JP7147982B2 - Three-dimensional cell shape evaluation method and cell observation device - Google Patents

Description

TECHNICAL FIELD The present invention relates to a method for evaluating the three-dimensional shape of cells, and a cell observation device therefor. The three-dimensional cell shape evaluation method and the cell observation device according to the present invention, for example, non-invasively evaluate the state of cells in the process of culturing pluripotent stem cells (ES cells, iPS cells, mesenchymal stem cells, etc.). is suitable for

In recent years, in the field of regenerative medicine, research using iPS cells, ES cells, or pluripotent stem cells such as mesenchymal stem cells has been actively conducted. In the research and development of regenerative medicine using such pluripotent stem cells, it is necessary to culture a large amount of undifferentiated cells that maintain pluripotency. Therefore, it is necessary to select an appropriate culture environment and stably control the environment, and to frequently check the state of cells during culture. In the field of regenerative medicine, it is important to confirm the state of cells in a non-destructive and non-invasive manner. Morphological evaluation using cell images obtained by a cell observation device is common for such non-destructive and non-invasive evaluation of cell conditions.

Since thin and transparent observation objects such as cells generally absorb little light, it is difficult to recognize the shape of the object from an optical intensity image. Therefore, conventionally, phase-contrast microscopes have been widely used for observation of cells. Since the phase-contrast microscope images the amount of phase change when light passes through an object, it is possible to obtain a phase-contrast image that visualizes even the morphology of transparent cells. In Patent Document 1, based on phase contrast images obtained with such a phase contrast microscope, good cell colonies consisting of only undifferentiated cells and bad cell colonies containing undifferentiated deviant cells are distinguished for pluripotent stem cells. A technique for doing so is disclosed.

However, pixel values (signal values) of a phase-contrast image obtained by a phase-contrast microscope cannot quantitatively evaluate information in the thickness direction of an object. Therefore, the method described in Patent Document 1 cannot perform morphological evaluation of the three-dimensional shape of cells, and the object is a colony of pluripotent stem cells, and the colony contains only undifferentiated cells or is undifferentiated. It is limited to a simple evaluation of containing deviated cells.

JP 2014-18184 A WO2016/084420

The present invention has been made to solve the above problems, and its main purpose is to easily evaluate the local three-dimensional shape of the cell based on the image reflecting the phase change obtained for the cell. An object of the present invention is to provide a three-dimensional cell shape evaluation method and a cell observation device that can

A three-dimensional cell shape evaluation method according to one aspect of the present invention, which has been made to solve the above problems, is a method for evaluating a three-dimensional shape of a cell,
a measurement step of irradiating one or more target cells to be evaluated with a laser beam and detecting interference light between light passing through the target cells and light passing around the target cells to obtain hologram data; ,
a phase image creating step of performing image reconstruction processing based on the hologram data to create a phase image of the target cell;
a differential image creating step of obtaining a spatial differential value for each pixel from the phase image and creating a differential image;
an evaluation step of evaluating the shape of the target cell based on the arithmetic processing result of the differential value of each pixel in the differential image;
and the evaluating step includes:
a histogram creating step of creating a histogram showing the appearance frequency distribution of the signal values of each pixel included in the range corresponding to the target cell in the differential image;
an index value calculation step of calculating an index value that quantifies the degree of the slope of the slope on the high signal value side of the peak appearing in the histogram;
includes .

Further, a cell observation device according to one aspect of the present invention, which has been made to solve the above problems, is a cell observation device using a holographic microscope,
a light source;
Hologram data by detecting interference light between light passing through the target cells and light passing around the target cells when a container containing one or more target cells is irradiated with light emitted from the light source unit, and light passing around the target cells is detected. a detection unit that acquires
a phase image creation unit that performs image reconstruction processing based on the hologram data and creates a phase image of the target cell;
a differential image creation unit that obtains a spatial differential value for each pixel from the phase image and creates a differential image;
an index value calculation unit that calculates an index value for evaluating the three-dimensional shape of the target cell based on the differential value of each pixel included in the range corresponding to the target cell in the differential image;
wherein the index value calculation unit includes a histogram creation unit that creates a histogram showing the appearance frequency distribution of the signal values of each pixel included in the range corresponding to the target cell in the differential image, and the number of peaks appearing in the histogram An index value is calculated by quantifying the degree of the gradient of the slope on the high signal value side .

Although the type of cells to be observed in the present invention is not particularly limited, they are typically pluripotent stem cells including iPS cells, ES cells, mesenchymal stem cells, and the like.

