Small particle measurement system and method for measuring small particles

Abstract

To improve the accuracy of making a measurement related to small particles by using a taken image.SOLUTION: The small particle measurement system according to an embodiment includes: a light source for emitting illumination light on liquid including small particles to measure; an objective lens for collecting the illumination light; an image formation lens for forming the collected illumination light into an image; an image sensor for taking an image of the imaged illumination light and outputting the taken image; a detection unit for detecting the small particles in the taken image; a calculation unit for calculating the accuracy of detecting small particles on the basis of the result of detection by the detection unit and a predetermined index; and a processing unit for determining the next processing on the basis of the accuracy and a target accuracy requested by a user.SELECTED DRAWING: Figure 9

Description

[Technical field]

[0001] An embodiment of the present invention relates to a microparticle measuring system and a microparticle measuring method. [Background technology]

[0002] Conventionally, organic wastewater treatment utilizes various useful microorganisms to decompose organic matter in wastewater and remove nitrogen and phosphorus. In this case, wastewater treatment is performed based on indicators such as sludge concentration and treated water quality. Therefore, it would be meaningful to be able to measure the concentration of useful microorganisms that contribute to organic matter decomposition and nitrogen removal.

[0003] Techniques for measuring the concentration of beneficial microorganisms (microparticles such as Bacillus) include colony counting and PCR (Polymerase Chain Reaction), but these methods require highly specialized equipment, and have problems such as the time required to transport samples to specialized facilities and the long measurement times.

[0004] Therefore, a technology has been proposed that uses the characteristics of light refraction by the microparticles being measured and detects microparticles (e.g., Bacillus spores) from captured images using image processing based on deep learning. This makes it unnecessary to transport samples to specialized facilities and enables the concentration of microparticles to be measured in a short time. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2016-147044 A [Patent Document 2] JP 2021-165657 A [Patent Document 3] JP 2017-23953 A [Patent Document 4] JP 2016-49055 A Summary of the Invention [Problem to be solved by the invention]

[0006] However, the above-mentioned deep learning method has a problem that the measurement accuracy is not constant. For example, the concentration of Bacillus in sludge is not uniform but has shades, and the number (concentration) of Bacillus shown in the captured image varies. Therefore, for example, when the number of Bacillus detected per captured image is small, the accuracy of the measurement result is low if the number of captured images is small. However, it is not always easy for an operator to judge whether the number of captured images is small or not.

[0007] Therefore, the present invention has been made in consideration of the above-mentioned circumstances, and has an object to provide a microparticle measuring system and a microparticle measuring method that can improve the accuracy when measuring microparticles using captured images. [Means for solving the problem]

[0008] The microparticle measuring system of the embodiment includes a light source that emits illumination light toward a liquid containing microparticles to be measured, an objective lens that collects the illumination light, an imaging lens that forms an image of the collected illumination light, an image sensor that captures the image of the illumination light and outputs a captured image, a detection unit that detects the microparticles shown in the captured image, a calculation unit that calculates the accuracy of microparticle detection based on the detection result by the detection unit and a predetermined index, and a processing unit that determines the next process based on the accuracy and a target accuracy required by a user. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a schematic diagram of a microparticle measuring system according to an embodiment. [Diagram 2] FIG. 2 is an explanatory diagram of parameters in a ray tracing matrix. [Diagram 3] FIG. 3 is an explanatory diagram of the relative transmitted light intensity etc. for Bacillus spores. [Figure 4]FIG. 4 is an explanatory diagram of the relative transmitted light intensity and the like for acrylic particles. [Diagram 5] FIG. 5 is a diagram showing an example of a captured image of sludge. [Figure 6] FIG. 6 is a diagram showing an example of a captured image and a teaching image used in deep learning. [Figure 7] FIG. 7 is a diagram showing an example of a Bacillus detection result by deep learning. [Figure 8] FIG. 8 is a diagram showing an example of a data table relating to the number of detected Bacilli and the 95% confidence interval. [Figure 9] FIG. 9 is a flowchart showing the processing performed by the microparticle measuring system of the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, embodiments of a microparticle measuring system and a microparticle measuring method according to the present invention will be described with reference to the drawings. Note that hereinafter, Bacillus spores are also simply referred to as Bacillus.

