered prior to lateral pixel accumulation. Before block 208, four boundary portion pixel values may be removed in turning to a one-dimensional result, taking into account boundary noise; in addition, the one-dimensional result may be smoothed.

At block 208, computing device 110 determines a maximum local peak position corresponding to a maximum of the three forward gradient values corresponding to the three second OCT images as the PRE layer position.

At block 210, computing device 110 determines an eye axis length of the target eye based on the corneal vertex position, the PRE layer position, and predetermined calibration parameters.

Specifically, the computing device 110 determines a spacing between the second OCT image and the first OCT image corresponding to the PRE-layer location. The eye axis length is then calculated by a predetermined formula based on the determined pitch, corneal vertex position, PRE layer position, average refractive index of the target eye, calibration parameters between the pixel and the true distance, and image longitudinal distance.

Referring to fig. 3, when the position of the corneal vertex in img01 is obtained, denoted as a (which may be understood as the distance of the corneal vertex from the bottom of the img01 image), the position of the PRE layer in img02-04 is denoted as b, c and d (which may be understood as the distance of the PRE layer from the top of img02, img03, img 04), respectively. If the anterior and posterior surfaces (cornea and retina) are imaged at img01, img02, the eye axis length length= (a+b) p/nx+s12 (1). If the front and rear surfaces are imaged at img01, img03, the eye axis length, lengh= [ (a+c) ×p+f ]/nx+s12+s23 (2). If the front and rear surfaces are imaged at img01, img04, the eye axis length lendh= [ (a+d) ×p+2f ]/nx+s12+s23+s34 (3). Where nx is the average refractive index (a known parameter) of the target eye. S is the spacing between the figures, S12 represents the spacing between img01 and img02, S23 represents the spacing between img02 and img03, S34 represents the spacing between img03 and img04, and the spacing between the different figures can be obtained in advance by calibration. p is a calibration parameter between the pixel and the true distance, which can be obtained in advance by calibration, and f is the image longitudinal length (known parameter).

In some embodiments, prior to block 212, computing device 110 may also determine whether a maximum of three forward gradient values corresponding to the three second OCT images is greater than a predetermined gradient threshold. The lowest most acceptable forward gradient value may be given as a predetermined gradient threshold, for example by a test instrument. Below the predetermined gradient threshold, indicating that the image is unclear or the signal is unreasonable, the computing device 110 may prompt an error or warning.

If computing device 110 determines that the maximum of the three forward gradient values corresponding to the three second OCT images is greater than the predetermined gradient threshold, then at block 212, an eye axis length of the target eye is determined based on the corneal vertex position, the PRE layer position, and the predetermined calibration parameters.

Therefore, the eye axis length can be calculated under the condition that the forward gradient value is in the normal range, and the accuracy of calculating the eye axis length is further improved.

The following describes the determination of calibration parameters, which can be obtained by calibration with a etalon.

First, the computing device 110 may acquire a standard eye axis length of the etalon and a third OCT image regarding the etalon and three fourth OCT images captured via the multi-path OCT system, the third OCT image including an anterior ocular segment region of the etalon, one of the three fourth OCT images including a retina of the etalon.

Computing device 110 may then determine a standard corneal vertex position for the etalon based on the third OCT image.

For each of the three fourth OCT images, the computing device 110 can perform lateral pixel accumulation on the fourth OCT image to generate a standard one-dimensional result; and carrying out local peak search on the one-dimensional result to obtain a standard maximum local peak position and a corresponding standard forward gradient value.

Subsequently, computing device 110 may determine a standard maximum local peak position corresponding to a maximum of the three standard forward gradient values corresponding to the three fourth OCT images as a standard PRE layer position. These steps are similar to those for the target eye and are referred to elsewhere herein and will not be repeated.

Finally, computing device 110 may determine calibration parameters based on the standard corneal vertex position, the standard PRE layer position, and the standard eye axis length.

Referring to fig. 4, taking the imaging of anterior and posterior surfaces (cornea and retina) at img_0, img_1 as an example, the eye axis length length= (a+b) p/nx+s12 (1), where the eye axis length length, a, b, nx, f is known, p and S12 can be obtained by varying the etalon eye axis length calibration. Taking the front and back surfaces (cornea and retina) imaged at img_0, img_1 as an example, fig. 9 shows a schematic diagram of the position a of the corneal vertex, the position b of the PRE layer, the image longitudinal length f, and the spacing S12 between images img_0 and img_1. Similarly, the case where the anterior and posterior surfaces (cornea and retina) are imaged at img_0, img_2, and the anterior and posterior surfaces (cornea and retina) are imaged at img_0, img_3, can be deduced in this way, and S23 and S34 can also be obtained by the formulas lengh= [ (a+c) ×p+f ]/nx+s12+s23 (2) and lengh= [ (a+d) ×p+2f ]/nx+s12+s23+s34 (3) via calibration. As shown in fig. 10, by changing the length of the ocular axis of the etalon such that the anterior and posterior surfaces (cornea and retina) are imaged at img_0, img_1 (indicated by the arrows in the first row of drawings), img_0, img_2 (indicated by the arrows in the second row of drawings), and img_0, img_3 (indicated by the arrows in the third row of drawings), respectively, p, S12, S23, and S34 can be obtained by calibration of the above formulas.