A phase image obtained by performing image reconstruction processing on hologram data also includes information in the thickness direction of the observed object, that is, the cell. Therefore, when the spatial differential value at each pixel of the phase image is obtained, the differential value shows a large value at the periphery (contour) of the cell, which is the boundary between the cell and the background, and the thickness of the cell at the periphery. The steeper the slope in the vertical direction, the larger the differential value. In other words, the differential value of each pixel and the differential image composed of a collection of the differential values contain information on a local site such as the cell periphery. Therefore, in the three-dimensional cell shape evaluation method according to one aspect of the present invention, the three-dimensional shape of the target cell, specifically evaluates the degree of inclination in the thickness direction at the cell periphery.

According to the three-dimensional cell shape evaluation method and cell observation device according to one aspect of the present invention, for example, the three-dimensional shape of a local site of pluripotent stem cells in culture can be evaluated non-destructively and non-invasively. can be done. In particular, by performing evaluation based on index values calculated according to a predetermined procedure from the differential value of each pixel that constitutes the differential image, even an operator with little experience or skill in such evaluation can obtain an accurate and highly reliable image. Evaluation can be easily performed. In addition, it is possible to improve the working efficiency in evaluating the state of cells, and in turn improve the productivity in cell culture.

1 is a schematic configuration diagram of a cell observation device that is an embodiment of the present invention; FIG. 4 is a flow chart showing a work procedure and a processing flow in a cell state evaluation work using the cell observation device of the present embodiment. FIG. 4 is a conceptual diagram for explaining imaging operation and image reconstruction processing in the cell observation device of the present embodiment; FIG. 4 is a diagram showing an example of a differential filter used in differential calculation in the cell observation device of the present embodiment; FIG. 4 is a diagram showing another example of a differential filter used in differential calculation in the cell observation device of the present embodiment; The figure which shows an example of the differential value histogram produced in the cell observation apparatus of this embodiment. FIG. 3 is a diagram showing an example of an IHM phase image and a differential image obtained therefrom in the cell observation device of the present embodiment; FIG. 4 is a diagram showing an example of three-dimensional cell shape evaluation using a differential value histogram in the cell observation device of the present embodiment; FIG. 4 is a diagram showing an example of three-dimensional cell shape evaluation using a differential value histogram in the cell observation device of the present embodiment; FIG. 4 is a diagram showing an example of three-dimensional cell shape evaluation using a differential value histogram in the cell observation device of the present embodiment;

An embodiment of a cell observation device according to the present invention and a three-dimensional cell shape evaluation method using the same will be described below with reference to the accompanying drawings.

[Configuration of the cell observation device of the present embodiment]
FIG. 1 is a schematic configuration diagram of a cell observation device according to this embodiment.

The cell observation device of this embodiment includes a microscopic observation section 1, a control/processing section 2, an input section 3 and a display section 4 which are user interfaces.
In this embodiment, the microscopic observation unit 1 is an in-line holographic microscope and includes a light source unit 10 including a laser diode or the like and an image sensor 11, and a cell 13 is included between the light source unit 10 and the image sensor 11. A culture plate 12 is placed.

The control/processing unit 2 controls the operation of the microscopic observation unit 1 and processes data acquired by the microscopic observation unit 1. The control/processing unit 2 includes an imaging control unit 20, a hologram data storage unit 21, and a phase information calculation unit. a section 22, an image reconstruction section 23, a reconstructed image data storage section 24, a differential image creation section 25, a differential value histogram creation section 26, a three-dimensional cell shape evaluation index calculation section 27, and a display processing section 28; , as functional blocks.

Usually, the entity of the control/processing unit 2 is a personal computer with predetermined software (computer program) installed, a workstation with higher performance, or a high-performance computer connected to such a computer via a communication line. is a computer system containing That is, the function of each block included in the control/processing unit 2 is implemented by executing software installed in a computer system including a single computer or a plurality of computers, and is stored in the computer or computer system. It can be embodied by processing using various types of data.

[Outline description of cell state evaluation in the cell observation device of the present embodiment]
Although various cells can be observed with the cell observation device of the present embodiment, mesenchymal stem cells, which are one type of pluripotent stem cells, are to be observed here as an example. For the purpose of cell observation necessary when culturing mesenchymal stem cells, a process of acquiring observation images of cells and evaluating the state of the cells being cultured from the observation images will be described.

FIG. 2 is a flow chart showing the work procedure and process flow in the cell state evaluation work using the cell observation device of the present embodiment. FIG. 3 is a conceptual diagram for explaining the shooting operation and image reconstruction processing.

