JP7084210B2 - A method for estimating the level of fibrosis of the fibrotic structure surrounding adipocytes, an estimation device and an estimation program, and a method for estimating facial skin motility, an estimation device and an estimation program. - Google Patents

Description

The present invention relates to a method, an estimation device and an estimation program for estimating the fibrosis level of the fibrous structure surrounding adipocytes, and an estimation method, an estimation device and an estimation program for the motility of facial skin.

The aging phenomenon of the skin with aging, that is, the appearance change such as wrinkles, sagging, and spots, is caused by the physiochemical change of the internal structure of the skin. In recent years, there has been much interest in elucidating the mechanism of aging changes in the internal structure of the skin for the purpose of suppressing such skin aging phenomena.

The skin is roughly divided into three layers: the epidermis, the dermis, and the hypodermis. The epidermis can be further classified into four layers, the stratum corneum, the stratum granulosum, the stratum spinosum, and the stratum basale, and the dermis located in the lower layer can be further classified into three layers, the papillary layer, the subpapillary layer, and the reticular layer. The subcutaneous tissue plays a role in supporting these epidermis and dermis.

Most of the subcutaneous tissue is subcutaneous fat composed of adipose lobules in which adipocytes form agglomerates, and has a heat insulating effect and a buffering action against external force. The fat leaflets are surrounded by connective tissues such as collagen fibers and elastin fibers in a mesh pattern to form a fibrous structure surrounding adipocytes.

Palpation has long been used as a method for determining the hardness of the skin, but with the development of ultrasonic elastography technology (for example, Patent Document 1), the physical properties of each layer constituting the skin, especially viscoelasticity, have been used. Quantitative measurement of elasticity is possible.

By the way, as a method for pathologically diagnosing fibrosis of body tissue, biopsy diagnosis (so-called "biopsy") is generally performed. However, it was difficult to perform biopsy frequently because it involved invasion of the subject. It is disclosed that "fibroscan" based on ultrasonic elastography can evaluate liver fibrosis non-invasively (Patent Document 2).

Further, in recent years, with the development of image analysis technology, a technique for analyzing changes in facial expressions has been researched and developed. Patent Document 3 discloses a technique for analyzing the followability of facial skin to changes in facial expressions.

Special Table 2009-539528 Gazette International Publication 2011/08214 Pamphlet Japanese Unexamined Patent Publication No. 2016-194901

An invasive method is known as a method for measuring the fibrosis level of skin adipocytes, but the burden on the subject is large and there is a problem. On the other hand, there is a method of using an ultrasonic diagnostic device as a method of measuring the fibrosis level, but in order to carry out this technique, an expensive analysis device is required, and there is a problem that the capital investment cost increases.
On the other hand, image analysis can be performed only with analysis software, and no expensive capital investment is required.
However, a technique for evaluating the fibrosis level of subcutaneous adipocytes by analyzing facial images or moving images has not been known so far.
In view of these problems, the first problem to be solved by the present invention is a novel technique that makes it possible to estimate the fibrosis level of subcutaneous adipocytes from external information that can be obtained by imaging or the like. Is to provide.

Further, the motility of the facial skin in the change of facial expression can be measured by an image analysis technique (Patent Document 3), but a technique for estimating this from the physiological and anatomical characteristics inside the skin is known. do not have.
Therefore, a second problem to be solved by the present invention is to provide a novel technique for estimating the motility of facial skin in facial expression changes from the physiological and anatomical characteristics inside the skin.

As a result of diligent research by the present inventors, it has been clarified that the motility of facial skin in the change of facial expression decreases with aging. It was also clarified that the collagen fibers surrounding the subcutaneous adipocytes present in the subcutaneous tissue become fibrotic with aging.
In other words, it was clarified that both facial skin motility and fibrosis correlate with age.

It was also found that the motility of the facial skin in the change of facial expression and the fibrosis level of subcutaneous adipocytes both have a correlation with the viscoelasticity of the subcutaneous tissue.

By combining these findings, the present inventors have obtained external information such as by taking images, "movement of facial skin in facial expression changes", and physiological and anatomy inside the skin. It was found that a correlation was established with the specific characteristic "level of fibrosis of subcutaneous adipocytes".

The present inventors have completed the present invention based on these findings.
That is, the present invention that solves the above-mentioned problems utilizes the correlation between the motility of facial skin in facial expression changes and the fibrosis level of subcutaneous adipocytes, and uses the measured value of the motility as an index. It is a method of estimating the fibrosis level, which comprises estimating the fibrosis level.
According to the present invention, the fibrosis level, which is an internal characteristic of the skin, can be estimated from the externally obtainable information of the motility of the skin of the face.

In a preferred embodiment of the present invention, the fiber is obtained from the measured value of the motility by using a regression equation with the measured value of the motility of the skin of the face in the change of facial expression as the explanatory variable and the evaluation value of the fibrosis level as the objective variable. It is characterized by calculating the conversion level.
By using a regression equation prepared in advance, the fibrosis level of subcutaneous adipocytes can be estimated more accurately.

In a preferred embodiment of the present invention, the motility is selected from the followability, elasticity and deformability of the skin of the face in the change of facial expression.

In a preferred embodiment of the present invention, the motility is the followability of the skin of the face in the change of facial expression.
The measured value of the followability is a difference in time at which the movement speeds of at least two markers set at arbitrary positions on the face are maximized in the change of facial expression.
The fibrosis level can be estimated more accurately by acquiring the followability by a quantitative numerical value called "time difference".

In a preferred embodiment of the present invention, the measured value of the followability is the maximum movement speed of the first marker set at an arbitrary position of the cheek in the change of the open facial expression from the expressionless state to the expression with the mouth open. It is characterized in that it is the difference between the time when the movement speed becomes maximum and the time when the movement speed of the second marker set at an arbitrary position on the cheek becomes maximum.
By measuring the movements of the jaw and cheek in the open facial expression change in this way, the followability can be easily quantified, and the fibrosis level can be estimated more accurately.

A preferred embodiment of the present invention is characterized in that the measured value of the followability is a difference calculated by the following steps.
(I) With an arbitrary point on the face as a reference point, the time change of the amount of change in the distance between the reference point and the first marker per unit time is measured, and the time at which the amount of change is maximum is specified. Step (ii) Step of measuring the time change of the amount of change in the distance between the reference point and the second marker per unit time, and specifying the time when the amount of change is maximum (iii) Step (i). ) And the step of obtaining the difference between the time specified in the step (ii) By providing the reference point in this way, the followability can be measured more accurately.

