JP2023142064A - Microparticle measuring system and microparticle measuring method - Google Patents

Abstract

To improve accuracy when performing measurements concerning a microparticle using a captured image.SOLUTION: A microparticle measuring system comprises: a light source for emitting illumination light onto a liquid containing a microparticle to be measured; an objective lens for condensing the illumination light; an imaging lens for imaging the condensed illumination light; an image sensor for capturing an image of the imaged illumination light and outputting the captured image; a detection section for detecting the microparticle appearing in the captured image; a calculation section for calculating microparticle detection reliability on the basis of a detection result by the detection section and a predetermined indicator; and a processing section for determining next processing on the basis of the reliability.SELECTED DRAWING: Figure 9

Description

Embodiments of the present invention relate to a microparticle measurement system and a microparticle measurement method.

Conventionally, for example, in organic wastewater treatment, various useful microorganisms have been used to decompose organic matter and remove nitrogen and phosphorus from wastewater. At that time, wastewater treatment is performed based on indicators such as sludge concentration and treated water quality, but it would be meaningful if the concentration of useful microorganisms that contribute to organic matter decomposition and nitrogen removal could be measured.

Conventional techniques for measuring the concentration of useful microorganisms (microparticles such as Bacillus) include, for example, the colony counting method and the PCR (Polymerase Chain Reaction) method. However, measurement using these methods requires highly specialized equipment, and there are problems in that it takes time and effort to transport the specimen to a specialized facility and the measurement time is long.

Therefore, a technique has been proposed in which microparticles (for example, Bacillus spores) are detected from captured images by image processing using deep learning, using the characteristics of light refraction by microparticles to be measured. This eliminates the need to transport the specimen to a specialized facility, and allows the concentration of microparticles to be measured in a short time.

Japanese Patent Application Publication No. 2016-147044

However, the above-described deep learning method has a problem in that the measurement accuracy is not constant. For example, the concentration of Bacillus in sludge is not uniform but has shades, and there are variations in the number (concentration) of Bacillus appearing in the captured image. Therefore, for example, if the number of bacilli detected per captured image is small, the accuracy of the measurement result will be low if the number of captured images is small. Furthermore, it is not always easy for the operator to determine whether or not there are a small number of captured images.

Therefore, the present invention has been made in view of the above circumstances, and provides a microparticle measurement system and a microparticle measurement method that can improve the accuracy when measuring microparticles using captured images. The task is to

A microparticle measurement system according to an embodiment includes a light source that emits illumination light to a liquid containing microparticles to be measured, an objective lens that focuses the illumination light, and an image of the focused illumination light. an imaging lens, an image sensor that captures the formed illumination light and outputs a captured image, a detection unit that detects the microparticles appearing in the captured image, and a detection result by the detection unit and a predetermined The present invention includes a calculation unit that calculates the reliability of microparticle detection based on the index, and a processing unit that determines the next process based on the reliability.

FIG. 1 is a schematic configuration diagram of a microparticle measurement system according to an embodiment. FIG. 2 is an explanatory diagram of parameters in the ray tracing matrix. FIG. 3 is an explanatory diagram of relative transmitted light intensity, etc. for Bacillus spores. FIG. 4 is an explanatory diagram of relative transmitted light intensity, etc. for acrylic particles. FIG. 5 is a diagram showing an example of a captured image of sludge. FIG. 6 is a diagram showing an example of a captured image and a teaching image used in deep learning. FIG. 7 is a diagram showing an example of Bacillus detection results by deep learning. FIG. 8 is a diagram showing an example of a reference table for reliability calculation. FIG. 9 is a flowchart showing processing by the microparticle measurement system of the embodiment.

DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of a microparticle measurement system and a microparticle measurement method of the present invention will be described with reference to the drawings. In addition, below, Bacillus spores are also simply called Bacillus.

FIG. 1 is a schematic configuration 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 drive section 14, a laser displacement meter 15, an objective lens 16, an imaging lens 17, an image sensor 18, a measurement control section 19, and an information A processing device 20 is provided. Note that the measurement control section 19 and the information processing device 20 may be configured integrally. Further, the information processing device 20 may be divided into two or more parts.

