Measuring device and measuring method

Abstract

The present invention relates to a measuring device and a measuring method. Formed components contained in a liquid are classified to improve the calculation accuracy of the number of formed components after classification. The measuring device comprises: an acquisition unit, which acquires a first image obtained by photographing a liquid containing formed components flowing in a flow path, and a second image photographed simultaneously with the first image and having a higher shooting magnification than the shooting magnification of the first image; and a calculation unit, which uses a cut image obtained by cutting out formed components contained in the first image and the second image to classify the formed components by type, and uses the total number of formed components cut out from the first image and included in a specified classification, and the ratio of each type of formed components cut out from the second image and included in the specified classification to the total number of formed components cut out from the second image and included in the specified classification, to calculate the number of formed components included in the specified classification.

Description

Technical Field

[0001] The present invention relates to a measuring device and a measuring method. Background Art

[0002] As one of the urine tests, the following method is known: a urine sample flowing in a flow path set in a flow tank is photographed, and the sediment components in the urine (tangible (solid) components in the urine such as blood cells, epithelial cells, casts, bacteria, crystals, etc.) are classified by components (for example, refer to patent documents 1 and 2).

[0003] Patent Document 1: Japanese Patent Application Laid-Open No. 06-288895

[0004] Patent Document 2: Japanese Patent Application Laid-Open No. 11-94727

[0005] The analysis of sediment components in urine includes a discrimination process for discriminating the type of sediment components in urine and a calculation process for calculating the number of sediment components. By photographing the urine specimen at a high magnification, the shape of the sediment components can be confirmed in detail, thereby improving the accuracy of the discrimination process. On the other hand, if the urine specimen is photographed at a high magnification, the photographing range becomes narrower, so the number of sediment components passing outside the photographing range increases, and the capture rate in the photographed image decreases.

[0006] When the number of sediment components passing outside the shooting range is reduced in order to improve the accuracy of the calculation process and thus expand the shooting range, the urine sample is photographed at a low magnification. If the urine sample is photographed at a low magnification, the detailed shape and structure of the sediment component cannot be confirmed, so the accuracy of the discrimination process is reduced. Such a problem is not limited to urine samples, and may also occur when the formed components contained in liquids other than urine such as blood and body fluids, artificial blood, etc. are discriminated and the number is counted. In patent document 1, a detector is used to detect diffuse light diffusely reflected by particles flowing in a flow tank, and an image is taken according to the detection signal. In patent document 2, a known standard sample is used to obtain an image validity coefficient before sample measurement, and the image processing particle number is calculated based on the total number of particles passing through the shooting area of ​​the flow tank, and the particle number is calculated by multiplying the image processing particle number by the particle image validity coefficient. However, in particle detection based on diffuse light, particles cannot be classified by component. Therefore, it is required to further improve the measurement accuracy. Summary of the invention

[0007] An object of the present invention is to improve the measurement accuracy in the measurement of classifying formed ingredients contained in a liquid and counting the number of the classified formed ingredients.

[0008] One aspect of the disclosed technology is exemplified by the following measuring device. The measuring device comprises: an acquisition unit that acquires a first image obtained by photographing a liquid containing a formed component flowing in a flow path and a second image photographed simultaneously with the first image and having a higher photographing magnification than the first image; and a calculation unit that uses a cut-out image obtained by cutting out the formed components contained in the first image and the second image and classifies the formed components by type, and calculates the number of formed components included in the specified classification by using the total number of formed components cut out from the first image and included in the specified classification and the ratio of each type of formed components to the total number of formed components cut out from the second image and included in the specified classification.

[0009] In the disclosed technology, it is possible to classify the formed components contained in the liquid, and improve the calculation accuracy of the number of the classified formed components. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a diagram showing a schematic configuration of a measuring device according to an embodiment.

[0011] Figure 2 This is a diagram showing the schematic structure of a flow cell.

[0012] Figure 3 It is a diagram showing a schematic structure of the vicinity of the merging portion and the tapered portion.

[0013] Figure 4 It is a diagram showing the distribution of the sheath fluid and the specimen flowing through the fourth channel.

[0014] Figure 5 It is a diagram showing an example of an image captured by the first imaging unit and the second imaging unit respectively.

[0015] Figure 6 This is a flowchart showing the process of classifying formed components in the embodiment.

[0016] Figure 7 This is a flowchart showing the flow of classification and number calculation of shaped components in the embodiment.

[0017] Figure 8 This is a diagram showing an example of the results of classification and number calculation of tangible components based on the first image.

[0018] Fig. 9 This is a diagram showing an example of the results of classification and ratio calculation of the tangible components based on the second image.

[0019] Fig.10 FIG. 1 is a diagram illustrating a correction result of a correction process in the embodiment.

[0020] Fig.11 This is a flowchart showing the flow of classification and number calculation of shaped components in the first modification.

[0021] Fig.12 This is a diagram showing an example of the results of classification and ratio calculation of the tangible components based on the second image.

[0022] Fig.13 FIG. 1 is a diagram illustrating a correction result of the correction process in the first modification example.

[0023] Fig.14 This is a flowchart showing the process of classifying and calculating the number of formed components in the second modification.

[0024] Fig.15 This is a diagram showing an example of the results of classification and ratio calculation of the tangible components based on the second image.

[0025] Fig.16 FIG. 2 is a diagram illustrating a correction result of the correction process in the second modification example.

[0026] Fig.17 This is a flowchart showing the process of classifying and calculating the number of formed components in the third modification.

[0027] Fig.18 This is a diagram showing an example of the results of classification and number calculation of tangible components based on the first image in the third modification.

[0028] Fig.19 This is a diagram showing an example of the results of classification and ratio calculation of tangible components based on the second image in the third modification.

[0029] Fig. 20 FIG. 1 is a diagram illustrating a correction result of the correction process in the third modification example.