Thus, the relevant calibration parameters can be obtained through the pre-calibration of the etalon so as to accurately calculate the length of the eye axis.

Fig. 5 shows a schematic diagram of an example of a method 500 for local area peak searching of one-dimensional results, according to an embodiment of the disclosure. In fig. 5, various actions may be performed, for example, by the computing device shown in fig. 1. It should be understood that method 500 may also include additional acts not shown and/or may omit acts shown, the scope of the present disclosure being not limited in this respect.

For each point in the one-dimensional result, computing device 110 may determine a first average of a first predetermined number of points around the point, a second average of a third predetermined number of points around the point forward spaced a second predetermined number of points, and a third average of a third predetermined number of points around the point backward spaced a second predetermined number of points at block 502.

For example, the average value of n1 points near each point is denoted as Peak.

For example, an average of n2 points around the forward interval m points is calculated and denoted as side1.

For example, the average of n2 points around the m points of the backward interval is calculated and is denoted as side2.

In some embodiments, reference coefficients coff1, coff2 may be preset for two sides, respectively, and values of side1 and side2 may be corrected, for example, side 1=coff1×side1, and side 2=coff2×side2.

At block 504, the computing device 110 determines whether the first average is greater than the second average and the first average is greater than the third average.

For example, it may be determined that Peak > side1 and Peak > side2 are satisfied at the same time, which corresponds to a true local Peak.

If the computing device 110 determines at block 504 that the first average is greater than the second average and the first average is greater than the third average, then at block 506 the difference between the first average and the second average is determined as the forward gradient value for that point. For example, for a point of a true local Peak, the forward gradient value for that point may be calculated as Peak-side1. Otherwise, the forward gradient value corresponding to the point is determined to be zero at block 508.

After calculating the forward gradient value for each point in the one-dimensional result, at block 510, the computing device 110 determines the point corresponding to the maximum of the plurality of forward gradient values corresponding to the one-dimensional result as the maximum local peak position of the one-dimensional result. The maximum local peak position may be denoted posMax and its corresponding forward gradient value may be denoted peakMax.

Thus, the maximum local peak position can be determined for the one-dimensional result, thereby determining the maximum local peak position in the OCT image.

Fig. 6 illustrates a schematic diagram of an example of a method 600 for determining a maximum local peak position corresponding to a maximum of three forward gradient values corresponding to three second OCT images as a PRE layer position, according to an embodiment of the present disclosure. In fig. 6, various actions may be performed, for example, by the computing device shown in fig. 1. It should be appreciated that method 600 may also include additional actions not shown and/or may omit shown actions, the scope of the present disclosure being not limited in this respect.

At block 602, computing device 110 determines a second OCT image from the three second OCT images that has a lowest signal-to-noise ratio.

For example, the second OCT image img02 in fig. 3 is determined to have the lowest signal-to-noise ratio.

At block 604, the computing device 110 performs a straight line fit with the longitudinal position and the pixel accumulation result in the one-dimensional result corresponding to the second OCT image with the lowest signal-to-noise ratio as the argument and the argument, respectively, to obtain a straight line fit result.

In some examples, the straight line fitting may be performed after the head-to-tail data of the one-dimensional result is removed. The element in the one-dimensional result can be expressed as (x, y), where x is the longitudinal position of the second OCT image and y is the pixel accumulation result. A straight line fit of y=ax+b can be performed on the one-dimensional results to remove oscillations and non-smoothing of the data therein.

At block 606, the computing device 110 normalizes the amount of strain in the line fit result to the maximum amount of strain in the line fit result, resulting in a normalized result.

For example, if the maximum strain amount is expressed as y0, the element in the normalized result may be expressed as (x, y/y 0), and y/y0 may be referred to as the depth correction factor coff.

At block 608, the computing device 110 corrects the three forward gradient values, respectively, based on the three normalized strain amounts in the normalization result that correspond to the three largest local peak locations, resulting in three corrected forward gradient values.

In some examples, the forward gradient value may be divided by the normalized strain amount (also referred to as a depth correction factor) to obtain a corrected forward gradient value. For example, if the maximum local peak position is posMax, the corresponding forward gradient value is peakMax, and the depth correction factor corresponding to posMax in the normalization result is coff, the corrected forward gradient value is peakMax/coff.

