Cell quality evaluation device, cell quality evaluation method, and program

Abstract

Provided is a cell quality evaluation apparatus which is configured such that the quality of cells is evaluated on the basis of a plurality of images obtained by an imaging device that takes images of a plurality of imaging regions selected from the whole of a culture vessel in which the cells are cultured to produce the plurality of images, the cell quality evaluation apparatus executing processing including: estimation processing for determining feature amounts respectively from the plurality of images and then calculating an average value of the feature amounts to estimate the quality of the cells relative to the whole of the culture vessel; introduction processing for introducing an estimation error for the quality determined by the estimation processing on the basis of the variation of the feature amounts in the plurality of images and imaging information associated with the areas of the plurality of the imaging regions; and imaging control processing for allowing the imaging device to re-take an image of at least one re-imaging region that is different from the plurality of imaging regions when the estimation error does not fall within an acceptable range.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a cell quality evaluation device, a cell quality evaluation method, and a program. [Background technology]

[0002] In the fields of regenerative medicine, drug discovery, etc., a technique is known for evaluating the quality of cells being cultured in a culture vessel by analyzing images obtained by capturing images of the culture vessel with an imaging device such as a microscope (see, for example, International Publication No. 2016 / 098271).

[0003] WO 2016 / 098271 discloses that a cell-occupied area ratio (hereinafter referred to as confluency), which is the ratio of the area occupied by cells in a culture vessel, is calculated based on multiple images acquired from multiple measurement areas (imaging regions) specified in the culture vessel. Confluency is an index that represents the quality of cells being cultured. Summary of the Invention [Problem to be solved by the invention]

[0004] When estimating the quality of cells based on images acquired from multiple imaging regions selected from the entire culture vessel as described in WO 2016 / 098271, the quality estimation error depends on the number and size of the imaging regions selected from the entire culture vessel. accuracy In order to improve the image quality, it is necessary to increase the number of imaging regions selected from the entire culture vessel or to increase the area of ​​the imaging region, thereby imaging the entire culture vessel.

[0005] While imaging the entire culture vessel is possible for small-volume culture vessels, such as wells in a well plate, it is difficult for large-volume culture vessels, such as T-flasks. While microscopes capable of imaging the entire large-volume culture vessel exist, these microscopes are large and expensive, making them difficult for average users to install and operate. Therefore, there is a need for the development of a cell quality evaluation device that can evaluate cell quality with high accuracy, even in large-volume culture vessels.

[0006] The technology disclosed herein aims to provide a cell quality evaluation device, a cell quality evaluation method, and a program that enable highly accurate evaluation of cell quality even in large-capacity culture vessels. [Means for solving the problem]

[0007] The cell quality evaluation device disclosed herein is a cell quality evaluation device that evaluates the quality of cells based on multiple images acquired from an imaging device that generates multiple images by imaging multiple imaging areas selected from the entire culture container in which cells are cultured, and is equipped with at least one processor. The processor executes processes including: an estimation process that estimates the quality of cells for the entire culture container by obtaining feature values ​​from each of the multiple images and calculating an average value of the feature values; a derivation process that derives an estimation error of the quality obtained by the estimation process based on the variation in feature values ​​in the multiple images and imaging information related to the areas of the multiple imaging areas; and an imaging control process that causes the imaging device to re-image at least one re-imaging area different from the multiple imaging areas if the estimation error is outside an acceptable range.

[0008] The imaging information is the number of images, and the estimation error is σ / N, where σ is the standard deviation of the feature amount and N is the number of images. 1 / 2 It is preferred that the .lambda.

[0009] In the imaging control process, it is preferable to set a re-image area based on the variation in the feature amount and the position information of the plurality of imaging areas.

[0010] In the imaging control process, it is preferable to set the re-imaging region based on the surface shape of the culture vessel.

[0011] The feature amount is preferably the cell area ratio, the area of ​​the cell nucleus, or the area ratio between the cell nucleus and the cytoplasm.

[0012] The feature amount is the cell occupancy rate, and in the imaging control process, it is preferable to make the imaging device re-image areas where the cell occupancy rate is around 50% more than other areas.

[0013] In the derivation process, it is preferable to derive the estimation error based on a table that defines the relationship between the ratio of the area of ​​the imaged imaged region to the entire culture vessel and the estimation error.