The preferred embodiment of the present invention is characterized in that the measured value of the followability is the slope of the regression line calculated by the following steps.
(I) With an arbitrary point on the face as a reference point, the time change of the amount of change in the distance between the reference point and the first marker per unit time is measured, and the time at which the amount of change is maximum is specified. (Ii') A plurality of second markers are set in parallel in the height direction of the face, and the time change of the amount of change in the distance between the reference point and each of the second markers per unit time is set. The difference between the time specified in the step (iii') and the time specified in the step (i) and the time related to each of the second markers specified in the step (ii) is obtained by measurement. Step (iv) The difference related to each second marker obtained in step (iii') is plotted for each coordinate of each second marker, regression analysis is performed, and the inclination of the regression line is calculated. Step In this way, a plurality of second markers are set, and the inclination of the regression line obtained as a result of performing regression analysis on the delay of the movement of each of the second markers with respect to the movement of the first marker is "measurement of followability". It can also be evaluated as a "value". This makes it possible to measure the followability more accurately and estimate the fibrosis level with high accuracy.

In a preferred embodiment of the present invention, a second marker is set above the line drawn horizontally from the top of the nose when there is no expression. In a more preferred embodiment, a second marker is set above the center line of the line drawn horizontally from the top of the nose and the line drawn horizontally from the outer corner of the eye when there is no facial expression.
The more distant the cheek from the chin, the more markedly the deterioration of followability with aging is observed. Therefore, by setting the second marker above the cheek as in this embodiment, the fibrosis level of subcutaneous adipocytes can be estimated more accurately.

A preferred embodiment of the present invention is characterized in that the followability is measured by using a motion capture moving image taken by setting the marker on the face.
By using the motion capture technique, the followability can be easily measured, and the fibrosis level of subcutaneous adipocytes can be easily estimated.

The present invention also relates to a fibrosis level estimation device that estimates the fibrosis level using the measured value of the motility as an index by utilizing the correlation.
That is, the estimation device of the present invention includes a storage means for storing correlation data showing the correlation and a storage means.
A fibrosis level calculating means for calculating the fibrosis level by collating the motility of the skin of the face with the change in the facial expression of the subject with the correlation data stored in the storage means.
It is characterized by having.

The present invention also relates to the fibrosis level estimation program that estimates the fibrosis level using the measured value of the motility as an index by utilizing the correlation.
That is, the estimation program of the present invention uses a computer.
As a fibrosis level calculation means for calculating the fibrosis level by collating the motility of the facial skin with the change in the facial expression of the subject with the correlation data showing the correlation.
It is characterized by making it work.

In addition, the present invention utilizes the correlation between the motility of facial skin in facial expression changes and the fibrosis level of subcutaneous adipocytes, and uses the fibrosis level of the subcutaneous adipocytes as an index to describe the motility. The present invention also relates to the method for estimating motility, which comprises estimating.
The present invention is the opposite of the above-mentioned method for estimating the fibrosis level of subcutaneous adipocytes. According to the present invention, the motility of facial skin can be estimated from the physiological and anatomical characteristics of the fibrosis level of subcutaneous adipocytes.

In a preferred embodiment of the present invention, the evaluation value of the fibrosis level of subcutaneous adipocytes is used as an explanatory variable, and a regression equation with facial skin motility in facial expression change as an objective variable is used to determine the fibrosis level of subcutaneous adipocytes. It is characterized in that the motility is calculated from the evaluation value.
By using the regression equation in this way, it is possible to more accurately estimate the motility of the facial skin in the change of facial expression.

A preferred embodiment of the present invention is characterized in that the fibrosis level of the subcutaneous adipocytes is measured by an ultrasonic diagnostic apparatus.

A preferred embodiment of the present invention is characterized in that an echo image of a subcutaneous tissue is acquired by an ultrasonic diagnostic apparatus, a histogram is generated from the image, and the fibrosis level of subcutaneous adipocytes is calculated as the skewness of the histogram. ..
By using the skewness calculated from the histogram as an index, the fibrosis level of subcutaneous adipocytes can be objectively evaluated, and the motility can be estimated more accurately.

The present invention also relates to the motility estimation device that estimates the motility measurement value using the fibrosis level as an index by utilizing the correlation.
That is, the estimation device of the present invention includes a storage means for storing correlation data showing the correlation and a storage means.
A motility calculating means for calculating the motility by collating the fibrosis level with the correlation data stored in the storage means.
It is characterized by having.

The present invention also relates to the motility estimation program that estimates the motility measurement value using the fibrosis level as an index by utilizing the correlation.
That is, the estimation program of the present invention uses a computer.
As a motility calculation means for calculating the motility by collating the fibrosis level with the correlation data showing the correlation.
It is characterized by making it work.

According to the present invention, the fibrosis level of subcutaneous adipocytes can be estimated from the motility of the skin of the face.
Further, according to the present invention, the motility of facial skin can be estimated from the fibrotic level of subcutaneous adipocytes.

The figure which shows the position of the reference point, the 1st marker and the 2nd marker set at the time of measuring the followability. A graph in which the coordinates of the second marker are on the horizontal axis, and the difference in time between the first marker and the second marker at the maximum change amount per unit time is on the vertical axis. The straight line in the graph represents the regression line. The figure which shows the point which is set at the time of the measurement of elasticity, and the distance between the point which should calculate the increase by a change of a facial expression. It is a hardware block diagram which shows one Embodiment of the fibrosis level estimation apparatus of this invention. It is a hardware block diagram which shows one Embodiment of the kinetic estimation apparatus of this invention. It is a reference photograph obtained by an electron microscope used for evaluating the degree of fibrosis of subcutaneous adipocytes. The photograph with the lowest degree of progression of fibrosis is a photograph having a score of 1, and the photograph with the highest degree of progression of fibrosis is a photograph with a score of 5. It is a scatter plot which shows the result of observing the subcutaneous adipocyte of 9 donors and scoring based on the reference photograph. It is a photograph showing the position of a marker and the change in facial expression in the motion capture analysis of Test Example 2. It is a graph which took the average value for each age separately for point 1 to point 7, and plotted this. It is a boxplot created by statistically processing the elasticity for each age group and a graph showing a regression line. It is an image which shows the distribution of viscoelasticity in the internal cross section of the skin obtained by the elastography analysis. It is a graph which shows the result of the regression analysis about the analysis result of the test example 2 and the test example 3. It is a schematic diagram of a skin model. It is a figure which shows the outline of FEM analysis. It is a figure which shows the position which measures the displacement in the Z direction in FEM analysis. It is a graph which shows the result of FEM analysis which plotted the time in the horizontal axis, and the displacement in the Z direction on the vertical axis. 6 is an imaging image showing the distribution of displacement in the Z direction in the XX cross section of the skin model over time. It is an image showing the fibrosis state in the subcutaneous fat layer obtained by ultrasonic analysis, and the histogram obtained by image processing. (A) Image with low degree of fibrosis, (B) Image with high degree of fibrosis, (C) Image with high skewness of histogram, (D) Image with low skewness of histogram It is a graph which shows the result of the regression analysis about the analysis result of Test Example 6.

