JP2019117490A - Counting device and method for counting - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a counting device and a counting method capable of accurately counting the number of objects in an area of interest. A counting device 1 includes an interference image acquisition unit 2 and a calculation unit 3. The interference image acquisition unit 2 has 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. [Selection diagram] Fig. 1

Description

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

  Techniques for counting the number of cells are described in Non-Patent Documents 1 to 3. The counting techniques described in these documents count the number of cells in an image or a region of interest by performing segmentation of individual cell areas 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, “Counting Clustered Cells using Distance Mapping,” International Conference on Informatics, Electronics & Vision (ICIEV), 2013. Koyuncu CF, Arslan S, Durmaz I, Cetin-Atalay R, Gunduz-Demir C, "Smart Markers for Watershed-Based Cell Segmentation," 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 area is long, and the counting error is large. In particular, in the case where the cells are aggregated as in the case where the cells are vertically stacked, the counting error is larger. Such problems exist 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-mentioned problems, and it is an object of the present invention to provide a counting device and a counting method capable of accurately counting the number of objects in a region of interest.

  The counting device of the present invention comprises: (1) an interference image acquisition unit for acquiring an interference image including one or more objects; and (2) an optical thickness image based on the interference image acquired by the interference image acquisition unit. And determining the integral of the optical thickness within the region of interest of the optical thickness image and the integral of the average optical thickness per object of the object within the region of interest. And an operation unit for obtaining the number of objects.

  In the counting device according to one aspect of the present invention, the computing unit is configured to, based on an area in which the object is distributed at a density lower than the distribution density of the object in the region of interest, in the interference image acquired by the interference image acquiring unit. An optical thickness image is determined to determine an integral of the average optical thickness per object.

  In the counting device according to another aspect of the present invention, when there are a plurality of types of objects, the computing unit stores an integral value of an average optical thickness per one of the plurality of types of objects. In addition, the integral value of the average optical thickness per one of the objects of any kind of them is selected and used.

  In the counting device according to still another aspect of the present invention, the interference image acquiring unit calculates an integral value of an average optical thickness per one object as compared to 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 a collection of objects, and the calculation unit is obtained from the interference image acquired by the interference image acquisition unit. Based on the integral of the optical thickness of the assembly of the optical thickness image and the integral of the average optical thickness per object, Find the number.

  The counting method of the present invention comprises (1) an image acquisition step of acquiring an interference image including one or more objects by an interference image acquisition unit, and (2) an optical based on the interference image acquired by the interference image acquisition unit. Dynamic thickness images are obtained, and based on the integral value of the optical thickness in the region of interest of the optical thickness image and the integral value of the average optical thickness per object, Calculating the number of objects in the region of interest.

  In the counting method according to one aspect of the present invention, in the calculating step, of the interference image acquired by the interference image acquiring unit, a region in which the object is distributed at a density lower than the distribution density of the object in the region of interest An optical thickness image is determined to determine an integral of the average optical thickness per object.

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

  In the counting method according to still another aspect of the present invention, in the image acquisition step, an average optical thickness per object is smaller than when an interference image including a region of interest is acquired by the interference image acquisition unit. The optical magnification at the time of acquiring the interference image including the area for obtaining the integral value of the length by the interference image acquisition unit is high.

  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 assembly among the optical thickness images determined from the above and the integrated value of the average optical thickness per object of the object Find the number of objects to be

  In the counting method according to still another aspect of the present invention, the flatness in the background of the optical thickness image is less than 5 nm as a 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 more aggregates of a population including a plurality of cell aggregates as an object are separated into individual cells. (1) The 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 of the population, and the calculation step is performed from the first interference image The integral value of the average optical thickness per cell is determined based on the determined optical thickness image, and the aggregate of the aggregate is determined based on the optical thickness image determined from the second interference image. The integral value of thickness is determined, and based on the integral value of the optical thickness of the aggregate and the integral value of the average optical thickness per cell, the number of cells included in the aggregate is calculated. Ask.