[0011] 1 is a schematic diagram of a microparticle measurement system 10 according to an embodiment. The microparticle measurement system 10 includes a light source 11, a stage 13, a stage driver 14, a laser displacement meter 15, an objective lens 16, an imaging lens 17, an image sensor 18, a measurement controller 19, and an information processor 20. The measurement controller 19 and the information processor 20 may be integrated into one unit. The information processor 20 may also be divided into two or more units.

[0012] The light source 11 emits illumination light L to a measurement sample SP (liquid, specimen) that contains microparticles to be measured.

[0013] The stage 13 supports a slide glass (preparation) 12 that holds a measurement sample SP. Note that a hemocytometer may be used in place of the slide glass.

[0014] A stage driver 14 moves the stage 13 in the vertical direction in FIG. 1 along the optical axis. The laser displacement meter 15 detects the position of the slide glass 12 by using a laser.

[0015] The objective lens 16 condenses the illumination light L into parallel light. The imaging lens 17 condenses the illumination light L, which has become parallel light, to form an image.

[0016] The image sensor 18 captures the illumination light imaged by the imaging lens 17 and outputs the captured image. The measurement control unit 19 controls the stage driving unit 14 and the image sensor 18 .

[0017] The information processing device 20 includes an acquisition unit 21, a detection unit 22, a calculation unit 23, a processing unit 24, a storage unit 25, a display unit 26, and an input unit 27.

[0018] The acquisition unit 21 acquires a captured image from the image sensor 18 .

[0019] The detection unit 22 detects microparticles, impurities, and the like that appear in the captured image.

[0020] The calculation unit 23 calculates the accuracy of microparticle detection based on the detection result by the detection unit 22 and a predetermined index. The index is, for example, the number of microparticles.

[0021] The processing unit 24 executes various information processes. For example, the processing unit 24 determines the next process based on the accuracy and the target accuracy required by the user.

[0022] When the accuracy is less than the target accuracy, the processing unit 24 determines and executes the following processes (1) to (5) as the next process. (1) A process of displaying on the display unit 26 a screen requesting the user to perform a predetermined operation to improve accuracy (2) A process of displaying on the display unit 26 a screen requesting the user to obtain an additional captured image. (3) Automatically acquiring additional captured images (4) Processing for displaying accuracy on the display unit 26 (5) A process of displaying on the display unit 26 the time required to complete the measurement, which is estimated from the number of microparticles and the total number of captured images.

[0023] The memory unit 25 stores the operating programs of each unit 21 to 24, various parameters, captured images acquired by the acquisition unit 21, detection results by the detection unit 22, calculation results such as accuracy by the calculation unit 23, processing results by the processing unit 24, etc.

[0024] The display unit 26 displays various information according to instructions from the processing unit 24 .

[0025] The input unit 27 is a means for a user to input information, such as a keyboard, a mouse, or a touch panel.

[0026] It should be noted that all or part of the processes performed by the above-mentioned units 21 to 24 may be executed by one processor (control unit) based on the operation programs and various parameters stored in the storage unit 25.

[0027] Next, the principle of measuring microparticles will be described. When illumination light is irradiated from the back side of a microparticle in a liquid, the illumination light is focused at a position according to the particle diameter and refractive index of the microparticle due to the lens effect of the microparticle.

[0028] The transmitted light intensity increases as the light approaches the focusing position, reaches a maximum at the focusing position, and decreases again as the light moves away from the focusing position. In other words, the position at which the transmitted light intensity is maximum is the focusing position. At this time, the focusing position can be identified by measuring the distance between the objective lens 16 and the position at which the transmitted light intensity is maximum.