[0030] Description of symbols

[0031] 1: imaging device; 12: light source; 13A: flow cell; 14: controller; 20: measuring device; 100A: first imaging unit; 100B: second imaging unit; 101: objective lens; 102: branching unit; 104: aperture. DETAILED DESCRIPTION

[0032] <Implementation Method>

[0033] The following further describes the embodiments. The structures of the embodiments shown below are examples, and the disclosed technology is not limited to the structures of the embodiments. The measuring device of the embodiments has the following structure, for example. The measuring device of the embodiments has:

[0034] an acquisition unit that acquires a first image obtained by photographing a liquid containing a formed component flowing in a flow path, and a second image photographed simultaneously with the first image and having a higher photographing magnification than the first image; and

[0035] A calculation unit that uses a cut image obtained by cutting out the formed components included in the first image and the second image to classify the formed components by type, and uses the total number of formed components cut out from the first image and included in the specified category and the ratio of each type of formed components cut out from the second image and included in the specified category to the total number of formed components cut out from the second image and included in the specified category, to calculate the number of formed components included in the specified category.

[0036] The present measuring device identifies the types of formed components contained in a liquid and counts the number of formed components by type. In the present measuring device, the liquid may be, for example, a liquid derived from a living body such as urine, blood, or body fluid, or a liquid not derived from a living body such as artificial blood or medicine. As formed components, when the specimen is urine, blood cells, epithelial cells, casts, bacteria, crystals, etc. may be cited.

[0037] The flow path is preferably formed so that the formed components contained in the liquid are uniformly distributed in the liquid and flow without staying in one place. By thinly spreading the formed components in the liquid, it is possible to suppress the reduction in the calculation accuracy of the number of formed components due to the overlapping images of multiple formed components in the first image or the second image. The flow path is formed in a flow tank, for example.

[0038] The acquisition unit acquires the first image and the second image. Here, the acquisition unit may acquire the first image and the second image from the camera unit that has captured the first image and the second image. In addition, the acquisition unit may acquire the first image and the second image that are pre-stored in the storage unit from the storage unit. In addition, the acquisition unit may receive the first image and the second image from other devices via a communication line or the like.

[0039] The imaging unit is, for example, a digital camera having an imaging element such as a charge coupled device (CCD) image sensor or a complementary metal oxide semiconductor (CMOS) image sensor.

[0040] Since the magnification of the first image is lower than that of the second image, a wider range is captured. Therefore, the number of formed components captured in the captured image of the first image is large, which is suitable for counting the number of formed components. In addition, the second image captured at a higher magnification than the first image can reflect the detailed shape and structure of the formed components. Therefore, the second image is suitable for classifying formed components. The present measuring device calculates the number of at least one type of formed components by using the number of formed components contained in the first image and the number of each type of formed components contained in the second image, thereby improving the calculation accuracy of the number of formed components.

[0041] The present measuring device may also have the following features. The calculation unit calculates the number of tangible components included in the specified classification by multiplying the total number of tangible components cut from the first image and included in the specified classification by the ratio of each type of tangible components cut from the second image and included in the specified classification to the total number of tangible components. Here, the specified classification (type) may, for example, specify all classifications, or may be a classification (type) specified by the person in charge of the measurement, or may be a classification (type) that is prone to misclassification. By having such a feature, the calculation accuracy of the number of tangible components in the specified classification (type) can be improved while suppressing the computational load of the present measuring device.

[0042] The measuring device may also have the following features. The calculation unit outputs a full component image configured with cut-out images representing each type of the formed ingredient based on the result of calculating the formed ingredient by type. By having such a feature, the measuring device can facilitate visual confirmation of the type and number of the formed ingredient.

[0043] The measuring device may also have the following features. The measuring device also includes a first camera unit for photographing the first image and a second camera unit for photographing the second image, and the focus positions on the optical axis of the first camera unit and the second camera unit are equal. By having such a feature, since the photographing range of the second image is included in the photographing range of the first image in a focused state, the calculation accuracy of the number of formed components can be improved. The technology involved in the above-described embodiment can also be understood from the aspect of the measuring method.

[0044] Hereinafter, the measuring device according to the embodiment will be further described with reference to the drawings. Figure 12 is a diagram showing a schematic structure of a measuring device according to an embodiment. The measuring device 20 includes a photographing device 1. The measuring device 20 photographs urine, for example, as a specimen, through the photographing device 1, and measures formed components in urine, for example, by analyzing the photographed image. However, the measuring device 20 can also be applied to the measurement of formed components in liquid specimens other than urine, such as blood or body fluids. Urine is an example of a "liquid containing formed components".

[0045] The imaging device 1 includes an imaging unit 10 for imaging a specimen, a light source 12 for imaging, and a flow cell unit 13. The flow cell unit 13 includes a stage (not shown) on which a flow cell 13A through which the specimen flows is fixedly arranged. The flow cell 13A is attachable to and detachable from the stage.

[0046] The imaging unit 10 includes an object lens 101, a branching portion 102, a first lens group 103A, a second lens group 103B, an aperture 104, a first camera 105A, and a second camera 105B. The first camera 105A and the second camera 105B use imaging elements such as CCD image sensors or CMOS image sensors to shoot. Hereinafter, the object lens 101, the branching portion 102, the first lens group 103A, the aperture 104, and the first camera 105A are collectively referred to as the first imaging unit 100A. In addition, the object lens 101, the branching portion 102, the second lens group 103B, and the second camera 105B are collectively referred to as the second imaging unit 100B. The first lens group 103A and the second lens group 103B each include an eyepiece, and there is a case where they also have an imaging lens. The flow tank 13A is arranged between the light source 12 and the object lens 101, and the light source 12 and the object lens 101 are shared by the first imaging unit 100A and the second imaging unit 100B. Although the object lens 101 can be a finite distance correction optical system or an infinity correction optical system, by making the object lens 101 a finite distance correction optical system, the imaging device 1 can be made compact. The first imaging unit 100A is an example of the "first imaging unit". The second imaging unit 100B is an example of the "second imaging unit".

