JP2019208904A - Estimation method, estimation device, and estimation program for fibrosis level of fibrous structure enclosing adipose cells and estimation method, estimation device, and estimation program for motility of skin of face - Google Patents

Abstract

To provide a new technology for enabling estimation of a fibrosis level of subcutaneous adipose cells from superficial information capable of being acquired using picture imaging or the like.SOLUTION: By utilizing correlation between a fibrosis level of subcutaneous adipose cells and motility of a skin of a face in an expression change, the fibrosis level is estimated using a measurement value of the motility as an index. In a preferable form of the invention, the motility is selected from followability, elasticity, and deforming of the skin of the face in the expression change. The followability is measured using a motion capture moving picture obtained by imaging the face on which a marker is set.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an estimation method, an estimation device, and an estimation program for a fibrosis level of a fiber structure that wraps fat cells, and an estimation method, an estimation device, and an estimation program for facial skin motility.

  Skin aging due to aging, that is, changes in appearance such as wrinkles, sagging, and spots are caused by physiochemical changes in the internal structure of the skin. In recent years, interest has been focused on elucidating the mechanism of aging changes in the internal structure of skin for the purpose of suppressing such skin aging phenomenon.

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

  Most of the subcutaneous tissue is subcutaneous fat composed of fat lobules in which adipocytes form agglomerates, and has heat retention and buffering action against external force. The fat leaflet is surrounded by a network of connective tissues such as collagen fibers and elastin fibers to form a fiber structure that encloses fat cells.

  Palpation has long been used as a method for judging the hardness of the skin, but due to the development of ultrasonic elastography technology (for example, Patent Document 1), the physical characteristics of each layer constituting the skin, in particular, the viscosity. Quantitative measurement of elasticity is possible.

  By the way, biological tissue diagnosis (so-called “biopsy”) is generally performed as a method of pathologically diagnosing fibrosis of body tissue. However, biopsy is difficult to perform frequently because it involves invasion of the subject. In “Fibroscan” based on the principle of ultrasonic elastography, it is disclosed that evaluation of liver fibrosis can be performed non-invasively (Patent Document 2).

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

JP-T 2009-539528 International Publication 2011/0108214 Pamphlet Japanese Patent Laid-Open No. 2006-194901

As a method for measuring the fibrosis level of skin adipocytes, an invasive method is known, but the burden on the subject is large and problematic. On the other hand, there is a method using an ultrasonic diagnostic apparatus as a method for measuring the fibrosis level. However, in order to implement this technique, there is a problem that an expensive analysis apparatus is required and the capital investment cost increases.
On the other hand, image analysis can be performed as long as there is analysis software, and expensive capital investment is unnecessary.
However, a technique for evaluating the fibrosis level of subcutaneous fat cells by analyzing facial images or moving images has not been known so far.
In view of such 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 acquired by imaging or the like. Is to provide.

The facial skin motility during facial expression changes can be measured by image analysis technology (Patent Document 3), but a technique for estimating this from the physiological and anatomical characteristics inside the skin is known. Absent.
Accordingly, a second problem to be solved by the present invention is to provide a novel technique for estimating facial skin motility in facial expression changes from physiological and anatomical characteristics inside the skin.

As a result of intensive studies by the present inventors, it has been clarified that the facial skin motility in the expression change decreases with aging. It was also clarified that collagen fibers encapsulating subcutaneous fat cells present in the subcutaneous tissue become fibrotic with aging.
In other words, both facial skin motility and fibrosis were found to correlate with age.

  We also found that there is a correlation between facial skin motility and facial adipocyte fibrosis level in facial expression changes and viscoelasticity of the subcutaneous tissue.

  By combining these findings, the present inventors are able to obtain external information that can be acquired by taking images, such as facial skin motility during facial expression changes, and the physiological and anatomical aspects of the skin. We found that there is a correlation with the “fibrosis level of subcutaneous adipocytes”, which is a characteristic of the skin.

The present inventors have completed the present invention based on these findings.
That is, the present invention that solves the above problems uses the correlation between the facial skin motility in the expression change and the fibrosis level of the subcutaneous fat cells as an index of the measured value of the motility. The fibrosis level estimation method, wherein the fibrosis level is estimated.
According to the present invention, it is possible to estimate the fibrosis level, which is an internal characteristic of the skin, from externally obtainable information such as facial skin motility.

In a preferred embodiment of the present invention, using a regression equation in which the measured value of facial skin motility in an expression change is an explanatory variable and the evaluation value of the fibrosis level is an objective variable, It is characterized by calculating a conversion level.
By using a regression equation prepared in advance, the fibrosis level of subcutaneous fat cells can be estimated more accurately.

  In a preferred embodiment of the present invention, the mobility is selected from the followability, stretchability, and deformability of the facial skin when changing facial expressions.