FIG. 3(a) is a schematic top view of the culture plate 12 used in the cell observation device of this embodiment. The culture plate 12 has six circular wells 12a formed in top view, and cells are cultured in each of the wells 12a. Here, the entire culture plate 12, that is, the entire rectangular range including six wells 12a is the observation target area. The microscopic observation unit 1 includes four sets of light source units 10 and image sensors 11 . Each pair of light source unit 10 and image sensor 11 collects hologram data of four quartered ranges 81 obtained by equally dividing the entire culture plate 12 into four, as shown in FIG. 3(a). That is, four sets of light source units 10 and image sensors 11 share the collection of hologram data over the entire culture plate 12 .

As shown in FIGS. 3(b) and 3(c), the range in which a set of the light source unit 10 and the image sensor 11 can capture images at one time includes one well 12a in the quadrant range 81. This range corresponds to one imaging unit 83 obtained by dividing a square range 82 into 10 equal parts in the X-axis direction and 12 equal parts in the Y-axis direction. One quadrant range 81 includes 15×12=180 imaging units 83 . The four light source units 10 and the four image sensors 11 are arranged near four vertices of a rectangle having the same size as the quadrant range 81 in the XY plane including the light source units 10 and the image sensors 11, respectively. The acquisition of holograms for four different imaging units 83 on the culture plate 12 is performed simultaneously.

When collecting hologram data, the operator first sets the culture plate 12 in which the cells 13 to be observed are cultured at a predetermined position in the microscopic observation unit 1, and enters the identification number, measurement date and time, etc. that identify the culture plate 12. After inputting information from the input unit 3, the execution of measurement is instructed. Upon receiving this measurement instruction, the imaging control section 20 controls each section of the microscopic observation section 1 to perform imaging (step S1).

That is, one light source unit 10 irradiates a predetermined region (one imaging unit 83) of the culture plate 12 with coherent light that spreads at a small angle (for example, about 10°). Coherent light (object light 15) that has passed through cells 13 on culture plate 12 interferes with light (reference light 14) that has passed through the area (usually medium) around cells 13 on culture plate 12, and is transmitted to the image sensor. 11 is reached. The object light 15 is light whose phase is changed when it passes through the cell 13 , while the reference light 14 is light that does not pass through the cell 13 and therefore does not undergo a phase change due to the cell 13 . Therefore, on the detection plane (image plane) of the image sensor 11, an interference image, that is, a hologram is formed between the object light 15 whose phase is changed by the cells 13 and the reference light 14 whose phase is not changed. Two-dimensional light intensity distribution data (hologram data) corresponding to is output from the image sensor 11 .

As described above, the four light source units 10 emit coherent light substantially simultaneously toward the culture plate 12, and the four image sensors 11 acquire hologram data of regions corresponding to different imaging units 83 on the culture plate 12. be done. Each time measurement at one measurement position is completed, the light source unit 10 and the image sensor 11 are moved by a moving unit (not shown) in the X-axis direction and the Y-axis direction by a distance corresponding to one imaging unit 83 in the XY plane. is moved step by step. As a result, the 180 imaging units 83 included in the quadrant range 81 are measured, and the entire culture plate 12 is measured using the four light source units 10 and the image sensors 11 as a whole. The hologram data thus obtained by the four image sensors 11 of the microscopic observation unit 1 are stored in the hologram data storage unit 21 together with attribute information such as measurement date and time.

When a series of measurements (photographs) as described above is completed, the phase information calculation unit 22 sequentially reads out the hologram data from the hologram data storage unit 21 and performs a light wave propagation calculation process (phase recovery process) to obtain a two-dimensional image. Recover the phase and amplitude information at each position. The spatial distribution of these pieces of information is obtained for each imaging unit 83 . When the phase information and amplitude information of all the imaging units 83 are obtained, the image reconstruction unit 23 forms a phase image of the entire observation target region, that is, an IHM phase image, based on the phase information and amplitude information. (Step S2).

That is, the image reconstruction unit 23 reconstructs the IHM phase image of each imaging unit 83 based on the spatial distribution of phase information calculated for each imaging unit 83 . Then, by performing a tiling process (see FIG. 3D) that joins the IHM phase images in the narrow range, an IHM phase image of the entire culture plate 12 is formed. Data forming the IHM phase image are stored in the reconstructed image data storage unit 24 . It should be noted that, during tiling processing, appropriate correction processing may be performed so that the IHM phase images at the boundaries of the imaging units 83 are smoothly connected.

When calculating the phase information and reconstructing the image as described above, a well-known algorithm disclosed in Patent Document 2 or the like may be used. Generally, in an IHM phase image, the outline (boundary) of a cell, which is transparent and difficult to see with an optical microscope, and the pattern inside the cell can be easily seen.

Based on the hologram data, in addition to the phase information, intensity information and pseudo-phase information obtained by merging the phase information and the intensity information are also calculated. Intensity image, IHM quasi-phase image) can also be created. Further, when creating an IHM phase image or the like based on hologram data, the IHM phase image or the like at a plurality of focal positions can be created respectively.