[0047] The branching portion 102 is, for example, a beam splitter such as a half-mirror. The branching portion 102 transmits a portion of the light that has passed through the flow cell 13A and the objective lens 101, and reflects the remaining portion, thereby branching the light into two directions. The light that has passed through the branching portion 102 among the branched light is incident on the imaging surface of the imaging element of the first camera 105A via the first lens group 103A. That is, the light that has passed through the branching portion 102 is used for imaging in the first imaging portion 100A. On the other hand, the light reflected by the branching portion 102 is incident on the imaging surface of the imaging element of the second camera 105B via the second lens group 103B. That is, the light reflected by the branching portion 102 is used for imaging in the second imaging portion 100B. The optical path of the light between the branching portion 102 and the first camera 105A is referred to as the first optical path, and the optical path of the light between the branching portion 102 and the second camera 105B is referred to as the second optical path. In addition, as Figure 1 As shown in the example, the branching portion 102 is arranged on the optical axis 11B of the objective lens 101. Figure 1 In FIG. 1 , the optical axis of the first optical path is represented by 111A, and the optical axis of the second optical path is represented by 111B.

[0048] The aperture 104 is disposed between the branch portion 102 and the first lens group 103A. That is, the aperture 104 is inserted into the first optical path. The aperture 104 is formed by opening a circular hole in a plate. The aperture 104 is disposed at a position where the optical axis 111A of the first lens group 103A passes through the central axis of the hole of the aperture 104 and is perpendicular to the optical axis 111A of the first optical path. The aperture 104 is an aperture that reduces the opening of the light irradiated to the first camera 105A by blocking the light of the peripheral portion in the first optical path. The aperture 104 deepens the depth of field of the first camera 105A.

[0049] The measuring device 20 is provided with a controller 14 as a control unit. The controller 14 includes a central processing unit (CPU) 14A, a read only memory (ROM) 14B, a random access memory (RAM) 14C, an electrically erasable programmable read only memory (EEPROM) 14D, and an interface circuit 14E. The CPU 14A, the ROM 14B, the RAM 14C, the EEPROM 14D, and the interface circuit 14E are connected to each other via a bus 14F.

[0050] The CPU 14A controls the entire measuring device 20 according to the program stored in the ROM 14B and read by the RAM 14C. The ROM 14B stores programs and data for operating the CPU 14A. The RAM 14C provides a work area for the CPU 14A and temporarily stores various data and programs. The EEPROM 14D stores various setting data, etc. The interface circuit 14E controls the communication between the CPU 14A and various circuits.

[0051] The interface circuit 14E is connected with control lines of the first imaging unit 100A, the second imaging unit 100B, the light source 12, the first pump 15A, and the second pump 15B. The first imaging unit 100A, the second imaging unit 100B, the light source 12, the first pump 15A, and the second pump 15B are controlled by a control signal from the CPU 14A. The first pump 15A is a pump that supplies sheath liquid to the flow tank 13A via the first supply tube 132A. The second pump 15B is a pump that supplies a specimen to the flow tank 13A via the second supply tube 133A. The sheath liquid is a liquid that controls the flow of the specimen in the flow tank 13A. As the sheath liquid, for example, in the case where the specimen is urine, physiological saline can be cited. However, a solution other than physiological saline can also be used as the sheath liquid.

[0052] Figure 2 2 is a diagram showing a schematic structure of the flow channel 13A. The flow channel 13A is formed by bonding the first plate 130 and the second plate 131 (for example, by thermocompression bonding). Figure 2 13A is a diagram of the flow channel 13A viewed from the first plate 130 side. Figure 2 The width direction of the flow cell 13A shown is set as the X-axis direction in the orthogonal coordinate system, the length direction is set as the Y-axis direction, and the thickness direction is set as the Z-axis direction. The sample photographed flows in the flow cell 13A along the Y-axis direction. The optical axis 11B of the objective lens 101 is arranged in the Z-axis direction.

[0053] The material of the flow tank 13A can be a material having a visible light transmittance of more than 90%, such as PMMA (acrylic resin), COP (cycloolefin polymer), PDMS (polydimethylsiloxane), PP (polypropylene), or quartz glass.

[0054] The first plate 130 is provided with a first supply port 132 for supplying a sheath liquid, a second supply port 133 for supplying a specimen, and a discharge port 134 for discharging the sheath liquid and the specimen. The first supply port 132, the second supply port 133, and the discharge port 134 penetrate the first plate 130 in the thickness direction. The first supply port 132 is provided at one end side in the longitudinal direction of the first plate 130. The second supply port 133 is provided at the other end side in the longitudinal direction of the first plate 130. The discharge port 134 is provided between the first supply port 132 and the second supply port 133 in the longitudinal direction of the first plate 130.

[0055] The first supply port 132, the second supply port 133, and the discharge port 134 are connected to each other through passages 135A, 135B, 136, and 138. These passages 135A, 135B, 136, and 138 are formed by being recessed from the surface on the bonding surface side of the first plate 130 so that the cross section becomes rectangular. In addition, the cross section of these passages 135A, 135B, 136, and 138 is formed in the width direction ( Figure 2 X-axis direction) than the depth direction ( Figure 2 When the first plate 130 and the second plate 131 are joined, the second plate 131 becomes a wall material for forming the passages 135A, 135B, 136, and 138.

[0056] The first supply port 132 is connected to the first passage 135A and the second passage 135B. The first passage 135A and the second passage 135B go around in opposite directions, advance along the outer edge of the first plate 130 toward the second supply port 133, and merge at the confluence 137. In addition, the third passage 136 is connected to the second supply port 133. The third passage 136 merges with the first passage 135A and the second passage 135B at the confluence 137. The confluence 137 is connected to the discharge port 134 via the fourth passage 138. The fourth passage 138 is formed with a tapered portion 138A, which is formed into a tapered shape in which the depth of the fourth passage 138 (the length in the plate thickness direction (Z-axis direction) of the first plate 130) gradually decreases as it approaches the discharge port 134 from the confluence 137. The tapered portion 138A is provided to be inclined at, for example, 2° to 8°.