[0014] The cell quality evaluation method disclosed herein is a cell quality evaluation method that evaluates the quality of cells based on multiple images acquired from an imaging device that generates multiple images by imaging multiple imaging areas selected from the entire culture vessel in which cells are cultured, and performs processing including: an estimation process that estimates the quality of cells for the entire culture vessel by obtaining feature values ​​from each of the multiple images and calculating the average value of the feature values; a derivation process that derives an estimation error of the quality obtained by the estimation process based on the variation in feature values ​​in the multiple images and imaging information related to the areas of the multiple imaging areas; and an imaging control process that causes the imaging device to re-image at least one re-imaging area different from the multiple imaging areas if the estimation error is outside an acceptable range.

[0015] The present disclosure programis a program that causes a computer to execute a process for evaluating the quality of cells based on a plurality of images acquired from an imaging device that generates a plurality of images by imaging a plurality of imaging regions selected from the entire culture vessel in which cells are cultured, and causes the computer to execute processes including: an estimation process that estimates the quality of cells in the entire culture vessel by obtaining feature amounts from each of the plurality of images and calculating an average value of the feature amounts; a derivation process that derives an estimation error of the quality obtained by the estimation process based on variations in the feature amounts in the plurality of images and imaging information related to the areas of the plurality of imaging regions; and an imaging control process that causes the imaging device to re-image at least one re-imaging region different from the plurality of imaging regions when the estimation error is outside an allowable range. [Effects of the Invention]

[0016] According to the technology of the present disclosure, it is possible to provide a cell quality evaluation device, a cell quality evaluation method, and a program that enable cell quality to be evaluated with high accuracy even in a large-capacity culture vessel. [Brief explanation of the drawings]

[0017] [Figure 1] FIG. 1 is a schematic diagram illustrating an example of the configuration of a microscope observation system. [Figure 2] FIG. 10 is a schematic diagram showing an example of a plurality of imaging regions set in a culture region. [Figure 3] FIG. 2 is a block diagram showing an example of an internal configuration of an information processing device. [Figure 4] FIG. 2 is a block diagram illustrating an example of a functional configuration of the information processing device. [Figure 5] FIG. 2 is a schematic diagram illustrating an example of an image acquired by an imaging device. [Figure 6] FIG. 10 is a schematic diagram illustrating an example of a quality estimation process performed by a quality estimation unit. [Figure 7] FIG. 10 is a schematic diagram illustrating an example of an estimation error derivation process performed by an estimation error derivation unit. [Figure 8] 10 is a schematic diagram showing an example of a re-imaging region set by an imaging control unit. FIG. [Figure 9]10 is a flowchart showing an example of the flow of a cell quality evaluation process. [Figure 10] 10 is a graph illustrating the dependency of quality estimation error on the number of captured images. [Figure 11] 10 is a graph illustrating an example of the relationship between confluency and estimation error. [Figure 12] 1A and 1B are diagrams illustrating images of cells seeded using the first seeding method and the second seeding method, where (A) is an example of an image of cells seeded using the first seeding method, and (B) is an example of an image of cells seeded using the second seeding method. [Figure 13] FIG. 1 is a schematic diagram showing an example of a culture vessel having a curved bottom surface. [Figure 14] 10A and 10B are schematic diagrams showing an example of a process for setting a re-imaging region based on the surface shape of a culture vessel. [Figure 15] FIG. 10 is a diagram illustrating an example of a table for deriving an estimation error. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] As an example, as shown in FIG. 1 , a microscope observation system 2 is composed of an information processing device 10 and a microscope device 20. The information processing device 10 is, for example, a desktop personal computer. A display 11, a keyboard 12, a mouse 13, and the like are connected to the information processing device 10. The keyboard 12 and the mouse 13 constitute an input device 14 through which a user inputs information. The input device 14 also includes a touch panel, etc. The information processing device 10 is an example of a "cell quality evaluation device" according to the technology of the present disclosure.

[0020] The microscope device 20 includes a mounting unit 21, a light source 22, an imaging device 23, and a drive unit 24. The microscope device 20 is a phase-contrast microscope or a bright-field microscope. A culture vessel 25 for culturing cells 30 is mounted on the mounting unit 21. The culture vessel 25 is, for example, a T-flask. A T-flask is an example of a large-capacity culture vessel. The cells 30 are cultured using a medium 33 filled in the culture vessel 25. For example, the cells 30 are pluripotent stem cells in an undifferentiated state, such as iPS (induced pluripotent stem) cells or ES (embryonic stem) cells. Note that the culture vessel 25 is not limited to a flask such as a T-flask, and may be a petri dish, a dish, a well plate, or the like.