In a preferred embodiment of the present invention, the mobility is the followability of the facial skin in facial expression changes,
The follow-up measurement value is a time difference at which the movement speeds of at least two markers set at arbitrary positions on the face in expression change are maximum.
The fibrosis level can be estimated more accurately by obtaining the followability by a quantitative value called “time difference”.

In a preferred embodiment of the present invention, the measured value of the followability is such that the movement speed of the first marker set at an arbitrary position of the chin in the opening facial expression change from the expressionless state to the facial expression with the mouth open is the maximum. And the time at which the movement speed of the second marker set at an arbitrary position on the cheek is maximized.
In this way, by measuring the movement of the jaw and cheek in the opening facial expression change, the followability can be easily quantified, and the fibrosis level can be estimated more accurately.

In a preferred aspect of the present invention, the measured value of the followability is a difference calculated by the following steps.
(I) Using an arbitrary point on the face as a reference point, the time change of the change amount per unit time of the distance between the reference point and the first marker is measured, and the time when the change amount is maximum is specified (Ii) a step of measuring a time change of a change amount per unit time of a distance between the reference point and the second marker, and specifying a time at which the change amount is maximum (iii) a step (i Step of obtaining the difference between the time specified in step (ii) and the time specified in step (ii) By providing the reference point in this way, the followability can be measured with higher accuracy.

In a preferred aspect of the present invention, the follow-up measurement value is an inclination of a regression line calculated by the following steps.
(I) Using an arbitrary point on the face as a reference point, the time change of the change amount per unit time of the distance between the reference point and the first marker is measured, and the time when the change amount is maximum is specified (Ii ′) A plurality of second markers are set in parallel in the height direction of the face, and a change in the amount of change per unit time of the distance between the reference point and each of the second markers is determined. A step of measuring and specifying a time when the amount of change is maximum (iii ′) A difference between the time specified in step (i) and the time related to each second marker specified in step (ii) is obtained. Step (iv) The difference relating to each second marker obtained in step (iii ′) is plotted for each coordinate of each second marker, regression analysis is performed, and the slope of the regression line is calculated. Step A plurality of second markers are set in this way, and the first It is also possible to evaluate the inclination of the regression line obtained as a result of the regression analysis with respect to the delay of the movement of each of the second markers with respect to the movement of each marker as the “following measurement value”. Thereby, followability can be measured more accurately and the fibrosis level can be estimated with high accuracy.

In a preferred embodiment of the present invention, the second marker is set above a line drawn in the horizontal direction from the top of the nose when there is no expression. In a more preferred form, when there is no expression, the second marker is set above the center line of the line drawn in the horizontal direction from the top of the nose and the line drawn in the horizontal direction from the corner of the eye.
As the position of the cheek is farther from the jaw, the follow-up deterioration with aging is more noticeably observed. Therefore, the fibrosis level of subcutaneous adipocytes can be estimated with higher accuracy by setting the second marker above the cheek as in this embodiment.

In a preferred aspect of the present invention, the follow-up property is measured using a motion capture moving image shot with the marker set on the face.
By using the motion capture technique, the followability can be easily measured, and the fibrosis level of the subcutaneous fat cells can be easily estimated.

The present invention also relates to a fibrosis level estimation apparatus that estimates the fibrosis level by using the correlation and using the measured value of motility as an index.
That is, the estimation apparatus of the present invention includes storage means for storing correlation data indicating the correlation,
The fibrosis level calculation means for calculating the fibrosis level by comparing the facial skin motility in the expression change of the subject with the correlation data stored in the storage means,
It is characterized by providing.

The present invention also relates to the fibrosis level estimation program that uses the correlation to estimate the fibrosis level using the measured value of motility as an index.
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 facial skin motility in the expression change of the subject with the correlation data indicating the correlation,
It is made to function.

Further, the present invention uses the correlation between the facial skin motility in the expression change and the fibrosis level of the subcutaneous fat cells, and the motility using the subcutaneous fibrocyte fibrosis level as an index. It is also related to the method for estimating motility.
The present invention is opposite to the method for estimating the fibrosis level of subcutaneous adipocytes described above. According to the present invention, facial skin motility can be estimated from physiological and anatomical characteristics such as fibrosis level of subcutaneous fat cells.

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

  In a preferred embodiment of the present invention, the fibrosis level of the subcutaneous fat cells is measured by an ultrasonic diagnostic apparatus.

In a preferred embodiment of the present invention, an echo image of a subcutaneous tissue is acquired by an ultrasonic diagnostic apparatus, a histogram is generated from the image, and a fibrosis level of the subcutaneous fat cell is calculated as a skewness of the histogram. .
By using the skewness calculated from the histogram as an index, the fibrosis level of the subcutaneous fat cells can be objectively evaluated, and the motility can be estimated with higher accuracy.

The present invention also relates to the motility estimation apparatus that estimates the measured value of motility using the correlation as an index of the fibrosis level.
That is, the estimation apparatus of the present invention includes storage means for storing correlation data indicating the correlation,
A motility calculating means for calculating the motility by comparing the fibrosis level with the correlation data stored in the storage means;
It is characterized by providing.