[0057] The first supply port 132 is connected to Figure 1 The first supply pipe 132A is shown as an example. The second supply port 133 is connected to Figure 1The second supply tube 133A is shown as an example. A discharge tube (not shown) is connected to the discharge port 134. The sheath liquid supplied from the first supply tube 132A to the first supply port 132 flows through the first passage 135A and the second passage 135B. The specimen supplied from the second supply tube 133A to the second supply port 133 flows through the third passage 136. Then, the sheath liquid and the specimen merge at the confluence portion 137 and flow through the fourth passage 138, and are discharged from the discharge port 134 to the discharge tube.

[0058] Figure 3 138A is a diagram showing a schematic structure of the vicinity of the confluence portion 137 and the tapered portion 138A. In the confluence portion 137 , the third passage 136 is arranged on the second plate 131 side. In the confluence portion 137 , the sample flows along the second plate 131 .

[0059] Figure 4 138 is a diagram showing the distribution of the sheath fluid and the specimen flowing through the fourth passage 138. Figure 4 After the sheath liquid and the specimen are provided to the upper side of the second plate 131, the sheath liquid and the specimen are merged at the confluence portion 137. Immediately after the sheath liquid and the specimen are merged at the confluence portion 137, the specimen in the sheath liquid is concentrated in a relatively narrow range on the wall side of the second plate 131 (the position of the AA line). Then, when the specimen flows in the tapered portion 138A, the specimen is pressed by the sheath liquid and expands in a flat shape along the wall near the wall of the second plate 131 (the position of the BB line). When the specimen flows further, the specimen leaves the wall of the second plate 131 through the tubular pinch effect and is lifted toward the center of the fourth passage 138 (the position of the CC line).

[0060] The distribution of the formed components is affected by the distribution of the specimen in the sheath liquid. The measuring device 20 can improve the measurement accuracy of the formed components by performing the imaging of the first imaging unit 100A and the second imaging unit 100B at a position where more formed components can be imaged. In the flow cell 13A, the flow of the specimen changes depending on the position in the Y-axis direction. Figure 4 At the CC line, the width of the specimen in the Z-axis direction becomes larger than that at the BB line. Figure 4 At the position of the CC line, the formed components in the specimen are distributed spread out in the Z-axis direction, and therefore it is not suitable for imaging the formed components.

[0061] On the other hand, Figure 4 At the position of the BB line, the sheath liquid flows in a manner that presses the specimen against the second plate 131 from above, and the specimen is squeezed by the sheath liquid in the optical axis direction and spreads thinly. Figure 4At the position of the BB line, the formed ingredients in the specimen exist in a manner that does not diffuse in the Z-axis direction. In addition, the sheath liquid and the specimen form laminar flows and are hardly mixed. Since such a position of the BB line is a position in the Y-axis direction suitable for photographing the formed ingredients, the measuring device 20 photographs the specimen at the position in the Y-axis direction. This position is called a photographing position, and the optical axis 11B of the objective lens 101 is aligned with the photographing position. That is, the flow cell 13A is formed at the photographing position in a manner that the formed ingredients are thinly spread in the specimen. At the photographing position, the formed ingredients are evenly distributed in the specimen.

[0062] In addition, the example in which the specimen contacts the wall surface of the flow cell 13A after passing through the tapered portion 138A of the flow cell 13A is described, but the structure of the flow cell and the flow of the specimen are not limited to this. In the measuring device 20, for example, a flow cell having the following structure may be used: after passing through the tapered portion 138A of the flow cell 13A, the sheath liquid surrounds the periphery of the specimen and the specimen is thinly stretched in the center of the sheath liquid.

[0063] Back to Figure 1 In the first imaging unit 100A, an aperture 104 is inserted into the first optical path to limit the amount of light irradiated to the first camera 105A (numerical aperture is reduced). On the other hand, an aperture equivalent to the aperture 104 is not provided in the second optical path. The first camera 105A sets the shooting magnification of the shooting position to the first magnification by adjusting the numerical aperture of the aperture 104, the magnification of each lens included in the first lens group 103A, and the distance between the objective lens 101 and the first lens group 103A. In addition, the second camera 105B sets the shooting magnification at the shooting position to the second magnification higher than the first magnification by adjusting the magnification of each lens included in the second lens group 103B and the distance between the objective lens 101 and the second lens group 103B. Here, for example, the first magnification may be 10 times and the second magnification may be 40 times. The focal positions of the first camera 105A and the second camera 105B on the optical axis are adjusted so as to focus on the same position in the specimen during imaging at the imaging position. That is, the focal positions of the first camera 105A and the second camera 105B are the same.

[0064] The CPU 14A causes the first camera 105A and the second camera 105B to simultaneously capture a static image of the formed components in the specimen flowing through the flow tank 13A. The static image is an enlarged image of the specimen. The lighting time of the light source 12 and the shooting time (exposure time) of the first camera 105A and the second camera 105B are synchronized by the CPU 14A. Parallel light is incident from the light source 12 into the flow tank 13A. The CPU 14A causes the light source 12 to light up once or multiple times during shooting. The lighting time of the light source 12 depends on the flow rate of the specimen, and is set to 0.1 to 10 μsec, for example, in order to keep the jitter of the subject within the allowable range. In one exposure, the number of formed components contained in one image can be increased by causing the light source 12 to emit light multiple times. The measuring device 20 can further improve the measurement accuracy of the formed components by capturing more formed components. In this case, the flashing timing of the light source 12 can be determined by considering the relationship between the flow rate of the specimen and the lighting time of the light source 12. In the measurement of one sample, for example, 100 to 1000 images are captured. The light source 12 may be, for example, a xenon lamp or a white LED, but is not limited thereto and may be another light source.