Next, the image reconstructing unit 23 performs a process of removing a background region in which cells are clearly absent from the created IHM phase image (step S3).
Various methods can be used as the background removal method, and texture analysis can be used as an example. Texture analysis is roughly divided into structural texture analysis and statistical texture analysis. The latter is suitable for extracting patterns and pattern features of local parts in an image. There are various methods for statistical texture analysis, including the density histogram method using density histograms, the density level difference method using difference statistics, the spatial density level dependence method using co-occurrence matrices, and the method using density co-occurrence matrices. , an appropriate method may be selected. Note that the pixel value of the portion removed as the background region may be set to a fixed value such as 0.

Specifically, the entire IHM phase image is divided into a plurality of fine sub-regions, and predetermined texture analysis is performed for each sub-region to calculate the texture feature amount. Since there is a clear difference in the texture feature value between the case where the small region is only the background region and the case where the small region includes at least part of the cell region, the small region corresponding to the background region is extracted from the texture feature value. Find it and remove it. Of course, such background removal processing is not essential and can be omitted.

Further, the image reconstructing unit 23 performs processing for removing noise components from the IHM phase image after the background removal processing using a Gaussian filter or the like (step S4). This noise removal process can also be omitted, like the background removal process.

Subsequently, the differential image generator 25 applies a differential filter to the signal value (pixel value) of each pixel of the IHM phase image after noise removal, and calculates a differential value for each pixel (step S5). As the differential filter, for example, a Laplacian filter, which is often used for image edge detection, can be used.

FIG. 4 is an example of a general 3×3 pixel Laplacian filter using four neighboring pixels in the vertical and horizontal directions. Also, a Sobel filter combining a vertical filter and a horizontal filter shown in FIG. 5 may be used as a differential filter. Once the differential value is obtained for each pixel, it is used to create a differential image corresponding to the IHM phase image.

Here, the display processing unit 28 may arrange the original IHM phase image and the differential image on the same screen and display them on the display unit 4 (step S6). The signal value of each pixel in the IHM phase image indicates the amount of phase delay of light passing through the cell, which reflects the optical thickness of the cell. Therefore, the steeper the slope in the thickness direction of the peripheral edge of the cell, the greater the differential value at the peripheral edge.

The differential value histogram creation unit 26 creates a histogram in which the horizontal axis represents the differential value and the vertical axis represents the number of appearances (the number of pixels) based on the differential values of all the pixels forming the differential image (step S7). In order to visually grasp the state of change in the number of occurrences with respect to an increase in the differential value, it is preferable to set the horizontal axis of the histogram as a linear axis and the vertical axis as a logarithmic axis.

FIG. 6 is an example of a differential value histogram. As shown in FIG. 6, the differential value histogram has a left-right asymmetrical peak with a steep slope on the low differential value side and a gentle slope on the high differential value side. Pixels included in the range on the low differential value side including the peak top of this peak are considered to be mainly pixels present in a background region that is not a cell or in a region where the thickness is relatively flat within the cell. On the other hand, pixels included in the range of gentle slopes on the high differential value side are considered to be pixels present mainly in the periphery (contour) of the cell. Therefore, the degree of the gradient of the slope on the high differential value side in the differential value histogram reflects the degree of slope in the thickness direction of the peripheral portion of the cell.

That is, among a large number of cells appearing in the original IHM phase image, the greater the proportion of cells with a steeper slope in the thickness direction of the peripheral edge of the cell, the higher the proportion of pixels with relatively high differential values. can be said to increase. When the percentage of pixels with relatively high differential values is high, the gradient of the slope on the right side of the peak in the differential value histogram becomes gentle. In FIG. 6, the slope of the peak indicated by A is gentler than that of the peak indicated by B, and it can be said that the percentage of cells with a steep slope in the thickness direction of the peripheral edge of the cell is high. Therefore, in the cell observation apparatus of the present embodiment, an index value is calculated so that the degrees of gradients of the slopes can be compared with each other in different differential value histograms, and the index value is used to determine the slope of the peripheral portion of the cell in the thickness direction. is steep or not, that is, the three-dimensional shape is evaluated.

Specifically, the three-dimensional cell shape evaluation index calculation unit 27 first performs a smoothing process by taking a moving average in the direction of change of the differential value in order to reduce variations and errors in the differential value histogram. After that, the half value width of the peak of the differential value histogram is calculated as an evaluation index value (step S8). The half-value width here is the width of the differential value in the number of appearances of the peak top of the peak which is half the number of appearances. For example, in FIG. The half-value width Wb is obtained for However, as described above, only the slope on the side with the higher differential value is meaningful here, so the differential value at the peak top and the horizontal slope indicating (the number of appearances of the peak top)×(1/2) The difference between the differential value corresponding to the point where the line and the slope on the side with the higher differential value intersect may be used instead of the half width. Since the half-value width increases as the slope of the peak becomes gentler, this half-value width can be used as an evaluation index value.

