Microparticle measuring system

Abstract

A microparticle measuring system according to an embodiment comprises: a light source that emits illumination light to a liquid containing microparticles to be measured; an objective lens that condenses the illumination light; an imaging lens that forms an image of the condensed illumination light; an image sensor that captures the formed image of the illumination light and outputs the captured image; a detection unit that detects the microparticles reflected in the captured image; a calculation unit that calculates concentrations of the microparticles and error ranges on the basis of the detection results by the detection unit; and a display control unit that causes a display unit to display a plurality of pairs of the concentrations and the error ranges in a time-series manner.

Description

Micro Particle Measurement System

[0001] An embodiment of the present invention relates to a micro particle measurement system.

[0002] Conventionally, in organic wastewater treatment, various useful microorganisms have been used to decompose organic substances in wastewater and remove nitrogen and phosphorus. At that time, wastewater treatment is carried out based on indicators such as sludge concentration and treated water quality. Therefore, it is meaningful to be able to measure the concentration of useful microorganisms that contribute to organic matter decomposition and nitrogen removal.

[0003] As techniques for measuring the concentration of useful microorganisms (micro particles such as Bacillus), for example, the colony count method and the PCR (Polymerase Chain Reaction) method exist. However, measurement by these methods requires highly specialized equipment, and there are problems such as the time and effort required to transport the sample to a specialized facility and the long measurement time.

[0004] Therefore, a technique has been proposed that utilizes the light refraction characteristics of the micro particles to be measured and detects micro particles (for example, Bacillus spores. Hereinafter, simply referred to as "Bacillus") from the captured image by image processing using deep learning. This eliminates the need to transport the sample to a specialized facility and enables the concentration of micro particles to be measured in a short time.

[0005] Special Table 2021 - 533945 Gazette, Special Table 2023 - 536243 Gazette

[0006] However, the Bacillus concentration in the sludge is not uniform and has variations, and there are variations in the number (concentration) of Bacillus reflected in the captured image. Therefore, the error range of the measurement result differs depending on the number of detected Bacillus.

[0007] Therefore, even if an operator checks the temporal change in the Bacillus concentration, it is not possible to determine whether the concentration has changed significantly, and it is not possible to judge whether countermeasures should be taken based on the concentration change.

[0008] Therefore, the present invention has been made in view of the above circumstances, and aims to provide a microparticle measurement system that displays information for the user to determine whether or not the concentration of microparticles measured using an image has changed significantly.

[0009] The microparticle measurement system of the embodiment comprises a light source that emits illumination light onto a liquid containing microparticles to be measured; an objective lens that focuses the illumination light; an imaging lens that forms an image of the focused illumination light; an image sensor that captures the image of the illumination light and outputs an image; a detection unit that detects the microparticles shown in the image; a calculation unit that calculates the concentration and error range of the microparticles based on the detection result by the detection unit; and a display control unit that displays a plurality of sets of the concentration and error range in a time series on a display unit.

[0010] Figure 1 is a schematic diagram of the microparticle measurement system of the embodiment. Figure 2 is an explanatory diagram of the parameters in the ray tracing matrix. Figure 3 is an explanatory diagram of the relative transmitted light intensity for Bacillus spores. Figure 4 is an explanatory diagram of the relative transmitted light intensity for acrylic particles. Figure 5 is a diagram showing an example of an image of sludge. Figure 6 is a diagram showing an example of an image and a training image used in deep learning. Figure 7 is a diagram showing an example of Bacillus detection results by deep learning. Figure 8 is a diagram showing an example of a data table regarding the number of Bacillus detected and the 95% confidence interval. Figure 9 is a flowchart showing the processing by the microparticle measurement system of the embodiment. Figure 10 is a diagram showing an example of the prior art display. Figure 11 is a diagram showing a first display example of the embodiment. Figure 12 is a diagram showing a second display example of the embodiment. Figure 13 is a diagram showing a third display example of the embodiment.

[0011] Hereinafter, embodiments of the microparticle measurement system of the present invention will be described with reference to the drawings.

[0012] Figure 1 is a schematic diagram of the microparticle measurement system 10 according to an embodiment. The microparticle measurement system 10 comprises a light source 11, a stage 13, a stage drive unit 14, a laser displacement meter 15, an objective lens 16, an imaging lens 17, an image sensor 18, a measurement control unit 19, and an information processing device 20. The measurement control unit 19 and the information processing device 20 may be configured as a single unit. Alternatively, the information processing device 20 may be configured as two or more separate units.