[0021] The light source 22 and the imaging device 23 are held by an arm 26. The mounting section 21 is disposed between the light source 22 and the imaging device 23. Specifically, the light source 22 is disposed above the culture vessel 25 mounted on the mounting section 21. The imaging device 23 is disposed below the mounting section 21 in a position facing the light source 22. The light source 22 emits illumination light L toward the culture vessel 25. Hereinafter, the emission direction of the illumination light L will be referred to as the Z direction, one direction perpendicular to the Z direction will be referred to as the X direction, and a direction perpendicular to the Z direction and the X direction will be referred to as the Y direction.

[0022] The imaging device 23 is, for example, a CMOS (Complementary Metal-Oxide Semiconductor) type image sensor. The imaging device 23 may be an image sensor provided with a color filter, or may be a monochrome image sensor. In addition to the image sensor, the imaging device 23 is provided with an optical system (not shown) including an objective lens. The imaging device 23 captures images of a plurality of imaging regions selected from the entire culture vessel 25.

[0023] The imaging device 23 captures images of a plurality of cells 30 (also referred to as a cell population 30A) irradiated with illumination light L from the light source 22 for each imaging region, and outputs the captured image obtained by imaging to the information processing device 10 as an image P.

[0024] The driving unit 24 is connected to the imaging device 23 and moves the imaging device 23 in two-dimensional directions. The light source 22 moves in conjunction with the movement of the imaging device 23. For example, the driving unit 24 is an XY stage that moves the imaging device 23 in the X and Y directions.

[0025] The information processing device 10 comprehensively controls the operations of the light source 22, the image capturing device 23, and the drive unit 24. As an example, as shown in Fig. 2, the information processing device 10 sets a plurality of image capturing areas IR within a culture region R corresponding to the entire culture vessel 25 (i.e., the entire cell adhesion surface), and controls the drive unit 24 to cause the image capturing device 23 to capture an image of the cell population 30A for each image capturing area IR. The image capturing area IR is an area that the image capturing device 23 captures in one image capturing operation.

[0026] When the culture vessel 25 is a T-flask, the area of ​​the culture region R is, for example, about 25 cm 2 The area of ​​the imaging region IR is, for example, about 4 mm 2 The area of ​​the imaging region IR depends on the imaging magnification of the optical system of the imaging device 23 and the size of the image sensor. The area of ​​the imaging region IR is inversely proportional to the square of the imaging magnification. For example, if the imaging magnification is changed from 10x to 4x, the area of ​​the imaging region IR becomes 6.25x.

[0027] 3, the computer constituting the information processing device 10 includes a storage device 40, a memory 41, a CPU (Central Processing Unit) 42, a communication unit 43, a display 11, and an input device 14. These are interconnected via a bus line 46.

[0028] The storage device 40 is a hard disk drive built into a computer constituting the information processing device 10 or connected via a cable or network. The storage device 40 may also be a disk array in which multiple hard disk drives are connected in series. The storage device 40 stores control programs such as an operating system, various application programs, and various data associated with these programs. Note that a solid state drive may be used instead of a hard disk drive.

[0029] The memory 41 is a work memory for executing processes by the CPU 42. The CPU 42 loads programs stored in the storage device 40 into the memory 41 and executes processes in accordance with the programs, thereby providing overall control over each part of the computer.

[0030] The communication unit 43 is a network interface that controls the transmission of various information via a network such as a LAN (Local Area Network). The display 11 displays various screens. The computer that constitutes the information processing device 10 accepts input of operation instructions from the input device 14 via the various screens.

[0031] The information processing device 10 estimates the quality of the cell population 30A based on a plurality of images P generated by the imaging device 23 capturing images of a plurality of imaging regions IR selected from the entire culture vessel 25, and if the estimation error is outside the allowable range, causes the imaging device 23 to perform re-imaging. The imaging regions to be imaged by the re-imaging are regions different from the plurality of imaging regions IR.

[0032] 4, an operating program 44 is stored in the storage device 40 of the information processing device 10. The operating program 44 is an application program for causing a computer to function as the information processing device 10. In other words, the operating program 44 is an example of a "program" according to the technology of the present disclosure.

[0033] The storage device 40 stores imaging area information 50 and tolerance range information 51 in addition to the operating program 44. The storage device 40 also stores a plurality of images P input from the imaging device 23. For example, the imaging area information 50 and tolerance range information 51 are input in advance by the user using the input device 14.

[0034] When the operating program 44 is started, the CPU 42 of the computer constituting the information processing device 10 works in cooperation with the memory 41, etc. to function as a read / write (hereinafter abbreviated as RW) control unit 60, an imaging control unit 61, a quality estimation unit 62, an estimated error derivation unit 63, and a display control unit 64.