The present invention also relates to the motility estimation program that uses the correlation to estimate the measured value of motility using the fibrosis level as an index.
That is, the estimation program of the present invention uses a computer,
As the motility calculating means for calculating the motility by collating the fibrosis level with the correlation data indicating the correlation,
It is made to function.

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

The figure which shows the position of the reference point, the 1st marker, and the 2nd marker which are set in the case of the followability measurement. The graph which makes the horizontal axis the coordinate of a 2nd marker, and makes the vertical axis | shaft the difference of the time when the variation | change_quantity per unit time of a 1st marker and a 2nd marker becomes the maximum. The straight line in the graph represents a regression line. The figure which shows the distance set between the point set in the measurement of elasticity, and the increase by expression change. 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 motility estimation apparatus of this invention. It is the reference | standard photograph acquired by the electron microscope used in order to evaluate the grade of fibrosis of a subcutaneous fat cell. A photograph with the lowest degree of progression of fibrosis is a photograph with a score of 1, and a photograph with the highest degree of progression of fibrosis is a photograph with a score of 5. It is a scatter diagram showing the result of observing the subcutaneous fat cells of nine donors and assigning scores based on a reference photograph. 10 is a photograph showing marker positions and facial expression changes in motion capture analysis of Test Example 2. It is the graph which took the average value for every age separately about point 1 to point 7, and plotted this. It is a graph showing a box-and-whisker diagram created by statistical processing for each age with respect to elasticity and a regression line. It is an imaging image showing distribution of viscoelasticity in the internal cross section of the skin obtained by elastography analysis. It is a graph showing the result of the regression analysis about the analysis result of the test example 2 and the test example 3. FIG. It is a schematic diagram of a skin model. It is a figure showing the outline | summary of FEM analysis. It is a figure showing the position which measures the displacement of the Z direction in FEM analysis. It is a graph showing the result of FEM analysis which plotted time on the horizontal axis and the displacement of the Z direction on the vertical axis. It is an imaging image which shows the distribution of the displacement of the Z direction in the XZ cross section of a skin model with time. It is the image obtained by ultrasonic analysis, the image showing the fibrosis state in a subcutaneous fat layer, and the histogram obtained by image processing. (A) An image with a low degree of fibrosis, (B) An image with a high degree of fibrosis, (C) An image with a high degree of distortion in the histogram, (D) An image with a low degree of distortion in the histogram 10 is a graph showing the results of regression analysis for the analysis results of Test Example 6.

<1> Method for Estimating Fibrosis Level of Subcutaneous Adipocytes Hereinafter, embodiments of the present invention will be described in detail.
Negative correlation between facial skin motility (hereinafter also simply referred to as motility) and facial fibrosis level (hereinafter also simply referred to as fibrosis level) surrounding adipocytes Is established. That is, the smaller the fibrosis level, the better the facial skin motility.
The present invention uses this correlation to estimate the fibrosis level of the fiber structure that wraps adipocytes from motility.

  “Facial skin motility during facial expression changes” refers to how the skin surface moves in accordance with facial expression changes. Specific examples of motility include the followability of the facial skin (hereinafter also simply referred to as followability) in facial expression changes and the stretchability of the facial skin (hereinafter also simply referred to as stretchability) in facial expression changes. In addition, the facial skin deformability (hereinafter, also simply referred to as “deformability”) due to changes in facial expression may be employed as the mobility.

  “Face following the skin of a facial expression change” refers to the degree of delay in facial skin movement that changes following a facial expression change. When facial expression changes, the facial skin changes with a delay, but the smaller the delay, the better the follow-up.

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

  The two markers set when measuring the followability can be set arbitrarily, but the position of the face that moves most significantly in the expression change is the first marker, and the position of any other face is the second. It is preferable to set a marker and measure the time difference at which the movement speed of these two markers is maximum.

As the “expression change” to be executed by the subject in the follow-up measurement, an opening expression change from an expressionless state to an expression with an open mouth can be particularly preferably exemplified.
In this case, the first marker 1 is preferably set 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, the second marker is preferably set 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 in the horizontal direction from the top of the nose when there is no expression, but preferably the second marker set above the line 41 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 corner of the eye (FIG. 1).

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

In step (i), an arbitrary point on the face is set as the reference point 3, and the time change of the change amount V1 per unit time of the distance L1 between the reference point 3 and the first marker 1 is measured. The time when V1 becomes maximum is specified (see FIG. 1).
In this way, an arbitrary point on the face is set as the reference point 3, and the movement of the first marker 1 and the second marker 2 is captured by the distance from the reference point 3. The relative evaluation of each movement of the 1st marker 1 and the 2nd marker 2 becomes possible without being done.