[0065] When the first imaging unit 100A and the second imaging unit 100B capture images of the light from the light source 12 that has passed through the flow cell 13A, two images having different capturing magnifications are acquired. Figure 5 1 is a diagram showing an example of an image captured by each of the first imaging unit 100A and the second imaging unit 100B. Figure 5 In FIG. 1 , the first image P1 is an example of an image of the shooting position captured by the first imaging unit 100A at a first magnification. In addition, the second image P2 is an example of an image of the shooting position captured by the second imaging unit 100B at a second magnification. Figure 5 As illustrated, the second image P2 is an image of a region in which a part of the first image P1 is enlarged. In addition, the focus positions on the optical axes of the first camera 105A and the second camera 105B are equal. Although the center positions of the first image P1 and the second image P2 in the XY plane are consistent, it is sufficient as long as the shooting range of the second image P2 is included in the shooting range of the first image P1. That is, the shooting area shot by the second camera unit 100B is included in the shooting area shot by the first camera unit 100A. The positional relationship of the shooting areas of the two camera units corresponds. The CPU 14A, for example, respectively acquires the first image P1 and the second image P2 captured simultaneously from the first camera unit 100A and the second camera unit 100B, and stores the acquired first image P1 and second image P2 in association in the RAM 14C. The first image P1 is an example of the "first image". The second image P2 is an example of the "second image".

[0066] Since the first image P1 is an image with a wider shooting range than the second image P2, it is suitable for finding the number of formed components. On the other hand, since the second image P2 is an image with a higher shooting magnification than the first image P1, it is suitable for morphological observation of cell nuclei and the like and classification of formed components. For example, the CPU 14A can calculate the number of formed components in the specimen based on the first image P1, classify the formed components in the specimen into various types based on the second image P2, and calculate the number of each of the classified types.

[0067] The CPU 14A grasps the position, size, and number of the formed components based on the images captured by the first camera unit 100A and the second camera unit 100B, determines the cutout size of the image based on the grasped size of the formed components, and generates a cutout image. The cutout image is an image obtained by comparing the background image and the captured image, surrounding the area where there is a difference, and cutting out the image inside.

[0068] Before generating the cut image, the CPU 14A uses the data of the stored images to generate an image as a background image by averaging the pixel values ​​of each pixel for each image. The pixel value can be the brightness of each pixel or the RGB value. The cut image is generated by the CPU 14A executing a program (cut processing) stored in the ROM 14B. The cut image is stored in the RAM 14C together with the cut position and the cut size. For example, the CPU 14A determines that the part that is different from the background image contains a formed component for each image of the first image P1 and the second image P2, and generates a cut image for all the formed components contained in the image. The CPU 14A classifies the cut images cut from the first image P1 for each formed component and counts the number of cut images classified into each classification item. It is also possible not to classify the cut image of the first image P1 for each formed component, but only to calculate the total number of formed components in the specimen. Furthermore, the CPU 14A observes the morphology of the formed components for each of the cut-out images cut out from the second image P2 , classifies the formed components by type, and calculates the number of each of the classified types.

[0069] Figure 6 This is a flowchart showing the process of classifying formed components in the embodiment. Figure 6 The illustrated flowchart is executed by the CPU 14A.

[0070] In S101, the CPU 14A acquires the first image P1 captured by the first camera unit 100A. In addition, the CPU 14A acquires the second image P2 captured by the second camera unit 100B. The CPU 14A stores the first image P1 and the second image P2 in the RAM 14C. The CPU 14A that executes the process of S101 is an example of an "acquisition unit". The process of S101 is an example of an "acquisition step".

[0071] In S102, the CPU 14A generates a first cut-out image by cutting out a tangible component from the first image P1. The CPU 14A stores the generated first cut-out image in the RAM 14C.

[0072] In S103, the CPU 14A acquires the position information and feature quantity of the first cut-out image stored in the RAM 14C in S102. The CPU 14A stores the position information and feature quantity of the first cut-out image in the RAM 14C in association with the first cut-out image. Examples of the feature quantity include color, shape, and size. The program pre-stored in the ROM 14B is used to acquire the feature quantity.

[0073] In S104, the CPU 14A cuts out a tangible component from the second image P2 to generate a second cut-out image. The CPU 14A stores the generated second cut-out image in the RAM 14C.

[0074] In S105, the CPU 14A acquires the feature amount of the second cut-out image stored in the RAM 14C in S104. The CPU 14A stores the feature amount of the second cut-out image in the RAM 14C in association with the second cut-out image.

[0075] In S106, the CPU 14A classifies and counts the tangible components based on the feature quantities acquired in S103 and S105. For the classification, a program pre-stored in the ROM 14B is used. Figure 7 : is a flowchart showing the process of classifying and counting the tangible components in the embodiment. Figure 7 Shows Figure 6 The detailed process of the processing in S106 is shown below.

[0076] In S1061, the CPU 14A classifies the tangible components by type based on the cut-out image cut out from the first image P1, and calculates the number of each type after classification. Figure 6 The feature quantity obtained in S103 is used to classify the tangible components and calculate their number. Figure 8 FIG. 1 is a diagram showing an example of the result of classification and number calculation of tangible components based on the first image P1. Figure 8In the , the tangible elements are classified into large categories and classification items that are further subdivided in the large categories. The large categories are classified into 8 items from "1" to "8". For example, in Figure 8 In the above example, the epithelial category of major category "3" is further subdivided into "squamous epithelial" and "other epithelial" categories. The cast category of major category "4" is further subdivided into "hyaline cast" and "other cast". All major categories may not have further subdivided categories. Figure 8 As shown, the CPU 14A calculates the number of each type of the formed ingredients and the total number of the formed ingredients. In addition, in S1061, the CPU 14A may calculate only the total number of the formed ingredients instead of calculating the number of each type of the formed ingredients.

