JP7470587B2 - Specimen characteristic determination device and specimen characteristic determination method - Google Patents

Description

The present invention relates to a technique for detecting sample information.

In recent years, with the evolution of IT and robotics, the automation of clinical specimen testing has progressed, and specimens collected for medical diagnosis are automatically measured by medical analysis equipment according to the test items. For example, when performing a complete blood count using a blood testing device, blood is extracted from a blood collection tube containing collected blood in order to check the concentration and amount of blood formed elements and substances contained therein, and measurements are performed according to the blood count items. The blood extraction process is performed automatically and mechanically, but if there is clotted blood in the blood collection tube, the nozzle may become clogged during extraction, causing the automation line to stop. Therefore, a work process is required in which a laboratory technician visually checks for the presence or absence of blood clottes before sending the blood to the blood testing device, which places a heavy burden on the laboratory technician.

In response to this, there is a method that uses a camera, for example, to mechanically determine whether or not a sample in a sample container has clotted without the use of human labor (Patent Document 1). This is a method that determines whether or not blood has clotted in a test tube or blood collection tube by going through a process in which a test tube containing blood is held vertically and then rotated a predetermined angle in the vertical plane.

There is also a method that can detect blood coagulation by simply irradiating light onto the blood contained in a container, without mechanically manipulating the container (Patent Document 2). This is a method that detects coagulation based on the second derivative of the wavelength with respect to the absorbance, using a light receiver that receives the transmitted light that has passed through the blood to be measured by irradiating light, and a calculation unit that calculates the absorbance over a specified wavelength range based on the received transmitted light.

Japanese Patent Application Laid-Open No. 11-248853 JP 2016-61585 A

Patent Document 1 is an effective method for observing large blood clots that appear and disappear due to the swaying of the liquid surface while rotating a transparent test tube, for example, but it is difficult to observe small clots buried in the blood. In addition, many blood collection tubes have printing or labels attached, making it difficult to observe such blood collection tubes.

In Patent Document 2, a light source is irradiated onto a sample and the absorbance of the transmitted near-infrared light of 750 to 1050 nm is measured, making it possible to confirm blood clots buried within the blood. However, some of the paint components used in the characters or figures such as barcodes printed or written on the body or label of the blood collection tube absorb light in the above-mentioned infrared range, making it difficult to identify blood clots.

In order to solve at least one of the above problems, the specimen characteristic discrimination device of the present invention comprises a vibration unit that applies vibration to a container for storing a specimen, a light source that irradiates the specimen with at least one of ultraviolet light, infrared light, and visible light, an imaging unit that captures the light irradiated from the light source and transmitted through the specimen, a shadow area detection unit that acquires one image or multiple images taken by the imaging unit at different times and detects shadow areas based on the gradation distribution of the acquired images, and a specimen abnormality discrimination unit that discriminates solid matter within the specimen based on at least one of the movement and shape change of the detected shadow area, wherein the light source, the container, and the imaging unit are fixed to the same system, and the vibration unit applies vibration to the entire system .

According to one aspect of the present invention, by observing the localized movement of shading in an image transmitted through a sample, disturbances caused by light absorption in the printing area of the blood collection tube or its label can be eliminated, providing a method for appropriately determining sample properties, such as blood coagulation. Problems, configurations, and effects other than those described above will be made clear by the description of the following embodiment.

1 is a block diagram illustrating an example of the configuration of a specimen characteristic discriminating device according to a first embodiment of the present invention. 1A and 1B are a side view and a block diagram illustrating an example of a device configuration for performing a tumbling motion of a specimen characteristic discriminating device according to a first embodiment of the present invention. 4 is a side view illustrating an example of a tumbling motion of the specimen characteristic discriminating device according to the first embodiment of the present invention. FIG. 1A and 1B are a side view and a block diagram illustrating an example of a device configuration for performing horizontal vibration motion in a specimen characteristic discriminating device according to a first embodiment of the present invention. 3 is a side view illustrating an example of a horizontal vibration motion of the specimen characteristic discriminating device according to the first embodiment of the present invention. FIG. FIG. 2 is a perspective view illustrating an example of a sample setting method of the sample characteristic determination device according to the first embodiment of the present invention. FIG. 2 is a perspective view illustrating an example of a pre-test process of the specimen characteristic discriminating device according to the first embodiment of the present invention. 1A and 1B are a side view and a perspective view for explaining an example of installing a slit for reducing adverse effects during imaging by a light source of the specimen characteristic determination device according to the first embodiment of the present invention. 1 is a perspective view illustrating an example of the placement of a slit and a light source for reducing adverse effects caused when an image is captured by the light source of the specimen characteristic determination device according to the first embodiment of the present invention. FIG. 13A and 13B are a side view and a block diagram illustrating an example of a device configuration that performs a tumbling motion in a specimen characteristic discrimination device according to a second embodiment of the present invention. 10 is a side view illustrating an example of a tumbling motion of a specimen characteristic discriminating device according to a second embodiment of the present invention. FIG. FIG. 2 is a block diagram illustrating an example of a sample property determining unit of the sample property determining device according to the first embodiment of the present invention. FIG. 2 is a block diagram illustrating an example of a shadow region detection unit of the sample characteristic discriminating device according to the first embodiment of the present invention. FIG. 2 is a block diagram illustrating an example of a specimen abnormality discrimination unit using image analysis in the specimen characteristic discrimination device according to the first embodiment of the present invention. 10A to 10C are diagrams illustrating an image in which the sample properties are normal in the first embodiment of the present invention. 10A to 10C are diagrams illustrating an image in which the sample properties are partially abnormal in the first embodiment of the present invention. 10A to 10C are diagrams illustrating an image in which the sample properties are generally abnormal in Example 1 of the present invention. FIG. 11 is a diagram for explaining the movement of shading when the specimen properties are normal in Example 1 of the present invention. 10A to 10C are diagrams illustrating the movement of shading when the specimen property is partially abnormal in Example 1 of the present invention. 10A to 10C are diagrams illustrating the movement of shading when the sample properties are generally abnormal in Example 1 of the present invention. FIG. 4 is a diagram illustrating an example of time-series learning data when the sample properties are normal in Example 1 of the present invention. FIG. 11 is a diagram illustrating an example of time-series learning data when the sample property is partially abnormal in Example 1 of the present invention. FIG. 11 is a diagram illustrating an example of time-series learning data when the sample properties are generally abnormal in Example 1 of the present invention. FIG. 2 is a block diagram illustrating an example of a specimen abnormality discriminating unit using a neural network in the specimen property discriminating device according to the first embodiment of the present invention. 4A to 4C are diagrams illustrating an example of vibration of a sample and a driving time of the sample characteristic discriminating device according to the first embodiment of the present invention. FIG. 11 is a block diagram illustrating an example of a sample property determining unit of a sample property determining device according to a second embodiment of the present invention. FIG. 11 is a block diagram illustrating an example of a region extraction unit of a specimen characteristic discriminating device according to a second embodiment of the present invention. FIG. 11 is a block diagram illustrating an example of a shadow region detection unit that does not use a memory unit for storing frame image data in a sample characteristic discriminating device according to a second embodiment of the present invention. 11A to 11C are diagrams illustrating an example of a detection method performed by an image speckle detection unit of a sample characteristic discriminating device according to a second embodiment of the present invention. 13A to 13C are diagrams illustrating an example of a region image after image speckles are detected by the sample characteristic discriminating device according to the second embodiment of the present invention.