The reference point 3 is preferably set at a location where the skin movement is poor or there is no movement in the opening facial expression change.
Since the skin of the forehead is difficult to move when the facial expression changes, it is preferable to set the reference point 3 at an arbitrary position of the forehead, more preferably at the top of the forehead, and even more preferably near the hairline (FIG. 1).

In 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 at which the change amount V2 is maximized is specified ( FIG. 1 front view right side).
Naturally, the reference point 3 in step (i) and step (ii) is the same.

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

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

  The embodiment of the step (i) in the present embodiment is the same as the above-described another form. A feature of the present embodiment is that a plurality of second markers are set in parallel in the face height direction, and a shift in the timing of movement of each second marker from the first marker is measured. This will be specifically described with reference to FIG.

  In the present embodiment, the second markers 21 to 23 are set in parallel in the face height direction (left side in front view in FIG. 1). In step (ii ′), the change in the amount of change V21 per unit time of the distance L21 between the reference point 3 and the second marker 21 and the distance L22 between the reference point 3 and the second marker 22 are as follows. Of the change amount V22 per unit time and the time change of the change amount V23 per unit time of the distance L23 between the reference point 3 and the second marker 23 are measured respectively. The time when each of 23 becomes the maximum is specified.

  In step (iii ′), the difference between the time specified in step (i) and the time associated with each second marker specified in step (ii) is obtained. Specifically, the difference between the time when the change amount V1 is maximum and the time when the change amounts V21 to 23 are maximum is obtained.

In step (iv), the difference relating to each second marker is plotted for each relative coordinate in the face on which each second marker is set, and regression analysis is performed to calculate the slope of the regression line. To do.
Specifically, the difference between the time when the change amount V1 is maximum and the time when the change amounts V21 to 23 are 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 of the face, the “coordinate” here is a coordinate in the height direction.
Of course, the vertical axis and the horizontal axis may be interchanged for plotting.

The method for specifying the coordinates of the second marker is not limited. For example, the relative distance based on the first marker or the reference point may be specified as the “coordinate”.
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 a graph. In this case, the coordinates of the respective second markers may be plotted at equal intervals in the horizontal axis direction (FIG. 2).

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

  In addition, although the form which set the 2nd marker 3 points | pieces is shown on the left side of the front view of FIG. 1, it is not limited to this, Preferably it is 3 points | pieces or more, More preferably, it is 5 points | pieces or more, More preferably, 7 Set a second marker above the point.

Of a plurality of second markers to be set, one point or two or more points are preferably set above the line 41, and more preferably set above the line 42 (FIG. 1).
In addition, it is preferable to set the second marker above and below the line 41 (FIG. 1).
Thereby, the precision of the regression analysis in process (iv) can be improved.

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

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

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

  Further, “the elasticity of the skin of the face when the facial expression changes” refers to the ease with which the skin can be stretched when the facial expression changes. For example, when there is a facial expression change that stretches the skin of the face, the higher the ratio of the increase in the distance in the extension direction in a certain area to the increase in the distance in the entire extension direction, the more excellent the elasticity. Can be evaluated.

The stretchability can be quantified by setting any three or more points on the skin in a substantially straight line and calculating the distance between the points in the facial expression change.
Specifically, 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 the points adjacent to each other, which are increased by the change in facial expression, is calculated. Then, the elasticity can be measured quantitatively by dividing the latter numerical value by the former numerical value.

An embodiment of the stretch quantification method will be described in more detail with reference to FIG.
In the present embodiment, seven points are set in a substantially straight line on the cheek of the face (FIG. 3). The number of points set on the cheek is not particularly limited to seven.

For convenience of explanation, as shown in FIG. 3, the points adjacent to each other are assumed to be X1 to X6, respectively.
In this embodiment, the increments (ΔX1 to ΔX6) of the respective distances X1 to X6 after changing the facial expression from the time of no expression (left of FIG. 3) (right of FIG. 3) are calculated.
Then, the ratio of the sum of the increase in the distance below the cheek (ΔX4 + ΔX5 + ΔX6) to the sum of the increase in the distance of the entire cheek (ΔX1 + ΔX2 +... ΔX6) is calculated. The value calculated by this calculation can be evaluated as a quantitative value of “stretchability” (see the following formula).

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

  For the quantitative measurement of stretchability, methods such as the optical flow method and the motion capture method that can be used for the followability measurement described above can be applied.

By the way, the surface (three-dimensional) includes a plurality of points (zero-dimensional) and lines (two-dimensional) as elements.
Here, the followability and elasticity described above are parameters that focus on the points set on the face (zero dimension) or the distance between the points (two dimensions), and the facial skin motility is measured using these parameters. Can do.
Therefore, it can be said that even when the face on the skin of the face including points and lines as elements is the measurement object, the motility of the skin of the face can be measured.

  In other words, in the present invention, the deformability of the face skin when the facial expression changes, more specifically, the manner of deformation (distortion) of the region set at an arbitrary position of the face that changes during the facial expression change is measured. Thus, it is possible to adopt a form in which the motility of the facial skin is measured.

