Biological sample analysis device

Abstract

The present disclosure aims to provide a technique capable of acquiring region and color information of an analysis target without causing a decrease in extraction accuracy of the analysis target region due to misextraction of the color of a color label, and performing measurement of the liquid amount of a specimen and discrimination of the specimen category. A biological specimen analysis apparatus according to the present disclosure crops a partial region from a color image of a biological sample tube, creates an unfolded image by joining the partial regions along the circumferential direction of the biological sample tube, and extracts a detection target region from the unfolded image (see Fig. 6B).

Description

TECHNICAL FIELD

[0001] The present disclosure relates to a biological sample analysis device that analyzes a biological sample housed in a biological sample tube to which a plurality of labels are attached. BACKGROUND

[0002] In order to increase the efficiency of clinical examinations such as blood tests, a technique has been proposed to automate the confirmation work of biological samples that has been performed by visual confirmation in the past. Among these, in the sample confirmation work before biochemical analysis, techniques have been sought to measure the volume of a sample stored in a container such as a blood collection tube, and to analyze the state of a blood sample such as the classification of serum (normal, hemolysis, jaundice, chyle, and the like) in the blood sample.

[0003] As such a technique for classifying the state of a sample, for example, Patent Literature 1 discloses a detection device that detects information of a biological sample in a biological sample tube to which a label is attached. In more detail, the detection device includes an imaging section that images the biological sample tube, a detection target region extraction section that extracts a detection target region from an image imaged by the imaging section, a label extraction section that extracts a label located on the imaging surface side of the imaging section from the image imaged by the imaging section, and an analysis section that determines the boundary position of the label within the detection target region extracted by the detection target region extraction section based on the boundary position of the label extracted by the label extraction section, performs measurement of the volume, and acquires information of the classification of the sample based on color information of the detection target region extracted from the image of the sample.

[0004] Further, for example, Patent Literature 2 discloses a liquid detection device that detects transmitted light by irradiating a blood collection tube with infrared light, determines the boundary of a label based on the first derivative value thereof, and estimates the serum volume of the blood collection tube.

[0005] PRIOR ART DOCUMENTS

[0006] PATENT LITERATURE

[0007] Patent Literature 1: JP Patent No. 2015-040696 A

[0008] Patent Literature 2: JP Patent No. 2004-037322 A SUMMARY

[0009] PROBLEMS TO BE SOLVED BY THE INVENTION

[0010] The technology described in Patent Literature 1 relates to a manner of extracting color information of a detection region by determining a boundary position of a label within an extracted detection target region based on a boundary position of the extracted label. However, in Patent Literature 1, regarding a specimen having a color label attached thereto having a color similar to that of a detection target, the color of the color label and the color of the detection target region are recognized, and separation of the color label region and the detection target region is not considered, and there is a problem of a decrease in color extraction accuracy due to erroneous extraction of the color of the color label.

[0011] Patent Literature 2 measures the amount of serum by finding a boundary of a label, but cannot obtain color information of an analysis target, and cannot discriminate the type of a specimen.

[0012] The present disclosure is made in view of such a situation, and proposes a technology that obtains region and color information of an analysis target without causing a decrease in extraction accuracy of an analysis target region due to erroneous extraction of the color of a color label, and enables measurement of the amount of a specimen and discrimination of the type of a specimen.

[0013] Means for solving the problem

[0014] The biological specimen analysis apparatus according to the present disclosure crops a partial region from a color image of a biological sample tube, creates an unfolded image by joining the partial regions along the circumferential direction of the biological sample tube, and extracts a detection target region from the unfolded image.

[0015] Effects of the invention

[0016] According to the biological specimen analysis apparatus according to the present disclosure, it is possible to accurately detect the color and amount of a sample composed of a plurality of components in a specimen having a color label attached thereto. Further features associated with the present disclosure are apparent from the description of the specification, the drawings, and the like. Modes of the present disclosure are achieved and realized by elements and combinations of various elements, and detailed descriptions and the appended claims attached hereto. It is to be understood that the description of the specification is merely illustrative, and does not limit the claims or applications of the present disclosure in any way. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a diagram showing an example of the overall structure of the biological specimen analysis apparatus 100 according to Embodiment 1.

[0018] Figure 2 is a diagram showing the overall functions realized by expanding the programs stored in the memory 107 or the storage device 108 in the controller (processor) 106.

[0019] Figure 3 is a diagram showing an example of the internal structure of the image processing section 201.

[0020] Figure 4 is a flowchart for explaining an outline of the biological sample analysis process performed by the biological sample analysis device 100.