At block 610, computing device 110 determines a maximum local peak position corresponding to a maximum of the three modified forward gradient values as the PRE layer position.

Therefore, under the condition that the background signal value is reduced along with the axial depth, the forward gradient value is corrected by the depth correction factor, the influence of axial depth attenuation of the signal is eliminated, and the position of the PRE layer is more accurately determined.

Fig. 7 illustrates a schematic diagram of an example of a method 700 for determining an eye axis length of a target eye, according to an embodiment of the disclosure. In fig. 7, various actions may be performed, for example, by the computing device shown in fig. 1. It should be appreciated that method 700 may also include additional actions not shown and/or may omit shown actions, the scope of the present disclosure being not limited in this respect.

For each of the three second OCT images, at block 702, computing device 110 obtains a first partial image from the second OCT image centered around a maximum local peak position for a predetermined number of points.

At block 704, computing device 110 performs a vertical pixel accumulation on the first partial image to obtain a lateral one-dimensional result.

At block 706, the computing device 110 determines a weighted result of the mean and maximum of the lateral one-dimensional results as a boundary threshold.

For example, a scaling factor may be set, denoted as shapeRatio1 and shapeRatio2. Calculating a boundary threshold value thred=shaperatio 1 x shapemean+shaperatio2 x shapeMax, wherein shapeMean represents the mean value and shapeMax represents the maximum value.

At block 708, computing device 110 determines a first location in the lateral one-dimensional result from the left that is greater than a boundary threshold as a left boundary; and determining a first position from the right side in the transverse one-dimensional result, which is larger than the boundary threshold value, as a right side boundary.

At block 710, computing device 110 obtains a second partial image from the second OCT image centered around the maximum local peak position, a predetermined number of points above and below each, and from the left boundary to the right boundary.

At block 712, computing device 110 fits a straight line based on each column maximum position of the second partial image.

At block 714, computing device 110 determines a slope of the straight line as the retinal inclination corresponding to the maximum local peak location.

At block 716, computing device 110 determines whether the retinal tilt corresponding to the PRE position is less than a predetermined tilt threshold.

If at block 716 computing device 110 determines that the retinal tilt corresponding to the PRE layer position is less than the predetermined tilt threshold, then at block 718 computing device 110 determines the eye axis length of the target eye based on the corneal vertex position, the PRE layer position, and the predetermined calibration parameters. Otherwise, an error or warning may be prompted. The predetermined tilt threshold is calibrated in advance, for example, by instrumentation.

Therefore, the length of the eye axis can be calculated under the condition that the inclination angle of the retina is in the normal range, and the accuracy of calculating the length of the eye axis is further improved.

Fig. 8 schematically illustrates a block diagram of an electronic device 800 suitable for use in implementing embodiments of the present disclosure. Device 800 may be used to implement computing device 100 of fig. 1. As shown, the device 800 includes a Central Processing Unit (CPU) 801 that can perform various suitable actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM) 802 or loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the RAM803, various programs and data required for the operation of the device 800 can also be stored. The CPU 801, ROM802, and RAM803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.

Various components in device 800 are connected to I/O interface 805, including: an input unit 806 such as a keyboard, mouse, etc.; an output unit 807 such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, or the like. The communication unit 809 allows the device 800 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The processing unit 801 performs the various methods and processes described above, such as performing the methods 200, 500-700. For example, in some embodiments, the methods 200, 500-700 may be implemented as a computer software program stored on a machine readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 800 via ROM802 and/or communication unit 809. One or more of the operations of the methods 200, 500-700 described above may be performed when a computer program is loaded into RAM803 and executed by CPU 801. Alternatively, in other embodiments, CPU 801 may be configured to perform one or more actions of methods 200, 500-700 by any other suitable means (e.g., by means of firmware).

The present disclosure may be methods, apparatus, systems, and/or computer program products. The multi-path OCT system can include the electronics 800 described above. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for performing aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.

The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.

Computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the technical improvement of the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for determining an eye axis length, comprising:

acquiring a first OCT image and three second OCT images of a target eye captured via a multi-path OCT system, the first OCT image including an anterior segment region of the target eye, one of the three second OCT images including a retina of the target eye;

determining a corneal vertex position of the target eye based on the first OCT image;

for each of the three second OCT images, performing the steps of:

performing lateral pixel accumulation on the second OCT image to generate a one-dimensional result;

carrying out local peak search on the one-dimensional result to obtain a maximum local peak position and a corresponding forward gradient value;

determining the maximum local peak position corresponding to the maximum value in the three forward gradient values corresponding to the three second OCT images as a PRE layer position; and

an ocular axis length of the target eye is determined based on the corneal vertex position, the PRE layer position, and predetermined calibration parameters.