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

  According to the present invention, the number of objects in a 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 the configuration of a sample. FIG. 3 is a view showing five interference images I1 to I5 acquired by the interference image acquisition unit 2. As shown in FIG. FIG. 4 is a view 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 view showing a cell nucleus staining image of the same field of view as the interference image of FIG. FIG. 6 is a table summarizing the integrated values of optical thickness, estimated cell number, true cell number and errors for each of nine fields of view. FIG. 7 is a graph showing the estimated number of cells and the number of true cells for each of nine fields of view. FIG. 8 is a flow chart for explaining the operation of the counting device 1 of the first embodiment and the counting method of the first embodiment. FIG. 9 is a view showing a visual field for describing a first preferred example of a method for obtaining an integral value of average optical thickness per cell. FIG. 9 (a) shows a field of view where cells are distributed at low density. FIG. 9 (b) shows a field of view where cells are distributed at high density. FIG. 10 is a view showing a visual field for describing a second preferred example of a method for obtaining an integral value of average optical thickness per cell. FIG. 11 is a schematic view of an optical thickness image of a region to be analyzed. FIG. 12 is a schematic view of an optical thickness image when each well of the multiwell plate is a region of interest to be analyzed. FIG. 13 is a view showing a plurality of cell populations 83 in the culture solution 82 contained in the container 80. As shown in FIG. FIG. 14 shows cells 73 dispersed and contained in wells 91 of container 90 and cell populations 83 contained in wells 92 of container 90. FIG. 15 is a flow chart for explaining the procedure for counting the number of cells contained in the cell population. FIG. 16 is a view showing an optical thickness image obtained from interference images acquired by the interference image acquisition unit 2 at optical magnifications different from each other. FIG. 16 (a) shows a low magnification optical thickness image that includes both the region of interest ROImain and the region ROIcon. FIG. 16 (b) shows a high magnification optical thickness image including only the region ROIcon. FIG. 17 is a flowchart illustrating the procedure of switching the optical magnification of the interference image acquisition unit 2 to count the number of cells.

  Hereinafter, with reference to the accompanying drawings, modes for carrying out the present invention will be described in detail. In the description of the drawings, the same elements will be denoted by the same reference symbols, without redundant description. The present invention is not limited to these exemplifications, is shown by the claims, and is intended to include all modifications within the scope and meaning equivalent to the 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 imaging device 18, a piezo element 21, a photodetector 22, and a phase control circuit. Including 23.

  The interference image acquisition unit 2 includes a Michelson interferometer as a two-beam interferometer, and acquires an interference image of one or more objects. The calculation unit 3 obtains an optical thickness image based on the interference image acquired by the interference image acquisition unit 2. Furthermore, the calculation unit 3 is based on the integral value of the optical thickness in the region of interest of the optical thickness image and the integral value of the average optical thickness per object. Find the number of objects in the region of interest.

  The subject is not limited to a specific cell or biological sample. For example, target cells include cultured cells, immortalized cells, primary cultured cells, cancer cells, adipocytes, liver cells, cardiomyocytes, nerve cells, glial cells, somatic stem cells, embryonic stem cells, pluripotent stem cells, iPS Cells, and cell clusters (colony or spheroid) produced from the cells, and the like can be mentioned. The object is not limited to a living body, and examples include industrial samples such as metal surface, semiconductor surface, glass surface, inside of semiconductor element, resin material surface, liquid crystal, polymer compound and the like.

  In the following description of the present embodiment, it is assumed that the object is a cell 73 in the culture solution 72 contained in the container 70, as shown in FIG. Inside the bottom of the container 70 a reflection enhancing coating 71 is provided.

  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, a light emitting diode (LED) light source, a superluminescent diode (SLD) light source, an amplified spontaneous emission (ASE) light source, or the like.

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

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

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

  The objective lens 14 is optically coupled to the beam splitter 12 and condenses the second branched light output from the beam splitter 12 on the reflection surface of the reference mirror 15. The objective lens 14 also receives the second branched light reflected by the reflection surface of the reference mirror 15 and outputs the second branched light to the beam splitter 12.

  The tube lens 16 is optically coupled to the beam splitter 12 forming the interference optical system, and images the interference light output from the beam splitter 12 on the imaging surface of the imaging device 18 through the beam splitter 17. The beam splitter 17 splits the light arriving from the tube lens 16 into two, outputs one of the split lights to the imager 18, and outputs the other split 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, for example, a CCD area image sensor and a CMOS area image sensor.