[0021] Figure 5 is a flowchart for explaining details of S402 (unfolding image synthesis process) of Figure 4

[0022] Figure 6A is a drawing showing a setting example of the image center as a given region in S501.

[0023] Figure 6B is an example of an unfolding image created in the process after S502.

[0024] Figure 7 is a drawing showing an example of the luminance distribution of the detection target region 117 imaged with the fixed camera 101.

[0025] Figure 8 is a drawing showing an example in which two linear reflection regions appear in the center of the biological sample tube 116.

[0026] Figure 9 is a flowchart for explaining details of S403 (sample information analysis process).

[0027] Figure 10 is a flowchart for explaining details of label calibration.

[0028] Figure 11 is a flowchart showing the unfolding image synthesis process in Embodiment 2.

[0029] Figure 12 is a drawing showing an example of the rotation axis of the biological sample tube 116.

[0030] Figure 13 is a flowchart showing an example of the rotation axis detection process (S1101) of the biological sample tube 116.

[0031] Figure 14 is a flowchart showing the unfolding image synthesis process in Embodiment 3.

[0032] Figure 15 is an example in which the image is deformed due to the curvature of the biological sample tube 116.

[0033] Figure 16 is a flowchart showing the detailed process of S403 in Embodiment 4. DETAILED DESCRIPTION

[0034] ​Embodiments of the present disclosure will be described below with reference to the accompanying drawings. In the drawings, functionally identical elements are sometimes shown with the same reference numerals. In addition, the drawings illustrate specific embodiments and installation examples in accordance with the principles of the present disclosure, but these are used for understanding the present disclosure, and are by no means used to limit the interpretation of the present disclosure.

[0035] In the present embodiment, its explanation is made sufficiently in detail for those skilled in the art to implement the present disclosure, but other installations / modalities are also possible, and it is to be understood that structural / constructional changes, substitution of various elements can be made without departing from the scope and spirit of the technical idea of the present disclosure. Therefore, the following description is not to be construed as limiting the interpretation to this.

[0036] In the present embodiment, for the sake of convenience, when necessary, it is explained by being divided into a plurality of divisions or embodiments, but except for the case where it is specifically indicated, they are not unrelated to each other, and are in a relationship where one is a part or all of the modification example, details, supplementary explanation, etc. of the other. In the case where the number of elements, etc. (including the number, numerical value, amount, range, etc.) is mentioned, except for the case where it is specifically indicated and the case where it is clearly limited to a specific number in principle, it is not limited to the specified number, and it can be more than the specified number, or it can be less than the specified number.

[0037] In the following embodiments, it is self-evident that the constituent elements (including element steps, etc.) are not necessarily required except for the case where it is specifically indicated and the case where it is considered to be clearly required in principle.

[0038] Similarly, in the following embodiments, when the shape, positional relationship, etc. of the constituent elements, etc. are mentioned, except for the case where it is specifically indicated and the case where it is considered to be clearly not such a case in principle, etc., it includes a case where it is substantially similar or analogous to the shape, etc. This is the same for the above-mentioned numerical values and ranges.

[0039] In all the drawings for explaining the embodiments, the same reference numerals are attached to the same members in principle, and sometimes the repeated explanation thereof is omitted.

[0040] <Embodiment 1>

[0041] <Device structure of biological sample analysis device>

[0042] Figure 1is a diagram showing a schematic configuration example of a biological sample analysis apparatus 100 according to Embodiment 1 of the present disclosure. The biological sample analysis apparatus 100 is provided with a fixed camera 101, a background plate 102, light sources 103a and 103b, light source drivers 104a and 104b, a biological sample tube support 105, a controller 106, a memory 107, a storage device 108, an input / output interface 109, a data bus 110, a gripping mechanism 111, a moving mechanism 112, an up-down control driver 113, a biological sample tube rotating mechanism 114, and a rotation control driver 115.

[0043] The controller 106 is configured by, for example, a processor (Central Processing Unit (CPU) or the like), reads various programs stored in the memory 107 or the storage device 108, expands them in an internal memory (not shown), and generates an image processing section described later, and executes at a given timing.

[0044] The input / output interface 109 is configured by a keyboard, a touch panel, a microphone, a switch, a display device, a printer, a speaker, and the like, and is used in display of an analysis result of a detected biological sample (a category, a quantity, and the like of the biological sample), a state check of a biological sample before analysis, and the like. Further, the input / output interface 109 accepts setting of a given region clipping condition described later, and is used in input of a parameter in transmission of data, analysis of a biological sample category and / or a biological sample quantity, and the like.