2. The method of claim 1, wherein performing a local peak search on the one-dimensional result comprises:

for each point in the one-dimensional result, performing the steps of:

determining a first average of a first predetermined number of points around the point;

determining a second average of a third predetermined number of points around the point forward-spaced a second predetermined number of points;

determining a third average of the third predetermined number of points around the point spaced backward from the point by the second predetermined number of points;

if the first average value is determined to be larger than the second average value and the first average value is determined to be larger than the third average value, determining the difference between the first average value and the second average value as a forward gradient value corresponding to the point; and

otherwise, determining the forward gradient value corresponding to the point as zero; and

and determining a point corresponding to the maximum value in the plurality of forward gradient values corresponding to the one-dimensional result as the maximum local peak position of the one-dimensional result.

3. The method of claim 1, wherein determining, as the PRE-position, a maximum local peak position corresponding to a maximum of three forward gradient values corresponding to the three second OCT images comprises:

determining a second OCT image with the lowest signal-to-noise ratio from the three second OCT images;

respectively performing linear fitting by taking a longitudinal position and a pixel accumulation result in a one-dimensional result corresponding to the second OCT image with the lowest signal-to-noise ratio as independent variables and dependent variables to obtain a linear fitting result;

normalizing the strain in the straight line fitting result by using the maximum strain in the straight line fitting result to obtain a normalized result;

based on three normalized dependent variables corresponding to three maximum local peak positions in the normalization result, respectively correcting the three forward gradient values to obtain three corrected forward gradient values; and

and determining the maximum local peak position corresponding to the maximum value in the three corrected forward gradient values as the PRE layer position.

4. The method of any of claims 1-3, wherein determining the axial length of the target eye comprises:

for each of the three second OCT images, performing the steps of:

acquiring a first partial image of each of a predetermined number of points around the maximum local peak position as a center from the second OCT image;

performing longitudinal pixel accumulation on the first partial image to obtain a transverse one-dimensional result;

determining a weighted result of the average value and the maximum value of the transverse one-dimensional result as a boundary threshold;

determining a position in the transverse one-dimensional result, which is first from the left side to be greater than the boundary threshold value, as a left side boundary, and a position in the transverse one-dimensional result, which is first from the right side to be greater than the boundary threshold value, as a right side boundary;

acquiring a second partial image of each of a predetermined number of points up and down centered around the maximum local peak position and from the left boundary to the right boundary from the second OCT image;

fitting a straight line based on each column maximum position of the second partial image; and

determining a slope of the line as a retinal tilt corresponding to the maximum local peak location; and

if it is determined that the retinal tilt corresponding to the PRE-layer position is less than a predetermined tilt threshold, determining an ocular axis length of the target eye based on the corneal vertex position, the PRE-layer position, and predetermined calibration parameters.

5. The method of any of claims 1-3, wherein determining the axial length of the target eye comprises:

if it is determined that the maximum of the three forward gradient values corresponding to the three second OCT images is greater than a predetermined gradient threshold, determining an eye axis length of the target eye based on the corneal vertex position, the PRE layer position, and a predetermined calibration parameter.

6. The method of any of claims 1-3, wherein determining the corneal vertex position of the target eye comprises:

acquiring a central predetermined number of points from the first OCT image to generate a first matrix;

for any mth row in the first matrix, determining a difference between a value of an mth row and an nth column in the first matrix and a value of an mth-1 row and an nth column as a value of an mth row and an nth column in a second matrix;

transversely accumulating the second matrix to obtain a longitudinal accumulation result; and

and determining the position corresponding to the maximum value in the longitudinal accumulation result as the cornea vertex position.

7. A method according to any one of claims 1-3, further comprising:

acquiring a standard eye axis length of a etalon and a third OCT image and three fourth OCT images of the etalon captured via the multi-path OCT system, the third OCT image including a anterior ocular segment region of the etalon, one of the three fourth OCT images including a retina of the etalon;

determining a standard corneal vertex position of the etalon based on the third OCT image;

for each of the three fourth OCT images, performing the following steps:

performing lateral pixel accumulation on the fourth OCT image to generate a standard one-dimensional result;

carrying out local peak search on the one-dimensional result to obtain a standard maximum local peak position and a corresponding standard forward gradient value;

determining a standard maximum local peak position corresponding to the maximum value in three standard forward gradient values corresponding to the three fourth OCT images as a standard PRE layer position; and

the calibration parameters are determined based on the standard corneal vertex position, the standard PRE layer position, and the standard eye axis length.

8. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

9. A multi-path OCT system comprising the electronic device of claim 8.

10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-7.