The light source 11 emits illumination light L to a measurement sample SP (liquid, specimen) containing microparticles to be measured.

The stage 13 supports a slide glass (preparation) 12 that holds a measurement sample SP.

The stage drive unit 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 using a laser.

The objective lens 16 condenses the illumination light L into parallel light.
The imaging lens 17 condenses the parallel illumination light L and forms an image.

The image sensor 18 captures the illumination light formed by the imaging lens 17 and outputs a captured image.
The measurement control section 19 controls the stage drive section 14 and the image sensor 18.

The information processing device 20 includes an acquisition section 21 , a detection section 22 , a calculation section 23 , a processing section 24 , a storage section 25 , and a display section 26 .

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

The detection unit 22 detects microparticles, contaminants, and the like appearing in the captured image.

The calculation unit 23 calculates the reliability of microparticle detection based on the detection result by the detection unit 22 and a predetermined index (details will be described later).

The processing unit 24 performs various information processing. For example, the processing unit 24 determines the next process based on the reliability. For example, when the reliability is less than a predetermined threshold, the processing unit 24 causes the display unit 26 to display a screen requesting the user (worker) to take a predetermined action to improve the reliability. For example, the processing unit 24 requests the user to obtain additional captured images when the reliability is less than a predetermined threshold.

Further, for example, the processing unit 24 may automatically acquire an additional captured image when the reliability is less than a predetermined threshold.

The storage unit 25 stores 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 reliability by the calculation unit 23, and the processing unit. 24 is stored.

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

Note that all or part of the processing performed by each of the units 21 to 24 described above may be executed by one processor (control unit) based on the operating program and various parameters stored in the storage unit 25. .

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

Note that the intensity of the transmitted light increases as it approaches the condensing position, reaches its maximum at the condensing position, and decreases as it moves away from the condensing position again. That is, the position where the transmitted light intensity is maximum is the light condensing position. At this time, the light condensing position can be specified by measuring the distance between the objective lens 16 and the position where the transmitted light intensity is maximum.

In this case, the optical path of the illumination light can be expressed by the following equation, so in addition to the distance between the objective lens 16 and the position where the transmitted light intensity is maximum, the particle size of the microparticles must be known. For example, the refractive index of a microparticle can be found by solving the equation expressed by the ray tracing matrix below.

FIG. 2 is an explanatory diagram of parameters in the ray tracing matrix. In the above ray tracing matrix, the radius of the microparticle PC is r, the refractive index of the microparticle is n, and the microparticle and the objective lens 16 when the transmitted light intensity of the illumination light L is maximum in the target microparticle. Let the distance between them be z. Further, the distance from the optical axis when the illumination light L is incident on the microparticle is defined as x ₀ , and the angle of incidence when the illumination light L is incident on the microparticle is defined as u ₀ . Further, the distance from the optical axis of the illumination light L that entered the image sensor 18 is defined as x ₁ , and the incident angle of the illumination light L that entered the image sensor 18 is defined as u ₁ .

Further, let the distance between the objective lens 16 and the imaging lens 17 be _l1 , and the distance between the imaging lens 17 and the image sensor 18 be _l2 . Further, the focal length of the objective lens is _f1 , and the focal length of the imaging lens 17 is _f2 .

As described above, if the refractive index of the microparticle is known, the diameter of the microparticle can be calculated by solving the equation expressed by the ray tracing matrix.

Furthermore, useful microorganisms used in organic wastewater treatment can be considered microparticles depending on the conditions. The condition is, for example, when useful microorganisms form spores. This is because when spores are formed, the shape etc. do not change and the shape is also almost constant depending on the useful microorganism.

Spores of useful microorganisms have a unique size (e.g. particle diameter) and a unique refractive index, so by treating them in the same way as microparticles, it is possible to detect such useful microorganisms and reduce the number of spores per observation field. (and, by extension, concentration).