[0013] The light source 11 emits illumination light L towards the measurement sample SP (liquid; specimen) containing the minute particles to be measured.

[0014] Stage 13 supports the slide glass (preparation slide) 12 that holds the sample SP for measurement. A hemocytometer may be used instead of the slide glass.

[0015] The stage drive unit 14 moves the stage 13 along the optical axis in the vertical direction shown in Figure 1. The laser displacement meter 15 detects the position of the slide glass 12 using a laser.

[0016] The objective lens 16 focuses the illumination light L to form parallel light. The imaging lens 17 focuses the now parallel illumination light L to form an image.

[0017] 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 drive unit 14 and the image sensor 18.

[0018] 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.

[0019] The acquisition unit 21 acquires the captured image from the image sensor 18.

[0020] The detection unit 22 detects minute particles, impurities, and other contaminants that are visible in the captured image.

[0021] The calculation unit 23 performs various calculation processes. For example, the calculation unit 23 calculates the bacillus concentration (concentration of minute particles) and error range based on the detection results from the detection unit 22. Specifically, for example, the calculation unit 23 calculates the bacillus concentration from the observation field range obtained from the captured image and the number of bacillus spores detected. An example of the formula for calculating the bacillus concentration is shown below: Bacillus concentration [spores / mL] = Number of bacillus detected [spores] / Observation field range of the captured image [mL]

[0022] Furthermore, the method for calculating the error range will be described later using Figure 8.

[0023] The processing unit 24 performs various information processing tasks (details will be described later).

[0024] The memory unit 25 stores the operation programs for each unit 21 to 24, various parameters, captured images acquired by the acquisition unit 21, detection results from the detection unit 22, calculation results from the calculation unit 23, and processing results from the processing unit 24.

[0025] The display unit 26 displays various information based on instructions from the processing unit 24. For example, the processing unit 24 displays multiple "concentration and error range pairs" in a time series on the display unit 26 (details will be described later using Figures 11 to 13).

[0026] Furthermore, the processing unit 24 may display multiple "concentration and error range pairs" and a set reference concentration on the display unit 26 in a time series (details will be described later using Figure 12).

[0027] The input unit 27 is a means of inputting information by the user, and can be a keyboard, mouse, touch panel, etc.

[0028] Furthermore, all or part of the processing performed in each of the above-mentioned parts 21 to 24 may be executed by a single processor (control unit) based on the operation program and various parameters stored in the storage unit 25.

[0029] Next, we will explain the measurement principle for minute particles. When illumination light is shone from the back side of minute particles in a liquid, the illumination light is focused to a position corresponding to the particle diameter and refractive index of the minute particles due to the lens effect of the minute particles.

[0030] Furthermore, the transmitted light intensity increases as you approach the focusing position, reaching its maximum at the focusing position, and then decreasing again as you move away from the focusing position. In other words, the position where the transmitted light intensity is maximum is the focusing position. At this time, the focusing position can be determined by measuring the distance between the objective lens 16 and the position where the transmitted light intensity is maximum.

[0031] In this case, the optical path of the illumination light can be expressed by the following equation. Therefore, if the particle size of the minute particles 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 minute particles can be determined by solving the equation expressed by the ray tracing matrix below.

[0032]

[0033] Figure 2 is an explanatory diagram of the parameters in the ray tracing matrix. In the ray tracing matrix described above, let r be the radius of the microparticle PC, let n be the refractive index of the microparticle, and let z be the distance between the microparticle and the objective lens 16 when the transmitted light intensity of the illumination light L is maximum for the target microparticle. Also, let x be the distance from the optical axis when the illumination light L is incident on the microparticle. ０ Let u be the angle of incidence when illumination light L is incident on a minute particle. ０ Let x be the distance from the optical axis of the illumination light L incident on the image sensor 18. １ The incident angle of the illumination light L incident on the image sensor 18 is set to u １ Let's assume that.

[0034] Furthermore, the distance between the objective lens 16 and the imaging lens 17 is l １ The distance between the imaging lens 17 and the image sensor 18 is set to l ２ Let's assume that the focal length of the objective lens is f １ The focal length of the imaging lens 17 is set to f ２ Let's assume that.