The following describes embodiments of the present invention with reference to the drawings, but the present invention is not necessarily limited to these embodiments. In each drawing describing the embodiments, the same components are given the same reference numerals, and repeated explanations will be omitted.

In this embodiment, an example of a specimen property determination device that measures abnormalities in specimen properties based on shadows in an image created by transmitted light is described, using a method for determining blood coagulation as an example.

Figure 1 is a block diagram illustrating an example of the configuration of a specimen characteristic determination device in embodiment 1 of the present invention.

The specimen characteristic discrimination device in this embodiment includes a vibration unit 10, an equipment control unit 100 that controls the device configured by the vibration unit, a specimen characteristic discrimination unit 200, and a display unit 115 that displays the discrimination results.

The device control unit 100 includes an imaging control unit 102, a light source control unit 109, a drive control unit 110, and a system control unit 105.

The imaging control unit 102 receives an imaging data signal 101 output from the imaging unit 123 (see FIG. 2), which is a part of the vibration unit 10, converts the compressed imaging data into an uncompressed video signal 104 such as an RGB signal, and outputs an imaging control signal 103 for adjusting exposure and focus according to the brightness and gradation of the acquired image. The system control unit 105 receives the imaging control signal 103 as an input, and outputs a drive control signal 107 to a motor 124 that moves the specimen, a light source control signal 106 that adjusts the brightness of a light source for illuminating the specimen, and a system status signal 108. The light source control unit 109 receives the light source control signal 106 as an input, and outputs a light source voltage signal 111 for changing the brightness of the light source, for example. The drive control unit 110 receives the drive control signal 107 as an input, and outputs a motor voltage signal 113 that controls the motor 124 for moving the specimen, and an arm control signal 112 for gripping the specimen.

The light source 120 is controlled by the light source control signal 106. For example, if there is a lack of light or halation, the imaging control unit 102 (see FIG. 2) adjusts the exposure using the camera aperture, etc., but if the lack of light or halation cannot be eliminated even after maximum control within the controllable range, the light source control signal 106 can be used to adjust the amount of light output by the light source 120.

Figure 2 is a side view and block diagram illustrating an example of the device configuration that performs the tumbling movement of the specimen characteristic discrimination device in Example 1 of the present invention.

The device in FIG. 2 includes a light source 120, an arm 122 capable of gripping a blood collection tube 121, an imaging unit 123 such as a color camera, an infrared camera, or an ultraviolet camera, and a motor 124. The positional relationship between the light source 120, the blood collection tube 121, and the imaging unit 123 is fixed by a shaft 130.

The light source 120 is controlled by a light source voltage signal 111 output from the light source control unit 109. The motor 124 and the arm 122 are controlled by a motor voltage signal 113 and an arm control signal 112 output from the drive control unit 110, respectively. The imaging unit 123 is controlled by an imaging control signal 103 output from the imaging control unit 102, and outputs an imaging data signal 101.

Figure 6A is a perspective view illustrating an example of a sample setting method for the sample characteristic determination device in Example 1 of the present invention.

The arm 122 that grasps the blood collection tube 121 containing the blood sample is set to rotate around its axis when the blood collection tube 121 is transported to a predetermined position with the blood collection tube 121 upright (i.e., the bottom of the blood collection tube 121 faces down and the longitudinal direction of the blood collection tube 121 is approximately parallel to the vertical direction) as shown in the figure, and then the arm 122 clamps and fixes the blood collection tube 121. The arm 122 then rotates in the opposite direction to return to its original position, so that the blood collection tube 121 is laid down on its side (i.e., the longitudinal direction of the blood collection tube 121 is approximately parallel to the horizontal direction). As shown in FIG. 2 etc., the specimen properties are determined in this state.

At this time, the blood collection tube 121 may be measured by the position sensor 133, and after the blood collection tube 121 is stopped at a specified position, the arm 122 may be rotated to clamp the blood collection tube 121. Here, instead of the position sensor 133, the imaging unit 123 may confirm the stopping position while capturing an image of the movement of the blood collection tube 121. Also, although FIG. 6A shows an example of an arm 122 that grasps multiple blood collection tubes 121, an arm 122 that grasps a single blood collection tube 121 may be used.

Here, the arm control signal 112 that automatically sets the sample is not necessarily required, and the operator of the sample property determination device may manually attach the blood collection tube 121 to the arm 122. In addition, if the imaging unit 123 does not support the imaging control signal 103 that automatically adjusts the exposure and focus, a fixed value may be manually set in advance, the brightness of the image may be calculated by the system control unit 105, and the light source control unit 109 may perform calibration so that an appropriate amount of light is output.

Figure 7A is a side view and a perspective view illustrating an example of a slit that reduces adverse effects when capturing an image by the light source 120 of the specimen characteristic determination device in Example 1 of the present invention.

When the blood collection tube 121 has labels attached, the more labels attached, the weaker the transmitted light becomes, so the output of the light source 120 must also be increased. However, when photographing the blood collection tube 121 with the light source 120 facing away from it as shown in the figure, increasing the output of the light source 120 causes halation due to backlighting. Therefore, by passing the light through a slit 134 as shown in the figure, the light that directly enters the camera can be reduced. Also, in the figure, the slit 134 is placed between the light source 120 and the blood collection tube 121, but the slit 134 may be placed in front of the blood collection tube 121 when viewed from the imaging unit 123. In other words, the light source 120, blood collection tube 121, and slit 134 may be arranged in this order.