The display processing unit 28 displays the created differential value histogram and the evaluation index values calculated as described above together on the display unit 4 (step S9). This makes it possible to visually present the evaluation result of the three-dimensional shape of the cell to the operator. For example, when comparing the state of the three-dimensional shape of cells for multiple samples (culture vessels) with different culture conditions such as the presence or absence of an activator that activates cells, the differential value histogram for the multiple samples are superimposed and the evaluation index values for the plurality of samples are displayed side by side.

[Another example of three-dimensional shape evaluation of cells]
The half-value width of the peak in the differential value histogram described above is an example of the evaluation index value, and another index value as described below can also be used.

FIG. 8 is an explanatory diagram of a method of calculating the appearance ratio, which is another example of the evaluation index value.
Here, arbitrary k (where k is an integer of 2 or more) differential values are selected in a high differential value range in which the difference in the slope in the thickness direction of the peripheral part of the cell appears remarkably, and the k differential values are selected. A ratio of the number of occurrences corresponding to the differential value is calculated as an index value. In general, k may be 2 as shown in FIG. A ratio Xb [%] is obtained.

FIG. 9 is an explanatory diagram of a method of calculating a differential value difference, which is another example of the evaluation index value.
Here, any L number of appearances (where L is an integer of 2 or more) in the appearance number range corresponding to the slope of the high differential value range in which the difference in the slope in the thickness direction of the peripheral part of the cell appears remarkably is selected, and the differential value difference (differential value width) corresponding to the L number of appearances is calculated as an index value. In general, L may be 2 as shown in FIG. sought.
Since the absolute value of the number of occurrences depends on the cell density, in order to eliminate the influence of the difference in cell density, it is preferable to perform processing such as normalizing the number of occurrences.

FIG. 10 is an explanatory diagram of a method of calculating the exponent part of the exponent approximation formula, which is another example of the evaluation index value. In the high differential value range where the difference in slope in the thickness direction of the cell periphery becomes significant, the decrease in slope can be approximated by an exponential function. Therefore, select two arbitrary differential values in the high differential value range, and the slope between the two differential values is y = a · e ^-bx (where a and b are arbitrary constants) is approximated by the exponential function of Constants a and b that minimize the approximation error should be searched for. The exponent constant b at this time reflects the degree of slope gradient and is not affected by the cell density. The constant b of the exponent part may be used as the index value.

In the differential value histogram, even in the high differential value range where the difference in the slope in the thickness direction of the cell peripheral part appears remarkably, in the range where the differential value is particularly high, the number of appearances is the number of cells or the confluency (over the entire image area) proportion of the area occupied by cells). Therefore, the number of occurrences in such a high differential value range is normalized by the cell number or confluency to provide an evaluation index value that does not depend on them.

In this case, the confluency or the number of cells in the cell image to be observed is obtained by a measurement method different from, that is, independent from, the measurement method by this device. As a measuring method, for example, a cell count using a hemocytometer or the like can be used. Then, in the differential value histogram, the number of occurrences at an arbitrary differential value within the high differential value range is obtained, and the normalized number of occurrences is evaluated by dividing this number of occurrences by the number of confluencies or cells obtained as described above. Calculate as an index value.

In the above-described embodiment and other examples, evaluation index values derived based on differential value histograms obtained from differential images are used to compare the three-dimensional shapes of samples containing a plurality of cells with different culture conditions, for example. However, the operator may be able to intuitively evaluate from the image. The simplest way to perform such a sensory evaluation is to display the IHM phase image and the differential image side by side on the screen of the display unit 4 in step S6, and the operator evaluates using the images. .

FIG. 7 is a display example of IHM phase images and differential images of stem cells of the same strain cultured under two different culture conditions with and without an activator. The luminance range of the images as displayed is fixed so that the images can be compared. In the differential image, the outline of the cell appears more clearly than in the IHM phase image, and the operator can qualitatively evaluate the sharpness of the outline in the differential image as the brightness of the image. Sensory evaluation based on such differential images is particularly useful when comparing the three-dimensional shapes of cells in a plurality of samples.

In addition, the operator can confirm the gradient of the slope on the high differential value side of the peak in the differential value histogram from the result of displaying the differential value histogram itself on the screen of the display unit 4 without using the evaluation index value. Alternatively, the degree of inclination in the thickness direction at the peripheral edge of the cell may be sensorily evaluated. In this case, as shown in FIG. 6, if differential value histograms for a plurality of samples to be evaluated are superimposed and displayed in different display colors, visual comparison is facilitated.