  The piezo element 21 moves the reflective surface in a direction perpendicular to the reflective surface of the reference mirror 15. The piezoelectric element 21 can adjust the optical path length difference (that is, the phase difference) between the two light beams in the two light beam interferometer by the movement of the reflection surface. The piezo element 21 can determine the position of the reflection surface of the reference mirror 15 with a resolution less than the wavelength. In the two-beam interferometer, the optical path length difference between the two beams 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 reflecting surface of the reference mirror 15 is L2, the optical path length between the two light beams in the two light beam interferometer The difference is 2 (L1-L2). If this 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. Assuming that the central wavelength of the output light of the light source 11 is λ0, the phase difference Δφ between the two light beams in the two light beam interferometer is represented by the following equation.

  The light detector 22 is optically coupled to the beam splitter 17, receives the interference light arriving from the beam splitter 17, and outputs a detection signal. The photodetector 22 is, for example, a photodiode, an avalanche photodiode, or a photomultiplier, and may be 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 light detector 22, and receives a detection signal output from the light detector 22. 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 an optical path length difference between the two light beams in the two light beam 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 feedback control based on the detection result. As a result, the optical path length difference between the two light beams in the two-beam interferometer can be stabilized at the set value (locked state).

  The interference image acquisition unit 2 can capture and acquire an interference image of the object (cell 73) with the image pickup device 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 imaging device 18 of the interference image acquisition unit 2. Arithmetic unit 3 may be a computer such as a personal computer and a tablet terminal. In addition, the computing unit 3 includes an input unit (keyboard, mouse, tablet terminal, etc.) for receiving an input from the operator, and a display unit (display, tablet terminal, etc.) for displaying an interference image, an optical thickness image, etc. You may have. Further, it is preferable that the calculation unit 3 has a function of displaying an image or the like on the screen and receiving specification of the area by the operator on the screen.

  The light output from the light source 11 is split into two light beams by the beam splitter 12 to be the first split light and the second split light, and the beam splitter 12 outputs the first split light and the second split light. The first branched light output from the beam splitter 12 is focused on the cells 73 in the container 70 by the objective lens 13, and is reflected by a reflection enhancing coating 71 provided on the inside of the bottom of the container 70. The first branched light reflected by the reflection enhancing coating 71 is input to the beam splitter 12 through the objective lens 13. The second branched light output from the beam splitter 12 is focused by the objective lens 14 on the reflection surface of the reference mirror 15, and is reflected by the reflection surface. The second branched light reflected by the reflection surface of the reference mirror 15 is input to the beam splitter 12 through the objective lens 14.

  The first split light input from the objective lens 13 to the beam splitter 12 and the second split light input from the objective lens 14 to the beam splitter 12 are multiplexed by the beam splitter 12 and interference light from the beam splitter 12 Is output. The interference light passes through the tube lens 16 and is branched into two by the beam splitter 17 and is received by the imaging device 18 and the light detector 22 respectively. A detection signal is output from the light detector 22 that has received the interference light, and the phase control circuit 23 detects the difference in optical path length between the two light beams in the two light beam interferometer based on the detection signal. Then, by feedback control on the piezo element 21 by the phase control circuit 23, the optical path length difference between the two light beams in the two light beam interferometer is stabilized at the set value (locked state). An interference image is acquired by the imaging device 18 that receives the interference light in the locked state, and the interference image is output to the calculation unit 3. Then, the arithmetic 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 the interference image in each state in a state in which the optical path length difference of the two-beam interferometer is stabilized with each of a plurality of different setting values. The calculation unit 3 can obtain a phase image based on the plurality of interference images acquired by the interference image acquisition unit 2 (see Patent Document 1). Furthermore, 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 light detector 22, and the phase control circuit 23, and stabilizes the phase difference. The interference image I1 is acquired by the imaging device 18 in the state as described above. Next, the interference image acquisition unit 2 stabilizes the phase difference of the interference light at “initial phase + π / 2” using the piezo element 21, the light detector 22, and the phase control circuit 23, and stabilizes the phase difference. The interference image I2 is acquired by the imaging device 18 in the state as described above. Similarly, the interference image acquisition unit 2 acquires the interference image I3 with the imaging device 18 in a state in which the phase difference of the interference light is stabilized at “initial phase + π”, and the phase difference of the interference light is “initial phase + 3π The interference image I4 is acquired by the imaging device 18 in a stabilized state at 1/2 ′ ′, and the interference image I5 is acquired by the imaging device 18 in a state where the phase difference of the interference light is stabilized at “initial phase + 2π”.