Figure 7B is a perspective view illustrating an example of the placement of the slit 134 and the light source 120 to reduce adverse effects during imaging by the light source 120 of the specimen characteristic determination device in Example 1 of the present invention.

The adjustment of the light intensity for the blood collection tube 121 depends on the presence and amount of printing or labels on the blood collection tube 121. Therefore, when there are multiple blood collection tubes 121, the light sources 120 are arranged in parallel as shown in the figure, and an appropriate light source voltage signal 111 is input to each light source 120, so that the light intensity can be adjusted for each blood collection tube 121.

Figure 3 is a side view illustrating an example of the tipping motion of the specimen characteristic determination device in Example 1 of the present invention.

Figure 3 corresponds to the upper part of the configuration in Figure 2, and shows the movement of the blood collection tube 121 and the blood sample therein when the light source 120, the blood collection tube 121, and the arm 122 holding them are fixed to the shaft 130 and perform a pendulum-like tumbling motion.

The left side of FIG. 3 shows a state in which the blood collection tube 121 is approximately horizontal (i.e., the longitudinal direction and horizontal direction of the blood collection tube 121 are approximately parallel) but tilted so that the tube bottom side is slightly lower. On the other hand, the right side of FIG. 3 shows a state in which the blood collection tube 121 is approximately horizontal but tilted so that the tube bottom side is slightly higher. The state on the left side and the state on the right side of FIG. 3 appear alternately due to the vibration that the motor 124 shown in FIG. 2 applies to the entire system including the light source 120, the blood collection tube 121, and the imaging unit 123.

In the state on the left side of Figure 3, the bottom side of the blood collection tube 121 is slightly lower, so the depth of the sample (i.e., the blood flowing in the blood collection tube 121) is deep at the bottom side and shallow on the opposite side. On the other hand, in the state on the right side of Figure 3, the bottom side of the blood collection tube 121 is slightly higher, so the depth of the sample is shallow at the bottom side and deep on the opposite side.

In this way, the applied vibration causes the depth of the blood sample to vary depending on the position within the blood collection tube 121 and to change over time. As described below, if the sample contains solid matter (e.g., clots or fibrin) that is not adhered to the wall of the blood collection tube 121, the solid matter will move around in the flowing blood and change position.

Figure 12A is a diagram illustrating an image in which the sample characteristics are normal in Example 1 of the present invention.

The left side of FIG. 12A is a side view showing a situation in which light passing through light source 120 passes through a sample (in this example, blood indicated by diagonal lines slanting upward to the right) in blood collection tube 121, projecting a shadow onto imaging unit 123. An example of an image captured at this time is shown on the right side of FIG. 12A. This example was captured when blood collection tube 121 was tilted, and a gradation of brightness or color tone (hereinafter also simply referred to as tone) appears depending on the difference in blood depth (i.e., thickness in the vertical direction, or in other words, the length of the blood portion of the distance that the light beam travels from light source 120 to imaging unit 123).

Figure 12B is a diagram illustrating an image in which the sample characteristics are partially abnormal in Example 1 of the present invention.

In this example, a partially abnormal specimen characteristic is one in which the blood collection tube 121 contains both normal blood flowing therein and clotted blood clots. In this case, light from the light source 120 passes through the specimen in the blood collection tube 121, and areas of different densities caused by clotted blood appear in the shadow projected on the imaging unit 123. In this way, areas of specimen abnormality can be extracted as shadows using the transmitted light.

Figure 12C is a diagram illustrating an image in which the sample characteristics are generally abnormal in Example 1 of the present invention.

In this example, a partially abnormal specimen characteristic is a state in which the entire blood in the blood collection tube 121 is coagulated and does not flow. In this case, light from the light source 120 passes through the specimen in the blood collection tube 121, and the shadow projected by the imaging unit 123 shows areas with extremely little shadow due to blood and areas with a lot of it. Such clots often adhere to the bottom of the tube, and the coagulated portion often retains its shape even if the blood collection tube is turned sideways, as shown in the right image, so the coagulated portion can be seen as a clear shadow.

Here, the light to be irradiated may be either visible light or infrared light. The near-infrared range in particular has a higher transmittance through blood than the visible range. Also, since coagulation occurs when fibrin polymerizes with platelets, in the near-infrared range the coagulated area appears as a light and dark shadow.

In addition, printed or handwritten labels that impair the visibility of images in the visible range are often not absorbed in the near-infrared range, depending on the paint. Therefore, when infrared light is irradiated, such label printing does not appear in the image, reducing disturbance. However, not all paint printed on labels or blood collection tubes is absorbed, so such printing can be a nuisance. Even if the printing does not appear, shading due to spots that appear on the adhesive surface between the label and blood collection tube may lead to false detection.

Figure 13A is a diagram illustrating the movement of shading when the sample characteristics are normal in Example 1 of the present invention.

For example, as shown on the left side of FIG. 13A, when the blood collection tube 121 is shaken, the blood surface wobbles, and the state of the blood changes, for example, depending on the inclination of the inside of the blood collection tube 121, the blood may be unevenly distributed toward the bottom of the tube, unevenly distributed on the opposite side of the tube bottom, or evenly distributed. By photographing each state, a grayscale image according to the movement is obtained, as shown on the right side of FIG. 13A. The image observed by the imaging unit 123 changes from a grayscale image 137 photographed when the tube bottom is raised, through a grayscale image 138 photographed when the blood collection tube 121 is horizontal, to a grayscale image 139 photographed when the tube bottom is lowered, depending on the movement of the blood collection tube 121. In the case of normal blood, a gradation image with a gradual change in brightness is obtained in each state.

Figure 13B is a diagram illustrating the movement of shading when the sample properties are partially abnormal in Example 1 of the present invention.

When partial clotting occurs, a shaded area 135 roughly equivalent in size to the size of the clot appears in the gradation image of the blood shades, and changes in shaded image 140, shaded image 141, shaded image 142 in response to the movement of the blood collection tube 121, as shown on the right. At this time, the shaded area of the clot also moves with the change in shade, so it can be identified as a clot. In addition, the spots caused by the printing on the blood collection tube 121 and the printing on the label affixed to the blood collection tube 121 do not move relative to the blood collection tube 121, so they can be distinguished from a clot.

Figure 13C is a diagram illustrating the movement of shading when the sample characteristics are generally abnormal in Example 1 of the present invention.