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

In addition, it is preferable to perform the measurement of motility for obtaining data used for creating a regression equation or regression model indicating the correlation between motility and fibrosis level by the above-described method.
More specifically, the motility of the statistically significant number of subjects is measured by the method described above, and the fibrosis level of subcutaneous adipocytes is measured by the method described later for the subject. Based on these measured values, a regression equation or a regression model is created with motility as an explanatory variable and fibrosis level as an objective variable.

<2> Method for Estimating Face Skin Motility As described above, a negative correlation is established between the facial skin motility and the fibrosis level of the fiber structure that wraps the fat cells. The present invention uses this correlation to estimate motility from the fibrosis level.
The correlation is preferably represented by an equation or model. As the formula or model, a single regression equation or a single regression model is preferably exemplified.

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

Since the invasive method imposes a burden on the subject, the fibrosis level of the subcutaneous fat cells is preferably evaluated by a non-invasive method.
A non-invasive method includes a method using an ultrasonic diagnostic apparatus. More specifically, a subcutaneous fat layer portion is cut out from an image of a tomographic surface of the skin obtained by an ultrasonic diagnostic apparatus and used as an analysis image. With respect to the acquired analysis image, the fibrosis level can be evaluated from the feature amount obtained using the image processing software.
Examples of such feature amounts include parameters calculated by converting an image into gray scale, histogram, binarization, and the like.

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

In the present invention, it is preferable that the analysis image is formed into a histogram, and the skewness of the histogram is adopted as the evaluation value of the fibrosis level.
Since a histogram with a low degree of skewness (showing a substantially normal distribution) indicates that the strain is small, it can be understood that the fibrosis level of subcutaneous fat cells is high. On the other hand, it can be determined that the fibrosis level of the subcutaneous fat cells is low from a histogram with a large skewness (indicating a non-normal distribution).

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

  In the above item <1> and this item, the method of measuring or evaluating the motility or fibrosis level as an index for estimation has been described. This explanation is also valid for the method of measuring or evaluating the motility or fibrosis level to create a regression equation or regression model.

<3> Apparatus for Estimating Fibrosis Level of Subcutaneous Adipocytes Hereinafter, an apparatus for estimating the fibrosis level of subcutaneous fat cells will be described with reference to FIG. The subcutaneous fat cell fibrosis level estimation apparatus of the present invention is an apparatus for carrying out the subcutaneous fat cell fibrosis level estimation method described in the above item <1>. Therefore, the description of the item <1> is also valid for the following apparatus for estimating the fibrosis level of subcutaneous fat cells.

  The subcutaneous fat cell fibrosis level estimation apparatus 1 according to the present invention stores a fibrosis level correlation data indicating a correlation between facial skin motility and fibrosis level of subcutaneous fat cells in expression change. 121, and fibrosis level calculation means 112 for calculating the fibrosis level by collating the motility of the facial skin in the expression change of the subject with the fibrosis level correlation data stored in the storage means 121. .

  As shown in FIG. 4, the subcutaneous fat cell fibrosis level estimation apparatus 1 includes a motility measurement unit 13, a ROM (Read Only Memory) 12 having a storage unit 121, and a CPU (Central) having a fibrosis level calculation unit 112. 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 digitizing means 111 that digitizes the facial skin motility in the change in the facial expression of the subject measured by the motility measurement unit 13. The CPU 11 includes a digitizing unit 111.

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

  The apparatus 1 for estimating the fibrosis level of subcutaneous adipocytes according to the present invention having such a configuration can easily measure the fibrosis level of the subcutaneous fat cells of the subject simply by measuring the motility of the facial skin in the change in the facial expression of the subject. Can be calculated.

  In another embodiment, instead of the mobility measurement unit 13 and the digitizing unit 111, a mobility input unit that inputs a measured value of mobility measured separately may be provided.

<4> Program for Estimating Fibrosis Level of Subcutaneous Adipocytes The present invention also relates to a program for estimating fibrosis level of subcutaneous fat cells, which causes a computer to execute the above-described method for estimating the fibrosis level of subcutaneous fat cells. Since the program of the present invention corresponds to each means in the CPU included in the above-described fibrosis level estimating apparatus of the present invention, it will be described with the reference numerals in FIG.

  The program for estimating the fibrosis level of subcutaneous fat cells according to the present invention includes a fibrosis level correlation data indicating the correlation between the motility and the fibrosis level of subcutaneous fat cells, and the facial skin motility in the change in expression of the subject. A computer is caused to function as the 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 cause a computer to function as the digitizing means 111 as shown in the block diagram of FIG.