[0035] The RW control unit 60 controls writing of various data to the storage device 40 and reading of various data from within the storage device 40. For example, the RW control unit 60 receives an image P output from the imaging device 23 of the microscope device 20 and writes it to the storage device 40.

[0036] The imaging control unit 61 acquires imaging area information 50 from the storage device 40 via the RW control unit 60, and causes the imaging device 23 to perform imaging operations based on the acquired imaging area information 50. The imaging area information 50 includes position information of multiple imaging areas IR (see FIG. 2 ) and information indicating the number of imaging areas IR. As a result of the imaging device 23 capturing images of the multiple imaging areas IR, multiple images P output from the imaging device 23 are written to the storage device 40 via the RW control unit 60.

[0037] The quality estimation unit 62 acquires the multiple images P stored in the storage device 40 via the RW control unit 60, and performs an estimation process to estimate the quality of the cells based on the multiple acquired images P. Specifically, the quality estimation unit 62 obtains a feature amount F from each of the multiple images P and calculates the average value of the feature amounts F, thereby estimating the quality of the cells in the entire culture vessel 25. Here, the multiple images P correspond to a sample with the entire culture vessel 25 as the population. The quality estimation unit 62 outputs the average value of the feature amounts F to the display control unit 64 as quality information 52.

[0038] In this embodiment, the quality estimation unit 62 derives the confluency (cell occupied area ratio) of the cell population 30A as the feature F and estimates the confluency of the cell population 30A relative to the entire culture vessel 25 by calculating the average value of the confluency.

[0039] The estimation error derivation unit 63 performs a derivation process to derive an estimation error 53 of quality by the quality estimation unit 62 based on the variation in the feature amount F in the multiple images P and imaging information related to the areas of the multiple imaging areas IR. In this embodiment, the imaging information related to the areas of the multiple imaging areas IR is the number of imaging areas IR included in the imaging area information 50 (i.e., the number of images P acquired by the imaging device 23). This is because the areas of the multiple imaging areas IR are proportional to the number of imaging areas IR.

[0040] Furthermore, in this embodiment, the estimated error derivation unit 63 derives the standard error SE based on the variation (i.e., standard deviation) of the feature F derived by the quality estimation unit 62 and the number of images P (i.e., the number of samples). The estimated error derivation unit 63 outputs the derived standard error SE to the imaging control unit 61 as the estimated error 53.

[0041] The imaging control unit 61 acquires the allowable range information 51 from the storage device 40 via the RW control unit 60, and determines whether the estimated error 53 input from the estimated error derivation unit 63 is within the allowable range. The allowable range is a range in which the quality estimation error by the quality estimation unit 62 is tolerable, and is set in advance by the user, for example.

[0042] When the estimation error is outside the allowable range, the imaging control unit 61 performs imaging control processing to cause the imaging device 23 to re-image at least one re-imaged area different from the multiple imaging areas. Information on the re-imaged area is included in the imaging area information 50. The imaging control unit 61 sets one or more re-imaged areas within the culture area R based on the imaging area information 50, and causes the imaging device 23 to image the set re-imaged areas.

[0043] When re-imaging is performed by the imaging device 23, the quality estimation unit 62 adds the image P obtained by re-imaging to the above-mentioned multiple images P, and estimates the quality of the cells for the entire culture vessel 25. When the quality estimation unit 62 estimates the quality again, the estimation error derivation unit 63 derives the estimation error 53 again.

[0044] If the imaging control unit 61 determines that the estimation error 53 is within the allowable range, the display control unit 64 causes the display 11 to display the quality information 52 output from the quality estimation unit 62.

[0045] Fig. 5 is an example of an image P acquired by the imaging device 23. As shown in Fig. 5, the image P shows a cell population 30A consisting of a plurality of cells 30. Each cell 30 is composed of a cell nucleus 31 and a cytoplasm 32. The area other than the cells 30 in the image P is a culture medium 33.

[0046] The quality estimation unit 62 uses a method based on image analysis or machine learning to identify the cells 30 in the image P. Then, the quality estimation unit 62 derives the confluency CF as the feature amount F by calculating the area ratio between the cells 30 and the culture medium 33 in the image P.

[0047] Fig. 6 shows an example of quality estimation processing by the quality estimation unit 62. Fig. 6 shows a case where the number of images P acquired by the imaging device 23, i.e., the number of samples, is N. In Fig. 6, images P1 to PN represent the N images P acquired by the imaging device 23. Furthermore, feature amounts F1 to FN represent the confluency CF derived from the images P1 to PN.