If clotting is present throughout, there will be areas with extremely little blood shadow and areas with extremely much, but as shown on the right side of Figure 13C, there will be almost no change between the grayscale images 143, 144, and 145. If clotting is present throughout, even when moved like this, areas with no liquid surface will appear, and the motionless solid shadow image 136 can be observed as clotting.

In this way, by analyzing video, it is possible to identify problematic clots that are difficult to distinguish from still images.

Figure 4 is a side view and block diagram illustrating an example of the equipment configuration that performs the horizontal vibration motion of the specimen characteristic discrimination device in Example 1 of the present invention.

The device in FIG. 4 includes a light source 120, an arm 122 capable of gripping a blood collection tube 121, an imaging unit 123 such as a camera, a mirror 132, and a horizontal vibrator 131, and the positional relationship between the light source 120, the blood collection tube 121, and the imaging unit 123 is fixed by a shaft 130. The light source 120 is controlled by a light source voltage signal 111 output from a light source control unit 109. The motor 124 and the arm 122 are controlled by a motor voltage signal 113 and an arm control signal 112 output from a drive control unit 110, respectively. The imaging unit 123 is controlled by an imaging control signal 103 output from an imaging control unit 102, and outputs an imaging data signal 101.

In this configuration, the imaging unit 123 measures the blood in the blood collection tube 121 through the mirror 132. However, this configuration is just one example, and the imaging unit 123 may be installed under the blood collection tube 121 so as to measure the blood in the blood collection tube 121 without using a mirror, as in the case shown in FIG. 2. Alternatively, in the system of FIG. 2, the imaging unit 123 may be installed so as to measure the blood in the blood collection tube 121 through a mirror, as in the case of FIG. 4.

Figure 5 is a side view illustrating an example of the horizontal vibration motion of the specimen characteristic determination device in Example 1 of the present invention.

The imaging unit 123, blood collection tube 121, light source 120, etc., which are fixed to the shaft 130, are swung left and right by the horizontal vibrator 131, and the blood in the blood collection tube 121 can also be swung. Any movement that can swung the blood can be used, and does not have to be a pendulum movement.

Figure 9 is a block diagram illustrating an example of the sample characteristic determination unit 200 of the sample characteristic determination device in Example 1 of the present invention.

The specimen characteristic discrimination unit 200a shown in FIG. 9 is an example of the specimen characteristic discrimination unit 200 in FIG. 1, and the input and output signals are the same as those described in FIG. 1. The specimen characteristic discrimination unit 200a includes a shadow area detection unit 300, a specimen abnormality discrimination unit 400, and a selector 203. The shadow area detection unit 300 receives the video signal 104 as an input signal and outputs a shadow area signal 201. The specimen abnormality discrimination unit 400 detects a distribution pattern of shadow areas that are specimen abnormalities from the shadow area signal 201, and outputs an abnormality discrimination signal 202. The selector 203 receives the system status signal 108 as an input, and selects and outputs 0 if the system status signal 108 is in standby, and selects and outputs the abnormality discrimination signal 202 if the system status signal 108 is in operation.

Figure 10 is a block diagram illustrating an example of the shadow region detection unit 300 of the sample characteristic determination device in Example 1 of the present invention.

The shadow area detection unit 300 includes a smoothing filter 301, a video data storage unit 303, an inter-frame difference detection unit 305, and a dynamic range adjustment unit 307. The smoothing filter 301 receives the video signal 104 as an input signal, smooths the video using a spatial filter, and outputs a smoothed video signal 302 in which noise has been removed. The video data storage unit 303 stores the smoothed video data. The inter-frame difference detection unit 305 receives the delayed smoothed video signal 304 delayed by one frame and the smoothed video signal 302 as input signals, detects smoothing and video movement and shape changes in the time axis direction using the signal difference, and outputs an inter-frame difference signal 306. The dynamic range adjustment unit 307 receives the inter-frame difference signal 306 as an input, adjusts the gradation distribution from the histogram of the inter-frame difference signal, and outputs a shadow area signal 201.

Figure 11 is a block diagram illustrating an example of a sample abnormality determination unit 400 using image analysis in a sample property determination device according to the first embodiment of the present invention.

The specimen abnormality discrimination unit 400a shown in FIG. 11 is an example of the specimen abnormality discrimination unit 400 in FIG. 9, and the input and output signals are the same as those described in FIG. 1. The specimen abnormality discrimination unit 400a includes a histogram acquisition unit 401 and a gradation spot detection unit 403. The histogram acquisition unit 401 receives the shadow area signal 201 as an input signal, and outputs a histogram 402. The gradation spot detection unit 403 receives the shadow area signal 201 and the histogram 402 as input signals, and outputs an abnormality discrimination signal 202.

Here, an example of the processing of the histogram acquisition unit 401 and the gradation spot detection unit 403 will be described. For example, the histogram acquisition unit 401 may calculate the difference value of pixel values (e.g., luminance values) between multiple frames for each pixel, and generate a histogram indicating the occurrence frequency of the difference values.

For example, when the difference value of pixel values is calculated for each pixel between the grayscale image 137 and the grayscale image 138 in FIG. 13A, the difference value tends to be small near the center of the blood collection tube 121 and large near both ends. However, the difference value is relatively small because it is due to the difference in depth of the blood, which is the sample. For this reason, the histogram of the occurrence frequency of the difference value is biased toward a range of relatively low difference values. When such a histogram is obtained, the gradation spot detection unit 403 may determine that the sample is normal.

In contrast, for example, when the pixel value difference value is calculated for each pixel between the grayscale image 140 and the grayscale image 141 in FIG. 13B, in addition to the same difference value as in FIG. 13A above, a difference value due to the shaded region 135 that moves or changes shape due to the vibration of the blood collection tube 121 appears. For example, if the vibration causes clots to move through the blood, causing the shaded region 135 to move as shown in FIG. 13B, a relatively large difference value appears at the edge portion. Also, for example, if the shaded region 135 is caused by a collection of small clots, the shape of the shaded region 135 may change due to the vibration of the blood collection tube 121, and in that case too, a relatively large difference value appears at the edge portion.

For this reason, the gradation speckle detection unit 403 may determine that solid matter such as clots floating in the blood is present when, for example, the frequency of occurrence of difference values greater than a predetermined threshold (first threshold) is higher than a predetermined threshold (second threshold) in a histogram of the occurrence frequency of pixel value difference values. When a histogram is obtained in which such a distribution of occurrence frequencies is superimposed on the distribution of occurrence frequencies in FIG. 13A, the gradation speckle detection unit 403 may determine that the sample contains flowing blood and solid matter such as clots guiding therethrough, that is, is partially abnormal.