<1> Method for Estimating Fibrosis Level of Subcutaneous Adipocytes Hereinafter, embodiments of the present invention will be described in detail.
There is a negative correlation between the motility of facial skin in facial expression changes (hereinafter, also simply referred to as motility) and the fibrotic level of the fibrotic structure surrounding adipocytes (hereinafter, also simply referred to as fibrotic level). Is established. That is, the smaller the fibrosis level, the better the motility of the skin on the face.
The present invention utilizes this correlation to estimate the fibrotic level of the fibrotic structure surrounding adipocytes from motility.

"Facial skin motility in facial expression changes" refers to the way the skin surface moves with facial expression changes. Specific examples of the motility include the followability of the facial skin in the change of facial expression (hereinafter, also simply referred to as followability) and the elasticity of the skin of the face in the change of facial expression (hereinafter, also simply referred to as elasticity). Further, as the motility, the deformability of the skin of the face due to the change of facial expression (hereinafter, also simply referred to as deformability) can be adopted.

"Facial skin followability in facial expression change" is the degree of delay in facial skin movement that changes in accordance with facial expression change. When a facial expression changes, the skin on the face changes in lagging with the movement, but the smaller the degree of the lag, the better the followability.

The followability can be quantitatively evaluated by observing any two points on the face when the facial expression changes and measuring the degree of deviation in the timing of the movements of these two points.
More specifically, the followability can be quantitatively measured as the difference in the time at which the movement speeds of at least two markers set at arbitrary positions on the face are maximized in the facial expression change.

The two markers set when measuring the followability can be arbitrarily set, but the position of the face that moves most remarkably in the facial expression change is the first marker, and the position of any other face is the second marker. It is preferable to set the markers and measure the difference in the time when the movement speeds of these two markers are maximized.

As the "facial expression change" to be performed by the subject in the measurement of the followability, a change in the open facial expression from the expressionless state to the facial expression with the mouth open can be particularly preferably exemplified.
In this case, it is preferable to set the first marker 1 at an arbitrary position on the jaw. More preferably, the first marker 1 is set near the tip of the jaw (FIG. 1).

On the other hand, it is preferable to set the second marker at an arbitrary position on the cheek (FIG. 1). The followability may be measured by the second marker 21 set below the line 41 drawn horizontally from the top of the nose when there is no expression, but the second marker set above the line 41 is preferable. 22, more preferably, the followability is measured based on the second marker 23 set above the center line 42 of the line 41 and the line 43 drawn horizontally from the outer corner of the eye (FIG. 1).

The followability is preferably measured by the three steps (i) to (iii) described below.
(I) Using any point on the face as a reference point, measure the time change of the amount of change in the distance between the reference point and the first marker per unit time, and specify the time when the amount of change is maximum. Step (ii) The step of measuring the time change of the amount of change in the distance between the reference point and the second marker per unit time, and specifying the time when the amount of change is maximum (iii). Step for obtaining the difference between the time specified in the step and the time specified in the step (ii) The following, each step will be described in detail.

In the step (i), an arbitrary point on the face is set as a reference point 3, and the time change of the change amount V1 of the distance L1 between the reference point 3 and the first marker 1 per unit time is measured and the change amount is measured. The time at which V1 is maximized is specified (see FIG. 1).
In this way, by setting an arbitrary point on the face as the reference point 3 and capturing the movements of the first marker 1 and the second marker 2 at the distance from the reference point 3, the movement of the head in the change of facial expression is affected by the movement of the head. Relative evaluation of each movement of the first marker 1 and the second marker 2 becomes possible without being performed.

The reference point 3 is preferably set to a place where the skin does not move or has little movement in the change of the open facial expression.
Since the skin of the forehead is difficult to move due to the change in facial expression, it is preferable to set the reference point 3 at an arbitrary position of the forehead, more preferably the upper part of the forehead, and more preferably near the hairline (FIG. 1).

In the step (ii), the time change of the change amount V2 per unit time of the distance L2 between the reference point 3 and the second marker 2 described above is measured, and the time when the change amount V2 becomes maximum is specified (i). FIG. 1 right side when viewed from the front).
As a matter of course, the reference point 3 in the step (i) and the step (ii) is the same.

In the step (iii), the difference between the time specified in the step (i) and the time specified in the step (ii) is obtained. In the step (i) and the step (ii), a graph in which the change amount V1 and the change amount V2 are plotted over time may be created so that the difference can be easily obtained visually.

Further, the followability may be measured by the four steps (i), (iii'), (iii') and (iv) described below.
(I) With an arbitrary point on the face as a reference point, the time change of the amount of change in the distance between the reference point and the first marker per unit time is measured, and the time at which the amount of change is maximum is specified. (Ii') A plurality of second markers are set in parallel in the height direction of the face, and the time change of the amount of change in the distance between the reference point and each of the second markers per unit time is set. The difference between the time specified in the step (iii') and the time specified in the step (i) and the time related to each of the second markers specified in the step (ii) is obtained by measurement. Step (iv) The difference related to each second marker obtained in step (iii') is plotted for each relative position on the face on which each second marker is set, and regression analysis is performed. Steps for calculating the slope of the regression line Each step will be described in detail below.

The embodiment of step (i) in this embodiment is the same as the other embodiment described above. A feature of this embodiment is that a plurality of second markers are set in parallel in the height direction of the face, and the deviation of the movement timing of each of the second markers from the first marker is measured. A specific description will be given with reference to FIG.