[0035] As mentioned above, if the refractive index of a minute particle is known, the particle size of the minute particle can be calculated by solving the equation expressed by the ray tracing matrix described above.

[0036] In addition, useful microorganisms used in organic wastewater treatment can be regarded as microparticles under certain conditions. The conditions include, for example, when the useful microorganisms form spores. When spores are formed, the shape and the like do not change, and the shape is also almost constant depending on the useful microorganisms.

[0037] Since the spores of useful microorganisms have a specific size (e.g., particle diameter) and a specific refractive index, by handling them in the same way as microparticles, it becomes possible to measure the detection of such useful microorganisms and the number (and thus the concentration) per observation field.

[0038] When measuring the concentration, by scanning the observation position (image capture position) along the optical axis direction, the number of useful microorganisms in the volume corresponding to the observation field × scanning distance can be measured, and thus the concentration can be measured.

[0039] By the way, in the case of spores of Bacillus genus strains (Bacillus spores) in sludge where the refractive index of light is known and the particle size is 1 μm or less, the position corresponding to the distance z at which the transmitted light intensity is maximum is located within the depth of field (effective focal position) corresponding to the focal length f in image acquisition. １ Therefore, based on a preset transmitted light intensity threshold, a portion having a light intensity above the threshold can be regarded as Bacillus spores.

[0040] In this case, the transmitted light intensity of the liquid containing Bacillus spores is greater than that of the liquid not containing Bacillus spores. Therefore, by setting the transmitted light intensity threshold for determining whether Bacillus spores are contained to be slightly higher than the transmitted light intensity in the liquid without spores, Bacillus spores can be reliably detected.

[0041] Furthermore, by using a set threshold to continuously move the sample in the optical axis direction while sequentially acquiring images, and combining this with deep learning (machine learning) that uses the transmitted light intensity for each location (each pixel) obtained from the acquired images and the size (particle diameter) of the Bacillus spores as minute particles as criteria, it becomes possible to improve the accuracy of Bacillus spore detection and counting, and ultimately, the measurement of Bacillus spore concentration.

[0042] When performing deep learning, for example, multiple samples with different concentrations of microparticles can be prepared in advance, and supervised learning can be performed for each sample so that the detection result by the user (operator) becomes equal to the detection result by deep learning, thereby obtaining detection results for microparticles according to the particle size and refractive index of the microparticles being studied.

[0043] Next, Figure 3 is an explanatory diagram of the relative transmitted light intensity for bacillus spores. More specifically, Figure 3 is a diagram that explains the relationship between the relative transmitted light intensity and the difference between the actual position of the objective lens 16 and the distance z between the bacillus spore and the objective lens 16 when the transmitted light intensity for bacillus spores is maximum.

[0044] Figure 4 is an explanatory diagram of the relative transmitted light intensity for acrylic particles. More specifically, Figure 4 is a diagram that explains the relationship between the relative transmitted light intensity and the difference in the 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 acrylic particles with a particle diameter of 30 μm.

[0045] First, images of Bacillus spores and acrylic particles were acquired at the focal point using the image sensor 18. Then, the stage 13 was moved vertically along the optical axis using the stage drive unit 14, and the positional difference Δz between the position of the objective lens 16 when the relative transmitted light intensity of each minute particle was maximum on the image sensor 18 and the actual position of the objective lens 16 was measured using the laser displacement meter 15.

[0046] Figure 3(A) shows the image acquired 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 imaging region. Furthermore, as shown in Figure 3(B), it was calculated that in a liquid containing Bacillus spores, the relative transmitted light intensity is maximum at a positional difference Δz = 0 μm.

[0047] In contrast, as shown in Figure 4(B), in the case of a liquid containing acrylic particles with a particle diameter of 30 μm, the relative transmitted light intensity is negative at a position difference of Δ0 μm where the relative transmitted light intensity is maximum in the Bacillus spore. That is, the transmitted light intensity is lower than the background light intensity. Also, as shown in Figure 4(A), the relative transmitted light intensity is minimum around the acrylic particles. And, as shown in Figure 4(B), it was calculated that in a liquid containing acrylic particles with a particle diameter of 30 μm, the relative transmitted light intensity is maximum outside of a position difference of Δz = ±15 μm.

