JP6884095B2 - Counting device and counting method - Google Patents

Description

The present invention relates to an apparatus and method for counting the number of objects in a region of interest.

Non-Patent Documents 1 to 3 describe techniques for counting the number of cells. The counting techniques described in these documents count the number of cells in an image or region of interest by segmenting individual cell regions in an image acquired as digital data.

International Publication No. 2016/121250

K. Liimatainen, P. Ruusuvuori, L. Latonen and H. Huttunen, "Supervised method for cell counting from brightfield focus stacks," 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, 2016, pp.391-394. H. A. Khan et.al, “CountingClustered Cells using Distance Mapping,” International Conference on Informatics, Electronics & Vision (ICIEV), 2013. Koyuncu C F, Arslan S, Durmaz I, Cetin-Atalay R, Gunduz-Demir C, “Smart Markers for Watershed-Based CellSegmentation,” PLOS ONE 7 (11): e48664. 2012.

In the counting techniques described in Non-Patent Documents 1 to 3, the time required for segmentation of the cell region is long, and the counting error is large. In particular, when the cells are aggregated as in the case where the cells are stacked one above the other, the counting error is even larger. Such a problem exists not only when counting the number of cells but also when counting the number of other objects.

The present invention has been made to solve the above problems, and an object of the present invention is to provide a counting device and a counting method capable of accurately counting the number of objects in the region of interest.

The counting device of the present invention has (1) an interference image acquisition unit that acquires an interference image including one or a plurality of objects, and (2) an optical thickness image based on the interference image acquired by the interference image acquisition unit. Is obtained, and based on the integrated value of the optical thickness in the region of interest in the optical thickness image and the integrated value of the average optical thickness per object, the integrated value in the region of interest. It is provided with a calculation unit for obtaining the number of objects.

In the counting device according to one aspect of the present invention, the calculation unit is based on the region of the interference image acquired by the interference image acquisition unit in which the objects are distributed at a density lower than the distribution density of the objects in the region of interest. The optical thickness image is obtained to obtain the integrated value of the average optical thickness per object.

In the counting device according to the other aspect of the present invention, when there are a plurality of types of objects, the arithmetic unit stores the integrated value of the average optical thickness of each of the plurality of types of objects. The integral value of the average optical thickness per object of any kind is selected and used.

In the counting device according to still another aspect of the present invention, the interference image acquisition unit is an integrated value of the average optical thickness per object as compared with the case of acquiring the interference image including the region of interest. The optical magnification is high when acquiring an interference image including a region for obtaining.

In the counting device according to still another aspect of the present invention, the interference image acquisition unit acquires an interference image including an aggregate of objects, and the calculation unit is obtained from the interference image acquired by the interference image acquisition unit. Optical Thickness An object contained in an aggregate based on the integrated value of the optical thickness of the aggregate in the image and the integrated value of the average optical thickness per object. Find the number.

The counting method of the present invention is based on (1) an image acquisition step of acquiring an interference image including one or a plurality of objects by an interference image acquisition unit, and (2) an optical image based on the interference image acquired by the interference image acquisition unit. The target thickness image is obtained, and based on the integrated value of the optical thickness in the region of interest in the optical thickness image and the integrated value of the average optical thickness per object, It includes a calculation step for finding the number of objects in the region of interest.

In the counting method according to one aspect of the present invention, in the calculation step, among the interference images acquired by the interference image acquisition unit, based on the region where the objects are distributed at a density lower than the distribution density of the objects in the region of interest. The optical thickness image is obtained to obtain the integrated value of the average optical thickness per object.

In the counting method according to the other aspect of the present invention, when there are a plurality of types of objects in the calculation step, the integrated value of the average optical thickness of each of the plurality of types of objects is stored. The integral value of the average optical thickness per object of any kind is selected and used.

In the counting method according to still another aspect of the present invention, in the image acquisition step, the average optical thickness per object is compared with the case where the interference image including the region of interest is acquired by the interference image acquisition unit. The optical magnification is high when the interference image acquisition unit acquires an interference image including a region for obtaining the integrated value.

In the counting method according to still another aspect of the present invention, in the image acquisition step, an interference image including an aggregate of objects is acquired by the interference image acquisition unit, and in the calculation step, the interference image acquired by the interference image acquisition unit. Included in the aggregate based on the integrated value of the optical thickness of the aggregate in the optical thickness image obtained from and the integrated value of the average optical thickness per object. Find the number of objects to be used.

In the counting method according to still another aspect of the present invention, the flatness of the optical thickness image in the background is less than 5 nm in terms of the standard deviation of the optical thickness.

In the counting method according to still another aspect of the present invention, the object may be a cell. In the counting method according to still another aspect of the present invention, in the image acquisition step, one or a plurality of aggregates in a population including a plurality of aggregates of cells as an object are separated into individual cells. The 1 interference image is acquired by the interference image acquisition unit, and the second interference image is acquired by the interference image acquisition unit for the remaining one or more aggregates in the population, and is obtained from the first interference image in the calculation step. The integrated value of the average optical thickness per cell is obtained based on the obtained optical thickness image, and the optical aggregate is based on the optical thickness image obtained from the second interference image. The integrated value of the thickness is obtained, and the number of cells contained in the aggregate is calculated based on the integrated value of the optical thickness of the aggregate and the integrated value of the average optical thickness per cell. Ask.