[0029] In this case, the optical path of the illumination light can be expressed by the following equation: Therefore, if the particle diameter of the microparticle is known in addition to the distance between the objective lens 16 and the position where the transmitted light intensity is maximum, the refractive index of the microparticle can be determined by solving the equation expressed by the following ray tracing matrix.

[0030]

number

[0031] 2 is an explanatory diagram of parameters in a ray tracing matrix. In the ray tracing matrix, the radius of the microparticle PC is r, the refractive index of the microparticle is n, and the distance between the microparticle and the objective lens 16 when the transmitted light intensity of the illumination light L in the target microparticle is maximum is z. Also, the distance from the optical axis when the illumination light L is incident on the microparticle is x0, and the incident angle when the illumination light L is incident on the microparticle is u0. Also, the distance from the optical axis of the illumination light L incident on the image sensor 18 is x1, and the incident angle of the illumination light L incident on the image sensor 18 is u1.

[0032] Furthermore, the distance between the objective lens 16 and the imaging lens 17 is denoted by l1, and the distance between the imaging lens 17 and the image sensor 18 is denoted by l2. The focal length of the objective lens is denoted by f1, and the focal length of the imaging lens 17 is denoted by f2.

[0033] Then, as described above, if the refractive index of the microparticle is known, the particle diameter of the microparticle can be calculated by solving the equation represented by the ray tracing matrix.

[0034] In addition, useful microorganisms used in organic wastewater treatment can be considered as microparticles under certain conditions, such as when the useful microorganisms form spores. When the useful microorganisms form spores, their shape does not change and the shape is almost constant for the useful microorganisms.

[0035] Since the spores of beneficial microorganisms have a specific size (e.g., particle diameter) and a specific refractive index, by treating them in the same way as microparticles, it is possible to detect such beneficial microorganisms and measure their number (and thus their concentration) per observation field.

[0036] When measuring the concentration, the observation position (image capture position) is scanned along the optical axis direction, and the number of beneficial microorganisms in a volume corresponding to the observation field of view x scanning distance is counted, thereby making it possible to measure the concentration.

[0037] Incidentally, for spores of the genus Bacillus (Bacillus spores) in sludge with a known refractive index and a particle size of 1 μm or less, it is known that the position corresponding to the distance z at which the transmitted light intensity is at its maximum is located within the depth of field (effective focal position) corresponding to the focal length f1 in image acquisition. Therefore, based on a preset transmitted light intensity threshold, the part having a light intensity equal to or greater than the threshold can be regarded as a Bacillus spore.

[0038] In this case, the transmitted light intensity through the liquid containing the Bacillus spores is greater than the transmitted light intensity through the liquid not containing the Bacillus spores. Therefore, by setting the threshold value of the transmitted light intensity for determining whether or not a liquid contains Bacillus spores to a value slightly higher than the transmitted light intensity in a liquid that does not contain spores, Bacillus spores can be detected reliably.

[0039] Furthermore, by using a set threshold value, the sample is continuously moved in the optical axis direction while sequentially capturing images, and by combining this with deep learning (machine learning) that uses the transmitted light intensity at each location (each pixel) obtained from the captured images and the size (particle diameter) of the Bacillus spores as microparticles as judgment criteria, it is possible to detect and count the Bacillus spores, and ultimately to measure the concentration of the Bacillus spores with high accuracy.

[0040] When performing deep learning, for example, multiple samples with different concentrations of microparticles are prepared in advance, and supervised learning is performed so that the manual detection results by a user (operator) for each sample are equivalent to the detection results obtained by deep learning, thereby obtaining microparticle detection results that correspond to the particle diameter and refractive index of the microparticles being learned.

[0041] Next, Fig. 3 is an explanatory diagram of the relative transmitted light intensity for Bacillus spores, etc. In detail, Fig. 3 is a diagram for explaining the relationship between the difference in the actual position of the objective lens 16 with respect to the distance z between the Bacillus spore and the objective lens 16 when the transmitted light intensity for the Bacillus spore is maximum, and the relative transmitted light intensity.