[0048] Furthermore, Figure 4(C) is an image taken when the relative transmitted light intensity is maximum in a liquid containing acrylic particles with a particle size of 30 μm. As shown in Figure 4(C), it can be seen that the relative transmitted light intensity is maximum at the center of the imaging region. And, as shown in Figure 4(D), it was calculated that the relative transmitted light intensity is maximum in a liquid containing acrylic particles with a particle size of 30 μm at a position difference Δz = 26 μm.

[0049] Based on these measurement results, the position difference Δz, which corresponds to the difference between the distance z from the Bacillus spores and 30 μm acrylic particles (which are the finest particles when the transmitted light intensity is maximum) to the objective lens 16 and the focal length of the objective lens, was calculated using the ray tracing matrix described above. The position difference Δz was found to be 0.9 μm for the liquid containing Bacillus spores and 22.5 μm for the liquid containing 30 μm acrylic particles, which is found to be in close agreement with the measurement results using the laser displacement meter 15. The distance between the objective lens 16 and the imaging lens 17 at this time was l １ = 130 mm, the distance between the imaging lens 17 and the image sensor 18 is l ２= 164.5 mm, the focal length of the objective lens is f １ = 4.1125 mm, the focal length of the imaging lens 17 is f ２ The length was set to 164.5 mm. Additionally, the r of the Bacillus spore was set to 1 μm and n to 1.4, while the n of the acrylic particle was set to 1.5.

[0050] In particular, it was found that the positional 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. From this, it was found that Bacillus spores can be detected by measuring the transmitted light intensity at the focal length.

[0051] In this way, by utilizing the characteristic that the center of a Bacillus spore glows brightly when the transmitted light intensity is at its maximum, it is possible to detect only Bacillus spores from the acquired image.

[0052] Next, Figure 5 shows an example of an image of sludge. As shown in Figures 5(a) and 5(b), in addition to Bacillus spores B, impurities C may also be visible in the image.

[0053] Next, Figure 6 shows examples of captured images and training images used in deep learning. (a) is a captured image showing a bacillus spore B and impurities C. The user provides the central position P of the bacillus spore B as ground truth data for this captured image, resulting in the training image shown in (b). By training the network to detect the central position P of the bacillus spore B in the captured image using these images, deep learning can be performed.

[0054] Next, Figure 7 shows an example of Bacillus detection results using deep learning. The detection unit 22 calculates the likelihood of Bacillus spore detection results using deep learning-based image processing (the probability (likelihood) that each pixel is the central position of a Bacillus spore).

[0055] Figure 7(a) is the input image (captured image). The detection unit 22 calculates a likelihood map, for example, shown in Figure 7(b). This likelihood map shows that the brighter the area, the higher the likelihood, and the darker the area, the lower the likelihood. The symbol Q corresponds to the Bacillus spore B (Figure 7(a)) and represents a region with a high likelihood.

[0056] The detection unit 22 then performs thresholding on this likelihood and selects pixels with a likelihood above a certain level as the central position of the Bacillus, thereby obtaining the detection result shown in Figure 7(c). In Figure 7(c), the symbol S indicates the central position of the detected Bacillus spore.

[0057] Figure 8 shows an example of a data table relating the number of Bacillus detected and the 95% confidence interval. It is assumed that when sludge containing Bacillus at a constant concentration is collected and an image is taken, the number of Bacillus detected in the image follows a Poisson distribution.

[0058] When k Bacillus bacteria are detected, and a confidence interval (error range) of the confidence coefficient α% is obtained using interval estimation with a Poisson distribution, the upper and lower limits of that confidence interval become the upper and lower limits of the error in the number of detected bacteria (i.e., the error in the measured concentration).

[0059] Figure 8 shows the 95% confidence interval for k Bacillus detections (1 ≤ k ≤ 20) assuming a Poisson distribution. For example, suppose 10 Bacillus are detected as a result of the measurement. From Figure 8, it can be seen that the relative error of this measurement result will fall within the range of -52.0% to +84.0% with a 95% probability.

[0060] Furthermore, the table in Figure 8 shows that the error range of the measurement result changes depending on the number of bacillus organisms detected. Therefore, even with the same concentration, the error range will differ depending on the number of bacillus organisms. For example, the calculated bacillus concentration will be the same whether a total of 2 bacillus organisms are detected from one image or a total of 20 bacillus organisms are detected from 10 images. However, calculating the respective error ranges from the table in Figure 8, the error range is -87.50% to +261.50% when 2 organisms are detected, while the error range is -38.90% to 54.45% when 20 organisms are detected. Therefore, even with the same concentration, the more organisms detected (i.e., the more images taken), the narrower the error range becomes, and the higher the accuracy of the measurement result.