In the present embodiment, the second markers 21 to 23 are set in parallel in the height direction of the face (left side of FIG. 1 front view). In the step (ii'), the time change of the amount of change V21 of the distance L21 between the reference point 3 and the second marker 21 per unit time, and the distance L22 between the reference point 3 and the second marker 22. The time change of the change amount V22 per unit time and the time change of the change amount V23 of the distance L23 between the reference point 3 and the second marker 23 per unit time are measured, and the change amount V21 to Identify the time at which each of the 23 is maximal.

In the step (iii'), the difference between the time specified in the step (i) and the time related to each of the second markers specified in the step (ii) is obtained. Specifically, the difference between the time when the change amount V1 becomes maximum and the time when the change amounts V21 to 23 become maximum is obtained.

In the step (iv), the difference relating to each second marker is plotted for each relative coordinate of the face on which each second marker is set, regression analysis is performed, and the slope of the regression line is calculated. do.
Specifically, the difference between the time when the amount of change V1 becomes maximum and the time when the amount of change V21 to 23 becomes maximum is plotted on the vertical axis, and the coordinates of the respective second markers are plotted on the horizontal axis (FIG. 2). Since the second marker is set in parallel in the height direction on the face, the "coordinates" here are the coordinates in the height direction.
As a matter of course, the vertical axis and the horizontal axis may be interchanged for plotting.

The method of specifying the coordinates of the second marker is not limited. For example, the relative distance with respect to the first marker or the reference point may be specified as "coordinates".
Further, when the second markers are set at equal intervals in the height direction, it is not necessary to determine the coordinates of the respective second markers as specific numerical values and plot them on the graph. In this case, the coordinates of each second marker may be plotted at equal intervals in the horizontal axis direction (FIG. 2).

After plotting on the graph, perform regression analysis. The method of regression analysis is not particularly limited, but the least squares method can be preferably exemplified.
The slope of the regression line obtained by the regression analysis (the numerical value of "a" in FIG. 2) is used as the measured value of the followability.

The left side of FIG. 1 shows a form in which three points of the second marker are set, but the present invention is not limited to this, and is preferably 3 points or more, more preferably 5 points or more, still more preferably 7. Set a second marker above the point.

Of the second markers to be set in plurality, one point or two or more points are preferably set above the line 41, and more preferably set above the line 42 (FIG. 1).
Further, it is preferable to set a second marker on either the upper side or the lower side of the line 41 (FIG. 1).
Thereby, the accuracy of the regression analysis in the step (iv) can be improved.

In the measurement of followability, the movement of each marker accompanying the change in the facial expression of the subject may be measured by any known method. A method of measuring based on a moving image including a change in the facial expression of a subject, such as an optical flow method or a motion capture method, can be preferably exemplified.
In this case, an image obtained by capturing a moving image of the face to be evaluated with a general camera device may be used, but it is preferable that the image has a resolution sufficient to withstand image analysis.

In general, a moving image is composed of a series of a large number of still images (frames), and the smoothness of the movement is represented by a frame rate representing the number of frames per unit time. Here, it is preferable to acquire the amount of change of the marker per unit time and acquire a moving image having a frame rate equal to or higher than that at which the maximum value can be specified.

An example of a form in which followability is measured by motion capture will be described. First, a reflection marker for motion capture is attached in advance to the positions of the reference point 3, the first marker 1, and the second marker 23 on the subject's face (FIG. 1). In that state, the subject is made to perform an opening facial expression change, and a moving image including the facial expression change is taken by a plurality of cameras. Then, by analyzing this moving image, changes in the three-dimensional coordinates of each marker are tracked, and the time at which the amount of change V1 per unit time of the distance L1 becomes maximum and the change per unit time of the distance L2. The time when the quantity V2 becomes maximum is specified, and the difference between these times, that is, the measured value of the followability is calculated.

Further, the "stretchability of the skin of the face due to the change of facial expression" means the ease of stretching of the skin when the change of facial expression occurs. For example, when there is a change in facial expression that stretches the skin of the face, the higher the ratio of the increase in the distance in the stretch direction in a certain region to the increase in the distance in the entire stretch direction, the more "excellent in elasticity". Can be evaluated.

The elasticity can be quantified by setting arbitrary three or more points on the skin in a substantially linear shape and calculating the distance between the points in the facial expression change.
Specifically, first, for all the points set on the face, the sum of the distances between the points adjacent to each other, which is increased by the change in facial expression, is calculated. At the same time, for some points existing in a specific area of the face, the sum of the distances between adjacent points increased by the change in facial expression is calculated. Then, by dividing the latter numerical value by the former numerical value, the elasticity can be quantitatively measured.

An embodiment of the method for quantifying elasticity will be described in more detail with reference to FIG.
In this embodiment, seven points are set on the cheeks of the face in a substantially straight line (FIG. 3). The points set on the cheeks are not particularly limited to seven.

For convenience of explanation, as shown in FIG. 3, the distances between points adjacent to each other are set to X1 to X6, respectively.
In the present embodiment, the increase in the distances (ΔX1 to ΔX6) of each of X1 to X6 after the opening facial expression is changed from the time of no expression (left in FIG. 3) (right in FIG. 3) is calculated.
Then, the ratio of the sum of the increases in the distance to the lower part of the cheeks (ΔX4 + ΔX5 + ΔX6) to the sum of the increases in the distance of the entire cheeks (ΔX1 + ΔX2 + ... ΔX6) is calculated. The value calculated by this calculation can be evaluated as a quantitative value of "stretchability" (see the formula below).

Elasticity = (ΔX4 + ΔX5 + ΔX6) / (ΔX1 + ΔX2 + ΔX3 + ΔX4 + ΔX5 + ΔX6)

For the quantitative measurement of elasticity, a method such as an optical flow method or a motion capture method that can be used for the measurement of followability described above can be applied.

By the way, a surface (three-dimensional) includes a plurality of points (zero-dimensional) and lines (two-dimensional) as elements.
Here, the above-mentioned followability and elasticity are parameters focusing on the points (zero-dimensional) set on the face or the distance between the points (two-dimensional), and the motility of the skin of the face is measured by these parameters. Can be done.
Therefore, it can be said that it is possible to measure the motility of the skin of the face even when the surface on the skin of the face including points and lines as elements is measured.

