Skin viscoelasticity index estimation method

Abstract

To provide an estimation method for estimating a skin viscoelasticity indicator.SOLUTION: A skin viscoelasticity indicator estimation method specifies one cell region of a horny layer image, and estimates a skin viscoelasticity indicator on the basis of a correlation between one or more horny layer parameter values calculated therefrom and a skin elasticity parameter measured by a skin viscoelasticity measurement device.SELECTED DRAWING: None

Description

[Technical Field]

[0001] The present invention relates to a method for estimating a skin viscoelasticity index. [Background technology]

[0002] The health of skin is evaluated using a variety of indices. For example, the presence or absence of a feeling of "firmness" in the skin is considered one of the indicators for assessing health and the degree of aging. "Firmness" can be divided into two types: firmness derived from the stratum corneum / epidermis, and firmness derived from the dermis. Dermal firmness, in particular, has the elasticity to push back when pressed with a finger, and quickly returns to its original state when the finger is removed; in physical terms, this is also called viscoelasticity. The viscoelasticity of the skin decreases with age, exposure to ultraviolet rays, and exposure to chemicals. For this reason, skin firmness can also be used as an indicator of skin aging. Methods and devices have been developed to measure the viscoelastic modulus of skin by treating the skin as a single elastic body, replacing the sensory evaluation method of "firmness." A representative skin viscoelasticity measuring device is the CUTOMETER (product name) (manufactured by Courage & Khazaka). This device draws the skin surface into a probe opening under negative pressure, measures the length of the skin drawn into the opening with a prism, then releases the suction and measures the displacement (return) when released in the same way. The viscoelastic modulus is calculated using this measurement result as a parameter (see Non-Patent Document 1). The viscoelastic modulus measured using this device is widely used in the cosmetics industry and cosmetic medical fields to evaluate the degree of skin aging and the effectiveness of cosmetics. However, the technology for measuring skin viscoelasticity using a skin viscoelasticity measuring device requires expensive probes and equipment, and requires skilled operation. As a result, measurements are only available at specialized salons and medical institutions, making it difficult to determine skin viscoelasticity indices cheaply, easily, and quickly. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Integral Corporation, "Skin Viscoelasticity Measuring Device CUTOMETER MPA580 Manual," revised May 2007 Summary of the Invention [Problem to be solved by the invention]

[0004] An object of the present invention is to provide a method for estimating a skin viscoelasticity index. [Means for solving the problem]

[0005] The present invention was made after extensive research to solve the above problems, and it was discovered that a skin viscoelasticity index can be estimated using the stratum corneum structure obtained from an observation image of the stratum corneum as an index.

[0006] Specifically, the means for solving the problems of the present invention are as follows. 1. Identify a single cell area in the stratum corneum image. A method for estimating a skin viscoelasticity index, characterized by estimating a skin viscoelasticity index based on one or more correlations selected from the following (1) to (5) between one or more stratum corneum parameter values ​​calculated from these and skin elasticity parameters measured by a skin viscoelasticity measuring device. (1) Correlation between the ratio of the length and width of a rectangle, the standard deviation of the average brightness of a single cell in the image, and the standard deviation of the median brightness of a single cell in the image as explanatory variables and the maximum suction height as the objective variable. (2) Correlation between the standard deviation of the brightness of each pixel of a single cell in the image as the explanatory variable and the final return / maximum suction height after release as the objective variable. (3) Correlation using one or more stratum corneum parameters selected from the group consisting of the standard deviation of luminance per cell, the minimum luminance per cell, the standard deviation of the standard deviation of luminance of each pixel of a single cell in an image, and the standard deviation of the minimum luminance of a single cell in an image as explanatory variables, and R7: correlation using the instantaneous return / maximum suction height after the release of negative pressure as the objective variable. (4) Correlation using one or more stratum corneum parameters selected from the group consisting of the standard deviation of the minimum brightness value of one cell in the image and the standard deviation of the standard deviation of the brightness of each pixel of one cell in the image as explanatory variables, and R5: correlation using the instantaneous return after releasing negative pressure / instantaneous deformation during suction as objective variables. (5) Correlation between one or more stratum corneum parameters selected from the group consisting of the area of ​​a single cell, the perimeter of a single cell, the rectangular area circumscribing a single cell, and the standard deviation of the median brightness of a single cell in the image as explanatory variables and R6: Correlation between the deformation over time during suction and the instantaneous deformation during suction as objective variables. 2. A method for estimating a skin viscoelasticity index described in 1, characterized in that the single-cell region is identified using a machine learning model that has previously trained a number of training stratum corneum images and single-cell regions based on visual evaluation of each training stratum corneum image. [Effects of the Invention]