  The calculation unit 3 calculates the phase image Φ by performing the calculation of the following equation (2) using the five interference images I1 to I5. arg is an operator for acquiring complex number transformations. i is an imaginary unit. After performing the phase unwrapping process and the background distortion correction process on the phase image 演算, the calculation unit 3 obtains the optical thickness image by obtaining the optical thickness OT by the following equation (3). Each parameter appearing in these formulas is a function of the pixel position (x, y), and the calculation of these formulas is performed for each pixel.

  With regard to background correction, a good (flat) background can be obtained by using a polynomial function (for example, Zernike polynomials) having x and y as variables. Also, high pass filtering may be performed if the spatial frequency of the distortion component in the background is sufficiently lower than the spatial frequency of the individual samples. The flatness in the background of the optical thickness image is preferably less than 5 nm in standard deviation of the optical thickness.

The 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.

  Below, an embodiment is described in more detail, showing the composition and the example of an image of a concrete example. An LED having a central wavelength of 730 nm was used as the light source 11. The focal length of each of the objective lens 13 and the objective lens 14 was 18 mm. The focal length of the tube lens 16 was 125 mm. The image pickup device size of the image pickup device 18 was 4.8 mm × 3.6 mm. The number of pixels of the imaging device 18 was 1280 pixels × 960 pixels. The visual field size at the sample surface was 691.2 μm × 518.4 μm. Melanoma-derived cultured cells SK-MEL-28 were used as cells 73 of the sample.

  FIG. 3 is a view showing five interference images I1 to I5 acquired by the interference image acquisition unit 2. As shown in FIG. FIG. 4 is a view showing an optical thickness image obtained by the calculation unit 3 based on the interference images I1 to I5 of FIG. From the optical thickness image of FIG. 4, the integral value of the optical thickness can be divided by the number of cells to determine the integral value of the average optical thickness per cell.

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

The integral value of the optical thickness was determined by multiplying the sum of the optical thicknesses OT of all the pixels by the sample area per pixel (0.54 μm × 0.54 μm). 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 fields of view different from the interference image of FIG. 3 are acquired, and the integral of the optical thickness of each field of view is the integral of the average optical thickness per cell (2 The number of cells present in each visual field was estimated by dividing by 12 × 10 ⁵ (nm × μm ² / cell). FIG. 6 is a table summarizing the integrated values of optical thickness, estimated cell number, true cell number, and errors for each of nine fields of view (FOV # 1 to 9). FIG. 7 is a graph showing the estimated number of cells and the number of true cells for each of nine fields of view. The true cell number was obtained by separately obtaining cell nucleus stained images of the same field of view and visually counting the images. 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 number of cells can be accurately counted in a short time by the counting method of the present embodiment.

  In the above example, when obtaining the integral value of the average optical thickness per cell, a cell nuclear staining image was used to accurately count the number of cells. In addition, in order to verify the estimation result of the cell number, a nuclear staining image was also used for the region of interest to be counted. Staining of cell nuclei is to ensure verification and is not essential.

  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 flow chart for explaining 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), optical adjustment is performed on the two-beam interferometer so that the interference image can be acquired by the interference image acquisition unit 2, and a plurality of interference images are acquired by the interference image acquisition unit 2 . In steps S2 to S5 (calculation step), the calculation unit 3 obtains an optical thickness image based on the plurality of interference images acquired by the interference image acquisition unit 2, and the region of interest in the optical thickness image The number of cells in the region of interest is determined based on the integral value of the optical thickness and the integral 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 as needed. In step S4, a region of interest (ROI) including a plurality of cells is set. The region of interest may be one or more. At step S5, the integral of the optical thickness within the region of interest of the optical thickness image is determined and divided by the integral of the average optical thickness per cell to obtain the interest. Estimate the number of cells in the area.