[0048] The quality estimation unit 62 calculates the average value FA of the features F1 to FN (that is, the average value of the confluency CF), and outputs the average value FA as the quality information 52.

[0049] FIG. 7 shows an example of the estimation error derivation process performed by the estimation error deriving unit 63. Estimation error derivation unit 63 The storage section 21 includes, for example, a standard deviation deriving section 65 and a standard error deriving section 66. In this embodiment, it is assumed that the distribution of the feature F for the entire (population) of the incubation vessel 25 follows a normal distribution. It is also assumed that the distribution of the average value FA (sample average) of the feature F for the images P1 to PN approaches a normal distribution as the number of samples N increases (i.e., it follows the central limit theorem).

[0050] The standard deviation deriving unit 65 derives the standard deviation σ representing the variation in the feature amount F based on, for example, the following formula (1): where N is the number of images P (number of samples), Fi is the feature amount Fi of image Pi, and FA is the average value of the features F1 to FN.

[0051]

number

[0052] The standard error derivation unit 66 derives the standard error SE based on, for example, the following formula (2). Here, σ is the standard deviation derived by the standard deviation derivation unit 65. N is the number of images P (number of samples). M is the number of elements in the population, that is, the number of imaging regions IR required to image the entire culture vessel 25. N / M in the following formula (2) represents the ratio of the area of ​​the imaging regions IR that have been imaged to the entire culture vessel 25 (culture region R). Note that the imaging regions IR that have been imaged also include re-imaged regions that have been re-imaged. Furthermore, the imaging region information 50 described above includes the number of elements M in the population.

[0053]

number

[0054] The standard error SE represents the standard deviation (i.e., the standard deviation of the sample mean) of the estimated value of quality (average value FA of feature F) estimated by the quality estimation unit 62. According to the above formula (2), it can be seen that the larger the number N of images P, the smaller the standard error SE, and the more accurate the quality estimation accuracy by the quality estimation unit 62. Furthermore, according to the above formula (2), it can be seen that the larger the ratio of the area of ​​the multiple imaging regions to the entire incubation vessel 25 (i.e., the closer the value of the area ratio N / M is to 1), the smaller the standard error SE.

[0055] Furthermore, when the number of elements M in the group is sufficiently large relative to the number N of images P, the quality estimation unit 62 may derive the standard error SE using the following formula (3) instead of the above formula (2).

[0056]

number

[0057] Fig. 8 shows an example of a re-imaging region IRS set by the imaging control unit 61. The re-imaging region IRS has the same shape and size as the imaging region IR. As shown in Fig. 8, for example, the re-imaging region IRS is set in a position in the culture region R that is different in the X and Y directions from the imaging region IR that has already been imaged.

[0058] Next, the cell quality evaluation process performed by the information processing device 10 will be described with reference to the flowchart shown in Fig. 9 as an example. First, in the information processing device 10, the CPU 42 executes the process based on the operating program 44, whereby the CPU 42 functions as the RW control unit 60, the imaging control unit 61, the quality estimation unit 62, the estimation error derivation unit 63, and the display control unit 64, as shown in Fig. 4.

[0059] 9, first, the imaging control unit 61 sets a plurality of imaging regions IR within the culture region R based on the imaging region information 50 (step S10). The imaging control unit 61 controls the microscope device 20 to cause the imaging device 23 to capture images of the set plurality of imaging regions IR (step S11). The information processing device 10 acquires a plurality of images P output from the microscope device 20 as a result of the imaging device 23 capturing images (step S12).

[0060] The quality estimation unit 62 estimates the quality of the cell as described above based on the multiple images P acquired from the microscope device 20 (step S13). The estimation error derivation unit 63 derives the estimation error 53 of the cell quality by the quality estimation unit 62 as described above (step S14).

[0061] The imaging control unit 61 is configured such that the estimation error derivation unit 63 Derivation The imaging control unit 61 determines whether the estimated error 53 is within the allowable range (step S15). If the imaging control unit 61 determines that the estimated error 53 is outside the allowable range (step S15: NO), the imaging control unit 61 sets a re-imaging region IRS within the culture region R as described above (step S16).

[0062] After step S16 is performed, the cell quality evaluation process proceeds again to step S11. In step S11, the imaging control unit 61 causes the imaging device 23 to capture an image of the re-imaging region IRS. In the following step S12, the information processing device 10 acquires the image P output from the microscope device 20 by the imaging device 23 capturing an image of the re-imaging region IRS.