[0042] Fig. 4 is an explanatory diagram of the relative transmitted light intensity for acrylic particles, etc. In detail, Fig. 4 is a diagram for explaining the relationship between the difference in the actual position of the objective lens 16 relative to the distance z between the acrylic particle (microparticle) and the objective lens 16 when the transmitted light intensity is maximum for an acrylic particle having a particle diameter of 30 μm, and the relative transmitted light intensity.

[0043] First, images of the Bacillus spores and acrylic particles were acquired at the focal position by the image sensor 18. Then, the stage 13 was moved vertically along the optical axis direction by the stage driving unit 14, and the position difference Δz between the position of the objective lens 16 when the relative transmitted light intensity of each microparticle on the image sensor 18 was maximized and the actual position of the objective lens 16 was measured by the laser displacement meter 15.

[0044] Figure 3(A) is an image captured when the relative transmitted light intensity is maximum in a liquid containing Bacillus spores. As shown in Figure 3(A), it can be seen that the relative transmitted light intensity is maximum at the center of the image capture area. And, as shown in Figure 3(B), in the liquid containing Bacillus spores, it was calculated that the relative transmitted light intensity is maximum at a position difference Δz = 0 μm.

[0045] In contrast, as shown in FIG. 4(B), in the case of a liquid containing acrylic particles with a particle diameter of 30 μm, the relative transmitted light intensity has a negative value at the position difference Δ0 μm where the relative transmitted light intensity of the Bacillus spores is maximum. In other words, it can be seen that the transmitted light intensity is lower than the background light intensity. Also, as shown in FIG. 4(A), it can be seen that the relative transmitted light intensity is minimum around the acrylic particles. And, as shown in FIG. 4(B), in the case of a liquid containing acrylic particles with a particle diameter of 30 μm, it was calculated that the relative transmitted light intensity is maximum outside the position difference Δz = ±15 μm.

[0046] Also, Fig. 4(C) is an image captured when the relative transmitted light intensity is maximum in a liquid containing acrylic particles with a particle diameter of 30 μm. As shown in Fig. 4(C), it can be seen that the relative transmitted light intensity is maximum at the center of the imaging area. And, as shown in Fig. 4(D), in a liquid containing acrylic particles with a particle diameter of 30 μm, it was calculated that the relative transmitted light intensity is maximum at a position difference Δz = 26 μm.

[0047] Based on this measurement result, the position difference Δz corresponding to the difference between the distance z from the Bacillus spores and acrylic particles having a particle diameter of 30 μm, which are microparticles at which the transmitted light intensity is maximized, to the objective lens 16 and the focal length of the objective lens was calculated using the above-mentioned ray tracing matrix for the Bacillus spores and acrylic particles having a particle diameter of 30 μm. The position difference Δz in the liquid containing the Bacillus spores was 0.9 μm, and the position difference Δz in the liquid containing acrylic particles having a particle diameter of 30 μm was 22.5 μm, which was found to be almost consistent with the measurement result using the laser displacement meter 15. In this case, the distance between the objective lens 16 and the imaging lens 17 was l1 = 130 mm, the distance between the imaging lens 17 and the image sensor 18 was l2 = 164.5 mm, the focal length of the objective lens was f1 = 4.1125 mm, and the focal length of the imaging lens 17 was f2 = 164.5 mm. In addition, the Bacillus spores had r = 1 μm and n = 1.4, and the acrylic particles had n = 1.5.

[0048] In particular, it was found that a position difference Δz=0.9 μm in the liquid containing Bacillus spores is effectively equal to the focal length of the objective lens 16 (within the depth of field), and the relative transmitted light intensity is maximum at the focal position. This demonstrates that in measuring Bacillus spores, it is possible to detect Bacillus spores by measuring the transmitted light intensity at the focal length.

[0049] In this way, by utilizing the characteristic of Bacillus spores that the center glows brightly when the transmitted light intensity is at its maximum, it is possible to detect only Bacillus spores from the captured image.