  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 view showing a visual field for describing a first preferred example of a method for obtaining an integral value of average optical thickness per cell. In this method, in the interference image acquired by the interference image acquisition unit 2, the operation unit 3 distributes cells at a density lower than the distribution density of cells in the region of interest (FIG. 9 (b)) (FIG. 9). Based on (a), an optical thickness image is determined to determine the integral 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. 9 (b) shows the distribution of cells in the region of interest. In the region where the distribution density of cells is low (Fig. 9 (a)), it is preferable that the individual cells be distributed apart from one another. Thus, using the optical thickness image of a region where the distribution density of cells is low (FIG. 9 (a)), the number of cells in that region can be accurately counted. The integral value of the average optical thickness can be accurately obtained. Note that the region where the distribution density of cells is low (FIG. 9A) and the region of interest (FIG. 9B) may be different regions within the same field of view or may be included in different fields of view. It may be.

  FIG. 10 is a view showing a visual field for describing a second preferred example of a method for obtaining an integral value of 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 in the region where the distribution density of the cells is low. It may be. In region A, individual cells are preferably distributed apart from one another. Also in this case, if the optical thickness image of the region A in which the distribution density of the cells is low is used, the number of cells in that region can be accurately determined, so the average optical thickness per cell can be obtained. The integral value of H can be accurately obtained.

  Also, when calculating the integral value of the average optical thickness per cell, it may be determined each time the number of cells in the region of interest is determined, or a value determined for the same kind of cells before that. May be stored in the storage unit of the calculation unit 3, and the stored value may be used. The computing unit 3 stores the integral value of the average optical thickness per cell of each of the plurality of types, and the average optical thickness per cell of any of these types of cells. It is also preferable to select and use the integral value of.

  In addition, in the case of using established cells (cell lines), the computing unit 3 records the integrated value of the average optical thickness per cell for several cell lines measured in advance by the storage unit. It is possible to In this case, the operator simply designates the cell type to obtain the in-plane integral value of the optical thickness of the field of view as the integral value of the average optical thickness per one of the designated cell types. The number of cells can be determined by dividing by. The integral value of the average optical thickness per cell may be stored by the storage unit of the computing unit 3 or may be downloaded to the storage unit of the computing unit 3 in a short period by downloading via the communication network. You may use what was memorized. In addition, HeLa, CHO, MCF7 etc. are mentioned as a established cell type frequently used in the field of biology.

Second Embodiment
In the second embodiment, the number of cells included in a cell population as a collection of objects is counted. The counting device of the second embodiment has the same configuration as 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. It has been difficult to count the number of cells contained in a cell population by the conventional cell counting method. A cell population is a collection of multiple cells in a colony-like or spheroid-like manner. A colony is typically one in which a plurality of cells form a spatially integrated group. A spheroid is a cell population in which cells are stacked, typically in a thickness of 100 μm or more, and formed into a sphere. One spheroid typically contains 1000 to several tens of thousands of cells.

  FIG. 11 is a schematic view of an optical thickness image of a region to be analyzed. 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 the 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 integrated value of the optical thickness of each cell population by the integrated value of the average optical thickness per cell to obtain an object contained in each cell population. The number of can be determined. The integral value of the average optical thickness per cell used here may be measured separately, or a typical value previously determined may be used.

  FIG. 12 is a schematic view of an optical thickness image when each well of the multiwell plate is a region of interest to be analyzed. In this figure, regions of interest ROI-1 to ROI-5 are set in five wells out of six wells, and each well contains one cell population. In the remaining one well, an area ROIcon including a plurality of individually separated cells is set. In this case, an integral value of the average optical thickness per cell can be obtained from the optical thickness image of the region ROIcon. Then, using this, it is possible to determine the number of objects included in the cell population of each of the regions of interest ROI-1 to ROI-5.

  In addition, in the case of 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 using FIG. 13 to FIG. FIG. 13 is a view showing a plurality of cell populations 83 in the culture solution 82 contained in the container 80. As shown in FIG. FIG. 14 shows cells 73 dispersed and contained in wells 91 of container 90 and cell populations 83 contained in wells 92 of container 90. FIG. 15 is a flow chart for explaining the procedure for counting the number of cells contained in the cell population.