[0061] Figure 9 is a flowchart showing the processing performed by the microparticle measurement system 10 of the embodiment. Before this processing, the measurement worker collects water from a water treatment device containing the microorganism (Bacillus) to be measured, performs a predetermined pretreatment (filtering, heating, etc.) on the collected sample, and sets the pretreated sample into the microparticle measurement system 10.

[0062] In step S1, the acquisition unit 21 acquires an image from the image sensor 18.

[0063] Next, in step S2, the detection unit 22 detects Bacillus spores that are visible in the captured image.

[0064] Next, in step S3, the calculation unit 23 calculates the concentration and error range of Bacillus based on the detection results in step S2.

[0065] Next, in step S4, the calculation unit 23 stores the combination of concentration and error range calculated in step S3, along with the measurement date and time, as time-series data in the storage unit 25.

[0066] Next, in step S5, the processing unit 24 displays the time-series data (data of multiple "concentration and error range pairs") stored in the storage unit 25 as a graph (in graph format) on the display unit 26 (details will be described later using Figures 11 to 13).

[0067] Next, the worker checks the displayed graph and determines whether there is a significant change between the latest measurement result and past measurement results, and inputs the result using the input unit 27. Then, in step S6, the processing unit 24 determines whether the information input by the worker using the input unit 27 indicates a significant difference compared to past measurement results. If the answer is Yes, the process proceeds to step S7; otherwise, the process ends.

[0068] In step S7, the processing unit 24 controls the water treatment device according to the measurement results. For example, if the processing unit 24 determines that the latest concentration is significantly lower than past concentrations, it implements control to increase the concentration. This control may be performed automatically or based on instructions from the user.

[0069] Next, we will explain an example of the display. Figure 10 shows an example of the display using the conventional technology. The horizontal axis shows the measurement date and time, with newer dates and times being further to the right. The vertical axis shows the Bacillus concentration. In the conventional technology, as shown in the figure, the Bacillus concentrations AP to DP for each measurement date and time were displayed.

[0070] However, as mentioned above, the concentration of Bacillus in the sludge is not uniform but varies, and there is variability in the number (concentration) of Bacillus particles visible in the captured images. Therefore, the margin of error in the measurement results differs depending on the number of Bacillus particles detected.

[0071] Therefore, even if a worker looks at this display and checks the change in Bacillus concentration over time, they cannot determine whether the concentration has changed significantly. For example, if concentration AP and concentration BP are compared, concentration BP is smaller, but the margin of error for each is unknown. Consequently, the worker cannot determine whether the concentration has decreased significantly or not. Therefore, displays like those in Figures 11 to 13 are used. In the following explanation, explanations of matters similar to those explained in the previous display examples will be omitted as appropriate.

[0072] Figure 11 shows a first display example of the embodiment. In this display example, in addition to the concentration of Bacillus, the error range is also displayed for each concentration. For example, for measurement date and time A, in addition to the concentration AP, the error range from the upper limit AP to the lower limit AL is also displayed. The same applies to measurement dates and times B, C, and D.

[0073] For example, comparing measurement date and time A with measurement date and time B, concentration BP appears to be lower than concentration AP. However, because the error range of concentration BP is large, and the error ranges of concentration AP and concentration BP overlap in some areas, statistically, it cannot be said that concentration BP is statistically significantly lower than concentration AP. To simplify the explanation below, the phrase "statistically," will be omitted.

[0074] Similarly, for measurement dates B and C, the error ranges for concentration BP and concentration CP partially overlap, so it cannot be said that concentration CP shows a significant change when compared to concentration BP.

[0075] However, when comparing measurement date and time A and measurement date and time C, the error ranges for concentration AP and concentration CP do not overlap. Therefore, it can be said that concentration CP is significantly lower than concentration AP.

[0076] Furthermore, comparing measurement date C with measurement date D, the error range for concentration DP is larger, but the error ranges for concentration CP and concentration DP do not overlap. Therefore, it can be said that concentration DP is significantly higher than concentration CP.

[0077] Figure 12 shows a second display example of the embodiment. Figure 12 shows an example of a time-series graph of Bacillus concentration when a reference concentration R is set.