If the shaded region 135 is caused by a label affixed to the blood collection tube 121 or by characters written on the label (or written directly on the blood collection tube 121), the shaded region 135 will not move or change shape due to vibration. In this case, the difference value of the shaded region 135 will be 0 or a value close to 0, and the sample will not be determined to be abnormal.

In addition, even if the clot adheres to the wall of the blood collection tube 121 and does not move, the shaded area 135 does not move or change shape, and the difference value of that part is 0 or close to 0, as in the case of a label, etc. Such a clot is unlikely to cause a nozzle blockage when blood is drawn from the blood collection tube 121, so such a sample does not need to be determined to be abnormal.

For example, when the difference in pixel value is calculated for each pixel between the grayscale image 143 and the grayscale image 144 in FIG. 13C, the pixel value does not change due to vibration, so the difference value is 0 or close to 0 throughout the entire image, and the difference value similar to that in the case of FIG. 13A above does not appear. In this case, there are no blood components that flow due to vibration, the entire sample is coagulated, and blood cannot be extracted from the blood collection tube 121, so the gradation spot detection unit 403 may determine that the entire sample is abnormal.

Figure 15 is a block diagram illustrating an example of a sample abnormality discrimination unit 400 using a neural network in a sample property discrimination device according to the first embodiment of the present invention.

The specimen abnormality discrimination unit 400b shown in FIG. 15 is an example of the specimen abnormality discrimination unit 400 in FIG. 9, and the input and output signals are the same as those described in FIG. 1. The specimen abnormality discrimination unit 400b includes a data conversion unit 410 and a state discrimination unit 412. The data conversion unit 410 receives the shadow area signal 201 as an input signal and converts it into data used by the state discrimination unit 412. The state discrimination unit 412 receives the converted data signal 411 as an input.

The state discrimination unit 412 may use, for example, a CNN (Convolutional Neural Network) using two-dimensional convolution, or may use a segmentation method such as SegNet or U-net. In this case, the input data contains time axis information because it is a conversion of inter-frame difference data. The state discrimination unit 412 may also use an RNN (Recurrent Neural Network) or a LSTM (Long short-term memory).

Figure 14A is a diagram illustrating an example of time-series learning data when the sample characteristics are normal in Example 1 of the present invention.

The graph at the bottom of Figure 14A shows the brightness distribution of the sample image along the lines (1) to (3) of the blood collection tube shown at the top of Figure 14A, connected in the time direction. Under normal conditions, a certain section, i.e., the section of the length of the blood collection tube, changes smoothly.

Figure 14B is a diagram illustrating an example of time-series learning data when the sample characteristics are partially abnormal in Example 1 of the present invention.

The graph at the bottom of Figure 14B shows the sample image connected in the time direction to the brightness distribution along the lines (1) to (3) of the blood collection tube shown at the top of Figure 14B. When there is a partial abnormality, the graph becomes distorted in a certain section, i.e., the length of the blood collection tube.

Figure 14C is a diagram illustrating an example of time-series learning data when the sample characteristics are generally abnormal in Example 1 of the present invention.

The graph at the bottom of Figure 14C shows the brightness distribution of the sample image along lines (1) to (3) of the blood collection tube shown at the top of Figure 14C, connected in the time direction. When there is a global abnormality, there is almost no change on the time axis in the graph. For example, in LSTM, a neural network can be obtained by learning this data.

In addition, in Figures 14A to 14C, a luminance distribution is generated that connects not only the luminance distribution obtained from one frame (e.g., lines (1) to (3) in frame 1) but also the luminance distributions obtained from multiple frames (e.g., line (1)' in frame 2, etc.) in the time direction.

In addition, although three lines are set in the examples of Figures 14A to 14C, in practice any number of lines greater than or equal to two may be set. In order to generate a luminance distribution that connects the luminance, the luminance of all pixels on the line may be obtained, or the luminance of pixels selected at a predetermined interval from the pixels on the line may be obtained.

In the examples of Figures 14A to 14C, a luminance distribution is generated by connecting the luminance of each frame, but a distribution may be generated by connecting the differences in pixel luminance between multiple frames. In that case, in a normal specimen as in Figure 14A, the difference value near both ends of the blood collection tube 121 in the longitudinal direction is large, and the difference value near the center of the blood collection tube 121 is small. In a partially abnormal specimen as in Figure 14B, a very large difference value appears in the coagulated area, superimposed on the luminance distribution similar to that in Figure 14A. In a generally abnormal specimen as in Figure 14C, the difference value is small across the entire line.

In addition, the lines (1) to (3) in the examples of Figures 14A to 14C are all oriented approximately parallel to the longitudinal direction of the blood collection tube 121, and have different starting points. Setting such lines is one example, and multiple lines may be set that are perpendicular to the longitudinal direction of the blood collection tube 121, and each have a different starting point.

By the way, clots can occur when blood cell components naturally settle and accumulate at the bottom of the tube, and often adhere to the tube walls. For this reason, it is necessary to shake the blood collection tube 121 to a degree that does not cause bubbles in the sample, and to remove the clots from the tube walls.

Figure 16 is a diagram illustrating an example of the vibration of a sample and the driving time of the sample property discrimination device in Example 1 of the present invention.

As shown in the graph in Figure 16, the sample can be quickly shaken several times before checking for coagulation in the sample characterization section, and then the coagulation test can be performed.

In this embodiment, the horizontal vibrator shown in Figure 4 is relatively easy to operate, but when using a device like that shown in Figure 2, it is difficult to move it quickly, including the load on the motor, when mixing by inversion.

Figure 6B is a perspective view illustrating an example of a pre-test process of the specimen characteristic determination device in Example 1 of the present invention.

Specifically, FIG. 6B shows an example process for removing clots adhering to the wall of a blood collection tube by rocking the arm 122 holding the blood collection tube to mix by inversion. Here, clots that do not come off from the wall even when inversion is performed remain attached to the wall and rarely clog the nozzle when blood is extracted during a blood test.

In the configuration of this embodiment, the specimen property determination unit may be implemented in an LSI or FPGA, or may be implemented by a program such as a CPU or GPU. Also, in the configuration of this embodiment, the imaging unit is installed below the specimen so that the optical path length of the transmitted light is short, and the image is captured from below, but it may also be captured from the side or from above.