In the counting method according to still another aspect of the present invention, in the image acquisition step, an interference image including a region for obtaining an integrated value of the average optical thickness per cell as an object is obtained as an interference image. At the time of acquisition by the acquisition unit, the nucleus of the cell is stained to acquire the interference image.

According to the present invention, the number of objects in the region of interest can be accurately counted.

FIG. 1 is a diagram showing the configuration of the counting device 1 of the first embodiment. FIG. 2 is a diagram showing a sample configuration. FIG. 3 is a diagram showing five interference images I1 to I5 acquired by the interference image acquisition unit 2. FIG. 4 is a diagram showing an optical thickness image obtained by the calculation unit 3 based on the interference images I1 to I5 of FIG. FIG. 5 is a diagram showing a cell nucleus stained image in the same field of view as the interference image of FIG. FIG. 6 is a table summarizing the integral value of the optical thickness, the estimated number of cells, the true number of cells, and the error for each of the nine visual fields. FIG. 7 is a graph showing the estimated cell number and the true cell number for each of the nine visual fields. FIG. 8 is a flowchart illustrating the operation of the counting device 1 of the first embodiment and the counting method of the first embodiment. FIG. 9 is a diagram showing a field of view for explaining a first preferred example of a method of obtaining an integral value of the average optical thickness per cell. FIG. 9A shows a field of view in which cells are distributed at a low density. FIG. 9B shows a field of view in which cells are densely distributed. FIG. 10 is a diagram showing a field of view for explaining a second preferred example of a method of obtaining an integral value of the average optical thickness per cell. FIG. 11 is a schematic view of an optical thickness image of the analysis target region. FIG. 12 is a schematic view of an optical thickness image when each well of the multi-well plate is used as a region of interest for analysis. FIG. 13 is a diagram showing a plurality of cell populations 83 in the culture medium 82 placed in the container 80. FIG. 14 is a diagram showing cells 73 individually dispersed and placed in wells 91 of container 90, and cell population 83 placed in wells 92 of container 90. FIG. 15 is a flowchart illustrating a procedure for counting the number of cells contained in a cell population. FIG. 16 is a diagram showing an optical thickness image obtained from an interference image acquired by the interference image acquisition unit 2 at different optical magnifications. FIG. 16A shows a low magnification optical thickness image that includes both the region of interest ROImain and the region ROIcon. FIG. 16B shows a high magnification optical thickness image containing only the region ROIcon. FIG. 17 is a flowchart illustrating a procedure for counting the number of cells by switching the optical magnification of the interference image acquisition unit 2.

Hereinafter, embodiments for carrying out the present invention will be described in detail with reference to the accompanying drawings. In the description of the drawings, the same elements are designated by the same reference numerals, and duplicate description will be omitted. The present invention is not limited to these examples, and is indicated by the scope of claims, and is intended to include all modifications within the meaning and scope equivalent to the scope of claims.

(First Embodiment)
FIG. 1 is a diagram showing the configuration of the counting device 1 of the first embodiment. The counting device 1 includes an interference image acquisition unit 2 and a calculation unit 3. The interference image acquisition unit 2 includes a light source 11, a beam splitter 12, an objective lens 13, an objective lens 14, a reference mirror 15, a tube lens 16, a beam splitter 17, an imager 18, a piezo element 21, a photodetector 22, and a phase control circuit. 23 is included.

The interference image acquisition unit 2 has a Michelson interferometer as a two-luminous flux interferometer, and acquires an interference image of one or a plurality of objects. The calculation unit 3 obtains an optical thickness image based on the interference image acquired by the interference image acquisition unit 2. Further, the calculation unit 3 is based on the integral value of the optical thickness in the region of interest in the optical thickness image and the integral value of the average optical thickness per object. , The number of the objects in the region of interest is obtained.

The object is not limited to a specific cell or biological sample. For example, as objects, cultured cells, immortalized cells, primary cultured cells, cancer cells, fat cells, liver cells, myocardial cells, nerve cells, glial cells, somatic stem cells, embryonic stem cells, pluripotent stem cells, iPS Examples thereof include cells and cell clusters (colony or spheroid) formed based on the cells. Further, the object is not limited to a living body, and examples thereof include industrial samples such as a metal surface, a semiconductor surface, a glass surface, an inside of a semiconductor element, a resin material surface, a liquid crystal, and a polymer compound.

In the following description of this embodiment, it is assumed that the object is cells 73 in the culture medium 72 placed in the container 70, as shown in FIG. 2 as a configuration example of the sample. A reflection-enhancing coating 71 is provided inside the bottom of the container 70.

The light source 11 outputs light. Preferably, the light source 11 outputs incoherent light. The light source 11 is, for example, a lamp-based light source such as a halogen lamp, an LED (Light emitting diode) light source, an SLD (Superluminescent diode) light source, an ASE (Amplified spontaneous emission) light source, or the like.