Of course, not only one evaluation index value but also a plurality of evaluation index values can be used for evaluation.

In the cell observation apparatus shown in FIG. 1, all processing is performed in the control/processing unit 2, but in general, calculation of phase information based on hologram data and image reconstruction processing require a huge amount of data. Calculations are required. In addition, the load of the cell state determination processing is also large. Therefore, a computer system is used in which a personal computer connected to the microscopic observation unit 1 is used as a terminal device, and the terminal device and a server, which is a high-performance computer, are connected via a communication network such as the Internet or an intranet. Such complicated calculations and processing are performed by a high-performance computer, and the roles are divided so that control of the microscopic observation unit 1 and display processing using processed data are performed by a relatively low-performance personal computer. good too.

In addition, in the cell observation apparatus of the above embodiment, an in-line holographic microscope is used as the microscopic observation unit 1, but other microscopes such as an off-axis type and a phase shift type may be used as long as the microscope is capable of obtaining a hologram. Naturally, it is possible to replace the system with a holographic microscope.

Further, the above-described embodiment and various modifications are examples of the present invention, and it is clear that any further modifications, modifications, and additions within the spirit of the present invention are included in the scope of the claims of the present application.

[Various aspects]
It will be appreciated by those skilled in the art that the exemplary embodiments described above are specific examples of the following aspects.

(Section 1) A method for evaluating a three-dimensional shape of a cell according to one aspect of the present invention is a method for evaluating a three-dimensional shape of a cell,
a measurement step of irradiating one or more target cells to be evaluated with a laser beam and detecting interference light between light passing through the target cells and light passing around the target cells to obtain hologram data; ,
a phase image creating step of performing image reconstruction processing based on the hologram data to create a phase image of the target cell;
a differential image creating step of obtaining a spatial differential value for each pixel from the phase image and creating a differential image;
an evaluation step of evaluating the shape of the target cell based on the arithmetic processing result of the differential value of each pixel in the differential image;
It has

(Section 11) A cell observation device of one aspect of the present invention is a cell observation device using a holographic microscope,
a light source;
Hologram data by detecting interference light between light passing through the target cells and light passing around the target cells when a container containing one or more target cells is irradiated with light emitted from the light source unit, and light passing around the target cells is detected. a detection unit that acquires
a phase image creation unit that performs image reconstruction processing based on the hologram data and creates a phase image of the target cell;
a differential image creation unit that obtains a spatial differential value for each pixel from the phase image and creates a differential image;
an index value calculation unit that calculates an index value for evaluating the three-dimensional shape of the target cell based on the differential value of each pixel included in the range corresponding to the target cell in the differential image;
is provided.

In the three-dimensional cell shape evaluation method according to item 1 and the cell observation device according to item 11, the phase image created by the phase image creation unit based on the hologram data obtained by the detection unit is the thickness of the cell It contains directional information, that is, three-dimensional information of the cell. Therefore, in the differential image created from the phase image by the differential image creation unit, the signal value (differential value) of the pixel in the site where the cell exists reflects the degree of change in the thickness of the cell. Therefore, the signal value of the pixel corresponding to the peripheral edge of the cell represents the degree of inclination in the thickness direction of the peripheral edge of the cell. is relatively large, and if the slope is gentle, the signal value is relatively small.

For example, the index value calculation unit performs predetermined arithmetic processing based on the signal value of each pixel in the differential image, calculates an index value for evaluating the three-dimensional shape of the target cell, and presents it to the user. In addition, instead of presenting such an index value, by displaying a differential image in which the brightness corresponding to the signal value is appropriately adjusted, the user can intuitively evaluate the three-dimensional shape of the target cell.

According to the three-dimensional cell shape evaluation method according to item 1 and the cell observation device according to item 11, for example, the three-dimensional shape of a local site of pluripotent stem cells in culture can be non-destructively and non-invasively can be evaluated. In particular, by presenting the index value calculated from the differential image to the user, even an operator lacking experience and skill can easily perform an accurate and highly reliable evaluation. In addition, it is possible to improve the working efficiency in evaluating the state of cells, and in turn improve the productivity in cell culture.

(Section 2) In the cell three-dimensional shape evaluation method according to Section 1, the evaluation step generates a histogram showing the appearance frequency distribution of the signal values of each pixel included in the range corresponding to the target cell in the differential image. and an index value calculation step of calculating an index value that quantifies the degree of slope of the slope on the high signal value side of the peak appearing in the histogram.