<5> Estimating Device for Facial Skin Motility in Change in Facial Expression Hereinafter, an apparatus for estimating facial skin mobility in a facial expression change will be described with reference to FIG. The apparatus for estimating motility of the present invention is an apparatus for carrying out the method for estimating the motility of facial skin in the facial expression change described in item <2>. Therefore, the description of the item <2> is applicable to the following motility estimation apparatus.

  The motility estimation apparatus 2 of the present invention includes a storage means 221 that stores motility correlation data indicating the correlation between facial skin motility and fibrosis level of subcutaneous fat cells in expression change, and subcutaneous fat of a subject. Motility calculating means 212 for calculating the motility by comparing the fibrosis level of the cell with the motility correlation data stored in the storage means 221.

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

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

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

  The motility estimation apparatus 2 of the present invention configured as described above can easily calculate the motility of the facial skin in the change in facial expression of the subject simply by measuring the fibrosis level of the subcutaneous fat cells of the subject. it can.

  In another embodiment, instead of the fibrosis level measurement unit 23 and the digitizing unit 211, a fibrosis level input unit that inputs a measured value of the fibrosis level measured separately may be provided.

<6> Program for estimating facial skin motility in facial expression change The present invention also relates to a motility estimation program for causing a computer to execute the above-described motility estimation method. Since the program of the present invention corresponds to each means in the CPU included in the above-described mobility estimation apparatus of the present invention, the program will be described with the reference numerals in FIG.

  According to the motility estimation program of the present invention, the fibrosis level of a subject's subcutaneous fat cells is collated with the motility correlation data indicating the correlation between the motility and the fibrosis level of the subcutaneous fat cells, and the motility is determined. A computer is made to function as the motility calculating means 212 for calculating.

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

<Test Example 1> Observation of change in collagen structure with aging Subcutaneous fat cells in subcutaneous tissues provided by nine donors 20 years of age or older were photographed with a scanning electron microscope. This electron micrograph was evaluated by a skilled evaluator and given a score of 1 to 5 for the degree of fibrosis of the subcutaneous fat cells. The evaluation was performed based on a five-step reference photograph (FIG. 6) with different degrees of fibrosis. The results are shown in FIG.

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

<Test Example 2> Measurement of facial skin motility in facial expression change (1) Measurement of follow-up 20 Japanese females in their 20s and 60s, each of which was 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 (points 1 to 1) aligned in the cheek height direction The reflection marker for motion capture of point 7) was pasted.
As shown in FIG. 8, the subject is asked to make a facial expression change (opening facial expression change) that extends in the vertical direction from the expressionless state (left in FIG. 8) to the open state (right in FIG. 8). Movie shooting (30 fps) was performed, and exercise information of each marker was acquired.

  In order to perform analysis with higher accuracy, from each of the 100 subjects, each generation 12 based on three points: 1) strength of facial expression, 2) synchrony of eye and mouth movement, and 3) timing of facial expression. A total of 60 people were selected for analysis and subjected to analysis.

The movement analysis of each marker was performed as follows.
First, the amount of change per unit time of the distance from point 0 to point 7 from the reference point was measured over time, and the time at which each amount of change was the maximum from the point of expression expression start was measured. Thereafter, the difference (follow-up property) between the time when the amount of change per unit time of the distance from the reference point to the point 0 is maximum and the time when the amount of change per unit time of the distance from the reference point to the points 1 to 7 is maximum. ) Was calculated. In this test, the difference in time was evaluated as a difference in frames of moving images (Δ frame).

  About the followability obtained in this way, the average value for every age was taken separately 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 movement delay 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 in the forties and later, the skin movement delay, that is, the follow-up performance decreases as the position of the cheek becomes farther from the jaw.

  The slope of the regression line shown in FIG.

(2) Measurement of stretchability Using the motion capture data obtained by the above-described follow-up measurement test, the stretchability of the facial skin in changing facial expressions was also measured.
Specifically, with respect to points 1 to 7, the distance between adjacent points is measured in the expressionless state (left side in FIG. 8) and the open state (right side in FIG. 8), and the distance increased by the change in opening facial expression. Was calculated.
And the elasticity was computed by dividing the sum total of the increase of the distance between points regarding a cheek lower part (points 4-7) by the sum total of the increase of the distance between points regarding the whole (points 1-7).

The elasticity obtained in this manner was plotted on a graph for each age, a box-and-whisker chart was created, and regression analysis was performed to draw a regression line. The results are shown in FIG.
As shown in FIG. 10, it became clear that the elasticity of the skin of the face in the expression change decreases with age.

<Test Example 3> Analysis of physical properties inside skin by elastography Viscoelasticity (strain) in 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). For the measurement of viscoelasticity, the measurement area is divided into a total of four layers: the skin surface layer (skin and dermis) and the upper layer of the subcutaneous tissue, the middle layer of the subcutaneous tissue, and the lower layer of the subcutaneous tissue. Calculated. The subcutaneous tissue upper layer, the subcutaneous tissue middle layer, and the subcutaneous tissue lower layer were set by dividing the subcutaneous tissue at a ratio of 1: 2: 1 in the depth direction.