[0045] The gripping mechanism 111 grips a biological sample tube 116 provided to the biological sample tube support 105. The up-down control driver 113 moves the gripping mechanism 111 in the up-down direction by controlling the moving mechanism 112.

[0046] The biological sample tube 116 provided to the biological sample tube support 105 is transported by a transport line, and is stopped by a stop mechanism (not shown) or the like. The moving mechanism 112 moves the biological sample tube 116 gripped by the gripping mechanism 111 to a position at which the biological sample tube 116 as a whole is imaged by the fixed camera 101.

[0047] The light of the light source 103a and the light source 103b are irradiated to the biological sample tube 116 as a whole by moving the biological sample tube 116 upward by the holding mechanism 111. More specifically, the biological sample tube 116 set to the biological sample tube holder 105 is held by the holding mechanism 111 and moved into the field of view of the fixed camera 101 by the moving mechanism 112. At this time, the moving amount of the moving mechanism 112 is controlled by the up-down control driver 113 to adjust the up-down position of the biological sample tube 116. The fixed camera 101 needs to acquire a two-dimensional color image of at least the detection target region 117 of the biological sample tube 116. Therefore, by determining the moving amount of the moving mechanism 112 by the up-down control driver 113 in correspondence with the length of the biological sample tube 116, the detection target region 117 can be moved into the field of view of the fixed camera 101 without depending on the length of the biological sample tube 116, and imaging is performed in a state where the detection target region 117 is illuminated by the light source 103a and the light source 103b.

[0048] The rotation control driver 115 rotates the biological sample tube 116 held by the holding mechanism 111 by controlling the biological sample tube rotation mechanism 114. The fixed camera 101 can image the entire circumference of the biological sample tube 116 by imaging while rotating the biological sample tube 116. The control of the rotation of the biological sample tube rotation mechanism 114 by the rotation control driver 115 and the synchronization method of imaging by the fixed camera 101 are described later.

[0049] The fixed camera 101 is, for example, an imaging device such as a CCD (Charge Coupled Device) image sensor, a CMOS (Complementary Metal-Oxide Semiconductor) image sensor, or the like. The fixed camera 101 acquires a two-dimensional color image of the biological sample tube 116 illuminated by the light source 103a and the light source 103b with the background plate 102 as a background, and inputs the image to the controller 106. By illuminating not with natural light but with the light source 103a and the light source 103b and with the background plate 102, for example, even in the case where the detection target region 117 is transparent such as serum, the deviation between the measurements of the colors of the detection target region 117 extracted for analysis, the deviation caused by the influence of external light, can be reduced, and the analysis accuracy can be improved.

[0050] The light source 103a and the light source 103b are constituted by, for example, white LEDs, and are controlled by the light source driver 104a and the light source driver 104b. The luminance of the light source 103a and the light source 103b has an influence on the color (pixel value) of the color image of the biological sample tube 116 taken by the fixed camera 101. Since the deviation in the luminance of the light source has an influence on the analysis result, for example, the evaluation of the camera using a jig for correcting the fading of the color, the analysis of the color of the background image of the biological sample tube 116, and the calibration of adjusting the luminance of the light source 103a and 103b are performed before the measurement, whereby the deviation in the analysis result due to the illumination can be reduced.

[0051] The color image taken by the above structure is used in the image processing and the analysis processing described later by the controller 106, and the controller 106 calculates (detects) the color and the amount of the detection target region 117. The memory 107 or the storage 108 stores, for example, the color characteristic amount of the serum and the like required for analyzing the color of the detection target region 117, and the information of the color of the detection target region 117, the analysis result, and the like are transferred to the controller 106 via the data bus 110, and are output (for example, displayed on the display screen of the display device) from the input / output interface 109.

[0052] <Functions implemented in the controller 106>

[0053] Figure 2 is a diagram showing the outline functions implemented by expanding the program stored in the memory 107 or the storage 108 in the controller (processor) 106. As shown in Figure 2 , the controller 106 has an image processing section 201, a region setting section 202, and a biological sample tube information acquisition section 203 as functions implemented as software.

[0054] The image processing section 201 generates an expanded image by the processing described later from a plurality of two-dimensional color images taken by the fixed camera 101, extracts the detection target region (for example, the serum region) 117 from the expanded image, and performs processing such as the determination of the color of the detection target and the determination of the type of the specimen, and the calculation of the amount of the solution.