[0007] The method for estimating a skin viscoelasticity index of the present invention uses a numerical value obtained from the stratum corneum structure as an index, and can estimate a skin viscoelasticity index very easily and accurately. DETAILED DESCRIPTION OF THE INVENTION

[0008] The present invention identifies a single cell region in an image of the stratum corneum, The present invention relates to a skin viscoelasticity index estimation method that estimates a skin viscoelasticity index based on the correlation between one or more of the stratum corneum parameter values ​​calculated from this and the analysis results obtained by a facial skin image analysis counseling system.

[0009] The skin viscoelasticity index estimation method of the present invention can estimate the following skin viscoelasticity indexes. R0 = Uf, an index of skin stretch and softness. The final stretch value 10 seconds after suction by the device. R2 = Ua / Uf, which indicates the overall elasticity including viscous deformation and is an index of the skin's viscoelasticity. The final skin return rate after suction is released. R7 = Ur / Uf, biological viscoelasticity (the ratio of rapid retraction to total elasticity), a measure of pure elasticity. The initial rate of return to the final elongation value. R5 = Ur / Ue, which indicates the net elasticity of the skin excluding viscous deformation and is an index of the skin's instantaneous recovery force. Initial speed of return rate after suction is released / Initial speed of elongation value at the start of suction. R6 = Uv / Ue, which indicates the ratio of viscoelastic stretch to elastic stretch, and is an index of the instantaneous viscoelasticity of the skin. The final skin stretch value excluding the initial velocity after suction / the initial velocity of the stretch value after suction of the skin. (Ue: Instantaneous deformation during suction, Uv: Deformation over time during suction, Uf: Maximum suction height, Ur: Instantaneous return after release of negative pressure, Ua: Final return after release)

[0010] Although the stratum corneum can be collected for imaging by either biopsy or tape stripping, tape stripping is preferred because it places less strain on the subject. Tape stripping involves applying adhesive tape to the skin and then peeling it off to collect the surface layer of the skin.

[0011] The corneal cells collected by tape stripping are imaged using transmitted light observation (including differential interference contrast, phase contrast, dark field observation, etc.) or reflected light observation to observe the cell morphology. Corneal cells are preferably observed unstained, but can also be stained if necessary. Cells can be observed using a microscope capable of cell observation, such as the Digital Microscope VHX-500 manufactured by Keyence Corporation or the Digital Microscope Dino-Lite manufactured by AnMo Electronics Corporation. There are no limitations on the observation conditions as long as the details of the cells can be confirmed, but examples include conditions such as a resolution of 1.0 μm / pixel or more and approximately 200,000 pixels or more.

[0012] The captured image of the stratum corneum is processed using an image processing system to identify a single cell region in the stratum corneum image. At this time, the area of ​​the cell region, the layer delamination region, the layer delamination rate, etc. can also be identified. A single cell area is the area of ​​an individual stratum corneum cell within the stratum corneum image. The cell region area is the total area of ​​the stratum corneum cell region in the stratum corneum image. The area of ​​multilayer peeling is the area of ​​the region where two or more layers of the stratum corneum have been peeled off. The layer delamination rate is the ratio of the "layer delamination area" to the "cell region area" (layer delamination area / cell region area).

[0013] Then, based on the identified single-cell area, etc., and depending on the type of skin viscoelasticity index to be estimated, one or more values ​​selected from the group consisting of single-cell area, single-cell perimeter, area of ​​a rectangle circumscribing a single cell, ratio of the length to the width of the rectangle, standard deviation of single-cell brightness, minimum single-cell brightness, standard deviation of average single-cell brightness in the image, standard deviation of median single-cell brightness in the image, standard deviation of minimum single-cell brightness in the image, and standard deviation of the brightness of each pixel of a single cell in the image are quantified. The stratum corneum image may be a color image, an image converted to grayscale by image processing, or both.