[0077] In S1062, the CPU 14A classifies the tangible components by type based on the second image P2, and calculates the ratio of each type of the classified tangible components to the total number of tangible components. Figure 6 The feature quantity obtained in S105 is used to classify the tangible components and calculate their proportions. Fig. 9 is a diagram showing an example of the results of the classification and ratio calculation of the tangible components based on the second image P2. Fig. 9 In, with Figure 8 Similarly, the formed elements are classified into red blood cells or white blood cells, and the number of formed elements is calculated for each classification. Fig. 9 In, with Figure 8 Likewise, the categories are classified into major categories "1" to "8". Fig. 9 As shown in the example, the CPU 14A calculates the number of each type of tangible ingredients. Fig. 9 As shown in the example, the CPU 14A further calculates the ratio of each type of the formed ingredients to the total number of the formed ingredients. The CPU 14A classifies the formed ingredients by type based on the second image P2 captured at a higher magnification than the first image P1, thereby classifying the formed ingredients with higher accuracy. Figure 8 and Fig. 9 In the present invention, formed elements are classified into various types such as "red blood cells", "white blood cells", "squamous epithelium", "other epithelium", "hyaline casts", "other casts", "bacteria", "crystals", "others", and "garbage / cell sheets", but the classification of formed elements is not limited to this.

[0078] In S1063, the CPU 14A performs correction processing. The CPU 14A corrects the number of each type of the formed components based on the total number of the formed components obtained from the cut-out image of the first image P1 calculated in S1061 and the ratio of each type of the formed components obtained from the cut-out image of the second image P2 calculated in S1062. Fig.10 This is a diagram illustrating the correction result of the correction processing performed in the embodiment. For example, since the ratio of "red blood cells" calculated in S1062 is "15.2"%, and the "total number" calculated in S1061 is "82", the CPU 14A uses the total number of each formed component based on the first image P1 and the existence ratio of each formed component obtained from the second image P2 to calculate the number of formed components. As an example, the total number of formed components "82" obtained by the first image P1 is multiplied by the ratio of red blood cells classified by the second image P2 "15.2%", and the corrected number of red blood cells is calculated to be "12". The corrected total number of formed components is the same as the total number of formed components obtained by the first image P1, and the corrected ratio of each classification is the same as the ratio obtained from the second image P2. By performing such correction processing on each formed component, the CPU 14A is able to obtain Fig.10 The correction results are shown.

[0079] Back to Figure 6 In S107, the CPU 14A outputs the correction result of the correction process performed in S106. For example, the CPU 14A may Fig.10 The list of correction results illustrated is output as a calculation result to a monitor, or is output to a printer for printing. In addition, for overall observation, the CPU 14A may also output a full component image as a calculation result, which is generated by randomly arranging cut images of the number of each type of formed component calculated in S106, the amount of specimen used for measurement, and the number of each component calculated as the magnification and image size displayed as the full component image on a single screen. The CPU 14A that executes the processing of S102 to S107 is an example of a "calculation unit". The processing of S102 to S107 is an example of a "calculation step".

[0080] The CPU 14A generates, for example, a full component image in which the first cut image or the second cut image is randomly arranged in a non-overlapping manner according to the number calculated in S106. Alternatively, the CPU 14A may enlarge a portion of the full component image and output an enlarged image in which the shapes of the shaped components can be easily observed. The full component image is, for example, a full component image obtained by enlarging a portion of the full component image in S106. Figure 5 An image obtained by changing the calculation result of calculating the number of each type of shaped component of the first image P1 and the second image P2 shown as an example.

[0081] In an embodiment, CPU 14A corrects the number of each type of formed ingredients according to the total number of formed ingredients calculated based on the first image P1 in S1061 and the ratio of each type of formed ingredients calculated based on the second image P2 in S1062. By combining the result of the high-magnification image suitable for counting effective ingredients with the result of the low-magnification image suitable for the classification of the types of formed ingredients, the measuring device 20 can improve the calculation accuracy of the number of each type of formed ingredients. In addition, when acquiring images with different shooting magnifications such as the first image and the second image, the light source 12 and the object lens 101 are respectively 1. Thus, the classification and calculation of the number of formed ingredients in the specimen can be realized at a lower cost.

[0082] In the embodiment, the CPU 14A outputs a full component image in which the first cut image or the second cut image is randomly arranged in a non-overlapping manner according to the number calculated in S106. In addition, in the embodiment, the CPU 14A may also enlarge a part of the full component image and output an enlarged image in which the shapes of each formed component can be easily observed. Therefore, according to the present embodiment, the types and numbers of formed components in urine can be easily observed.

[0083] In the embodiment, the focal positions on the optical axes of the first camera 105A and the second camera 105B are equal. Therefore, the magnifications of the first image P1 and the second image P2 are different. In the first image P1, a wide range of shooting can be performed, but the classification accuracy of the formed components is lower than that of the second image P2. In this embodiment, by calculating the number of each formed component using the number of formed components calculated using the first image P1 and the existence ratio of each classification calculated using the second image P2, the counting accuracy of the number of each type of formed components can be improved.

[0084] (First Modification)

[0085] In the embodiment, the number of each type of the formed ingredients is corrected for all major categories based on the total number of formed ingredients calculated based on the first image P1 and the ratio of each type of the formed ingredients calculated based on the second image P2. In the first variant, the process of correcting the number of formed ingredients for a specified major category is described. In the first variant, the embodiment Figure 7 The illustrated processing is modified. The information indicating the designated major category may be pre-stored in ROM 14B, for example, or the designated major category may be selected by a user operation. The same reference numerals are given to the same components as those in the embodiment, and their description is omitted. The first modified example is described below with reference to the accompanying drawings.

[0086] Fig.11 1061a, the CPU 14A calculates the total number of formed components classified as the epithelial components of the designated large category "3" based on the first image P1. Here, the result of the classification and number calculation of formed components based on the first image P1 is assumed to be Figure 8 In the illustrated state, the designated major category is epithelial category "3". The CPU 14A adds the number of "squamous epithelial cells" "12" and the number of "other epithelial cells" "1" classified into the major category "3" epithelial category to calculate the total number of "13". The CPU 14A that executes the process of S1061a is an example of the "first calculation unit".