The beam splitter 12 is optically coupled to the light source 11 to form a Michelson interferometer, which is a two-luminous flux interferometer. The beam splitter 12 may be, for example, a half mirror having a ratio of transmittance to reflectance of 1: 1. The beam splitter 12 splits the light output from the light source 11 into two luminous fluxes to obtain first-branched light and second-branched light. The beam splitter 12 outputs the first branch light to the objective lens 13 and outputs the second branch light to the objective lens 14.

Further, the beam splitter 12 inputs the first branch light reflected by the reflection enhancing coating 71 and passed through the objective lens 13, and inputs the second branch light reflected by the reference mirror 15 and passed through the objective lens 14. Then, the beam splitter 12 combines the input first branch light and the second branch light, and outputs the interference light to the tube lens 16.

The objective lens 13 is optically coupled to the beam splitter 12 and focuses the first branch light output from the beam splitter 12 on the cells 73 in the container 70. Further, the objective lens 13 inputs the first branch light reflected by the reflection enhancing coating 71 and outputs it to the beam splitter 12.

The objective lens 14 is optically coupled to the beam splitter 12 and focuses the second branch light output from the beam splitter 12 on the reflecting surface of the reference mirror 15. Further, the objective lens 14 inputs the second branch light reflected by the reflecting surface of the reference mirror 15 and outputs the second branch light to the beam splitter 12.

The tube lens 16 is optically coupled to the beam splitter 12 constituting the interference optical system, and the interference light output from the beam splitter 12 is imaged on the imaging surface of the imager 18 via the beam splitter 17. The beam splitter 17 splits the light arriving from the tube lens 16 into two, outputs one branched light to the imager 18, and outputs the other branched light to the photodetector 22. The beam splitter 17 may be, for example, a half mirror.

The imager 18 is optically coupled to the beam splitter 17 and receives the interference light arriving from the beam splitter 17 to acquire an interference image. The imager 18 is an image sensor such as a CCD area image sensor and a CMOS area image sensor, for example.

The piezo element 21 moves the reflective surface in a direction perpendicular to the reflective surface of the reference mirror 15. The piezo element 21 can adjust the optical path length difference (that is, the phase difference) between the two luminous fluxes in the two luminous flux interferometer by the movement of the reflecting surface. The piezo element 21 can determine the position of the reflecting surface of the reference mirror 15 with a resolution less than the wavelength. In the two-luminous flux interferometer, the optical path length difference between the two luminous fluxes is variable.

Assuming that the optical distance from the beam splitter 12 to the reflection enhancing coating 71 is L1 and the optical distance from the beam splitter 12 to the reflection surface of the reference mirror 15 is L2, the optical path length between the two light beams in the two light beam interferometers. The difference is 2 (L1-L2). If the optical path length difference is equal to or less than the coherent length of the output light of the light source 11, the imager 18 can acquire a clear interference image. When the central wavelength of the output light of the light source 11 is λ0, the phase difference Δφ between the two luminous fluxes in the two luminous flux interferometer is expressed by the following equation.

The photodetector 22 is optically coupled to the beam splitter 17, receives interference light arriving from the beam splitter 17, and outputs a detection signal. The photodetector 22 may be, for example, a photodiode, an avalanche photodiode, a photomultiplier tube, a line sensor (linear sensor), a CCD area image sensor, a CMOS area image sensor, or the like.

The phase control circuit 23 is electrically connected to the photodetector 22 and inputs a detection signal output from the photodetector 22. Further, the phase control circuit 23 is electrically connected to the piezo element 21 and controls the adjustment operation of the optical path length difference by the piezo element 21. The phase control circuit 23 detects the optical path length difference between the two luminous fluxes in the two luminous flux interferometer based on the input detection signal. Then, the phase control circuit 23 controls the adjustment operation of the optical path length difference by the piezo element 21 by the feedback control based on the detection result. As a result, the optical path length difference between the two luminous fluxes in the two luminous flux interferometer can be stabilized by the set value (locked state).

The interference image acquisition unit 2 can acquire an interference image of an object (cell 73) by taking an image with the imager 18 in the locked state. The calculation unit 3 obtains an optical thickness image of the object based on the interference image acquired by the imager 18 of the interference image acquisition unit 2. The calculation unit 3 may be a computer such as a personal computer or a tablet terminal. Further, the calculation unit 3 includes an input unit (keyboard, mouse, tablet terminal, etc.) that receives input from the operator, and a display unit (display, tablet terminal, etc.) that displays an interference image, an optical thickness image, and the like. You may have it. Further, it is preferable that the calculation unit 3 has a function of displaying an image or the like on a screen and receiving an operator's designation of an area on the screen.