[0014] The area of ​​one cell is the average area of ​​each individual stratum corneum cell in the stratum corneum image. The cell perimeter is the average value of the perimeters of individual stratum corneum cells in an image of the stratum corneum. The rectangular area circumscribing one cell is the average area of ​​the smallest rectangle that covers the entire individual stratum corneum cell in the stratum corneum image across all cells. The ratio of the long and short sides of a rectangle is the average ratio of the long side to the short side (long side / short side) of the rectangle with the smallest area that covers the entire individual stratum corneum cell in the stratum corneum image for all cells.

[0015] The standard deviation of brightness per cell is the value obtained by calculating the standard deviation of brightness within the cell region of each stratum corneum cell in the stratum corneum image. The minimum luminance value of one cell is the average of the minimum luminance values ​​within the cell region of each individual stratum corneum cell in the stratum corneum image across all cells. The average brightness of one cell is the average brightness value within the cell region of each individual stratum corneum cell in the stratum corneum image, averaged over all cells. The single-cell median brightness is the average of the median brightness values ​​within the cell region of each individual stratum corneum cell in the stratum corneum image across all cells. The standard deviation of the brightness of each pixel in a cell in an image is the standard deviation of the brightness within the cell region of each individual stratum corneum cell in the stratum corneum image (standard deviation of brightness within one cell) across all cells in the stratum corneum image. Hereinafter, the stratum corneum parameter value refers to a numerical value of one or more values ​​selected from the group consisting of the area of ​​a single cell, the perimeter of a single cell, the area of ​​a rectangle circumscribing a single cell, the ratio of the length and width of the rectangle, the standard deviation of the brightness of a single cell, the minimum brightness of a single cell, the standard deviation of the average brightness of a single cell in an image, the standard deviation of the median brightness of a single cell in an image, the standard deviation of the minimum brightness of a single cell in an image, and the standard deviation of the brightness of each pixel of a single cell in an image.

[0016] The digitization using an image processing system can be performed using a known image processing system, such as the image processing software provided with the digital microscope described above or commercially available image processing software. Alternatively, an image processing system can be used that includes a machine learning model that has previously trained a plurality of training stratum corneum images and a single-cell region (the region of each individual cell) in each training stratum corneum image based on visual evaluation. This machine learning model can also be trained to train a cell region (the region where cells are present) based on visual evaluation, a multilayer delamination region (the region where two or more layers of stratum corneum have been delaminate) based on visual evaluation, etc. The stratum corneum image to be quantified is input into this machine learning model, a single-cell area of ​​this stratum corneum image is output, and stratum corneum parameter values ​​(single-cell area, single-cell perimeter, area of ​​the rectangle circumscribing the single cell, ratio of the rectangular sides, standard deviation of single-cell brightness, minimum single-cell brightness, standard deviation of the average single-cell brightness in the image, standard deviation of the median single-cell brightness in the image, standard deviation of the minimum single-cell brightness in the image, and standard deviation of the standard deviation of the brightness of each pixel of a single cell in the image) are calculated based on the single-cell area output by the machine learning model, thereby enabling the rapid calculation of stratum corneum parameter values ​​equivalent to those based on visual evaluation. Furthermore, the single-cell area output by the image processing system equipped with the machine learning model can also be visually corrected by a human to determine stratum corneum parameter values.

[0017] The method for estimating the skin viscoelasticity index using the quantified stratum corneum parameter value as an index is not particularly limited, and known methods can be used. For example, the stratum corneum parameter value may be subjected to simple regression analysis or multiple regression analysis. The multiple regression analysis may be linear multiple regression analysis or nonlinear multiple regression analysis. Furthermore, parameters other than the single-cell area, single-cell perimeter, rectangular area circumscribing a single cell, rectangular side ratio, single-cell brightness standard deviation, single-cell brightness minimum value, standard deviation of the single-cell brightness average value in the image, standard deviation of the single-cell brightness median value in the image, standard deviation of the single-cell brightness minimum value in the image, and standard deviation of the brightness of each pixel of a single-cell in the image may be included as explanatory variables of the multiple regression analysis. [Example]

[0018] <Machine learning sample> Corneal cells were collected from the faces of 412 women (aged 18-87 years, mean age 46.8 years) using the tape stripping method. The collected stratum corneum cells were photographed unstained using a digital microscope (Keyence Corporation, VHX-5000) with 8-bit (RGB color), 0.41 μm / pixel resolution, and 1600 x 1200 pixels using transmitted light. Two to three fields of view were photographed for each sample and used for machine learning.