[0063] In step S13, the quality estimation unit 62 estimates the quality of the cell by adding the image P obtained by re-imaging to the above-mentioned multiple images P. In step S14, the estimation error derivation unit 63 again derives the estimation error 53.

[0064] In this way, steps S11 to S16 are repeated until it is determined in step S15 that the estimated error 53 is within the allowable range. Each time reimaging is performed, the estimated error 53 becomes smaller.

[0065] Thereafter, if it is determined in step S15 that the estimation error 53 is within the allowable range (step S15: YES), the display control unit 64 causes the quality information 52 output from the quality estimation unit 62 to be displayed on the display 11 (step S17). This completes the cell quality evaluation process.

[0066] In the microscope observation system 2, the cell quality evaluation process shown in FIG. 9 is performed periodically (for example, once a day) during cell culture.

[0067] As described above, the cell quality evaluation device of the present disclosure performs re-imaging of at least one re-imaged region when the quality estimation error is outside the allowable range, thereby making it possible to maintain a certain level of quality estimation accuracy with a minimum number of imaging attempts. In other words, the cell quality evaluation device of the present disclosure makes it possible to evaluate cell quality with high accuracy even in a large-capacity culture vessel.

[0068] Fig. 10 illustrates the dependency of quality estimation error on the number of captured images. Fig. 10 also compares the estimation error when the imaging magnification of the imaging device 23 is 10x and when it is 4x. Fig. 10 shows an example in which confluency is used as the feature F, and plots lines of ±2SE centered on the average value FA of the feature F.

[0069] 10, it can be seen that the smaller the imaging magnification, the smaller the quality estimation error, and the more the number of images N is increased, the more quickly the estimated quality approaches the true value. This is because the smaller the imaging magnification, the larger the size of the imaging region IR, and the greater the ratio of the area of ​​the imaging region IR that has been imaged to the entire incubation vessel 25 (i.e., the area ratio N / M). Note that the imaging region IR that has been imaged also includes the re-imaged region IRS that has been re-imaged.

[0070] [Variations] Various modifications of the above embodiment will be described below.

[0071] In the above embodiment, when re-imaging is performed, the imaging control unit 61 sets the re-imaging region IRS based on information previously set as the imaging region information 50, but the re-imaging region IRS may also be set based on the variation in the feature amount F and the position information of the multiple imaging regions IR. For example, the imaging control unit 61 sets the re-imaging region IRS in a region within the culture region R that is different from the multiple imaging regions IR and in which the variation in the feature amount F is large.

[0072] Furthermore, when the feature amount F is confluency, the imaging control unit 61 may set re-imaged regions IRS based on the confluency of each imaging region IR derived by the quality estimation unit 62. For example, the imaging control unit 61 sets more re-imaged regions IRS in regions where the confluency is around 50% within the culture region R than in other regions. That is, the imaging control unit 61 sets more re-imaged regions IRS in regions where the confluency is around 50% than in other regions. Area are reimaged more than other areas.

[0073] Fig. 11 illustrates the relationship between confluency and estimation error, showing the distribution of confluency when cells are seeded using the first seeding method and the distribution of confluency when cells are seeded using the second seeding method.

[0074] Figure 12 shows examples of images P when cells are seeded using the first seeding method and the second seeding method. Figure 12(A) is an example of image P when cells are seeded using the first seeding method. Figure 12(B) is an example of image P when cells are seeded using the second seeding method.

[0075] Figure 11 shows that the quality estimation error becomes large when the confluency is around 50%. Area By re-imaging the area more frequently than other areas, the estimation error can be converged more quickly.

[0076] Furthermore, the imaging control unit 61 may set the re-imaging region IRS based on the surface shape of the culture vessel 25. This is because the distribution of the cells 30 varies depending on the surface shape of the culture vessel 25.

[0077] As an example, as shown in FIG. 13, the bottom surface (i.e., cell adhesion surface) 25A of the culture vessel 25 may not be flat but may be curved. In the example shown in FIG. 13, the bottom surface of the culture vessel 25 is lower at the periphery than at the center. In such a case, cells 30 are less likely to gather in the center of the culture vessel 25 and are more likely to gather at the periphery. That is, the confluency is lower in the center of the culture vessel 25 and higher in the periphery. Since there is a tendency for the estimation error to be larger in regions with lower confluency than in regions with higher confluency, it is preferable that the imaging control unit 61 set a larger number of re-imaged regions IRS in the center of the culture region R than in the periphery.