[0050] Next, Fig. 5 is a diagram showing examples of captured images of sludge. As shown in Fig. 5(a) and (b), in addition to Bacillus spores B, impurities C may also be captured in the captured images.

[0051] Next, Fig. 6 shows examples of captured images and training images used in deep learning. (a) is a captured image showing Bacillus spores B and impurities C. A user provides the central position P of Bacillus spores B as correct answer data for this captured image, which is used as the training image shown in (b). Deep learning can be performed by training a network to detect the central position P of Bacillus spores B in the captured image using these images.

[0052] 7 is a diagram showing an example of a Bacillus detection result by deep learning. The detection unit 22 calculates the likelihood of the Bacillus spore detection result by image processing using deep learning (the possibility (likelihood) that each pixel is the center position of the Bacillus spore).

[0053] Fig. 7(a) is an input image (captured image). The detection unit 22 calculates a likelihood map shown in Fig. 7(b), for example. In this likelihood map, the brighter the area, the higher the likelihood, and the darker the area, the lower the likelihood. The symbol Q is a portion where the likelihood is high, corresponding to Bacillus spore B (Fig. 7(a)).

[0054] The detection unit 22 then performs threshold processing on this likelihood and determines that a pixel having a likelihood equal to or greater than a certain level is the center position of the Bacillus spore, thereby obtaining the detection result shown in Fig. 7(c). In Fig. 7(c), the symbol S indicates the center position of the detected Bacillus spore.

[0055] 8 is a diagram showing an example of a data table relating to the number of detected Bacillus and the 95% confidence interval. When sludge containing Bacillus at a certain concentration is sampled and an image is taken, it is assumed that the number of detected Bacillus in the image follows a Poisson distribution.

[0056] When the number of detected Bacilli is k, if a confidence interval with a reliability coefficient of α% is calculated by interval estimation using the Poisson distribution, the upper and lower limits of the confidence interval become the upper and lower limits of the error in the number of detected Bacilli (i.e., the error in the measured concentration).

[0057] Figure 8 shows the 95% confidence interval when the number of detected Bacilli is k (1≦k≦20) assuming a Poisson distribution. For example, assume that 10 Bacilli are detected as a measurement result. Figure 8 shows that the relative error of this measurement result falls within the interval of -52.0% to +84.0% with a 95% probability.

[0058] Here, for example, it is assumed that the allowable range of the relative error between the true concentration and the measured concentration is set to −43.8% to +77.8% as the target accuracy required by the user.

[0059] Moreover, the relative error can be calculated by the following formula (1). Relative error = (measured concentration - true concentration) / true concentration) Equation (1)

[0060] As can be seen from FIG. 8, the relative error between the number of detected objects and the lower limit of the confidence interval is -44.0% when the number of detected objects is 15, and -42.81% when the number of detected objects is 16. This shows that a detection number of 16 or more is sufficient.

[0061] In addition, the relative error between the number of detected objects and the upper limit of the confidence interval is 79.0% when the number of detected objects is 11, and 74.75% when the number of detected objects is 12. This shows that a detection number of 12 or more is sufficient.

[0062] Therefore, in order to achieve the target accuracy required by the user, it is understood that the measurement should be carried out so that the total number of detected Bacilli is 16 or more.

[0063] 9 is a flowchart showing the processing by the microparticle measuring system of the embodiment. Here, a target accuracy desired by the user is input, and measurement is performed to achieve that target accuracy.

[0064] First, in step S1, the information processing device 20 accepts an input of a target accuracy to be achieved by a measurement operator using the input unit 27. The measurement operator inputs, for example, a target value of the relative error between the true concentration and the measured concentration (for example, an allowable range: −43.8% to +77.8%) as the target accuracy.

[0065] Next, the measurement operator collects water from the water treatment device in which the microorganism (bacillus) to be measured is present, performs the specified pretreatment (filter treatment, heat treatment, etc.) on the collected water sample, and sets the sample after pretreatment in the microparticle measurement system 10.

[0066] Next, in step S2, the acquisition unit 21 acquires a captured image from the image sensor 18.