[0019] Using the image annotation software Labelme, cellular regions, delaminated regions, and single-cell regions were visually identified to obtain training images of the stratum corneum. We machine-learned each training stratum corneum image and the cellular area, multilayered peeled area, and single-cell area based on visual evaluation, and obtained a machine learning model that can output the cellular area, multilayered peeled area, and single-cell area.

[0020] <Sample> Corneal cells were collected from the faces of 227 women (28-87 years old, average age 47.2 years) using the tape stripping method. Three to five samples were collected from each person, for a total of 966 samples. The sample preparation method was the same as for the machine learning samples. The sample images were analyzed using an image processing device equipped with a machine learning model, and the following values ​​were quantified for each stratum corneum image: area of ​​a single cell, perimeter of a single cell, area of ​​the rectangle circumscribing a single cell, ratio of the length and width of the rectangle, standard deviation of luminance of a single cell, minimum luminance of a single cell, standard deviation of average luminance of a single cell within the image, standard deviation of median luminance of a single cell within the image, standard deviation of minimum luminance of a single cell within the image, and standard deviation of the luminance of each pixel of a single cell within the image, to determine stratum corneum parameter values. In addition, the circularity, approximate 4-6 polygon, mean brightness per cell, median brightness per cell, maximum brightness per cell, standard deviation of maximum brightness per cell in the image, standard deviation of area per cell in the image, standard deviation of perimeter per cell in the image, standard deviation of circularity per cell in the image, and standard deviation of approximate 4-6 polygon in the image were also quantified to determine stratum corneum parameter values. Circularity is the average value of the circularity of each individual stratum corneum cell area (calculated by 4π×(area) / (square of perimeter)) for all cells in one image. The approximate tetrahexagon is a regular tetrahexagon placed within the stratum corneum cell area, with the center of gravity of the shape of each individual stratum corneum cell at the center, and the angle and size are adjusted so that the area of ​​the area protruding from the regular tetrahexagon is minimized. When the area of ​​the protruding area is minimized, the area of ​​the protruding area is divided by the area of ​​the stratum corneum cell, and the n that gives the smallest value among regular n-gons (n ​​is 4 to 6) is defined as the approximate n-gon, and the n of each stratum corneum cell in the stratum corneum image is averaged across all cells. The maximum brightness value of one cell is the average of the maximum brightness values ​​within the cell region of each individual stratum corneum cell in the stratum corneum image across all cells.

[0021] For the 966 samples, the single-cell area, cell area, and layer-stratified peeled area determined by visual evaluation were compared with the single-cell area, cell area, and layer-stratified peeled area analyzed using an image processing device equipped with a machine learning model. The results were as follows: area of ​​single-cell region by visual evaluation / area of ​​single-cell region analyzed by an image processing device equipped with a machine learning model = 117%, area of ​​cell region by visual evaluation / area of ​​cell region analyzed by an image processing device equipped with a machine learning model = 95%, area of ​​layer-peeled region by visual evaluation / area of ​​layer-peeled region analyzed by an image processing device equipped with a machine learning model = 86%, and the single-cell region, cell region, and layer-peeled region by visual evaluation were equivalent to the single-cell region, cell region, and layer-peeled region analyzed by an image processing device equipped with a machine learning model.