  First, a population consisting of a plurality of cell populations is prepared (step S10). One or more cell populations 83 in the population are randomly taken out (step S11), dispersed into individual cells 73 by pipetting and other operations, and placed in the 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 sample dispersed in each single cell 73 is pipetted until it becomes uniform enough to be a cell suspension, and seeded in the well 91 at a low density at which individual discrimination is easy. Let this be a control group. On the other hand, the remaining cell population 83, which is not dispersed and remains in the form of a cell population, is placed as it is in the well 92 of the container 90 shown in FIG. 14 (step S13). Let this be a cell measurement group.

  The well 91 and the well 92 are physically separated in area. Inside the bottom of the well 91 is provided a reflection enhancing coating 93. Inside the bottom of the well 92 is provided a reflection enhancing coating 94. The wells 91 and 92 are sealed with a cover glass 95. In addition, the observation container for cell suspension and the observation container for spheroid observation may be isolated as long as the well part for injecting the sample is separated, and the exterior part 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 sample injection part is isolated although the exterior part is integrated. One well 91 is seeded with cells 73 seeded to a low density, and the other well 92 is seeded with a cell population 83.

  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 arithmetic unit 3 obtains an optical thickness image from the interference image and performs background correction (step S14), joins a plurality of optical thickness images as necessary (step S15), and the optical thickness The region ROIcon is set in the image (step S16), and the integral value of the average optical thickness per cell in the region ROIcon is determined (step S17).

  Further, the interference image acquisition unit 2 acquires an interference image of the cell population 83 in the well 92. The arithmetic unit 3 obtains an optical thickness image from the interference image and performs background correction (step S18), joins a plurality of optical thickness images as necessary (step S19), and the optical thickness A 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 calculates the number of cells contained 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. It asks for (Step S22).

  The container 90 used here is desirably sterilized prior to observation. If this container 90 is sterilized, the cell population 83 seeded in the wells 92 is not dispersed one by one or stained, so the culture is continued even after counting the number of cells. It becomes possible. Also, in the process of measurement by this method, some cell populations among the cell populations included in the original population lose their function as cell populations by being seeded at low density in a dispersed state. However, most other cell populations in the population can remain functional as cell populations after 100% cell number inspection.

  Although the container 90 is described as a multi-well plate, the container in which the cells 73 seeded at 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 uses the fact that the optical magnification at the time of acquiring the interference image by the interference image acquisition unit 2 is variable, compared to when 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 integral value of the average optical thickness per cell.

  When using a cell population which is a collection of cells as a region of interest, it is desirable to image a large area with a low-power optical system when observing the region of interest ROImain for which it is desired to count the number of cells. On the other hand, when determining the integral value of the average optical thickness per cell, discrimination of each cell is difficult in an image captured by a low magnification optical system, so imaging is performed with a high magnification optical system. It is desirable to do. Therefore, preferably, the optical magnification of the interference image acquisition unit 2 is switched to observe the region ROIcon for obtaining the integral value of the average optical thickness per cell at high magnification, while The region of interest ROImain for which the number of cells is to be counted should be observed at low magnification.

  FIG. 16 is a view showing an optical thickness image obtained from interference images acquired by the interference image acquisition unit 2 at optical magnifications different from each other. FIG. 16 (a) shows an optical thickness image including both a region of interest ROImain for which the number of cells is to be counted, and a region ROIcon for obtaining an integral value of the average optical thickness per cell. Show. FIG. 16 (b) shows an optical thickness image not including the region of interest ROImain but including only the region ROIcon. The optical magnification is higher at the time of acquiring the optical thickness image of FIG. 16B than at the time of acquiring the optical thickness image of FIG.

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

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

  Moreover, the interference image acquisition part 2 acquires an interference image by low magnification about a high density area | region. The arithmetic unit 3 obtains an optical thickness image from the interference image and performs background correction (step S35), joins a plurality of optical thickness images as necessary (step S36), and the optical thickness The region of interest ROI is set in the image (step S37), and the integral value of the optical thickness of 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 ((3) Step S37).

  By switching the optical magnification of the interference image acquisition unit 2, the number of pixels corresponding to one cell area is different between the optical thickness image of the high density area and the optical thickness image of the low density area, The sum of optical thicknesses in one cell area is different from one another. However, if the sum of the optical thickness and the sample area per pixel is used as the integral value of the optical thickness, the sample area per pixel is inversely proportional to the optical magnification, regardless of the optical magnification. The integral value of the optical thickness in the same region is the same. In addition, what is necessary is just to calibrate using the value of the square of an optical magnification, when using the sum total of optical thickness as an integral value of optical thickness.