[0067] Next, in step S3, the detection unit 22 detects Bacillus spores appearing in the captured image.

[0068] Next, in step S4, calculation unit 23 calculates the accuracy of Bacillus detection based on the detection result by detection unit 22 and a predetermined index. Note that processing unit 24 may cause display unit 26 to display the calculated accuracy.

[0069] Next, in step S5, the processing unit 24 determines whether or not the target accuracy has been achieved, and if Yes, ends the processing, and if No, proceeds to step S6.

[0070] In step S6, the processing unit 24 determines whether the number of repetitions ("number of times step S6 has been passed" - 1 (times)) has exceeded a number threshold (for example, about 5 to 10 times), and if Yes, ends the process, and if No, proceeds to step S7. Note that if Yes, it means that the accuracy continues to not reach the target accuracy or higher even after performing an operation (predetermined operation) to improve the accuracy a certain number of times or more, and the measurement result is determined to be "concentration below the lower limit of measurement" or "measurable", and the measurement is terminated. In that case, the display unit 26 may display that the measurement result is "concentration below the lower limit of measurement" or "measurable".

[0071] In step S7, the processing unit 24 causes the display unit 26 to display a screen requesting the user to perform a predetermined operation to improve accuracy. In this case, the processing unit 24 may also cause the display unit 26 to display the accuracy of the current measurement result.

[0072] The process in step S7 is the process (1) among the above-mentioned processes (1) to (5), and processes (2) and (3) may be executed instead of the process (1).

[0073] (Repost (2), (3)) (2) A process of displaying on the display unit 26 a screen requesting the user to obtain an additional captured image. (3) Automatically acquiring additional captured images

[0074] Regarding (2), for example, a case may be considered in which an additional captured image is acquired after shifting the glass slide 12 to change the imaging area, or a case may be considered in which an additional captured image is acquired after replacing the glass slide 12 with another one.

[0075] Regarding (3), if the configuration of the microparticle measurement system 10 is different from that described above and automatic acquisition of additional new captured images is possible, the processing unit 24 automatically acquires additional new captured images. In that case, for example, a device for automatically shifting the slide glass 12 may be provided.

[0076] In step S7, the processes (4) and (5) may also be performed. (Repost (4), (5)) (4) Processing for displaying accuracy on the display unit 26 (5) A process of displaying on the display unit 26 the time required to complete the measurement, which is estimated from the number of microparticles and the total number of captured images.

[0077] For (5), the time required to complete the measurement is predicted, and "x minutes remaining until completion" or the like is displayed on the display unit 26. Specifically, for example, when requesting acquisition of additional images, the time required to acquire the additional images can be calculated from the information acquired up to now (number of images taken, time required for measurement, etc.). For example, if 10 captured images have been acquired in 10 minutes up to now, and 5 additional captured images are to be acquired, the time required to acquire the additional images can be calculated as "5 minutes" by proportional calculation.

[0078] After step S7, the user performs a predetermined action, and then the process returns to step S2. In the case of (3), the user does not perform a predetermined action, and a captured image is automatically acquired in the next step S2.

[0079] In this way, the microparticle measuring system 10 of this embodiment calculates the accuracy of Bacillus detection, and determines the next process based on the accuracy and the target accuracy required by the user, thereby improving the accuracy of Bacillus measurement. For example, if the accuracy is below the target accuracy, a specific task is required of the operator, so that even an operator without specialized knowledge can easily perform the appropriate task.

[0080] Furthermore, by appropriately displaying the accuracy calculated from the measurement results, workers can easily check the accuracy of the measurement results and quantitatively evaluate it.

[0081] Furthermore, if the microparticle measurement system 10 is configured to be capable of automatically acquiring additional new captured images, then when the accuracy is below the target accuracy, additional new captured images can be automatically acquired, thereby reducing the burden on the operator.

[0082] In the above, the microparticle measuring system 10 is configured as a standalone system, but the present invention is not limited to this. For example, the microparticle measuring system 10 may acquire an image by the image sensor 18 on the local terminal side, transfer the image to a cloud server via a communication interface and a communication network, process the image by the information processing device 20 on the cloud server side, and display the processing result on the local terminal.