In addition, although the example of identifying sample properties in this embodiment has been described as identifying blood coagulation in blood, it may also be possible to identify fibrin in serum or plasma, for example.

As described above, according to this embodiment, by observing the localized changes in shading in the image transmitted through the sample, disturbances caused by light absorption in the printing area of the blood collection tube or its label can be eliminated, providing a method for appropriately determining sample properties, such as blood coagulation.

In the first embodiment, a biological information detection device was described that can eliminate disturbances caused by light absorption by the printing on the blood collection tube or the printing area of the label by observing the movement of shading that appears locally in the image transmitted through the sample, and provides a method for appropriately determining sample properties, such as blood coagulation. In the second embodiment, a configuration is described in which only the driving unit for mixing by inversion is used in the first embodiment of the present invention. Except for the differences described below, each part of the sample property determination device in the second embodiment has the same function as each part in the first embodiment that is assigned the same reference numeral, and therefore a description of those parts is omitted.

The specimen characteristic discrimination device in this embodiment includes a vibration unit 10c instead of the vibration unit 10 shown in FIG. 1, an instrument control unit 100b instead of the instrument control unit 100 that controls the devices that make up the vibration unit, a specimen characteristic discrimination unit 200b instead of the specimen characteristic discrimination unit 200, and a display unit 115 that displays the discrimination results.

Figure 8A is a side view and block diagram illustrating an example of the device configuration for performing the tumbling movement of the specimen characteristic discrimination device in Example 2 of the present invention.

The device in FIG. 8A includes a light source 120, an arm 122 that can grasp a blood collection tube 121, an imaging unit 123 such as a color camera, an infrared camera, or an ultraviolet camera, and a motor (not shown) that drives the arm 122. The positional relationship between the light source 120, the blood collection tube 121, and the imaging unit 123 is fixed by a shaft 130.

The light source 120 is controlled by a light source voltage signal 111 output from the light source control unit 109. The arm 122 is controlled by an arm control signal 112 output from the drive control unit 110b. The imaging unit 123 is controlled by an imaging control signal 103 output from the imaging control unit 102, and outputs an imaging data signal 101.

Figure 8B is a side view illustrating an example of the tipping motion of the sample characteristic determination device in Example 2 of the present invention.

Figure 8B corresponds to the upper part of the configuration in Figure 8A and shows the light source 120, blood collection tube 121, and arm 122 gripping them. In this example, arm 122 is rotatably attached to shaft 130, and shows the movement of the blood collection tube and blood when arm 122 tumbles like a pendulum. Unlike the configurations in Figures 2 and 4, in this configuration, blood collection tube 121 moves relatively between imaging unit 123 and light source 120, so the brightness of the image and the size of blood collection tube 121, which is the subject, change. Therefore, since the target area cannot be fixed from the image, it is necessary to correct the image according to the distance or size of the subject.

Figure 17 is a block diagram illustrating an example of the sample property determination unit 200b of the sample property determination device in Example 2 of the present invention.

The specimen characteristic discrimination unit 200b includes an area extraction unit 250, a shadow area detection unit 300, a specimen abnormality discrimination unit 400, and a selector 203. The area extraction unit 250 receives the video signal 104 and the drive control signal 107 as input signals, and outputs a target video signal 211. The shadow area detection unit 300 receives the target video signal 211 as input signals, and outputs a shadow area signal 201. The specimen abnormality discrimination unit 400 detects distribution spots of shadow areas that are specimen abnormalities from the shadow area signal 201, and outputs an abnormality discrimination signal 202. The selector 203 receives the system status signal 108 as input, and selects and outputs 0 if the system status signal 108 is in standby, and selects and outputs the abnormality discrimination signal 202 if the system status signal 108 is in operation.

Figure 18 is a block diagram illustrating an example of the region extraction unit 250 of the sample characteristic determination device in Example 2 of the present invention.

The region extraction unit 250 includes an edge detection unit 251, a parameter generation unit 256, an affine transformation unit 253, and a target discrimination unit 255. The edge detection unit 251 receives the video signal 104 as an input signal, calculates the contour of the blood collection tube, and outputs an edge signal 252. The parameter generation unit 256 receives the drive control signal 107 as an input signal, and outputs a region parameter signal 257 that calculates region parameters according to the state of the arm. The affine transformation unit 253 receives the video signal 104, the edge signal 252, and the region parameter signal 257 as input signals, and outputs an adjusted video signal 254. The target discrimination unit 255 receives the threshold parameter and the adjusted video signal 254 as input signals, and separates the background and the target image using, for example, a brightness threshold value.

Figure 19 is a block diagram illustrating an example of a shadow region detection unit 300b that does not use a memory unit for storing frame image data in a specimen characteristic discrimination device according to embodiment 2 of the present invention.

The shadow area detection unit 300b includes a smoothing filter 301, a histogram creation unit 310, a dynamic range adjustment unit 312, and an image speckle detection unit 314. The smoothing filter 301 receives the image signal 104 as an input signal, smooths the image using a spatial filter, and outputs a smoothed image signal 302 from which noise has been removed. The histogram creation unit 310 creates a histogram 311 from the smoothed image signal 302. The dynamic range adjustment unit 312 receives the histogram 311 and the smoothed image signal 302 as input signals, and outputs a dynamic range adjustment signal 313 after adjusting the dynamic range of the image. The image speckle detection unit 314 receives the dynamic range adjustment signal 313 as an input signal, and outputs a shadow area signal 201. The image speckle detection unit 314 may classify based on the high and low of the frequency (spatial frequency) obtained by the Fourier transform, or may classify based on the variance value.

Figure 20A is a diagram illustrating an example of a detection method by the image speckle detection unit 314 of the sample characteristic discrimination device in Example 2 of the present invention.

Specifically, FIG. 20A shows an image captured by the imaging unit 123 after smoothing, dynamic range adjustments, and other adjustments have been applied. Rectangle 146 is a convolution kernel centered on the target pixel. The image mottle detection unit 314 can perform a Fourier transform or find a variance value within the range of rectangle 146.

Figure 20B is a diagram illustrating an example of an area image after image mottle detection by a sample characteristic discrimination device in Example 2 of the present invention.

Specifically, in FIG. 20B, rectangle 146 is a convolution kernel centered on the target pixel, and a Fourier transform or variance calculation is performed within the range of the rectangle, and the degree of variation is binarized by, for example, determining the high and low frequency or the large and small variance values based on a threshold value.