[0087] In S1062a, the CPU 14A calculates the ratio of each of the formed components classified into the designated major classification "3" of epithelium based on the second image P2. Fig.12 1062a is a diagram showing an example of the result of the classification and proportion calculation of the formed components based on the second image P2. The CPU 14A adds up the number of "squamous epithelium" classified as the epithelial class of the major category "3", "8", and the number of "other epithelium", "2", and calculates the total number of formed components classified as the major category "3", "10". The CPU 14A calculates the proportion of "squamous epithelium" in the major category "3", "80.0%", and the proportion of "squamous epithelium" in the major category "3", "20.0%", based on the total number of formed components classified as the major category "3" calculated based on the second image P2 and the numbers of "squamous epithelium" and "other epithelium". The CPU 14A that executes the process of S1062a is an example of the "second calculation unit".

[0088] In S1063a, the CPU 14A corrects the number of "squamous epithelium" and "other epithelium" based on the total number of epithelial cells classified into the major category "3" calculated based on the first image P1 in S1061a and the respective ratios of "squamous epithelium" and "other epithelium" as the classification items calculated based on the second image P2 in S1062a. The CPU 14A calculates the number of "squamous epithelium" as "10" as a correction value by multiplying the total number of formed elements classified into the major category "3" calculated based on the first image P1 "13" by the ratio "80%" of "squamous epithelium" calculated based on the second image P2. In addition, CPU 14A calculates the number of "other epithelium" "3" as a correction value by multiplying the total number of shaped components "13" classified into the major category "3" calculated based on the first image P1 by the proportion of "other epithelium" "20%" calculated based on the second image P2. The total number of shaped components after correction is the same as the total number of shaped components obtained by the first image P1. In addition, the number of corrected classification items can also be calculated by subtracting the corrected number of other classification items from the total number included in the specified major category. In the above example, the corrected number of "other epithelium" "3" can also be calculated by subtracting the corrected number of "flat epithelium" (10) from the total number of "epithelium" (13). And vice versa. By performing such correction processing on each shaped component classified into the specified major category, CPU 14A is able to obtain Fig.13 The correction results are shown.

[0089] According to the first modification, the number of tangible components can be calculated with high accuracy for the designated major classification, and the processing load of the CPU 14A can be reduced compared to the embodiment in which the correction process is performed for all major classifications. In addition, in the first modification, the case in which all classifications are designated corresponds to the above embodiment.

[0090] (Second Modification)

[0091] In the second modification, in the classification based on the tangible components of the first image P1, a large classification that is prone to misclassification is specified, and the specified classification is corrected. Figure 7 The illustrated processing is modified. The information for specifying the major category that is prone to misclassification may be pre-stored in ROM 14B, for example, or the major category specified by the user's operation may be selected. The same reference numerals are given to the same components as those in the embodiment, and their description is omitted. The second modification example is described below with reference to the accompanying drawings.

[0092] Fig.141 is a flowchart showing the process of classifying and counting the tangible components in the second variant. In S1061b, the CPU 14A calculates the total number of tangible components classified into the large category that is prone to misclassification based on the classification result of the tangible components based on the first image P1. Here, the result of the classification and counting of the tangible components based on the first image P1 is Figure 8 In the illustrated state, the major categories designated as major categories that are prone to misclassification are "1", "5", "6", and "8". The CPU 14A uses the first image P1 to calculate the total number of formed components classified as "red blood cells", "bacteria", "crystals", and "trash / cell pieces". "Red blood cells", "bacteria", "crystals", and "trash / cell pieces" are formed components classified as "1", "5", "6", and "8" as major categories. The CPU 14A that executes the process of S1061b is an example of the "first calculation unit".

[0093] In S1062b, the CPU 14A calculates the respective proportions of the tangible components in the large category where misclassification is likely to occur, based on the classification result of the tangible components based on the second image P2. Fig.15 : is a diagram showing an example of the results of the classification and proportion calculation of the formed components based on the second image P2. The CPU 14A cuts out the formed components from the second image P2, and calculates the total number of classification items (formed components) classified into the major classifications "1", "5", "6", and "8". Here, the CPU 14A calculates the total number of "red blood cells", "bacteria", "crystals", and "garbage / cell pieces" as "49". The CPU 14A calculates the proportions of "red blood cells", "bacteria", "crystals", and "garbage / cell pieces" relative to the calculated total number. For example, the CPU 14A divides the number of "red blood cells" "10" by the total number "49" contained in the specified classification, and calculates the proportion of "red blood cells" as "20.4%". The CPU 14A that executes the processing of S1062b is an example of the "second calculation unit".

[0094] In S1063b, the CPU 14A corrects the number of each shaped component classified into a large category prone to misclassification based on the total number calculated using the first image P1 in S1061b and the ratio of each shaped component calculated based on the second image P2 in S1062b. Fig.161 is a diagram illustrating the correction result of the correction processing performed in the second variant. For example, the CPU 14A calculates "13" as the number of red blood cells after correction by multiplying the ratio "20.4%" of "red blood cells" calculated based on the second image P2 by the total number of "64" of formed elements classified as "1", "5", "6", and "8" in the first image P1 as the major categories that are prone to misclassification. The CPU 14A can obtain "13" as the number of red blood cells after correction by performing such correction processing on each formed element classified as the major category that is prone to misclassification. Fig.16 The total number of the tangible components after correction is the same as the total number of the tangible components obtained from the first image P1.

[0095] According to the second modification, the number of tangible components can be calculated with high accuracy for large categories that are prone to misclassification, and the processing load of the CPU 14A can be reduced compared to the embodiment in which the correction process is executed for all large categories.

[0096] (Third Modification)

[0097] If the shaped components are classified based on the second image P2 captured at a high magnification, it is possible to classify the shaped components in more detail and with higher accuracy than the classification based on the first image P1. In the third variant, the classification of the shaped components based on the second image P2 in more detail than the classification based on the first image P1 is described. Figure 7 The illustrated process is modified. The same reference numerals are given to the same components as those in the embodiment, and their description is omitted. Hereinafter, a third modification example will be described with reference to the drawings.