[0078] 14 shows an example of a process for setting a re-imaged region IRS based on the surface shape of the incubation vessel 25. In this example, the storage device 40 stores surface shape information 70 representing the shape of the bottom surface 25A of the incubation vessel 25. Imaging control unit 61 acquires the surface shape information 70 from the storage device 40, and sets the re-imaged region IRS based on the acquired surface shape information 70.

[0079] Furthermore, in the above embodiment, the estimated error derivation unit 63 derives the estimated error 53 based on the above formulas (1) and (2) or (1) and (3). However, the estimated error 53 may be derived based on a table stored in advance in the storage device 40 or the like. As shown in FIG. 15 as an example, table T defines the relationship between the ratio of the area of ​​the imaged imaging region IR to the entire incubation vessel 25 (i.e., the area ratio N / M) and the estimated error 53. The specific values ​​stored in table T are determined, for example, by experiment. Using such a table is useful when the estimated error 53 cannot be determined using the above formulas (1) and (2) or (1) and (3).

[0080] Furthermore, in the above embodiment, the user can input imaging region information 50 and tolerance range information 51 in advance using the input device 14. If the capacity of the culture vessel 25 to be used is not constant, the area of ​​the culture vessel 25 may be input or selected as the imaging region information 50. This makes it possible to derive the number of elements M of the population described above for each culture vessel 25 to be used from the relationship between the area of ​​the culture vessel 25 and the area of ​​the imaging region IR.

[0081] In addition, in the above embodiment, the feature F relating to the quality of the cell 30 is confluency, but instead of confluency, the area of ​​the cell nucleus 31 or the area ratio between the cell nucleus 31 and the cytoplasm 32 may be used.

[0082] Various modifications are possible to the hardware configuration of the computer that constitutes the information processing device 10. For example, the information processing device 10 can be configured with multiple computers that are separated as hardware in order to improve processing power and reliability.

[0083] In this way, the hardware configuration of the computer of the information processing device 10 can be changed as appropriate depending on the required performance such as processing power, safety, reliability, etc. Furthermore, not only the hardware but also application programs such as the operating program 44 can be duplicated or stored in a distributed manner across multiple storage devices in order to ensure safety and reliability.

[0084] In the above embodiment, the following various processors can be used as the hardware structure of processing units that perform various processes, such as the RW control unit 60, the imaging control unit 61, the quality estimation unit 62, the estimation error derivation unit 63, and the display control unit 64. As described above, the various processors include a CPU, which is a general-purpose processor that executes software (operating program 44) and functions as various processing units, as well as dedicated electrical circuits, such as a programmable logic device (PLD), a processor whose circuit configuration can be changed after manufacture, such as an FPGA, and an application specific integrated circuit (ASIC), which is a processor having a circuit configuration designed specifically for performing specific processes.

[0085] A single processing unit may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs and / or a combination of a CPU and an FPGA). Also, multiple processing units may be configured with a single processor.

[0086] Examples of configuring multiple processing units with a single processor include, first, a form in which one processor is configured with a combination of one or more CPUs and software, and this processor functions as multiple processing units, as typified by computers such as client and server. Second, a form in which a processor is used to realize the functions of an entire system including multiple processing units with a single IC (Integrated Circuit) chip, as typified by systems on chips (SoCs). In this way, various processing units are configured using one or more of the above-mentioned various processors as a hardware structure.

[0087] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements.

[0088] Furthermore, the above-described embodiments and modifications can be combined as appropriate within the scope of not causing any contradiction. Furthermore, the present disclosure extends to not only programs that cause a computer to execute various processes, but also computer-readable storage media that non-temporarily store the programs.