(Item 12) In the cell observation device according to item 11, the index value calculation unit generates a histogram showing the frequency distribution of the signal values of each pixel included in the range corresponding to the target cell in the differential image. and calculating an index value that quantifies the degree of the gradient of the slope on the high signal value side of the peak appearing in the histogram.

Here, the reason why the slope on the high signal value side of the peak appearing in the histogram is used is that the slope on the low signal value side and the area near the peak top mainly reflect the pixel information corresponding to the background portion in the phase image. This is because the slope on the high signal value side exclusively reflects the information of the pixels corresponding to the periphery of the cell.

According to the three-dimensional cell shape evaluation method described in item 2 and the cell observation device described in item 12 , by using a histogram of differential values to calculate the index value, the target cell to be evaluated can be obtained. It is possible to accurately capture the tendency of the entire observed image and obtain an index value that satisfactorily reflects the degree of inclination in the thickness direction of the periphery of the target cell. Thereby, the reliability of evaluation of the three-dimensional shape of cells can be improved.

The method of obtaining the index value from the histogram is not particularly limited as long as it can quantify the degree of the slope of the slope on the high signal value side of the peak appearing in the histogram, but for example, the following method can be adopted. .

(Items 3 and 13) In the three-dimensional cell shape evaluation method according to Item 2 and the cell observation device according to Item 12, the index value is a plurality of can be the ratio of the numbers of occurrences respectively corresponding to given signal values of .

(Items 4 and 14) In the three-dimensional cell shape evaluation method according to Item 2 and the cell observation device according to Item 12, the index value is a plurality of can be the difference between signal values respectively corresponding to a predetermined number of occurrences of .

(Items 5 and 15) In the cell three-dimensional shape evaluation method according to Item 2 and the cell observation device according to Item 12, the index value is the slope of the peak on the high signal value side with an exponential function. It can also be the value of the exponent part of the exponential function when approximated.

(Items 6 and 16) In the cell three-dimensional shape evaluation method according to Item 2 and the cell observation device according to Item 12, the index value is a predetermined value on the slope on the high signal value side of the peak. The number of occurrences corresponding to the signal value can also be a value obtained by dividing the number of cells or confluency observed in the phase image.
It should be noted that the number of cells and confluency are preferably obtained by other measurement methods that do not use differential images.

According to the three-dimensional cell shape evaluation method described in items 3 to 6 and the cell observation device described in items 13 to 16, a highly reliable index value can be calculated by simple arithmetic processing. can.

(Section 7) In the three-dimensional cell shape evaluation method according to any one of items 1 to 6, the evaluation step includes the differential image created by the differential image creating step and the differential image before creating the differential image. and an image display step of simultaneously displaying the phase image on the display section, and the evaluation by the user based on the differential image and the phase image can be made possible.

(Section 8) In the cell three-dimensional shape evaluation method according to any one of Sections 1 to 6, the evaluation step includes the signal value of each pixel included in the range corresponding to the target cell in the differential image and a histogram display step of displaying the histogram on a display unit, allowing the user to evaluate based on the histogram.

According to the three-dimensional cell shape evaluation method described in items 7 and 8, not quantitative evaluation based on the index value described above, but sensory and qualitative evaluation by the user (operator), for example, the three-dimensional shape of the cell It is possible to determine whether cells are good or bad based on the difference in shape. By making judgments by looking at differential images and histograms, the operator can find an abnormal state such as foreign matter contamination that does not appear in the index value or is difficult to appear in, for example, and can avoid erroneous evaluation.

(Item 9) In the three-dimensional cell shape evaluation method according to any one of items 1 to 8, the phase image creating step includes performing noise removal processing using a Gaussian filter on the phase image. A removal step may be included.

(Item 17) Similarly, in the cell observation device according to any one of items 11 to 16, the phase image creation unit performs noise removal processing using a Gaussian filter on the phase image. can be

(Item 10) The three-dimensional cell shape evaluation method according to any one of items 1 to 9, wherein the phase image creating step removes information on portions other than the target cell in the phase image. An extraction step may be included.

(Item 18) Similarly, in the cell observation device according to any one of items 11 to 17, the phase image creation unit removes information on portions other than the target cell in the phase image. can do.

According to the three-dimensional cell shape evaluation method described in items 9 and 10 and the cell observation device described in items 17 and 18, noise appearing in the phase image obtained by image reconstruction is removed. It is possible to obtain a differential image that more accurately reflects the information in the thickness direction of the cell. This improves the accuracy of evaluation of the three-dimensional shape of cells.