[0098] Fig.17 This is a flowchart showing the flow of classification of tangible ingredients and calculation of the number of tangible ingredients in the third modification. In S1061c, the CPU 14A classifies the tangible ingredients and calculates the total number of tangible ingredients based on the first image P1. Fig.18 is a diagram showing an example of the results of the classification and number calculation of the tangible components based on the first image P1 in the third variant. Fig.18 As shown in the example, the formed elements are classified into eight categories, namely, "red blood cells", "white blood cells", "epithelium", "casts", "bacteria", "crystals", "others", and "trash / cell pieces". In addition, as the "total number" of the formed elements, "101" is obtained. The CPU 14A that executes the process of S1061c is an example of the "first calculation unit".

[0099] In S1062c, the CPU 14A classifies the tangible components and calculates their proportions based on the second image P2. Fig.191 is a diagram showing an example of the result of the classification and proportion calculation of the formed components based on the second image P2 in the third variant. In the third variant, the second image P2 is used at a high magnification to classify the formed components in a more detailed manner than the classification based on the first image P1 in S1061c. For example, the formed components classified as "red blood cells" in the classification of S1061c are classified into two classification items of "uniform red blood cells" and "deformed red blood cells" in the classification of S1062c. CPU14A also calculates the proportion of each of the classified formed components. For example, the "total number" of the formed components detected from the second image P2 is "80" and the "number" of "uniform red blood cells" is "8", so CPU 14A calculates the "proportion" of "uniform red blood cells" as "10.0"%. CPU 14A that executes the processing of S1062c is an example of the "second calculation unit".

[0100] In S1063c, the CPU 14A calculates the number of each shaped ingredient based on the total number of shaped ingredients calculated based on the first image P1 in S1061c and the respective ratios of the shaped ingredients classified in detail based on the second image P2 in S1062c. Fig. 20 1 is a diagram illustrating the correction result of the correction process performed in the third variant. For example, the CPU 14A calculates the number of "uniform red blood cells" after correction as "10" based on the total number "101" calculated in S1061c and the ratio "10.0"% of "uniform red blood cells" calculated in S1062c. The CPU 14A can obtain the number of "uniform red blood cells" after correction by performing such correction process on each formed element. Fig. 20 The correction results are shown.

[0101] In the third modification, the types of tangible components that are difficult to classify in the first image P1 can also be classified based on the second image P2, and the number of tangible components of each type classified based on the second image P2 can be corrected based on the total number calculated based on the first image P1. According to the third modification, the number of each tangible component of the type that is difficult to classify in the first image P1 can also be calculated with high accuracy. In addition, in the third modification, all categories are specified and correction is performed according to all categories.

[0102] (Other variations)

[0103] In the above-described embodiments and variations, the second image P2 is a part of the shooting range of the first image P1. However, the shooting range of the second image P2 may not be included in the shooting range of the first image P1. For example, the shooting range of the first image P1 may be arranged on the upstream side or downstream side of the shooting range of the second image P2. Even in the case where the second image P2 is not included in the shooting range of the first image P1, the number of tangible components included in the classification specified by the same method as the above-described method can be calculated.

[0104] In the above-described embodiments and variations, the imaging device 1 performs imaging in a bright field, but the imaging device 1 may also perform imaging in a dark field, phase difference, differential interference, polarized light, fluorescence, etc. For example, when imaging in a dark field, it is sufficient to irradiate the flow cell 13A with light from the light source 12 so that the reflected light from the flow cell 13A is incident on the objective lens 101.

[0105] In the above-described embodiments and variations, the camera 1 has a second camera unit 100B that shoots at a second magnification higher than the first magnification, but the camera 1 may also have two or more camera units that shoot at a magnification higher than the first magnification. In this case, it is preferred that the shooting range of the camera unit that shoots at a magnification higher than the first magnification is included in the shooting range shot by the first camera unit 100A. That is, it is preferred that the camera unit that shoots at a magnification higher than the first magnification shoots an enlarged image of a part of the image shot by the first camera unit 100A.

[0106] The embodiments and modifications disclosed above can be combined.

Claims

1. A measuring device, comprising: an acquisition unit that acquires a first image obtained by photographing a liquid containing a formed component flowing in a flow path, and a second image photographed simultaneously with the first image and having a higher photographing magnification than the first image; and A calculation unit that uses a cut image obtained by cutting out the formed components included in the first image and the second image to classify the formed components by type, and uses the total number of formed components cut out from the first image and included in the specified category and the ratio of each type of formed components cut out from the second image and included in the specified category to the total number of formed components cut out from the second image and included in the specified category, to calculate the number of formed components included in the specified category.

2. The measuring device according to claim 1, wherein The imaging range of the second image is a part of the imaging range of the first image.

3. The measuring device according to claim 1, wherein The calculation unit calculates the number of tangible components included in the specified classification by multiplying the total number of tangible components cut out from the first image and included in the specified classification by the ratio of each type of tangible components cut out from the second image and included in the specified classification to the total number of tangible components cut out from the second image and included in the specified classification.

4. The measuring device according to claim 1, wherein The calculation unit outputs a total component image in which cutout images representing the types of the formed ingredients are arranged, based on the number of the formed ingredients calculated for each type.

5. The measuring device according to any one of claims 1 to 4, wherein: The measuring device further includes a first imaging unit for capturing the first image and a second imaging unit for capturing the second image. The first imaging unit and the second imaging unit have the same focal position on the optical axis.

6. A method for determination, wherein: The total number of formed components included in a designated classification in the image classified for each category is calculated using a cut-out image obtained by cutting out formed components included in a first image obtained by photographing a liquid including formed components flowing in a flow path, using a cut-out image obtained by cutting out the formed components included in the second image, wherein the ratio of each type of formed components included in the designated category to the total number of formed components included in the designated category in the image classified according to each type is calculated, wherein the second image is obtained by photographing the liquid including the formed components flowing in the flow path simultaneously with the first image, and the photographing magnification of the second image is higher than the photographing magnification of the first image, The number of formed ingredients included in the designated classification is calculated using the total number of formed ingredients calculated based on the first image and the ratio of each type of formed ingredients calculated based on the second image.

7. The assay method according to claim 6, wherein The imaging range of the second image is a part of the imaging range of the first image.

Images