When measuring the concentration, the number of useful microorganisms in the volume corresponding to the observation field x scanning distance is measured by scanning the observation position (image capture position) along the optical axis direction. measurement becomes possible.

By the way, for spores of a Bacillus strain in sludge (Bacillus spores) with a known refractive index of light and a particle size of 1 μm or less, the position corresponding to the distance z where the transmitted light intensity is maximum is determined in image acquisition. is known to be located within the depth of field (effective focal position) corresponding to the focal length f ₁ of . Therefore, based on a preset transmitted light intensity threshold, a portion having a light intensity equal to or higher than the threshold can be regarded as a Bacillus spore.

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

Furthermore, using the set threshold value, images were taken sequentially while moving the sample in the optical axis direction, and the transmitted light intensity for each location (each pixel) and Bacillus as microparticles were obtained from the captured images. By combining deep learning (machine learning) that uses the size of spores (particle diameter) as a criterion, it becomes possible to detect and measure the number of Bacillus spores, and by extension, measure the concentration of Bacillus spores with high accuracy. .

When performing deep learning, for example, multiple samples with different concentrations of microparticles are prepared in advance, and a supervised method is used to make the manual detection result of the user (operator) equal to the detection result of deep learning for each sample. Learning may be performed to obtain microparticle detection results based on the particle diameter and refractive index of the microparticle to be learned.

Next, FIG. 3 is an explanatory diagram of relative transmitted light intensity, etc. for Bacillus spores. Specifically, FIG. 3 shows the difference in the actual position of the objective lens 16 with respect to the distance z between the Bacillus spores and the objective lens 16 when the transmitted light intensity for the Bacillus spores is maximum, the relative transmitted light intensity, FIG.

Moreover, FIG. 4 is an explanatory diagram of relative transmitted light intensity, etc. for acrylic particles. More specifically, FIG. 4 shows the actual position of the objective lens 16 with respect to the distance z between the acrylic particles (microparticles) and the objective lens 16 when the transmitted light intensity is maximum for acrylic particles with a particle diameter of 30 μm. FIG. 3 is a diagram illustrating the relationship between the difference between the two and the relative transmitted light intensity.

First, images of Bacillus spores and acrylic particles at the focal position were acquired by the image sensor 18.
After that, the stage 13 is moved vertically along the optical axis direction by the stage drive unit 14, and the position of the objective lens 16 when the relative transmitted light intensity of each microparticle becomes maximum on the image sensor 18, and the actual position of the objective lens 16 are determined. The positional difference Δz between the position of the objective lens 16 and the position of the objective lens 16 was measured by the laser displacement meter 15.

FIG. 3(A) is a captured image when the relative transmitted light intensity reaches the maximum in a liquid containing Bacillus spores. As shown in FIG. 3A, it can be seen that the relative transmitted light intensity is maximum at the center of the imaging region. As shown in FIG. 3(B), it was calculated that in the liquid containing Bacillus spores, the relative transmitted light intensity was maximized at a positional difference Δz=0 μm.

On the other hand, 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 is It has a negative value. That is, it can be seen that the transmitted light intensity is lower than the background light intensity. Moreover, as shown in FIG. 4(A), it can be seen that the relative transmitted light intensity is minimum around the acrylic particles. As shown in FIG. 4(B), it was calculated that in a liquid containing acrylic particles with a particle diameter of 30 μm, the relative transmitted light intensity was maximized outside the positional difference Δz=±15 μm.

Moreover, FIG. 4(C) is a captured image when the relative transmitted light intensity becomes maximum in a liquid containing acrylic particles with a particle diameter of 30 μm. As shown in FIG. 4C, it can be seen that the relative transmitted light intensity is maximum at the center of the imaging region. As shown in FIG. 4(D), in a liquid containing acrylic particles with a particle diameter of 30 μm, the relative transmitted light intensity was calculated to be maximum at a position difference Δz=26 μm.