In this example, the signal is divided into two regions, but it may be divided into three or more regions, and the high and low frequencies or the large and small variance values may be displayed as continuous values.

In addition, a barcode is often printed on the label attached to the blood collection tube 121, and moire occurs when capturing an image in the visible range. When the subject moves, the moire also changes in response to the movement, which hinders the detection of variation. Therefore, the image speckle detection unit 314 may, for example, remove dark areas below a certain brightness before determining the degree of variation, and then replace the images with the average brightness in the above rectangular range and use an average filter to smooth the images.

As described above, this embodiment can provide a method for optimally determining sample properties while reducing costs by minimizing the number of drive units in the device.

The above-described embodiments of the present invention may include the following examples:

(1) A specimen property determination device comprising: a vibration unit (e.g., motor 124, horizontal vibrator 131, or a motor that drives arm 122 in FIG. 8A) that applies vibration to a container (e.g., blood collection tube 121) that stores a specimen; a light source (e.g., light source 120) that irradiates the specimen with at least one of ultraviolet light, infrared light, and visible light; an imaging unit (e.g., imaging unit 123) that captures the light that is irradiated from the light source and transmitted through the specimen; a shadow area detection unit (e.g., shadow area detection unit 300 or 300b) that acquires one image or multiple images at different times (e.g., at least one of grayscale images 137 to 145) captured by the imaging unit and detects a shadow area based on the gradation distribution of the acquired image; and a specimen abnormality determination unit (e.g., specimen abnormality determination unit 400, 400a, or 400b) that determines solid matter in the specimen (e.g., clots or fibrin in blood) based on at least one of the movement and shape change of the detected shadow area.

This allows the presence or absence of sample properties, such as the presence or absence of blood clots, to be appropriately determined without disturbances caused by light absorption in the printing area of the blood collection tube or its label.

(2) In (1) above, the shadow area detection unit acquires multiple images and detects the shadow area based on the gradation distribution of the pixel value differences between the images (for example, the processing of the shadow area detection unit 300 shown in FIG. 10).

This allows shadowed areas to be properly detected and sample characteristics to be determined based on them.

(3) In (1) above, the shadow area detection unit detects shadow areas based on the spatial frequency or variance of pixel values of one image (for example, the processing of the shadow area detection unit 300b shown in FIG. 10).

This allows shadowed areas to be properly detected and sample characteristics to be determined based on them.

(4) In (1) above, the light source, container, and imaging unit are fixed to the same system, and the vibration unit applies vibration to the entire system (for example, the configurations of Figures 2 to 5).

As a result, the size and shape of the specimen in the image captured by the imaging unit are not affected by vibration, making it easier to process the specimen's characteristics based on the image data.

(5) In the above (1), the shadow area detection unit detects a shadow area based on the video data captured by the imaging unit when at least a predetermined vibration (e.g., the vibration of mixing by inversion in FIG. 16) is applied to the container and then further vibration (e.g., the vibration of determining the sample properties in FIG. 16) is applied to the container.

This allows any clots that are attached to the container walls but are easily removed to be removed, making them easier to detect.

(6) In the above (1), the specimen abnormality discrimination unit discriminates the solid matter using a discriminator (e.g., state discrimination unit 412) that has been trained based on images of specimens containing solid matter and images of specimens not containing solid matter.

This allows the characteristics of the sample to be properly identified.

(7) In (6) above, the discriminator identifies solid objects based on a string of pixel values that connects pixel values on multiple lines on the image (e.g., lines (1) to (3) in Figures 14A to 14C).

This allows the characteristics of the sample to be properly determined based on the features of the two-dimensional image.

(8) In (7) above, the discriminator identifies solid objects based on a column of pixel values that connects columns of pixel values obtained from multiple images (e.g., frames 1 and 2 in Figures 14A to 14C).

This allows the characteristics of the specimen to be appropriately determined based on the characteristics of the two-dimensional image, including changes over time.

(9) In the above (1), the specimen abnormality discrimination unit (e.g., the histogram acquisition unit 401 and the gradation spot detection unit 403 of the specimen abnormality discrimination unit 400a) detects at least one of the movement and the change in shape of the shadow region based on the occurrence frequency of the difference values of the pixel values of multiple images.

This allows the characteristics of the sample to be properly identified.

(10) In the above (9), the sample abnormality determination unit detects at least one of movement and change in shape of the shadow region when the frequency of occurrence of difference values that exceed a predetermined first threshold value among the difference values of pixel values of multiple images exceeds a predetermined second threshold value.

This allows the characteristics of the sample to be properly identified.

(11) In the above (1), the positional relationship between the container and the imaging unit (e.g., the positional relationship between the blood collection tube 121 and the imaging unit 123 shown in FIG. 8A) changes due to vibration of the container, and the device further includes a region extraction unit (e.g., region extraction unit 250) that transforms (e.g., affine transforms) the image to absorb the change in the shape of the container in the image caused by the change in positional relationship, and the shadow region detection unit detects a shadow region based on the image transformed by the region extraction unit.

This eliminates the need to vibrate the light source and imaging unit, simplifying the mechanisms of the drive unit and other components, and enabling sample properties to be determined efficiently and at low cost.

(12) In the above (1), a slit (e.g., slit 134) is further provided to block light from the light source that reaches the imaging unit without passing through the container.

This prevents halation caused by unnecessary light, allowing the properties of the sample to be properly determined.

(13) In the above (12), the slit is provided between the container and the imaging unit.

This allows for more efficient use of the light from the light source.

(14) In the above (1), the specimen abnormality determination unit determines that a portion of the specimen is abnormal (e.g., determination based on FIG. 13B) if the specimen contains solid matter that moves within the liquid in response to the vibration applied by the vibration unit, based on at least one of the movement and shape change of the shadowed area, and determines that the entire specimen is abnormal (e.g., determination based on FIG. 13C) if the entire specimen is solid matter.

This allows the characteristics of the sample to be properly determined.

The present invention is not limited to the above-described embodiments, but includes various modifications. For example, the above-described embodiments have been described in detail to provide a better understanding of the present invention, and are not necessarily limited to those having all of the configurations described. It is also possible to replace part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. It is also possible to add, delete, or replace part of the configuration of each embodiment with other configurations.