[0078] Here, as an example of a reference concentration, the dominance concentration of Bacillus (for example, 10 ８ Set the [CFU (Colony forming unit) / g]. If the Bacillus concentration falls below the dominant concentration, the efficiency of sewage treatment may decrease, so it is important to maintain the liquid at a level where the Bacillus concentration is higher than the dominant concentration.

[0079] Looking at the graph in Figure 12, we can see that at measurement dates and times B and C, both concentrations BP and CP are below the reference concentration R. However, at measurement date and time B, the error ranges for reference concentration R and concentration BP overlap, so we cannot say that concentration BP is significantly lower than the reference concentration R. In contrast, at measurement date and time C, the error ranges for reference concentration R and concentration CP do not overlap, so we can say that concentration CP is significantly lower than the reference concentration R.

[0080] Furthermore, the processing unit 24 (display control unit) may display multiple "concentration and error range pairs" in a table format on the display unit 26 in chronological order. Figure 13 shows a third display example of the embodiment. When a user sees such a display, they can make the same judgment as when looking at a graph (Figures 11 and 12) by comparing the error ranges for each measurement date and time. In addition, because the error range is shown with specific numerical values, it is possible to more accurately determine whether the concentration has changed beyond the error range.

[0081] (Modified Version) Next, a modified version will be described. Explanations of matters similar to those in the above-described embodiment will be omitted as appropriate. In the modified version, the processing unit 24 (determination unit) determines whether or not the concentration has changed significantly. The processing unit 24 (display control unit) also displays the determination result on the display unit 26.

[0082] Regarding the flowchart in Figure 9, steps S1 to S5 are the same as in the embodiment.

[0083] After step S5, in step S6, the processing unit 24 (determination unit) determines whether the concentration has changed significantly. For example, the processing unit 24 determines whether the latest concentration has changed significantly compared to the concentration measured on a specific past date, based on the time change and error range of the Bacillus concentration. Here, the specific past date may be set to a date specified by the worker, or the date may be set automatically by the fine particle measurement system 10. Alternatively, instead of the concentration measured on a specific date, the comparison may be made using, for example, the average or median of multiple concentrations measured in the past.

[0084] The processing unit 24 (display control unit) displays the determination result on the display unit 26. The processing unit 24 may only display the determination result on the display unit 26 if the determination result indicates a significant difference. Step S7 is the same as in the embodiment.

[0085] In this way, the microparticle measurement system 10 of this embodiment calculates the concentration and error range of Bacillus and displays multiple sets of concentration and error range in a time series on the display unit 26. In other words, it can display information that allows the user to determine whether or not the concentration of Bacillus measured using the captured image has changed significantly. By looking at such a display, the user can easily determine whether or not the change in concentration is significant.

[0086] Furthermore, multiple sets of concentrations and error ranges can be displayed in a time series, for example, in a graph format (Figures 11 and 12). This allows users to visually determine whether or not the change in Bacillus concentration is significant.

[0087] Furthermore, multiple sets of concentrations and error ranges can be displayed in a time series, for example, in a tabular format (Figure 13). This allows users to more accurately determine whether changes in the concentration of fine particles are significant by looking at the values ​​in the table.

[0088] Furthermore, the processing unit 24 may automatically determine whether the concentration has changed significantly, and the result of this determination may be displayed on the display unit 26. This allows the user to immediately understand whether the concentration has changed significantly by looking at the determination result. Therefore, for example, if it is recognized that the condition of the liquid has deteriorated due to a decrease in concentration, countermeasures can be taken immediately.

[0089] Furthermore, as shown in Figure 12, in addition to multiple sets of concentrations and error ranges in a time series, the set reference concentration R may also be displayed on the display unit 26. This allows the user to determine whether the change in concentration is significant in relation to the reference concentration R by looking at such a display.

[0090] The above description has focused on the case where the microparticle measurement system 10 is configured as a standalone system, but it is not limited to this configuration. For example, the microparticle measurement system 10 may acquire captured images using an image sensor 18 on the local terminal side, transfer the captured images to a cloud server via a communication interface and communication network, process them using an information processing device 20 on the cloud server side, and display the processing results on the local terminal.

[0091] 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) or RAM (Random Access Memory), an external storage device such as an HDD (Hard Disk Drive), a display device such as a display device, and an input device such as a keyboard or mouse, thus having a hardware configuration that utilizes a typical computer.