DESCRIPTION OF SYMBOLS 1... Microscopic observation part 10... Light source part 11... Image sensor 12... Culture plate 12a... Well 13... Cell 14... Reference light 15... Object light 2... Control and processing part 20... Photography control part 21... Hologram data storage part 22... Phase information calculation unit 23 Image reconstruction unit 24 Reconstructed image data storage unit 25 Differential image creation unit 26 Differential value histogram creation unit 27 Three-dimensional cell shape evaluation index calculation unit 28 Display processing unit 3 Input unit 4 …Display

Claims (16)

A method for evaluating the three-dimensional shape of cells,
a measurement step of irradiating one or more target cells to be evaluated with a laser beam and detecting interference light between light passing through the target cells and light passing around the target cells to obtain hologram data; ,
a phase image creating step of performing image reconstruction processing based on the hologram data to create a phase image of the target cell;
a differential image creating step of obtaining a spatial differential value for each pixel from the phase image and creating a differential image;
an evaluation step of evaluating the shape of the target cell based on the arithmetic processing result of the differential value of each pixel in the differential image;
and the evaluating step includes:
a histogram creating step of creating a histogram showing the appearance frequency distribution of the signal values of each pixel included in the range corresponding to the target cell in the differential image;
an index value calculation step of calculating an index value that quantifies the degree of the slope of the slope on the high signal value side of the peak appearing in the histogram;
Cell three-dimensional shape evaluation method including . 2. The three-dimensional cell shape evaluation method according to claim 1 , wherein said index value is a ratio of the number of appearances corresponding to a plurality of predetermined signal values on the slope of said peak on the high signal value side. 2. The three-dimensional cell shape evaluation method according to claim 1 , wherein said index value is a difference between signal values respectively corresponding to a plurality of predetermined occurrence numbers on a slope on the high signal value side of said peak. 2. The three-dimensional cell shape evaluation method according to claim 1 , wherein said index value is a value of an exponential part of an exponential function obtained by approximating a slope of said peak on the high signal value side with said exponential function. The index value is a value obtained by dividing the number of appearances corresponding to a predetermined signal value on the slope of the high signal value side of the peak by the number or confluency of cells observed in the phase image. The cell three-dimensional shape evaluation method described. The evaluation step includes an image display step of simultaneously displaying the differential image created by the differential image creation step and the phase image before creation of the differential image on a display unit,
2. The three-dimensional cell shape evaluation method according to claim 1, wherein evaluation by a user based on said differential image and phase image is possible. 2. The three-dimensional cell shape evaluation method according to claim 1, wherein said evaluation step includes a histogram display step of displaying said histogram on a display unit, and enables evaluation by a user based on said histogram. 2. The three-dimensional cell shape evaluation method according to claim 1, wherein said phase image creation step includes a noise removal step of performing noise removal processing using a Gaussian filter on the phase image. 2. The three-dimensional cell shape evaluation method according to claim 1, wherein the phase image creation step includes a cell extraction step of removing information on portions other than the target cell in the phase image. A cell observation device using a holographic microscope,
a light source;
Hologram data by detecting interference light between light passing through the target cells and light passing around the target cells when a container containing one or more target cells is irradiated with light emitted from the light source unit, and light passing around the target cells is detected. a detection unit that acquires
a phase image creation unit that performs image reconstruction processing based on the hologram data and creates a phase image of the target cell;
a differential image creation unit that obtains a spatial differential value for each pixel from the phase image and creates a differential image;
an index value calculation unit that calculates an index value for evaluating the three-dimensional shape of the target cell based on the differential value of each pixel included in the range corresponding to the target cell in the differential image;
wherein the index value calculation unit includes a histogram creation unit that creates a histogram showing the appearance frequency distribution of the signal values of each pixel included in the range corresponding to the target cell in the differential image, and the number of peaks appearing in the histogram A cell observation device that calculates an index value that quantifies the degree of slope gradient on the high signal value side . 11. The cell observation device according to claim 10 , wherein said index value is a ratio of the number of appearances corresponding to a plurality of predetermined signal values on the slope of said peak on the high signal value side. 11. The cell observation device according to claim 10 , wherein said index value is a difference between signal values respectively corresponding to a plurality of predetermined numbers of appearances on a slope on the high signal value side of said peak. 11. The cell observation device according to claim 10 , wherein said index value is a value of an exponential part of an exponential function obtained by approximating a slope of said peak on the high signal value side by said exponential function. 11. The index value is a value obtained by dividing the number of occurrences corresponding to a predetermined signal value on the slope on the high signal value side of the peak by the number or confluency of cells observed in the phase image. The cell observation device described. 11. The cell observation device according to claim 10 , wherein the phase image creating section performs noise removal processing using a Gaussian filter on the phase image. 11. The cell observation device according to claim 10 , wherein the phase image creating section removes information on portions other than the target cell in the phase image.

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