Viscoelasticity refers to the property of combining both viscosity and elasticity. Therefore, in evaluating viscoelasticity, both viscosity and elasticity are evaluated. However, it is difficult to clearly distinguish between viscosity and elasticity in living tissue, and viscoelasticity is generally evaluated mainly by elastic modulus (Young's modulus).
Further, viscoelasticity can be evaluated by “strain” based on Hooke's law (the following formula 1). Therefore, in this test example, “strain” was measured for viscoelasticity inside the skin.

Formula 1

<Test Example 4> Regression Analysis Regression analysis was performed on the measured value of the followability (slope of the regression line) obtained in Test Example 2 and the measured value of the 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 became clear that a positive correlation is established between the followability of the facial skin in the expression change and the viscoelasticity of the subcutaneous tissue.

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

The skin model was constructed by laminating materials having different Young's moduli (FIG. 13). The layer imitating the dermis was set to a thickness of 2 mm, the upper layer of the subcutaneous tissue was 3 mm, the middle layer of the subcutaneous tissue was 6 mm, and the lower layer of the subcutaneous tissue was 3 mm (FIG. 13).
In this test, a skin model imitating the skin characteristics of the younger age group and a skin model imitating the characteristics of the old age skin were prepared and analyzed.
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.

Assuming that the skin of the cheek moves through a ligament connected to the deep muscle, a column corresponding to the ligament is connected to a part of the layer imitating the lower layer of the subcutaneous tissue, and this column is connected to 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 stop for 1 second after being displaced in the X direction for 3 seconds at a speed of 0.5 cm / s. During this movement, the displacement in the Z direction of the layer simulating the dermis (top layer) was plotted over time.
In addition, the point which observed the displacement of a Z direction was made into the part located back with respect to the displacement direction of a ligament rather than the part corresponded directly above the part to which the column imitating a ligament was connected (FIG. 15). . The results are shown in FIGS.

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

When the above results are combined, the skin model that imitates the skin whose subcutaneous tissue is harder is delayed in the deformation timing in the Z direction than the skin model that imitates the soft skin of the subcutaneous tissue (the follow-up property deteriorates) )It has been shown.
The result of Test Example 4 supports the results of Test Examples 2 to 4 in which there is a positive correlation between the distortion of the subcutaneous tissue (that is, viscoelasticity) and the followability of the facial skin in the expression change. It is.

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

  Further, a subcutaneous fat portion was cut out from the ultrasonic image of the same subject, and a histogram was created using this as an analysis image 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 of the left figure representing an image with a low degree of fibrosis was 1.62, and the skewness of a right figure representing an image with a high degree of fibrosis was 0.84. .

<Test Example 7> Regression Analysis A regression analysis was performed on the measured value 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 fat cells 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.
As the fibrosis level is high, the degree of distortion is small. Therefore, 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 fat cells. It became.

<Discussion>
The results of Test Example 1 indicate that collagen fibers encapsulating subcutaneous fat cells present in the subcutaneous tissue become fibrotic with aging.
In addition, the results of Test Example 2 indicate that the facial skin motility (followability, stretchability) in expression changes decreases with age.
In other words, Test Examples 1 and 2 revealed that both facial skin motility and fibrosis level correlated with age.

Furthermore, the result of Test Example 4 shows that a positive correlation is established between the followability of the facial skin in the expression change and the viscoelasticity of the subcutaneous tissue.
On the other hand, the result of Test Example 7 shows that a negative correlation is established between the viscoelasticity of the subcutaneous tissue and the fibrosis level of the subcutaneous fat cells.
That is, according to Test Examples 4 and 7, it became clear that a correlation between the followability of the facial skin and the fibrosis level of the subcutaneous fat cells in the expression change is established between the viscoelasticity of the subcutaneous tissue. .

  These results indicate that a correlation is established between “following of the skin of the face in expression change” and “fibrosis level of subcutaneous fat cells”.

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

  Considering these in total, it was shown by the above test example that the fibrosis level of subcutaneous adipocytes can be estimated by using the facial skin motility in the expression change including followability and stretchability as an index. Similarly, it was shown that the facial skin motility in the expression change can be estimated using the fibrosis level of subcutaneous fat cells as an index.

  The present invention can be applied to skin analysis technology.