That is, in the present invention, the deformability of the skin of the face due to the change in facial expression, more specifically, the method of deformation (distortion method) of the region set at an arbitrary position of the face, which changes due to the change in facial expression, is measured. Therefore, it can be used as a form for measuring the motility of the skin of the face.

The specific method for measuring the deformability is not particularly limited. For example, a method of acquiring the shape of an arbitrary region set on the skin of the face as an image before and after the facial expression change by an optical flow method, a motion capture method, or the like, and performing distortion analysis / deformation analysis on the shape can be exemplified.

It is also preferable to measure the motility for the acquisition of data used for creating a regression equation or a regression model showing the correlation between the motility and the fibrosis level by the above-mentioned method.
More specifically, the motility of a statistically significant number of subjects is measured by the method described above, and the fibrosis level of subcutaneous adipocytes is measured for the same subject by the method described later. Based on these measured values, a regression equation or regression model with motility as the explanatory variable and fibrosis level as the objective variable is created.

<2> Method for estimating facial skin motility As described above, a negative correlation is established between facial skin motility and the fibrotic level of the fibrotic structure surrounding adipocytes. The present invention utilizes such a correlation to estimate motility from the level of fibrosis.
The above correlation is preferably expressed by an equation or model. As the equation or model, a simple regression equation or a simple regression model is preferably mentioned.

The method for evaluating the level of fibrosis is not particularly limited.
As an invasive method, there is a method of calculating a relative evaluation value using a photo scale. More specifically, a plurality of images of subcutaneous adipocytes having different levels of fibrosis are prepared in advance. Using this as a reference photograph, a score is given to the image of subcutaneous adipocytes collected from the subject.

Since the invasive method imposes a burden on the subject, the fibrosis level of subcutaneous adipocytes is preferably evaluated by a non-invasive method.
As a non-invasive method, a method using an ultrasonic diagnostic apparatus can be mentioned. More specifically, the subcutaneous fat layer portion is cut out from the image of the tomographic surface of the skin obtained by the ultrasonic diagnostic apparatus and used as an image for analysis. The fibrosis level of the acquired image for analysis can be evaluated from the feature amount obtained by using the image processing software.
Examples of such features include parameters calculated by grayscale, histogram, and binarize an image.

As the ultrasonic elastography apparatus, for example, "ARIETTA E70" or "Noblue" manufactured by Hitachi, Ltd., "Accuson S2000e" manufactured by Siemens Healthcare, or the like can be used.

In the present invention, it is preferable to make an image for analysis into a histogram and adopt the skewness of this histogram as an evaluation value of the fibrosis level.
Since the histogram with low skewness (showing a substantially normal distribution) shows that the strain is small, it can be seen that the fibrosis level of subcutaneous adipocytes is high. On the contrary, from the histogram with a large skewness (showing a non-normal distribution), it can be discriminated that the fibrosis level of subcutaneous adipocytes is low.

As the image processing software, any known software such as the open source "ImageJ" may be used.

In addition, in the above <1> item and this item, the method of measuring or evaluating the motility or fibrosis level as an index for estimation was explained. This description is also valid for methods of measuring or assessing motility or fibrosis levels for creating regression equations or models.

<3> Device for estimating the fibrosis level of subcutaneous adipocytes Hereinafter, a device for estimating the level of fibrosis of subcutaneous adipocytes will be described with reference to FIG. The apparatus for estimating the fibrosis level of subcutaneous adipocytes of the present invention is an apparatus for carrying out the method for estimating the fibrosis level of subcutaneous adipocytes described in the above item <1>. Therefore, the above description of item <1> is also valid for the following device for estimating the fibrosis level of subcutaneous adipocytes.

The device 1 for estimating the fibrosis level of subcutaneous fat cells of the present invention is a storage means for storing fibrosis level correlation data showing a correlation between facial skin motility and the fibrosis level of subcutaneous fat cells in facial changes. 121 is provided with a fibrosis level calculation means 112 for calculating the fibrosis level by collating the motility of the skin of the face with respect to the facial expression change of the subject with the fibrosis level correlation data stored in the storage means 121. ..

As shown in FIG. 4, the device 1 for estimating the fibrosis level of subcutaneous adipocytes includes a motility measuring unit 13, a ROM (Read Only Memory) 12 including a storage means 121, and a CPU (Central) including a fibrosis level calculating means 112. It has a Processing Unit) 11 and a fibrosis level display unit 14.

In a preferred embodiment of the present invention, it is preferable to provide a quantifying means 111 for quantifying the motility of the skin of the face in the facial expression change of the subject measured by the motility measuring unit 13. The CPU 11 includes a digitizing means 111.

The fibrosis level display unit 14 is a display that displays an estimated value of the fibrosis level of subcutaneous adipocytes calculated by the fibrosis level calculation means 112.

The device 1 for estimating the fibrosis level of subcutaneous adipocytes of the present invention having such a configuration can easily measure the fibrosis level of subcutaneous adipocytes of the subject only by measuring the motility of the skin of the face in the facial expression change of the subject. Can be calculated.

In another embodiment, instead of the motility measuring unit 13 and the digitizing means 111, a motility input unit for inputting a separately measured motility measurement value may be provided.

<4> Subcutaneous adipocyte fibrosis level estimation program The present invention also relates to a subcutaneous adipocyte fibrosis level estimation program that causes a computer to execute the above-mentioned subcutaneous adipocyte fibrosis level estimation method. The program of the present invention will be described with reference to FIG. 4 in order to correspond to each means in the CPU included in the above-mentioned device for estimating the fibrosis level of the present invention.

The program for estimating the fibrosis level of subcutaneous adipocytes of the present invention is a fibrosis level correlation data showing the correlation between the motility and the fibrosis level of subcutaneous adipocytes in the motility of the facial skin in the facial expression change of the subject. It is characterized in that a computer functions as a fibrosis level calculation means 112 for collating and calculating the fibrosis level.

The fibrosis level estimation program of the present invention is preferably configured to allow the computer to function as the quantifying means 111, as shown in the block diagram of FIG.

<5> Estimating device for facial skin motility in facial expression changes Hereinafter, an explanation will be added for an estimation device for facial skin motility in facial expression changes with reference to FIG. The motility estimation device of the present invention is a device for carrying out the method for estimating the motility of the facial skin in the facial expression change described in the item <2> above. Therefore, the explanation of the item <2> above is also valid for the following motility estimation device.