The light output from the light source 11 is split into two luminous fluxes by the beam splitter 12 to be the first branch light and the second branch light, and the first branch light and the second branch light are output from the beam splitter 12. The first branching light output from the beam splitter 12 is focused on the cells 73 in the container 70 by the objective lens 13 and reflected by the reflection enhancing coating 71 provided inside the bottom of the container 70. The first branch light reflected by the reflection enhancing coating 71 is input to the beam splitter 12 via the objective lens 13. The second branch light output from the beam splitter 12 is focused by the objective lens 14 on the reflecting surface of the reference mirror 15, and is reflected by the reflecting surface. The second branch light reflected by the reflecting surface of the reference mirror 15 is input to the beam splitter 12 via the objective lens 14.

The first branching light input from the objective lens 13 to the beam splitter 12 and the second branching light input from the objective lens 14 to the beam splitter 12 are combined by the beam splitter 12 and interfered with by the beam splitter 12. Is output. After passing through the tube lens 16, the interference light is split into two by the beam splitter 17, and is received by the imager 18 and the photodetector 22 respectively. A detection signal is output from the photodetector 22 that has received the interference light, and the phase control circuit 23 detects the optical path length difference between the two luminous fluxes in the two luminous flux interferometer based on the detection signal. Then, the feedback control of the piezo element 21 by the phase control circuit 23 brings the optical path length difference between the two luminous fluxes in the two luminous flux interferometers to a stabilized state (locked state) at the set value. An interference image is acquired by the imager 18 that receives the interference light in the locked state, and the interference image is output to the calculation unit 3. Then, the calculation unit 3 obtains an optical thickness image of the object (cell 73) based on the interference image.

The counting device 1 obtains an optical thickness image from a plurality of interference images by a phase shift method. That is, the interference image acquisition unit 2 acquires an interference image in each state by setting the optical path length difference of the two-luminous flux interferometer to be stabilized at each of a plurality of different set values. The calculation unit 3 can obtain a phase image based on a plurality of interference images acquired by the interference image acquisition unit 2 (see Patent Document 1). Further, the calculation unit 3 can obtain an optical thickness image from this phase image.

For example, the interference image acquisition unit 2 stabilizes the phase difference of the interference light at a certain initial phase by feedback control using the piezo element 21, the photodetector 22, and the phase control circuit 23, and stabilizes the phase difference. The interference image I1 is acquired by the imager 18 in this state. Next, the interference image acquisition unit 2 stabilizes the phase difference of the interference light by "initial phase + π / 2" by using the piezo element 21, the photodetector 22, and the phase control circuit 23, and stabilizes the phase difference. The interference image I2 is acquired by the imager 18 in this state. Similarly, the interference image acquisition unit 2 acquires the interference image I3 by the imager 18 in a state where the phase difference of the interference light is stabilized by "initial phase + π", and the phase difference of the interference light is "initial phase + 3π". The interference image I4 is acquired by the imager 18 in a state stabilized by / 2 ”, and the interference image I5 is acquired by the imager 18 in a state where the phase difference of the interference light is stabilized by“ initial phase + 2π ”.

Using these five interference images I1 to I5, the calculation unit 3 performs the calculation of the following equation (2) to obtain the phase image Φ. arg is an operator that gets the complex number transformation. i is an imaginary unit. After performing the phase unwrap processing and the background distortion correction processing on the phase image Φ, the calculation unit 3 obtains the optical thickness OT by the following equation (3) to obtain the optical thickness image. It should be noted that each parameter appearing in these equations is a function of the pixel position (x, y), and the calculation of these equations is performed for each pixel.

For background correction, a good (flat) background can be obtained by using a polynomial function (for example, Zernike polynomial) with x and y as variables. Further, when the spatial frequency of the distortion component in the background is sufficiently lower than the spatial frequency of each sample, high-pass filtering processing can be performed. The flatness of the background of the optical thickness image is preferably less than 5 nm in terms of the standard deviation of the optical thickness.

This optical thickness OT represents the amount of phase change given to the light transmitted through the sample. The thickness of the cells 73 is d, an average refractive index of the cells 73 and n _c, and the refractive index of the culture solution 72 and n _m, the optical thickness OT is given by the following equation.

Hereinafter, the embodiments will be described in more detail while showing the configuration and image examples of specific examples. An LED having a center wavelength of 730 nm was used as the light source 11. The focal lengths of the objective lens 13 and the objective lens 14 were 18 mm. The focal length of the tube lens 16 was 125 mm. The image sensor size of the image sensor 18 was 4.8 mm × 3.6 mm. The number of pixels of the imager 18 was 1280 pixels × 960 pixels. The field of view size on the sample surface was 691.2 μm × 518.4 μm. Melanoma-derived cultured cells SK-MEL-28 were used as sample cells 73.

FIG. 3 is a diagram showing five interference images I1 to I5 acquired by the interference image acquisition unit 2. FIG. 4 is a diagram showing an optical thickness image obtained by the calculation unit 3 based on the interference images I1 to I5 of FIG. By dividing the integrated value of the optical thickness by the number of cells from the optical thickness image of FIG. 4, the integrated value of the average optical thickness per cell can be obtained.