1 Fibrosis level estimation device 11 CPU
111 Numerical means 112 Fibrosis level calculating means 12 ROM
121 storage means 13 motility measurement unit 14 fibrosis level display unit 2 motility estimation device 21 CPU
211 Numerical value means 212 Mobility calculation means 22 ROM
221 storage means 23 fibrosis level measuring unit 24 motility display unit

Claims (18)

  Using the correlation between facial skin motility and subcutaneous adipocyte fibrosis level in facial expression changes, and estimating the fibrosis level using the measured value of motility as an index, The method for estimating the fibrosis level.   Calculating the fibrosis level from the measured value of the motility using a regression equation having the measured value of the facial skin motility in the expression change as an explanatory variable and the evaluation value of the fibrosis level as an objective variable; The estimation method according to claim 1, wherein the estimation method is characterized.   The estimation method according to claim 1, wherein the motility is selected from followability, elasticity, and deformability of a facial skin in expression changes. The mobility is the followability of the skin of the face in facial expression changes;
The estimation according to claim 3, wherein the measured value of the followability is a time difference at which the movement speed of at least two markers set at arbitrary positions on the face in expression change is maximum. Method.   The follow-up measurement value is a time when the movement speed of the first marker set at an arbitrary position of the chin in the opening facial expression change from the expressionless state to the facial expression with the mouth open is maximum, The estimation method according to claim 4, wherein the estimation method is a difference from a time at which the movement speed of the second marker set at an arbitrary position is maximum. The estimation method according to claim 5, wherein the follow-up measurement value is a difference calculated by the following steps.
(I) Using an arbitrary point on the face as a reference point, the time change of the change amount per unit time of the distance between the reference point and the first marker is measured, and the time when the change amount is maximum is specified (Ii) a step of measuring a time change of a change amount per unit time of a distance between the reference point and the second marker, and specifying a time at which the change amount is maximum (iii) a step (i Step for obtaining the difference between the time specified in) and the time specified in step (ii) The estimation method according to claim 5, wherein the measured value of the followability is an inclination of a regression line calculated by the following steps.
(I) Using an arbitrary point on the face as a reference point, the time change of the change amount per unit time of the distance between the reference point and the first marker is measured, and the time when the change amount is maximum is specified (Ii ′) A plurality of second markers are set in parallel in the height direction of the face, and a change in the amount of change per unit time of the distance between the reference point and each of the second markers is determined. A step of measuring and specifying a time when the amount of change is maximum (iii ′) A difference between the time specified in step (i) and the time related to each second marker specified in step (ii) is obtained. Step (iv) The difference relating to each second marker obtained in step (iii ′) is plotted for each coordinate of each second marker, regression analysis is performed, and the slope 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 a line drawn in the horizontal direction from the top of the nose during no expression.   The second marker is set above a center line between a line drawn in the horizontal direction from the top of the nose and a line drawn in the horizontal direction from the corner of the eye at the time of expressionlessness. The estimation method according to claim 8.   The estimation method according to any one of claims 4 to 9, wherein the follow-up property is measured using a motion capture moving image captured by setting the marker on a face. The fibrosis level for estimating the fibrosis level using the measured value of the motility as an index using the correlation between the motility of the facial skin in the expression change and the fibrosis level of the subcutaneous fat cells An estimation device for
Storage means for storing correlation data indicating the correlation;
The fibrosis level calculation means for calculating the fibrosis level by comparing the facial skin motility in the expression change of the subject with the correlation data stored in the storage means,
An apparatus for estimating the fibrosis level, comprising: The fibrosis level for estimating the fibrosis level using the measured value of the motility as an index using the correlation between the motility of the facial skin in the expression change and the fibrosis level of the subcutaneous fat cells An estimation program of
Computer
As a fibrosis level calculation means for calculating the fibrosis level by collating the facial skin motility in the expression change of the subject with the correlation data indicating the correlation,
A program for estimating the fibrosis level, characterized by causing it to function.   Using the correlation between facial skin motility and subcutaneous adipocyte fibrosis level in facial expression changes, and estimating the motility using the subcutaneous adipocyte fibrosis level as an index The method for estimating the motility.   Using the regression equation with the evaluation value of the fibrosis level of the subcutaneous fat cells as the explanatory variable and the motility of the facial skin in the expression change as the objective variable, the motility is calculated from the evaluation value of the fibrosis level of the subcutaneous fat cells. The estimation method according to claim 13, wherein the estimation method is calculated.   The estimation method according to claim 13 or 14, wherein the fibrosis level of the subcutaneous fat cells is measured by ultrasound.   The estimation method according to claim 15, wherein an echo image of the subcutaneous tissue is acquired by ultrasound, a histogram is generated from the image, and a fibrosis level of the subcutaneous fat cell is calculated as a skewness of the histogram. . Using the correlation between facial skin motility and facial adipocyte fibrosis level in facial expression changes, the motility measurement value is estimated using the fibrosis level as an index. An estimation device,
Storage means for storing correlation data indicating the correlation;
A motility calculating means for calculating the motility by comparing the fibrosis level with the correlation data stored in the storage means;
The motility estimation apparatus comprising: Using the correlation between facial skin motility and facial adipocyte fibrosis level in facial expression changes, the motility measurement value is estimated using the fibrosis level as an index. An estimation program,
Computer
As the motility calculating means for calculating the motility by comparing the fibrosis level with the correlation data indicating the correlation,
The motility estimation program characterized in that it is made to function.