Based on this measurement result, using the above-mentioned ray tracing matrix, the objective lens is selected from Bacillus spores and acrylic particles with a particle size of 30 μm, which are microparticles at which the transmitted light intensity is maximum, for Bacillus spores and acrylic particles with a particle size of 30 μm. When we calculated the positional difference Δz corresponding to the difference between the distance z to 16 and the focal length of the objective lens, we found that the positional difference Δz in the liquid containing Bacillus spores was 0.9 μm, and in the liquid containing acrylic particles with a particle size of 30 μm. It was found that the positional difference Δz was 22.5 μm, which almost coincided with the measurement result using the laser displacement meter 15. At this time, the distance between the objective lens 16 and the imaging lens 17 is l ₁ =130 mm, the distance between the imaging lens 17 and the image sensor 18 is l ₂ =164.5 mm, and the focal length of the objective lens is f ₁ =4. The focal length of the imaging lens 17 was f ₂ =164.5 mm. Moreover, r=1 μm and n=1.4 for Bacillus spores, and n=1.5 for acrylic particles.

In particular, 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. Understood.
From this, it was found that Bacillus spores can be detected by measuring the intensity of transmitted light at a focal length.

In this way, only the Bacillus spores can be detected from the captured image by utilizing the characteristic of the Bacillus spores that the center shines brightly when the transmitted light intensity is maximum.

Next, FIG. 5 is a diagram showing an example of a captured image of sludge. As shown in FIGS. 5A and 5B, in addition to Bacillus spores B, contaminants C may also be seen in the captured image.

Next, FIG. 6 is a diagram showing an example of a captured image and a teaching image used in deep learning. (a) is a captured image showing Bacillus spores B and contaminants C. The user gives the center position P of the Bacillus spore B as correct data to this captured image, resulting in a teaching image shown in (b). Using these images, deep learning can be performed by training the network to detect the center position P of Bacillus spore B in the captured image.

Next, FIG. 7 is a diagram showing an example of Bacillus detection results by deep learning. The detection unit 22 calculates the likelihood (probability (likelihood) that each pixel is the center position of a Bacillus spore) of the detection result of Bacillus spores by image processing using deep learning.

FIG. 7(a) is an input image (captured image). The detection unit 22 calculates, for example, a likelihood map shown in FIG. 7(b). This likelihood map shows that the brighter the map, the higher the likelihood, and the darker the map, the lower the likelihood. The symbol Q indicates a portion where the likelihood is high corresponding to Bacillus spore B (FIG. 7(a)).

Then, the detection unit 22 performs threshold processing on this likelihood and determines a pixel having a likelihood of a certain value or more as the center position of the bacillus, 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.

Next, the reliability threshold will be explained in detail. The reliability threshold is set based on, for example, the number of images required to measure the dominant concentration of Bacillus, the minimum concentration that the microparticle measurement system 10 can measure, the measurement error of the microparticle measurement system 10, etc. do.

Further, as the reliability threshold, for example, a value preset in the microparticle measurement system 10 may be used, or a different value may be set for each site where the microparticle measurement system 10 is introduced. .

Specifically, the reliability threshold is set to, for example, 1.0 for each of the following examples of reliability indicators. In that case, the measurement work is performed so that the reliability becomes 1.0 or more. Furthermore, if it is desired to further increase the reliability of the concentration measurement results, the threshold value may be set to a value greater than 1.0. Conversely, if the reliability does not need to be high, the threshold value may be set smaller than 1.0.

Examples of reliability indicators will be described below.

(The reliability index is the number of microparticles)
When the reliability index is the number of microparticles, the calculation unit 23 calculates the reliability based on the number of detected microparticles.

For example, it is assumed that if L Bacillus spores are measured, the measured concentration and the actual concentration will statistically match. At this time, the reliability is calculated using the following formula.

The biometric method (standard counting method) has the idea that if approximately 30 target organisms are measured, the measured concentration will statistically match the actual concentration. Therefore, for example, the variable L can be set to 30.

Note that in that case, the reliability may be calculated using the reference table shown in FIG. 8 instead of the above-mentioned formula. However, the variable L=30 is an example, and the range of the number of Bacilli and the reliability value can be set arbitrarily.