This optical thickness image contains 53 cells. Here, the number of cells at the edge of the visual field and a part of which is out of the visual field was counted as 0.5. The true value of the number of cells in this optical thickness image was determined by visually counting in the cell nucleus stained image of FIG. FIG. 5 is a diagram showing a cell nucleus stained image in the same field of view as the interference image of FIG.

The integrated value of the optical thickness was obtained by multiplying the total optical thickness OT of all the pixels by the sample area (0.54 μm × 0.54 μm) per pixel. As a result, the integrated value of the optical thickness was ^{1.12 × 10 7 (nm × μm} 2). Therefore, the integral value of the average optical thickness per cell was estimated to be ^{2.12 × 10 5 (nm × μm} 2 / cell).

Optical thickness images of nine visual fields different from the interference image of FIG. 3 are acquired, and the integrated value of the optical thickness of each visual field is the integrated value of the average optical thickness per cell (2). by dividing ^{.12 × 10 5 (nm × μm} 2 / cell)), were estimated the number of cells present in each field. FIG. 6 is a table summarizing the integral value of the optical thickness, the estimated number of cells, the true number of cells, and the error for each of the nine visual fields (FOV # 1 to 9). FIG. 7 is a graph showing the estimated cell number and the true cell number for each of the nine visual fields. The true number of cells was determined by separately acquiring a cell nucleus stained image of the same field of view and visually counting the image. FIG. 6 also shows the error between the estimated cell number and the true cell number. The average absolute value of the error was 4.5%. It was confirmed that the counting method of the present embodiment can accurately count the number of cells in a short time.

In the above example, a cell nucleus stained image was used to accurately count the number of cells when obtaining the integrated value of the average optical thickness per cell. In addition, in order to verify the estimation result of the cell number, a nuclear-stained image was also used for the region of interest to be counted. Staining of cell nuclei is not essential, as it ensures verification.

Next, the operation of the counting device 1 of the present embodiment and the procedure of the counting method of the present embodiment will be described with reference to FIG. FIG. 8 is a flowchart illustrating the operation of the counting device 1 of the first embodiment and the counting method of the first embodiment.

In step S1 (image acquisition step), the two-beam interferometer is optically adjusted so that the interference image acquisition unit 2 can acquire the interference image, and the interference image acquisition unit 2 acquires a plurality of interference images. .. In steps S2 to S5 (calculation step), the calculation unit 3 obtains an optical thickness image based on a plurality of interference images acquired by the interference image acquisition unit 2, and the region of interest in the optical thickness image is obtained. The number of cells in the region of interest is determined based on the integrated value of the optical thickness within and the integrated value of the average optical thickness per cell.

More specifically, in step S2, an optical thickness image is obtained based on the plurality of interference images acquired by the interference image acquisition unit 2, and background correction is performed on this image. In step S3, a plurality of optical thickness images are joined, if necessary. In step S4, a region of interest (ROI) containing a plurality of cells is set. The area of interest may be one or may be plural. In step S5, the integral value of the optical thickness in the region of interest in the optical thickness image is obtained, and this is divided by the integral value of the average optical thickness per cell to be of interest. Estimate the number of cells in the region.

Next, a preferred example of a method for obtaining an integral value of the average optical thickness per cell will be described.

FIG. 9 is a diagram showing a field of view for explaining a first preferred example of a method of obtaining an integral value of the average optical thickness per cell. In this method, in the calculation unit 3, among the interference images acquired by the interference image acquisition unit 2, the region (FIG. 9) in which the cells are distributed at a density lower than the distribution density of the cells in the region of interest (FIG. 9B). Based on (a)), the optical thickness image is obtained to obtain the integrated value of the average optical thickness per cell. FIG. 9 (a) shows the distribution of cells having a density lower than the distribution density of cells in the region of interest. FIG. 9B shows the distribution of cells within the region of interest. In the region where the distribution density of cells is low (FIG. 9A), it is preferable that the individual cells are distributed apart from each other. In this way, by using the optical thickness image of the region where the distribution density of cells is low (FIG. 9 (a)), the number of cells in the region can be accurately counted, so that the number of cells per cell can be accurately counted. The integral value of the average optical thickness can be accurately obtained. The region where the distribution density of cells is low (FIG. 9 (a)) and the region of interest (FIG. 9 (b)) may be different regions in the same visual field, or are included in different visual fields. There may be.

FIG. 10 is a diagram showing a field of view for explaining a second preferred example of a method of obtaining an integral value of the average optical thickness per cell. As shown in this figure, the region A for obtaining the integral value of the average optical thickness per cell is a part of the region of interest B and the distribution density of the cells is low. There may be. In region A, the individual cells are preferably distributed apart from each other. In this case as well, if the optical thickness image of the region A having a low distribution density of cells is used, the number of cells in the region can be accurately determined, so that the average optical thickness per cell can be obtained. The integral value of the optics can be obtained accurately.

Further, when calculating the integral value of the average optical thickness per cell, it may be calculated every time the number of cells in the region of interest is calculated, or the value obtained for cells of the same type before that. May be stored in the storage unit of the calculation unit 3 and the stored value may be used. The calculation unit 3 stores an integral value of the average optical thickness per cell of each of the plurality of types, and the calculation unit 3 stores the average optical thickness per cell of any one of the types. It is also preferable to select and use the integral value of the optics.