[0022] <Regression analysis of stratum corneum structure analyzed by an image processing device equipped with a machine learning model of skin viscoelasticity index> Name of skin viscoelasticity index measuring device: Cutometer (registered trademark) (manufactured by Courage + Khazaka Electronic GmbH (Cologne, Germany)) Subjects: 335 people (ages 20-92, average age 44.3) Measurement site and procedure After washing their faces, the subjects rested for 10 minutes, after which the skin viscoelasticity of the cheeks was measured using a Cutometer (registered trademark), and the stratum corneum of the cheeks was sampled using the tape stripping method. The collected stratum corneum was imaged using a Dino-Lite digital microscope (AnMo Electronics Corporation) under conditions of 8-bit RGB, 0.41 μm / pixel, 1600 × 1200 pixels. Cell shape was recognized using AI and binarization, and each stratum corneum parameter value was calculated. Regression analysis was performed using each stratum corneum parameter value as the explanatory variable and the R value of skin viscoelasticity as the objective variable. JMP (registered trademark) 16.2.0 (SAS Institute Inc., NC, USA) was used for the analysis. The R values ​​of skin viscoelasticity, which are skin viscoelasticity indexes, are R0, R2, R7, R5, and R6.

[0023] "Simple regression analysis" Simple regression analysis was performed between each stratum corneum parameter value and the skin viscoelasticity index. For those where a correlation was observed, the correlation coefficient r, p value, and the equation of the simple regression line (Y = aX + b, where X is each stratum corneum parameter value and Y is the skin viscoelasticity index) are shown in Table 1. [Table 1]

[0024] As shown in Table 1, each stratum corneum parameter value described in (1) to (5) of the present invention had a correlation (|r|≧0.2) with the skin viscoelasticity index described in each of (1) to (5).

[0025] "Multiple regression analysis" For each skin viscoelasticity index, 20 stratum corneum parameter values, including those for which no correlation was observed, were used as explanatory variables for multiple regression analysis. The equation for the multiple regression line (Y = a1X1 + a2X2 + + a 14 X 14 +b, Xn are the stratum corneum parameter values, and Y is the amount of marker protein) are shown in Tables 2 and 3.

[0026] [Table 2]

[0027] [Table 3]

[0028] As shown in Tables 2 and 3, a higher correlation with the skin viscoelasticity index was observed when each of the stratum corneum parameter values ​​described in (1) to (5) of the present invention was combined with other parameter values.

Claims

1. Identify a single cell area in the stratum corneum image, A method for estimating a skin viscoelasticity index, characterized by estimating a skin viscoelasticity index based on one or more correlations selected from (1) to (5) below between one or more stratum corneum parameter values ​​calculated from these and skin elasticity parameters measured by a skin viscoelasticity measuring device. (1) Correlation in which one or more stratum corneum parameters selected from the group consisting of the ratio of the length and width of a rectangle, the standard deviation of the average brightness of a single cell in an image, and the standard deviation of the median brightness of a single cell in an image are used as explanatory variables, and R0 is the maximum suction height as the objective variable. (2) Correlation between the standard deviation of the brightness of each pixel of a cell in the image as the explanatory variable and the final return / maximum suction height after release as the objective variable. (3) Correlation in which one or more stratum corneum parameters selected from the group consisting of the standard deviation of luminance per cell, the minimum luminance per cell, the standard deviation of the standard deviation of luminance of each pixel of a single cell in an image, and the standard deviation of the minimum luminance value of a single cell in an image are used as explanatory variables, and R7: correlation in which the instantaneous return / maximum suction height after the release of negative pressure is used as the objective variable. (4) A correlation in which one or more stratum corneum parameters selected from the group consisting of the standard deviation of the minimum brightness value of one cell in the image and the standard deviation of the standard deviation of the brightness of each pixel of one cell in the image are used as explanatory variables, and R5 is the correlation in which the instantaneous return after releasing negative pressure / instantaneous deformation during suction is used as the objective variable. (5) Correlation in which one or more stratum corneum parameters selected from the group consisting of the area of ​​a single cell, the perimeter of a single cell, the rectangular area circumscribing a single cell, and the standard deviation of the median brightness of a single cell in an image are used as explanatory variables, and R6: Correlation in which the deformation over time during suction / instantaneous deformation during suction are used as objective variables.

2. The method for estimating a skin viscoelasticity index according to claim 1, characterized in that the single-cell region is identified using a machine learning model that has previously been trained on a plurality of training stratum corneum images and single-cell regions based on visual evaluation of each training stratum corneum image.

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