  Further, as a feature of the interference image acquisition unit 2, the optical thickness is a physical quantity determined by the refractive index and thickness of the sample, and has a property that it does not depend on the magnification of the imaging system and the intensity of the illumination light. Therefore, there is no problem in calculating the image of the low magnification area after obtaining the integral value of the average optical thickness per cell at high magnification.

  DESCRIPTION OF SYMBOLS 1 ... Counting apparatus, 2 ... Interference image acquisition part, 3 ... Calculation part, 11 ... Light source, 12 ... Beam splitter, 13, 14 ... Objective lens, 15 ... Reference mirror, 16 ... Tube lens, 17 ... Beam splitter, 18 ... Imaging device, 21: Piezo element, 22: Photodetector, 23: Phase control circuit, 70: Container, 71: Reflection-enhanced coating, 72: Culture fluid, 73: Cell, 80: Container, 82: Culture fluid, 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 including one or more objects;
An optical thickness image is obtained based on the interference image acquired by the interference image acquisition unit, and an integral value of the optical thickness in a region of interest of the optical thickness image and one of the objects. An arithmetic unit for determining the number of the objects in the region of interest based on an integral value of the average optical thickness of each layer;
A counting device comprising The arithmetic unit is configured to calculate the optical thickness based on a region in which the target is distributed at a density lower than a distribution density of the target in the region of interest in the interference image acquired by the interference image acquisition unit. An image is obtained to obtain an integral value of an average optical thickness per one of the objects.
The counting device according to claim 1. The computing unit stores, when there are a plurality of types of objects, an integral value of the average optical thickness per one of the plurality of types of objects, and any one of the types is Select and use the integral value of the average optical thickness per object
The counting device according to claim 1. The interference image acquisition unit includes an interference image including a region for obtaining an integral value of an average optical thickness per one object compared to when 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 a collection of objects;
The arithmetic unit is configured to calculate an integrated value of the optical thickness of the aggregate among the optical thickness images obtained from the interference image acquired by the interference image acquisition unit, and an average per one object. The number of objects contained in the assembly is determined based on the integral value of the typical 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 more objects by an interference image acquisition unit;
An optical thickness image is obtained based on the interference image acquired by the interference image acquisition unit, and an integral value of the optical thickness in a region of interest of the optical thickness image and one of the objects. Calculating the number of the objects in the region of interest based on the integral value of the average optical thickness of each layer;
Counting method. In the calculation step, the optical thickness is determined based on a region in which the target is distributed at a density lower than a distribution density of the target in the region of interest in the interference image acquired by the interference image acquiring unit. An image is obtained to obtain an integral value of an average optical thickness per one of the objects.
A counting method according to claim 6. In the calculating step, when there are a plurality of types of objects, an integral value of an average optical thickness per one of the plurality of types of objects is stored, and any one of them is stored. Select and use the integral value of the average optical thickness per object
A counting method according to claim 6 or 7. In the image acquisition step, an area for obtaining an integral value of an average optical thickness per one of the objects, as compared to when an interference image including the area of interest is acquired by the interference image acquisition unit. High optical magnification when acquiring an interference image including the
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 calculating step, an integral value of the optical thickness of the aggregate among the optical thickness images obtained from the interference image acquired by the interference image acquiring unit, and an average per object of the object. The number of objects contained in the assembly is determined based on the integral value of the typical 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 standard deviation of 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 more aggregates of a population including a plurality of cell aggregates as the object separated into individual cells. And acquiring a second interference image for the remaining one or more aggregates of the population by the interference image acquisition unit,
In the calculation step, an integral value of an average optical thickness per cell is determined based on an optical thickness image determined from the first interference image, and is determined from the second interference image. The integral of the optical thickness of the assembly is determined based on the selected optical thickness image, and the integral of the optical thickness of the assembly and the average optical thickness per cell Determine the number of cells contained in the assembly, based on the integral value of
A counting method according to claim 12. When the interference image acquisition unit acquires an interference image including a region for obtaining an integral value of an average optical thickness per cell as the object in the image acquisition step, the cells Staining the nuclei of the to obtain the interference image,
A counting method according to claim 12 or 13.