(The reliability index is the number of captured images)
When the reliability index is the number of captured images, the calculation unit 23 calculates the reliability based on the number of captured images.

In organic wastewater treatment, the concentration at which Bacillus becomes dominant is 10 ⁵ [cells/ml] or higher. The reliability is calculated from the number of captured images required to measure this density.

For example, when the Bacillus concentration is 10 ⁵ [cells/ml], if ``m Bacilli appear per captured image'', in order to ``measure L organisms to be measured'', it is necessary to An image is required. Therefore, the reliability is calculated using the following formula.

Since reliability is calculated based on the number of images, it is easy for operators to understand. That is, for example, if the reliability does not meet the standard, it is sufficient to simply increase the number of captured images.

Furthermore, if the minimum concentration that the microparticle measurement system 10 can measure (or that it wants to measure) is determined, the reliability can be determined in the same way.

For example, when the minimum concentration that can be measured by the microparticle measurement system 10 is 10 ³ [particles/ml], if "n Bacilli are seen per captured image", in order to "measure L organisms to be measured", requires L/n images. Therefore, the reliability is calculated using the following formula.

(The reliability index is the likelihood of Bacillus spore detection results)
When the reliability index is the likelihood of the detection result of Bacillus spores, the calculation unit 23 calculates the reliability based on the likelihood of the detection result of Bacillus spores by image processing using deep learning.

For example, when the reliability standard is "the average value of the likelihood of Bacillus detection results is r or more", the reliability is calculated using the following formula.

(The reliability index is the detection result of foreign matter)
When the reliability index is a detection result of a contaminant other than Bacillus spores, the calculation unit 23 calculates the reliability based on the detection result of the contaminant.

For example, when the reliability standard is "the number of pixels occupied by contaminants other than Bacillus is less than or equal to s", the reliability is calculated using the following formula.

Furthermore, in addition to the number of pixels occupied by foreign objects, the reliability may be calculated using the size and number of foreign objects.

Next, FIG. 9 is a flowchart showing processing by the microparticle measurement system 10 of the embodiment. First, the work before this process will be explained.

First, the measurement operator samples water from the water treatment equipment where the microorganism (bacillus) to be measured is present. Perform predetermined pretreatment (filter treatment, heat treatment, etc.) on the sampled water sample. The pretreated sample is set in the microparticle measurement system 10.

Next, in step S1 of FIG. 9, the acquisition unit 21 acquires a captured image from the image sensor 18.

Next, in step S2, the detection unit 22 detects Bacillus spores appearing in the captured image.

Next, in step S3, the calculation unit 23 calculates the reliability of microparticle detection based on the detection result in step S2 and a predetermined index.

Next, in step S4, the processing unit 24 determines whether the reliability calculated in step S3 is greater than or equal to a threshold value, and if Yes, the process ends, and if No, the process proceeds to Step S5.

In step S5, the processing unit 24 causes the display unit 26 to display a screen requesting the user to take a predetermined action to improve reliability. For example, the processing unit 24 requests the user to obtain additional captured images. The user, looking at this display, shifts the slide glass 12 to take an image with the image sensor 18, or replaces the slide glass 12 and takes an image with the image sensor 18, in order to increase the reliability.

Other operations performed by the user include, for example, filter processing to remove impurities, heat treatment to spore-form viable Bacillus bacteria, and injection of an activator.

In addition, if the reliability does not exceed the threshold even after performing operations to increase the reliability a certain number of times, the measurement result will be marked as ``concentration below the measurement lower limit'' or ``unmeasurable,'' and the measurement will be terminated. It's okay.

For example, if the configuration of the microparticle measurement system 10 is different and the specimen is directly imaged without using the slide glass 12, the specimen may be shaken or otherwise changed before imaging with the image sensor 18. Good too.

Further, for example, if the configuration of the microparticle measurement system 10 is different and it is possible to automatically acquire an additional new captured image, the processing unit 24 automatically acquires the additional new captured image. You may also do so. In that case, for example, it is assumed that a device for automatically shifting the slide glass 12 is provided.