[0089] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0090] The above explanation allows one to understand the following techniques. [Additional note 1] 1. A cell quality evaluation device that evaluates the quality of cells based on a plurality of images acquired from an imaging device that generates a plurality of images by capturing images of a plurality of imaging regions selected from an entire culture vessel in which cells are cultured, at least one processor; The processor: an estimation process of estimating the quality of cells in the entire culture vessel by obtaining a feature amount from each of the plurality of images and calculating an average value of the feature amounts; a derivation process of deriving an estimation error of the quality obtained by the estimation process based on the variation in the feature amounts in the plurality of images and imaging information related to areas of the plurality of imaging regions; an imaging control process for causing the imaging device to re-image at least one re-image area different from the plurality of imaging areas when the estimation error is outside an allowable range; A cell quality evaluation device that performs a process including: [Additional note 2] The imaging information is the number of images, The estimation error is σ / N, where σ is the standard deviation of the feature amount and N is the number of images. 1 / 2 represented by Item 1. A cell quality evaluation device according to claim 1. [Additional note 3] In the imaging control process, the re-imaging area is set based on the variation in the feature amount and position information of the plurality of imaging areas. Item 10. The cell quality evaluation device according to item 1 or 2. [Additional note 4] In the imaging control process, the re-imaging region is set based on a surface shape of the culture vessel. Item 10. The cell quality evaluation device according to item 1 or 2. [Additional note 5] The feature amount is a cell occupancy area ratio, an area of ​​a cell nucleus, or an area ratio between a cell nucleus and a cytoplasm. Item 4. The cell quality evaluation device according to any one of items 1 to 4. [Additional note 6] the feature amount is a cell-occupied area ratio, and the imaging control process causes the imaging device to re-image a region where the cell-occupied area ratio is around 50% more than other regions. Item 4. The cell quality evaluation device according to any one of items 1 to 4. [Additional note 7] In the derivation process, the estimation error is derived based on a table that defines a relationship between a ratio of an area of ​​an imaged imaging region to an entire area of ​​the incubation vessel and the estimation error. 7. The cell quality evaluation device according to claim 1, wherein the cell quality evaluation device is a cell quality evaluation device.

Claims

1. 1. A cell quality evaluation device that evaluates the quality of cells based on a plurality of images acquired from an imaging device that generates a plurality of images by capturing images of a plurality of imaging regions selected from an entire culture vessel in which cells are cultured, at least one processor; The processor: an estimation process of estimating the quality of cells in the entire culture vessel by obtaining a feature amount from each of the plurality of images and calculating an average value of the feature amounts; a derivation process of deriving an estimation error of the quality obtained by the estimation process based on the variation in the feature amounts in the plurality of images and imaging information related to areas of the plurality of imaging regions; an imaging control process for causing the imaging device to re-image at least one re-image area different from the plurality of imaging areas when the estimation error is outside an allowable range; A cell quality evaluation device that performs a process including:

2. The imaging information is the number of images, The estimation error is expressed as σ / N, where σ is the standard deviation of the feature amount and N is the number of images. １／２ represented by The cell quality evaluation device according to claim 1 .

3. In the imaging control process, the re-imaging area is set based on the variation in the feature amount and position information of the plurality of imaging areas. The cell quality evaluation device according to claim 1 .

4. In the imaging control process, the re-imaging region is set based on a surface shape of the culture vessel. The cell quality evaluation device according to claim 1 .

5. The feature amount is a cell occupancy area ratio, an area of ​​a cell nucleus, or an area ratio between a cell nucleus and a cytoplasm. The cell quality evaluation device according to claim 1 .

6. the feature amount is a cell-occupied area ratio, and the imaging control process causes the imaging device to re-image a region where the cell-occupied area ratio is around 50% more than other regions. The cell quality evaluation device according to claim 1 .

7. In the derivation process, the estimation error is derived based on a table that defines a relationship between a ratio of an area of ​​an imaged imaging region to an entire area of ​​the incubation vessel and the estimation error. The cell quality evaluation device according to claim 1 .

8. 1. A cell quality evaluation method for evaluating the quality of cells based on a plurality of images acquired from an imaging device that generates a plurality of images by capturing images of a plurality of imaging regions selected from an entire culture vessel in which cells are cultured, comprising: an estimation process of estimating the quality of cells in the entire culture vessel by obtaining a feature amount from each of the plurality of images and calculating an average value of the feature amounts; a derivation process of deriving an estimation error of the quality obtained by the estimation process based on the variation in the feature amounts in the plurality of images and imaging information related to areas of the plurality of imaging regions; an imaging control process for causing the imaging device to re-image at least one re-image area different from the plurality of imaging areas when the estimation error is outside an allowable range; A cell quality evaluation method that performs a process including the steps of:

9. A program that causes a computer to execute a process of evaluating the quality of cells based on a plurality of images acquired from an imaging device that generates a plurality of images by capturing images of a plurality of imaging regions selected from the entirety of a culture vessel in which cells are cultured, an estimation process of estimating the quality of cells in the entire culture vessel by obtaining a feature amount from each of the plurality of images and calculating an average value of the feature amounts; a derivation process of deriving an estimation error of the quality obtained by the estimation process based on the variation in the feature amounts in the plurality of images and imaging information related to areas of the plurality of imaging regions; an imaging control process for causing the imaging device to re-image at least one re-image area different from the plurality of imaging areas when the estimation error is outside an allowable range; A program that causes a computer to execute a process including the above.

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