When using a cell line, the calculation unit 3 records the integrated value of the average optical thickness of each of several cell lines measured in advance by the storage unit. It is possible to keep it. In this case, the operator simply specifies the cell type to obtain the in-plane integral value of the optical thickness of the visual field, and the integral value of the average optical thickness per cell type specified. The number of cells can be obtained by dividing by. The integrated value of the average optical thickness per cell may be stored in the storage unit of the calculation unit 3, or may be downloaded via the communication network and stored in the storage unit of the calculation unit 3 in a short period of time. The memorized one may be used. In addition, examples of the strained cell types frequently used in the field of biology include HeLa, CHO, and MCF7.

(Second Embodiment)
The second embodiment counts the number of cells contained in a cell population as an aggregate of objects. The counting device of the second embodiment has a configuration similar to the configuration shown in FIG.

In the present embodiment, the region of interest to be analyzed includes a cell population in which cells overlap each other. With the conventional cell number counting method, it has been difficult to count the number of cells contained in a cell population. A cell population is a collection of multiple cells in a colony-like or spheroid-like manner. A colony is typically a plurality of cells forming a spatially cohesive population. A spheroid is a cell population in which cells are typically stacked to a thickness of 100 μm or more to form a sphere. A spheroid typically contains 1000 to tens of thousands of cells.

FIG. 11 is a schematic view of an optical thickness image of the analysis target region. In this figure, three regions of interest ROI1, ROI2 and ROI3 are set, and one cell population is included in each region of interest. The interference image acquisition unit 2 acquires an interference image including these three regions of interest in the field of view. The calculation unit 3 obtains an optical thickness image from the interference image acquired by the interference image acquisition unit 2, and calculates the integrated value of the optical thickness of the cell population in each region of interest in the optical thickness image. Ask. Then, the calculation unit 3 divides the integral value of the optical thickness of each cell population by the integral value of the average optical thickness per cell, so that the object included in each cell population is included. The number of can be obtained. The integrated value of the average optical thickness per cell used here may be measured separately or a typical value obtained in advance may be used.

FIG. 12 is a schematic view of an optical thickness image when each well of the multi-well plate is used as a region of interest for analysis. In this figure, of the six wells, five wells have regions of interest ROI-1 to ROI-5, and each well contains one cell population. The remaining one well is set with a region ROIcon containing a plurality of individually isolated cells. In this case, the integral value of the average optical thickness per cell can be obtained from the optical thickness image of the region ROIcon. Then, using this, the number of objects contained in each cell population of the regions of interest ROI-1 to ROI-5 can be determined.

Further, in the analysis of a cell population, when the integral value of the average optical thickness per cell is unknown, the method described below can be used. This will be described with reference to FIGS. 13 to 15. FIG. 13 is a diagram showing a plurality of cell populations 83 in the culture medium 82 placed in the container 80. FIG. 14 is a diagram showing cells 73 individually dispersed and placed in wells 91 of container 90, and cell population 83 placed in wells 92 of container 90. FIG. 15 is a flowchart illustrating a procedure for counting the number of cells contained in a cell population.

First, a population composed of a plurality of cell populations is prepared (step S10). One or more cell populations 83 of the population are randomly taken out (step S11), dispersed in individual cells 73 by operations such as pipetting, and placed in wells 91 of the container 90 shown in FIG. Insert (step S12). The number of cell populations to be dispersed preferably does not exceed half of the total number. The samples dispersed in each cell 73 are pipetted until sufficiently uniform to form a cell suspension, which is then seeded in wells 91 at a low density for easy individual discrimination. This is the control group. On the other hand, the remaining cell population 83, which has not been dispersed and has maintained its morphology as a cell population, is directly placed in the well 92 of the container 90 shown in FIG. 14 (step S13). This is referred to as a cell measurement group.

The well 91 and the well 92 are physically separated from each other. A reflection-enhancing coating 93 is provided inside the bottom of the well 91. A reflection-enhancing coating 94 is provided inside the bottom of the well 92. Wells 91 and 92 are sealed with a cover glass 95. The observation container for cell suspension and the observation container for spheroid observation need only have the well portion for injecting the sample isolated, and even if the outer portion of the container is physically integrated. I do not care. The container 90 shown in FIG. 14 has two wells 91 and 92 in which the exterior portion is integrated but the sample injection portion is isolated. The low-density seeded cells 73 are seeded in one well 91, and the cell population 83 is seeded in the other well 92.

After such pretreatment, the interference image acquisition unit 2 acquires an interference image of the low-density seeded cells 73 in the well 91. The calculation unit 3 obtains an optical thickness image from the interference image, corrects the background (step S14), joins a plurality of optical thickness images as necessary (step S15), and joins the plurality of optical thickness images (step S15). The region ROIcon is set in the image (step S16), and the integrated value of the average optical thickness per cell in the region ROIcon is obtained (step S17).