The motility estimation device 2 of the present invention includes a storage means 221 for storing motility correlation data showing a correlation between the motility of facial skin and the fibrosis level of subcutaneous fat cells in a change in facial expression, and the subcutaneous fat of a subject. The motility calculation means 212 for calculating the motility by collating the fibrosis level of the cell with the motility correlation data stored in the storage means 221 is provided.

As shown in FIG. 5, the motility estimation device 2 includes a fibrosis level measuring unit 23, a ROM 22 including a storage means 221, a CPU 21 including a motility calculating means 212, and a motility display unit 24.

In a preferred embodiment of the present invention, it is preferable to provide a quantifying means 211 for quantifying the fibrosis level of the subject measured by the fibrosis level measuring unit 23. The CPU 21 includes a digitizing means 211.

The motility display unit 24 is a display that displays an estimated value of motility calculated by the motility calculation means 212.

The motility estimation device 2 of the present invention having such a configuration can easily calculate the motility of the facial skin in the facial expression change of the subject only by measuring the fibrosis level of the subcutaneous adipocytes of the subject. can.

In another embodiment, instead of the fibrosis level measuring unit 23 and the quantifying means 211, a fibrosis level input unit for inputting a separately measured fibrosis level measurement value may be provided.

<6> Estimating Program of Facial Skin Motility in Facial Expression Changes The present invention also relates to a motility estimation program in which a computer executes the above-mentioned motility estimation method. The program of the present invention will be described with reference to the reference numeral of FIG. 5 in order to correspond to each means in the CPU included in the above-mentioned motility estimation device of the present invention.

The motility estimation program of the present invention collates the fibrosis level of subcutaneous adipocytes of a subject with motility correlation data showing the correlation between motility and the fibrosis level of subcutaneous adipocytes to obtain the motility. As the motility calculation means 212 to be calculated, it is characterized in that a computer is made to function.

As shown in the block diagram of FIG. 5, the motility estimation program of the present invention is preferably configured to function as a digitizing means 211.

<Test Example 1> Observation of changes in collagen structure with aging Subcutaneous adipocytes in the subcutaneous tissue provided by 9 donors aged 20 years or older were photographed with a scanning electron microscope. This electron micrograph was evaluated by a skilled evaluator and scored 1-5 for the degree of fibrosis of subcutaneous adipocytes. The evaluation was performed based on a five-stage reference photograph (FIG. 6) in which the degree of progress of fibrosis was different. The results are shown in FIG.

As shown in FIG. 7, the age of the donor and the degree of fibrosis of subcutaneous adipocytes were significantly correlated. This result indicates that the fibrosis of subcutaneous adipocytes progresses with aging.

<Test Example 2> Measurement of facial skin motility in changes in facial expression (1) Measurement of followability 20 Japanese women in their 20s and 60s, 20 in each generation, were used as subjects, for a total of 100 subjects. As shown in FIG. 8 on the subject's face, one point (reference point) above the forehead (near the hairline), one point on the chin (point 0), and seven points parallel to the cheek height direction (points 1 to 1). The reflection marker for motion capture of point 7) was attached.
As shown in FIG. 8, the subject was asked to make a facial expression change (opening facial expression change) extending in the vertical direction from the expressionless state (Fig. 8 left) to the open state (Fig. 8 right), and this was performed by three cameras. A moving image was taken (30 fps), and the motion information of each marker was acquired.

In order to perform more accurate analysis, each generation 12 from 100 subjects based on 3 points: 1) facial expression intensity, 2) eye-mouth movement synchronization, and 3) facial expression expression timing. A total of 60 people were selected for analysis.

The motion analysis of each marker was performed as follows.
First, the amount of change per unit time at the distance from the reference point to points 0 to 7 was measured over time, and the time from the start of facial expression expression to the time when each amount of change became maximum was measured. After that, the difference between the time when the amount of change in the distance from the reference point to point 0 per unit time is maximum and the time when the amount of change in the distance from the reference point to points 1 to 7 per unit time is maximum (followability). ) Was calculated. In this test, the time difference was evaluated as the frame difference (Δ frame) of the moving image.

For the followability obtained in this way, the average values for each age group were separately taken for points 1 to 7, and this was plotted on a graph. Regression analysis was performed on the obtained data and a regression line was drawn. The results are shown in FIG.

As shown in FIG. 9, in the 20s and 30s, there is no delay in movement with respect to the jaw (point 0) from the lower part of the cheek (point 7 in FIG. 8) to the upper part (point 1 in FIG. 8). On the other hand, it was shown that after the 40s, the movement of the skin was delayed, that is, the followability was lowered, as the cheeks were farther from the chin.

The slope of the regression line shown in FIG. 9 was used as the measured value of the followability and used for the regression analysis shown in Test Example 3.

(2) Measurement of elasticity Using the motion capture data obtained by the above followability measurement test, the elasticity of the skin of the face due to changes in facial expressions was also measured.
Specifically, with respect to points 1 to 7, the distance between points adjacent to each other is measured in the expressionless state (Fig. 8 left) and the open state (Fig. 8 right), and the distance increased by the change in the open expression. Was calculated.
Then, the elasticity was calculated by dividing the sum of the increase in the distance between points for the lower cheek (points 4 to 7) by the sum of the increase in the distance between points for the whole (points 1 to 7).

The elasticity obtained in this way was plotted on a graph for each age group, a boxplot was created, regression analysis was performed, and a regression line was drawn. The results are shown in FIG.
As shown in FIG. 10, it was clarified that the elasticity of the skin of the face in the change of facial expression decreases with age.

<Test Example 3> Analysis of skin internal physical properties by elastography The viscoelasticity (strain) inside the skin was measured using elastography (Hitachi) for a total of 18 subjects who performed the motion capture analysis of Test Example 2. (Fig. 11). Regarding the measurement of viscoelasticity, the measurement area is divided into a total of four layers: the surface layer of the skin (dermis and dermis), the upper layer of the subcutaneous tissue, the middle layer of the subcutaneous tissue, and the lower layer of the subcutaneous tissue, and the relative viscoelasticity of each layer is determined. Calculated. The upper layer of the subcutaneous tissue, the middle layer of the subcutaneous tissue, and the lower layer of the subcutaneous tissue were set by dividing the subcutaneous tissue at a ratio of 1: 2: 1 in the depth direction.