After the predetermined operation requested in step S5 is completed, the processing from step S1 onwards is executed again.

In this way, according to the microparticle measurement system 10 of the present embodiment, the accuracy of microparticle measurement is improved by calculating the reliability of microparticle detection and determining the next process based on the reliability. be able to. In other words, by requesting a worker to perform a specific task when reliability is low, even a worker without specialized knowledge can easily perform an appropriate task.

In addition, although the case where the microparticle measurement system 10 is comprised stand-alone was demonstrated above, it is not limited to this. For example, the microparticle measurement system 10 acquires a captured image by the image sensor 18 on the local terminal side, transfers the captured image to a cloud server via a communication interface and a communication network, and transmits the captured image to the cloud server by the information processing device 20 on the cloud server side. The processing may be performed and the processing results may be displayed on a local terminal.

Further, the microparticle measurement system 10 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 a RAM (Random Access Memory), and an HDD (Hard Disk Drive). It is equipped with an external storage device such as , a display device such as a display device, and an input device such as a keyboard and mouse, and has a hardware configuration that uses a normal computer.

Further, the program executed by the microparticle measurement system 10 of this embodiment is a file in an installable format or an executable format, and can be stored on a DVD (Digital Versatile Disk), USB (Universal Serial Bus) memory, SSD (Solid State It is recorded on a computer-readable recording medium such as a semiconductor storage device such as a computer drive.

Alternatively, the program may be stored on a computer connected to a network such as the Internet, and provided by being downloaded via the network. Further, the program may be provided or distributed via a network such as the Internet.

Further, the program may be configured to be provided by being incorporated in a ROM or the like in advance.

DESCRIPTION OF SYMBOLS 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 section, 20... Information processing device, 21... Acquisition section, 22... Detection section, 23... Calculation section, 24... Processing section, 25... Storage section, 26... Display section

Claims (9)

a light source that emits illumination light to a liquid containing microparticles to be measured;
an objective lens that focuses the illumination light;
an imaging lens that forms an image of the condensed illumination light;
an image sensor that captures the imaged 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 reliability of microparticle detection based on the detection result by the detection unit and a predetermined index;
a processing unit that determines the next process based on the reliability;
Microparticle measurement system equipped with The microparticle measurement according to claim 1, wherein, when the reliability is less than a predetermined threshold, the processing unit causes a display unit to display a screen requesting a user to perform a predetermined operation to improve the reliability. system. The index is the number of microparticles,
The microparticle measuring system according to claim 1, wherein the calculation unit calculates the reliability based on the number of the detected microparticles. The index is the number of captured images,
The microparticle measurement system according to claim 1, wherein the calculation unit calculates the reliability based on the number of captured images. The microparticles are Bacillus spores whose refractive index of light in sludge is known,
The index is the likelihood of the detection result of the Bacillus spores,
The microparticle measurement system according to claim 1, wherein the calculation unit calculates the reliability based on the likelihood of the Bacillus spore detection result obtained by image processing using deep learning using the captured image. The microparticles are Bacillus spores whose refractive index of light in sludge is known,
The indicator is a detection result of impurities other than the Bacillus spores,
The detection unit detects the foreign matter appearing in the captured image,
The microparticle measurement system according to claim 1, wherein the calculation unit calculates the reliability based on the detection result of the contaminants. The microparticle measurement system according to claim 1, wherein the processing unit requests the user to acquire the additional captured image when the reliability is less than a predetermined threshold. The microparticle measurement system according to claim 1, wherein the processing unit automatically acquires the additional captured image when the reliability is less than a predetermined threshold. a light source that emits illumination light to 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; A microparticle measurement method using a microparticle measurement system comprising an image sensor that captures 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 shown in the captured image;
a calculation step in which the calculation unit calculates the reliability of microparticle detection based on the detection result from the detection step and a predetermined index;
A method for measuring microparticles, including a processing step in which the processing section determines the next processing based on the reliability.

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