In addition, the interference image acquisition unit 2 acquires an interference image of the cell population 83 in the well 92. The calculation unit 3 obtains an optical thickness image from the interference image, corrects the background (step S18), joins a plurality of optical thickness images as necessary (step S19), and joins the plurality of optical thickness images (step S19). The region of interest ROI is set in the image (step S20), and the integrated value of the optical thickness of the cell population 83 in the region of interest ROI is obtained (step S21).

Then, the calculation unit 3 determines the number of cells included in the cell population 83 based on the integral value of the optical thickness of the cell population 83 and the integral value of the average optical thickness per cell. Obtain (step S22).

The container 90 used here is preferably sterilized before observation. If the container 90 is sterilized, the cell population 83 seeded in the well 92 is neither dispersed in each cell nor stained, so that the culture is continued even after counting the number of cells. It becomes possible. In addition, in the process of measurement by this method, some cell populations among the cell populations originally included in the population lose their functions as cell populations when they are seeded at low density in a dispersed state. Most other cell populations in the population can remain functioning as a cell population even after a 100% cell count test.

Although the container 90 has been described as a multi-well plate, the container in which the cells 73 seeded at a low density are seeded and the container in which the cell population is seeded may be physically separated. In that case, the container 80 shown in FIG. 13 may be used as it is as one of the containers.

(Third Embodiment)
The third embodiment utilizes the fact that the optical magnification when the interference image acquisition unit 2 acquires the interference image is variable, as compared with the case where the interference image acquisition unit 2 acquires the interference image including the region of interest. The optical magnification is increased when the interference image acquisition unit 2 acquires an interference image including a region for obtaining an integrated value of the average optical thickness per cell.

When the cell population, which is an aggregate of cells, is set as the region of interest, it is desirable to image a large area with a low-magnification optical system when observing the region of interest ROImain for which the number of cells is to be counted. On the other hand, when determining the integrated value of the average optical thickness per cell, it is difficult to discriminate each cell from the image captured by the low-magnification optical system, so the image is captured by the high-magnification optical system. It is desirable to do. Therefore, preferably, the region ROIcon for switching the optical magnification of the interference image acquisition unit 2 to obtain the integrated value of the average optical thickness per cell is observed at a high magnification, while observing at a high magnification. It is advisable to observe the ROImain region of interest for which the number of cells is to be counted at a low magnification.

FIG. 16 is a diagram showing an optical thickness image obtained from an interference image acquired by the interference image acquisition unit 2 at different optical magnifications. FIG. 16A shows an optical thickness image including both the region of interest ROImain for which the number of cells is to be counted and the region ROIcon for obtaining the integral value of the average optical thickness per cell. Shown. FIG. 16B shows an optical thickness image that does not include the region of interest ROImain but contains only the region ROIcon. The optical magnification is higher when the optical thickness image of FIG. 16B is acquired than when the optical thickness image of FIG. 16A is acquired.

FIG. 17 is a flowchart illustrating a procedure for counting the number of cells by switching the optical magnification of the interference image acquisition unit 2.

First, a cell sample containing both the low-density region and the high-density region is prepared (step S30). In the low density region as compared with the high density region, cells are dispersed and present at a low density. The interference image acquisition unit 2 acquires an interference image at a high magnification in a low density region. The calculation unit 3 obtains an optical thickness image from the interference image, performs background correction (step S31), joins a plurality of optical thickness images as necessary (step S32), and joins a plurality of optical thickness images (step S32), and the optical thickness is obtained. The region ROIcon is set in the image (step S33), and the integrated value of the average optical thickness per cell in the region ROIcon is obtained (step S34).

In addition, the interference image acquisition unit 2 acquires an interference image at a low magnification in a high-density region. The calculation unit 3 obtains an optical thickness image from the interference image, performs background correction (step S35), joins a plurality of optical thickness images as necessary (step S36), and joins the plurality of optical thickness images (step S36). The region of interest ROI is set in the image (step S37), and the integrated value of the optical thickness of the cells in the region of interest ROI is obtained (step S38). Then, the calculation unit 3 obtains the number of cells in the high-density region based on the integral value of the optical thickness in the high-density region and the integral value of the average optical thickness per cell (the integral value of the optical thickness in the high-density region). Step S37).

By switching the optical magnification of the interference image acquisition unit 2, the number of pixels corresponding to one cell region differs between the optical thickness image in the high density region and the optical thickness image in the low density region. The sum of the optical thicknesses in one cell region is different from each other. However, if a value obtained by multiplying the total optical thickness by the sample area per pixel is used as the integrated value of the optical thickness, the sample area per pixel is inversely proportional to the optical magnification, regardless of the optical magnification. However, the integrated value of the optical thickness in the same region is the same. When the total optical thickness is used as the integral value of the optical thickness, the calibration may be performed using the square value of the optical magnification.

Further, as a feature of the interference image acquisition unit 2, the optical thickness is a physical quantity determined by the refractive index and the thickness of the sample, and has the property of not depending on the magnification of the imaging system and the intensity of the illumination light. Therefore, there is no problem even if the integral value of the average optical thickness per cell is obtained at high magnification and then the calculation for the image in the low magnification region is performed.