The above configurations, functions, processing units, processing means, etc. may be realized in hardware, in part or in whole, for example by designing them as integrated circuits. The above configurations, functions, etc. may be realized in software, by a processor interpreting and executing a program that realizes each function. Information on the programs, tables, files, etc. that realize each function can be stored in storage devices such as non-volatile semiconductor memory, hard disk drives, and SSDs (Solid State Drives), or in computer-readable non-transitory data storage media such as IC cards, SD cards, and DVDs.

In addition, the control lines and information lines shown are those considered necessary for the explanation, and not all control lines and information lines on the product are necessarily shown. In reality, it can be assumed that almost all components are interconnected.

101 Imaging data signal 103 Imaging control signal 104 Video signal 106 Light source control signal 107 Drive control signal 108 System status signal 111 Light source voltage signal 112 Arm control signal 113 Motor voltage signal 114 Abnormality detection signal 135 Shadow area 136 Solid shadow area 201 Shadow area signal 202 Abnormality determination signal 203 Selector 210 Target video signal 211 Target video signal 252 Edge signal 254 Adjusted video signal 257 Area parameter signal 258 Threshold parameter 302 Smoothed video signal 304 Delayed smoothed video signal 306 Inter-frame difference signal 311 Histogram 313 Dynamic range adjustment signal 402 Histogram 411 Conversion data signal

Claims (14)

a vibration unit that applies vibration to a container that contains a sample;
a light source that irradiates the specimen with at least one of ultraviolet light, infrared light, and visible light;
an imaging unit that captures an image of light irradiated from the light source and transmitted through the specimen;
a shadow region detection unit that acquires one image or a plurality of images taken at different times by the imaging unit and detects a shadow region based on a gradation distribution of the acquired image;
a specimen abnormality discriminating unit that discriminates solid matter in the specimen based on at least one of the movement and the change in shape of the detected shadow region ,
The light source, the container, and the imaging unit are fixed in the same system,
The specimen characteristic discriminating device according to claim 1, wherein the vibration unit applies vibration to the entire system . The specimen characteristic determination device according to claim 1,
The shadow region detection unit obtains the plurality of images and detects the shadow region based on a gradation distribution of differences in pixel values between the images. The specimen characteristic determination device according to claim 1,
The shadow region detection unit detects the shadow region based on a spatial frequency or a variance of pixel values of the one image. a vibration unit that applies vibration to a container that contains a sample;
a light source that irradiates the specimen with at least one of ultraviolet light, infrared light, and visible light;
an imaging unit that captures an image of light irradiated from the light source and transmitted through the specimen;
a shadow region detection unit that acquires one image or a plurality of images taken at different times by the imaging unit and detects a shadow region based on a gradation distribution of the acquired image;
a specimen abnormality discriminating unit that discriminates solid matter in the specimen based on at least one of the movement and the change in shape of the detected shadow region,
The shadow area detection unit detects the shadow area based on image data captured by the imaging unit while the container is still being vibrated after at least a predetermined vibration has been applied to the container. The specimen characteristic determination device according to claim 1,
The specimen abnormality discrimination unit discriminates the solid matter using a discriminator that has been trained based on images of a specimen containing solid matter and images of a specimen not containing solid matter. The specimen characteristic determination device according to claim 5,
The discriminator discriminates the solid object based on a string of pixel values that connects pixel values on a plurality of lines on the image. The specimen characteristic determination device according to claim 6,
The discriminator discriminates the solid object based on a string of pixel values obtained by connecting strings of pixel values acquired from the plurality of images. a vibration unit that applies vibration to a container that contains a sample;
a light source that irradiates the specimen with at least one of ultraviolet light, infrared light, and visible light;
an imaging unit that captures an image of light irradiated from the light source and transmitted through the specimen;
a shadow region detection unit that acquires one image or a plurality of images taken at different times by the imaging unit and detects a shadow region based on a gradation distribution of the acquired image;
a specimen abnormality discriminating unit that discriminates solid matter in the specimen based on at least one of the movement and the change in shape of the detected shadow region,
The sample abnormality determination unit detects at least one of a movement and a change in shape of the shadow region based on an occurrence frequency of difference values of pixel values of the plurality of images. The specimen characteristic determination device according to claim 8,
The sample abnormality determination unit detects at least one of a movement and a change in shape of the shadowed region when the frequency of occurrence of difference values between pixel values of the multiple images that exceed a predetermined first threshold value exceeds a predetermined second threshold value. a vibration unit that applies vibration to a container that stores a sample;
a light source that irradiates the specimen with at least one of ultraviolet light, infrared light, and visible light;
an imaging unit that captures an image of light irradiated from the light source and transmitted through the specimen;
a shadow region detection unit that acquires one image or a plurality of images taken at different times by the imaging unit and detects a shadow region based on a gradation distribution of the acquired image;
a specimen abnormality discriminating unit that discriminates solid matter in the specimen based on at least one of the movement and the change in shape of the detected shadow region,
a positional relationship between the container and the imaging unit is changed by the vibration of the container;
A region extraction unit converts the image so as to absorb a change in the shape of the container in the image caused by a change in the positional relationship,
The shadow region detection unit detects the shadow region based on the image converted by the region extraction unit. The specimen characteristic determination device according to claim 1,
A specimen characteristic determining device, further comprising a slit for blocking light from the light source that reaches the imaging unit without passing through the container. The specimen characteristic determination device according to claim 11,
The specimen characteristic determining device, wherein the slit is provided between the container and the imaging unit. The specimen characteristic determination device according to claim 1,
The specimen abnormality determination unit determines, based on at least one of the movement and shape change of the shadowed area, that a portion of the specimen is abnormal if the specimen contains solid matter that moves within the liquid in response to the vibrations applied by the vibration unit, and determines that the entire specimen is abnormal if the entire specimen is composed of solid matter. A method for discriminating specimen characteristics using a specimen characteristics discriminating device having a vibration unit, a light source, an imaging unit, a shadow region detection unit, and a specimen abnormality discriminating unit, comprising:
The vibration unit applies vibration to a container for storing a sample,
the light source irradiates the specimen with at least one of ultraviolet light, infrared light, and visible light;
The imaging unit captures an image of light irradiated from the light source and transmitted through the specimen,
the shadow region detection unit acquires one or more images captured by the imaging unit, and detects a shadow region based on a gradation distribution of the acquired images;
the specimen abnormality determination unit determines solid matter in the specimen based on at least one of the movement and the change in shape of the detected shadow region;
The light source, the container, and the imaging unit are fixed in the same system,
The method for determining a specimen characteristic, wherein the vibration unit applies vibration to the entire system.

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