[0083] Furthermore, the information processing device 20 of this embodiment includes a control device such as a CPU (Central Processing Unit), a storage device such as a ROM (Read Only Memory) and a RAM (Random Access Memory), an external storage device such as an HDD (Hard Disk Drive), a display device such as a display device, and input devices such as a keyboard and a mouse, and has a hardware configuration that utilizes a normal computer.

[0084] In addition, the program executed by the information processing device 20 of this embodiment is provided as a file in an installable or executable format, recorded on a computer-readable recording medium such as a semiconductor storage device such as a DVD (Digital Versatile Disk), a USB (Universal Serial Bus) memory, or an SSD (Solid State Drive).

[0085] The program may be stored on a computer connected to a network such as the Internet and provided by being downloaded via the network.The program may be provided or distributed via a network such as the Internet.The program may be provided by being pre-installed in a ROM or the like.

[0086] Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and spirit of the invention, and are included in the scope of the invention and its equivalents described in the claims.

[0087] For example, the target accuracy to be achieved may be a numerical value stored in the storage unit 25, rather than being input by the user. [Explanation of symbols]

[0088] 10...microparticle measurement system, 11...light source, 12...slide glass, 13...stage, 14...stage drive unit, 15...laser displacement meter, 16...objective lens, 17...imaging lens, 18...image sensor, 19...measurement control unit, 20...information processing device, 21...acquisition unit, 22...detection unit, 23...calculation unit, 24...processing unit, 25...storage unit, 26...display unit, 27...input unit

Claims

1. a light source that emits illumination light to a liquid containing microparticles to be measured; an objective lens for collecting the illumination light; an imaging lens that forms an image from the condensed illumination light; an image sensor that captures the focused illumination light and outputs a captured image; A detection unit that detects the microparticles captured in the captured image; a calculation unit that calculates the accuracy of microparticle detection based on a detection result by the detection unit and a predetermined index; a processing unit that determines a next process based on the accuracy and a target accuracy required by a user; A microparticle measuring system comprising:

2. 2. The microparticle measuring system according to claim 1, wherein the processing unit determines and executes, as the next process, a process of displaying on a display unit a screen requesting a user to perform a specified operation to improve the accuracy when the accuracy is less than the target accuracy.

3. the index is the number of the microparticles, The microparticle measuring system according to claim 1 , wherein the calculation section calculates the accuracy based on the number of the detected microparticles.

4. 2. The microparticle measuring system according to claim 1, wherein the processing unit determines and executes, as the next process, a process of displaying a screen on a display unit requesting a user to obtain an additional captured image when the accuracy is less than the target accuracy.

5. The microparticle measuring system according to claim 1 , wherein the processing unit, when the accuracy is less than the target accuracy, determines and executes, as the next process, a process of automatically acquiring an additional captured image.

6. The microparticle measuring system according to claim 1 , wherein the processing unit determines and executes, as the next process, a process of displaying the accuracy on a display unit when the accuracy is less than the target accuracy.

7. 2. The microparticle measuring system according to claim 1, wherein, when the accuracy is less than the target accuracy, the processing unit determines and executes, as the next process, a process of displaying on a display unit a time required to complete the measurement, which is estimated from the number of microparticles and the total number of captured images.

8. A microparticle measuring method using a microparticle measuring system including a light source that emits illumination light to a liquid containing microparticles to be measured, an objective lens that collects the illumination light, an imaging lens that forms an image of the collected illumination light, an image sensor that captures the image of the illumination light and outputs a captured image, a detection unit, a calculation unit, and a processing unit, a detection step in which the detection unit detects the microparticles appearing in the captured image; a calculation step in which the calculation unit calculates accuracy of microparticle detection based on a detection result from the detection step and a predetermined index; and a processing step in which the processing unit determines a next process based on the accuracy and a target accuracy required by a user.

Images