In addition, viscoelasticity refers to a property that combines both viscosity and elasticity. Therefore, in the evaluation of viscoelasticity, both viscosity and elasticity are evaluated. However, it is difficult to clearly distinguish between viscosity and elasticity in living tissues, and viscoelasticity is generally evaluated mainly by elastic modulus (Young's modulus).
In addition, viscoelasticity can be evaluated by "strain" based on Hooke's law (Equation 1 below). Therefore, in this test example, "strain" was measured with respect to the viscoelasticity inside the skin.

式１

Equation 1

<Test Example 4> Regression analysis A regression analysis was performed on the measured value of followability (inclination of the regression line) obtained in Test Example 2 and the measured value of viscoelasticity of the upper layer of the subcutaneous tissue obtained in Test Example 3. The results are shown in FIG.
As shown in FIG. 12, it was clarified that a positive correlation was established between the followability of the skin of the face in the change of facial expression and the viscoelasticity of the subcutaneous tissue.

<Test Example 5> Verification test Part of the skin was cut out for the "positive correlation between the followability of facial skin in facial expression changes and the viscoelasticity of the subcutaneous tissue" obtained as a result of Test Examples 2-4. It was verified by FEM analysis targeting a skin model (10 cm × 5 cm × 1.4 cm) composed of a rectangular multi-layered structure simulating a vertical portion.

The skin model was constructed by laminating materials having different Young's moduli (FIG. 13). The thickness of the layer imitating the dermis was set to 2 mm, the thickness of the upper layer of the subcutaneous tissue was set to 3 mm, the thickness of the middle layer of the subcutaneous tissue was set to 6 mm, and the thickness of the lower layer of the subcutaneous tissue was set to 3 mm (FIG. 13).
In this test, a skin model that imitated the characteristics of the skin of the young layer and a skin model that imitated the characteristics of the skin of the old layer were created and analyzed for each.
Table 1 shows the physical characteristics of each layer of the skin model. As shown in Table 1, Poisson's ratio and density are common in young and old skin models.

It is assumed that the skin on the cheek is moving through the ligament connected to the deep muscle, and a pillar corresponding to the ligament is connected to a part of the layer imitating the lower layer of the subcutaneous tissue, and this pillar is X. The skin model was moved by displacing in the direction (Fig. 14). At this time, the side surface of the skin model was fixed so as not to be displaced.
The movement by the pillar imitating the ligament was performed so as to be displaced in the X direction for 3 seconds at a speed of 0.5 cm / s and then stopped for 1 second. During this movement, the displacement of the dermis-like layer (top layer) in the Z direction was plotted over time.
The point where the displacement in the Z direction was observed was a portion located behind the displacement direction of the ligament from the portion directly above the portion to which the pillar imitating the ligament was connected (FIG. 15). .. The results are shown in FIGS. 16 and 17.

As shown in FIGS. 16 and 17, the skin model of pattern 2 (old age), which is inferior (hard) to the viscoelasticity of the upper layer of the subcutaneous tissue as compared with the young skin model, has a small displacement in the Z direction and Z. It was found that the timing of the displacement in the direction was late.

Summarizing the above results, the skin model in which the subcutaneous tissue imitates hard skin has a delayed timing of deformation in the Z direction (the followability deteriorates) as compared with the skin model in which the subcutaneous tissue imitates soft skin. )It has been shown.
The results of Test Example 4 support the results of Test Examples 2 to 4 that there is a positive correlation between the strain of the subcutaneous tissue (that is, viscoelasticity) and the followability of the facial skin in the change of facial expression. Is.

<Test Example 6> Analysis of skin internal physical properties by elastography For 140 subjects, elastographic images of the inside of the skin were acquired using elastography (Hitachi Seisakusho), and viscoelasticity (strain) was measured. Regarding the measurement of viscoelasticity, the measurement area is divided into a total of four layers: the surface layer of the skin (dermis and dermis), the upper layer of the subcutaneous tissue, the middle layer of the subcutaneous tissue, and the lower layer of the subcutaneous tissue, and the relative viscoelasticity of each layer is determined. Calculated. The upper layer of the subcutaneous tissue, the middle layer of the subcutaneous tissue, and the lower layer of the subcutaneous tissue were set by dividing the subcutaneous tissue at a ratio of 1: 2: 1 in the depth direction.

In addition, a subcutaneous fat portion was cut out from an ultrasonic image of the same subject, and a histogram was created using this as an image for analysis using image analysis software (ImageJ). The skewness of this histogram was calculated using image analysis software (ImageJ) (FIG. 18). In the histogram shown in FIG. 18, the skewness in the left figure showing an image with a low degree of fibrosis was 1.62, and the skewness in the right figure showing an image with a high degree of fibrosis was 0.84. ..

<Test Example 7> Regression analysis A regression analysis was performed on the measured values of the viscoelasticity of the upper layer of the subcutaneous tissue obtained in Test Example 6 and the skewness indicating the fibrosis level of the subcutaneous adipocytes obtained in the same test. The results are shown in FIG.
As shown in FIG. 19, a positive correlation is established between the viscoelasticity of the subcutaneous tissue and the skewness of the histogram of the ultrasonic image of the subcutaneous fat layer.
Since the skewness decreases as the fibrosis level increases, the results shown in FIG. 19 clearly show that a negative correlation is established between the viscoelasticity of the subcutaneous tissue and the fibrosis level of the subcutaneous adipocytes. It became.

<Discussion>
The results of Test Example 1 show that the collagen fibers surrounding the subcutaneous adipocytes present in the subcutaneous tissue become fibrotic with aging.
In addition, the results of Test Example 2 show that the motility (following ability, elasticity) of the facial skin in the change of facial expression decreases with aging.
That is, from Test Examples 1 and 2, it was clarified that both the motility of the facial skin and the level of fibrosis correlate with age.

Furthermore, the results of Test Example 4 show that a positive correlation is established between the followability of the skin of the face in the change of facial expression and the viscoelasticity of the subcutaneous tissue.
On the other hand, the results of Test Example 7 show that a negative correlation is established between the viscoelasticity of the subcutaneous tissue and the fibrosis level of the subcutaneous adipocytes.
That is, from Test Examples 4 and 7, it was clarified that the followability of facial skin in the change of facial expression and the fibrosis level of subcutaneous adipocytes both have a correlation with the viscoelasticity of the subcutaneous tissue. ..

These results indicate that a correlation is established between "following of facial skin in facial expression changes" and "fibrotic level of subcutaneous adipocytes".