1 ... Counting device, 2 ... Interference image acquisition unit, 3 ... Calculation unit, 11 ... Light source, 12 ... Beam splitter, 13, 14 ... Objective lens, 15 ... Reference mirror, 16 ... Tube lens, 17 ... Beam splitter, 18 ... Imager, 21 ... Piezo element, 22 ... Optical detector, 23 ... Phase control circuit, 70 ... Container, 71 ... Reflection enhancing coating, 72 ... Culture solution, 73 ... Cell, 80 ... Container, 82 ... Culture solution, 83 ... Cell population, 90 ... container, 91, 92 ... well, 93, 94 ... reflection-enhancing coating, 95 ... cover glass.

Claims (14)

An interference image acquisition unit that acquires an interference image containing one or more objects,
An optical thickness image is obtained based on the interference image acquired by the interference image acquisition unit, and the integrated value of the optical thickness in the region of interest in the optical thickness image and one of the objects. A calculation unit that obtains the number of the objects in the region of interest based on the integrated value of the average optical thickness per hit.
A counting device equipped with. The calculation unit has the optical thickness based on the region of the interference image acquired by the interference image acquisition unit in which the object is distributed at a density lower than the distribution density of the object in the region of interest. An image is obtained to obtain an integrated value of the average optical thickness of each of the objects.
The counting device according to claim 1. When there are a plurality of types of the objects, the calculation unit stores an integral value of the average optical thickness of each of the plurality of types of objects, and any of the above types of objects. Select and use the integral value of the average optical thickness per object.
The counting device according to claim 1 or 2. The interference image acquisition unit obtains an interference image including a region for obtaining an integrated value of an average optical thickness per object as compared with the case of acquiring an interference image including the region of interest. High optical magnification when acquiring,
The counting device according to any one of claims 1 to 3. The interference image acquisition unit acquires an interference image including an aggregate of objects, and obtains an interference image.
The calculation unit is the integral value of the optical thickness of the aggregate in the optical thickness image obtained from the interference image acquired by the interference image acquisition unit, and the average value per object. The number of objects contained in the aggregate is obtained based on the integral value of the optical thickness.
The counting device according to any one of claims 1 to 4. An image acquisition step of acquiring an interference image including one or a plurality of objects by an interference image acquisition unit, and
An optical thickness image is obtained based on the interference image acquired by the interference image acquisition unit, and the integrated value of the optical thickness in the region of interest in the optical thickness image and one of the objects. An arithmetic step of finding the number of objects in the region of interest based on the integrated value of the average optical thickness per hit, and
A counting method comprising. In the calculation step, the optical thickness of the interference image acquired by the interference image acquisition unit is based on the region in which the object is distributed at a density lower than the distribution density of the object in the region of interest. An image is obtained to obtain an integrated value of the average optical thickness of each of the objects.
The counting method according to claim 6. In the calculation step, when there are a plurality of types of the objects, the integrated value of the average optical thickness of each of the plurality of types of objects is stored, and any of the types of objects is stored. Select and use the integral value of the average optical thickness per object.
The counting method according to claim 6 or 7. In the image acquisition step, a region for obtaining an integrated value of the average optical thickness per object as compared with the case where an interference image including the region of interest is acquired by the interference image acquisition unit. The optical magnification is high when the interference image including the above is acquired by the interference image acquisition unit.
The counting method according to any one of claims 6 to 8. In the image acquisition step, an interference image including an aggregate of objects is acquired by the interference image acquisition unit.
In the calculation step, the integrated value of the optical thickness of the aggregate in the optical thickness image obtained from the interference image acquired by the interference image acquisition unit and the average value per object. The number of objects contained in the aggregate is obtained based on the integrated value of the optical thickness.
The counting method according to any one of claims 6 to 9. The flatness in the background of the optical thickness image is less than 5 nm in terms of the standard deviation of the optical thickness.
The counting method according to any one of claims 6 to 10. The object is a cell,
The counting method according to any one of claims 6 to 11. In the image acquisition step, the interference image acquisition unit acquires a first interference image of one or a plurality of aggregates separated into individual cells from a population including a plurality of cell aggregates as an object. At the same time, a second interference image is acquired by the interference image acquisition unit for the remaining one or more aggregates in the population.
In the calculation step, the integrated value of the average optical thickness per cell is obtained based on the optical thickness image obtained from the first interference image, and is obtained from the second interference image. The integrated value of the optical thickness of the aggregate is obtained based on the optical thickness image, and the integrated value of the optical thickness of the aggregate and the average optical thickness per cell of the aggregate are obtained. The number of cells contained in the aggregate is obtained based on the integrated value of.
The counting method according to claim 12. In the image acquisition step, when the interference image acquisition unit acquires an interference image including a region for obtaining an integrated value of the average optical thickness of each cell as an object, the cell To obtain the interference image by staining the nuclei of
The counting method according to claim 12 or 13.

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