[0092] Furthermore, the program executed by the information processing device 20 of this embodiment is provided as an installable or executable file, recorded on a computer-readable recording medium such as a DVD (Digital Versatile Disk), USB (Universal Serial Bus) memory, or SSD (Solid State Drive).

[0093] Furthermore, the program may be provided by storing it on a computer connected to a network such as the Internet and allowing users to download it via the network. Alternatively, the program may be provided or distributed via a network such as the Internet. Furthermore, the program may be provided pre-installed in ROM or similar media.

[0094] While several embodiments of the present invention have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These novel embodiments can be carried out in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents.

[0095] For example, the method for detecting Bacillus spores using captured images is not limited to deep learning; other methods such as template matching may also be used.

[0096] Furthermore, in the embodiments and modifications described above, the degree of overlap of the error ranges when comparing two concentrations was used as an indicator to determine whether or not the concentration had changed significantly. That is, if the two error ranges overlapped in at least part, it was determined that there was no significant difference, and if the two error ranges did not overlap at all, it was determined that there was a significant difference. However, the determination method is not limited to this. As another method, for example, statistical hypothesis testing may be used to determine whether or not there is a significant difference.

[0097] Furthermore, the confidence interval (error range) is not limited to a 95% confidence interval; other confidence intervals of different sizes, such as a 99% confidence interval or a 90% confidence interval, may also be used.

[0098] Furthermore, when displaying graphs as shown in Figures 11 and 12, for example, the user may specify a single measurement date and time, and the measurement dates and times with a significant difference in concentration and those without a significant difference in concentration may be color-coded accordingly. Also, if there is a significant change in concentration for N consecutive days (N: an integer of 2 or more), for example, the measurement dates and times with a significant change in concentration may be displayed in a different color from the other measurement dates and times so that the user can easily recognize them.

[0099] Furthermore, when displaying graphs as shown in Figures 11 and 12, it is also possible to display the upper and lower limits of the error range. This would make it easier for the user to determine whether the two error ranges overlap in at least part.

[0100] (Note) The above description of embodiments discloses the following technologies: (1) A microparticle measurement system comprising: a light source that emits illumination light onto a liquid containing microparticles to be measured; an objective lens that focuses the illumination light; an imaging lens that forms an image of the focused illumination light; an image sensor that captures the imaged illumination light and outputs an image; a detection unit that detects the microparticles shown in the image; a calculation unit that calculates the concentration and error range of the microparticles based on the detection result by the detection unit; and a display control unit that displays a plurality of sets of the concentration and error range in time series on a display unit. (2) The microparticle measurement system according to (1), wherein the display control unit displays a plurality of sets of the concentration and error range in time series on the display unit in graph format. (3) The microparticle measurement system according to (3), wherein the display control unit displays a plurality of sets of the concentration and error range in time series on the display unit in tabular format. (4) The fine particle measurement system according to (1), further comprising a determination unit for determining whether the concentration has changed significantly, wherein the display control unit causes the determination result by the determination unit to be displayed on the display unit. (5) The fine particle measurement system according to (1), wherein the display control unit causes a plurality of sets of the concentration and the error range, and a set reference concentration to be displayed on the display unit in a time series.

[0101] 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 microparticle measurement system comprising: a light source that emits illumination light onto a liquid containing microparticles to be measured; an objective lens that focuses the illumination light; an imaging lens that forms an image of the focused illumination light; an image sensor that captures the imaged illumination light and outputs an image; a detection unit that detects the microparticles visible in the image; a calculation unit that calculates the concentration and error range of the microparticles based on the detection results from the detection unit; and a display control unit that displays a plurality of sets of the concentration and error range in a time series on a display unit.

2. The fine particle measurement system according to claim 1, wherein the display control unit causes a plurality of sets of concentration and error range in a time series to be displayed on the display unit in graph format.

3. The fine particle measurement system according to claim 1, wherein the display control unit causes a plurality of sets of concentration and error range in a time series to be displayed on the display unit in a table format.

4. The fine particle measurement system according to claim 1, further comprising a determination unit for determining whether or not the concentration has changed significantly, wherein the display control unit causes the determination result by the determination unit to be displayed on the display unit.

5. The fine particle measurement system according to claim 1, wherein the display control unit causes a plurality of sets of concentrations and error ranges, and a set reference concentration, to be displayed on the display unit in a time series.

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