In addition, elasticity is a parameter that indicates the movement of the skin of the face as well as followability, and since both are negatively correlated with age (Test Example 2), not only followability but also elasticity is fibrotic. It can be said that there is a correlation with the level.

Taking these into consideration comprehensively, it was shown that the above-mentioned test examples can estimate the fibrosis level of subcutaneous adipocytes using the motility of facial skin in facial expression changes, including followability and elasticity, as an index. Similarly, it was shown that the motility of facial skin in facial expression changes can be estimated using the fibrosis level of subcutaneous adipocytes as an index.

The present invention can be applied to skin analysis techniques.

1 Fibrosis level estimation device 11 CPU
111 Quantifying means 112 Fibrosis level calculating means 12 ROM
121 Storage means 13 Motility measuring unit 14 Fibrosis level display unit 2 Motility estimation device 21 CPU
211 Numerical means 212 Motility calculation means 22 ROM
221 Memory means 23 Fibrosis level measurement unit 24 Motility display unit

Claims (18)

It is characterized by estimating the fibrosis level using the measured value of the motility as an index by using the correlation between the motility of the skin of the face and the fibrosis level of subcutaneous adipocytes in the change of facial expression. The method for estimating the fibrosis level. Using a regression equation with the measured value of facial skin motility in facial expression change as the explanatory variable and the evaluation value of the fibrosis level as the objective variable, the fibrosis level can be calculated from the measured value of the motility. The estimation method according to claim 1, which is characterized. The estimation method according to claim 1 or 2, wherein the motility is selected from the followability, elasticity and deformability of the skin of the face in the change of facial expression. The motility is the followability of the skin of the face in the change of facial expression.
The estimation according to claim 3, wherein the measured value of the followability is a difference in time at which the movement speeds of at least two markers set at arbitrary positions of the face are maximized in the facial expression change. Method. The measured value of the followability is the time when the movement speed of the first marker set at an arbitrary position of the jaw is maximized in the change of the open facial expression from the expressionless state to the expression with the mouth open, and the cheek. The estimation method according to claim 4, wherein the difference from the time at which the movement speed of the second marker set at an arbitrary position becomes maximum. The estimation method according to claim 5, wherein the measured value of the followability is a difference calculated by the following steps.
(I) With an arbitrary point on the face as a reference point, the time change of the amount of change in the distance between the reference point and the first marker per unit time is measured, and the time at which the amount of change is maximum is specified. Step (ii) Step of measuring the time change of the amount of change in the distance between the reference point and the second marker per unit time, and specifying the time when the amount of change is maximum (iii) Step (i). ) And the difference between the time specified in step (ii) The estimation method according to claim 5, wherein the measured value of the followability is a slope of a regression line calculated by the following steps.
(I) With an arbitrary point on the face as a reference point, the time change of the amount of change in the distance between the reference point and the first marker per unit time is measured, and the time at which the amount of change is maximum is specified. (Ii') A plurality of second markers are set in parallel in the height direction of the face, and the time change of the amount of change in the distance between the reference point and each of the second markers per unit time is set. The difference between the time specified in the step (iii') and the time specified in the step (i) and the time related to each of the second markers specified in the step (ii) is obtained by measurement. Step (iv) The difference related to each second marker obtained in step (iii') is plotted for each coordinate of each second marker, regression analysis is performed, and the inclination of the regression line is calculated. Process The estimation method according to any one of claims 5 to 7, wherein the second marker is set above the line drawn horizontally from the top of the nose when there is no facial expression. A fifth aspect of the present invention, wherein the second marker is set above the center line of the line drawn horizontally from the top of the nose and the line drawn horizontally from the outer corner of the eye when there is no facial expression. The estimation method according to any one of 8. The estimation method according to any one of claims 4 to 9, wherein the followability is measured by using a motion capture moving image taken by setting the marker on the face. The fibrosis level for estimating the fibrosis level using the measured value of the motility as an index by using the correlation between the motility of the skin of the face and the fibrosis level of subcutaneous adipocytes in the change of facial expression. It is an estimation device of
A storage means for storing the correlation data showing the correlation, and
A fibrosis level calculating means for calculating the fibrosis level by collating the motility of the skin of the face with the change in the facial expression of the subject with the correlation data stored in the storage means.
The device for estimating the level of fibrosis. The fibrosis level for estimating the fibrosis level using the measured value of the motility as an index by using the correlation between the motility of the skin of the face and the fibrosis level of subcutaneous adipocytes in the change of facial expression. Is an estimation program for
Computer,
As a fibrosis level calculation means for calculating the fibrosis level by collating the motility of the facial skin with the change in the facial expression of the subject with the correlation data showing the correlation.
The fibrosis level estimation program, characterized by functioning. It is characterized in that the motility is estimated by using the fibrosis level of the subcutaneous adipocytes as an index by utilizing the correlation between the motility of the skin of the face and the fibrosis level of the subcutaneous adipocytes in the change of facial expression. The method for estimating motility. Using a regression equation with the evaluation value of the fibrosis level of subcutaneous adipocytes as the explanatory variable and the motility of the facial skin in the change of facial expression as the objective variable, the motility is calculated from the evaluation value of the fibrosis level of the subcutaneous adipocytes. The estimation method according to claim 13, wherein the estimation is performed. The estimation method according to claim 13, wherein the fibrosis level of the subcutaneous adipocyte is measured by ultrasonic waves. The estimation method according to claim 15, wherein an echo image of the subcutaneous tissue is acquired by ultrasonic waves, a histogram is generated from the image, and the fibrosis level of subcutaneous adipocytes is calculated as the skewness of the histogram. .. Using the correlation between the motility of facial skin in facial expression changes and the fibrosis level of subcutaneous adipocytes, the measured value of the motility is estimated using the fibrosis level as an index. It ’s an estimation device,
A storage means for storing the correlation data showing the correlation, and
A motility calculating means for calculating the motility by collating the fibrosis level with the correlation data stored in the storage means.
The motility estimation device, characterized in that. Using the correlation between the motility of facial skin in facial expression changes and the fibrosis level of subcutaneous adipocytes, the measured value of the motility is estimated using the fibrosis level as an index. It ’s an estimation program,
Computer,
As a motility calculation means for calculating the motility by collating the fibrosis level with the correlation data showing the correlation.
The motility estimation program, characterized in that it functions.

Images