[0055] The region setting section 202 is a processing section that realizes the function by which the user (operator) can set / alter the given region (cropped region) in the cropping processing of the two-dimensional color image for generating the expanded image described later.

[0056] For example, if the user specifies the reference coordinate value, width, height, and the like within the captured image of the given region using the touch panel as the input / output interface 109, the region setting section 202 sets the given region with respect to the two-dimensional color image of the object. Alternatively, a setting file describing the reference coordinate value, width, height, and the like within the captured image of the given region can be stored in the storage 107 or the storage device 108, and the given region can be set / changed by reading the information of the setting file from the program.

[0057] The biological sample tube information acquisition section 203 is a processing section that acquires the information of the color information (range of label color distribution) and / or size of the color-labeled tube 118 extracted by the detection target region 117 extraction processing described later and / or the size (diameter and length in the long axis direction) information of the biological sample tube 116 of the object of analysis, and performs setting / change.

[0058] For example, if the user inputs the color information (range of label color distribution) and / or size information of the color-labeled tube 118 and the size information of the biological sample tube 116 using the touch panel as the input / output interface 109, the biological sample tube information acquisition section 203 sets / changes the color or / size information of the color-labeled tube 118 extracted in the detection target region 117 extraction processing described later and the size information of the biological sample tube 116. Alternatively, a setting file describing the information of the label color can be stored in the storage 107 or the storage device 108, and the color or size information of the color-labeled tube 118 and the size information of the biological sample tube 116 can be set / changed by reading the information of the setting file from the program.

[0059] <Internal structure of the image processing section>

[0060] Figure 3 is a view showing an example of the internal structure of the image processing section 201. As shown in Figure 3 , the image processing section 201 includes a region cropping section 301, an unwinding chart synthesis section 302, a detection target region extraction section 303, and an analysis section 304. Details of the operation of each processing section will be described later, and if briefly described, each of the following processes is executed.

[0061] The region cropping section 301 performs a process of cropping the region set by the region setting section 202 from each two-dimensional color image.

[0062] The unwinding chart synthesis section 302 performs a process of generating one unwinding chart by linking the plurality of images cropped by the region cropping section 301 along the circumference of the biological sample tube 116.

[0063] The detection target region extraction section 303 performs processing for extracting the detection target region (e.g., serum region) 117 according to color information of the detection target (e.g., serum region) obtained in advance or color information of the label attached to the biological sample tube obtained in the biological sample tube information obtaining section 203.

[0064] The analysis section 304 performs the following processing: processing for determining the color of the detection target region 117 extracted by the detection target region extraction section 303 and judging the category of the biological specimen according to the color information thereof; and processing for calculating the liquid amount of the biological specimen from the detection target region 117 extracted by the detection target region extraction section 303.

[0065] <Outline of Biological Specimen Analysis Processing>

[0066] Figure 4 is a flowchart for explaining an outline of the biological specimen analysis processing performed by the biological specimen analysis apparatus 100 of the present embodiment. In the following description, the action subject of each step is set to the corresponding processing section, but since each processing section is realized by a program stored in the memory 107 or the storage device 108, the controller 106 set as the execution subject of the program can also be the action subject of each step, and the description will be explained in a changed manner.

[0067] According to the biological specimen analysis processing of the present embodiment, in the biological sample tube 116 to which the biological sample tube 116 with a color label 118 is attached, the color label 118 of a color similar to that of the detection target region 117 is prevented from being erroneously extracted as a part of the detection target region 117, and the resolution accuracy of the color and amount of the detection target region 117 is improved.

[0068] ( Figure 4 : Step S401: Obtain a plurality of two-dimensional color images

[0069] The image processing section 201 obtains a plurality of two-dimensional color images of the biological sample tube 116 imaged by the fixed camera 101.

[0070] ( Figure 4 : Step S401: Regarding the imaging method

[0071] The imaging method of the biological sample tube 116 will be described. In order to analyze the color and amount of the detection target region 117 from a two-dimensional color image, a two-dimensional color image of the region in which the detection target region 117 is exposed among the biological sample tubes 116 to which the labels are attached is needed. In order to implement the development image generation processing of the development image synthesizing section 302 described later, a plurality of two-dimensional color images taken while the biological sample tube 116 is rotated are needed. Therefore, the rotation of the biological sample tube 116 is controlled by the biological sample tube rotation mechanism 114 and the rotation control driver 115 based on the instruction of the controller 106, and a plurality of two-dimensional color images are imaged while the biological sample tube 116 is rotated. For example, the biological sample tube rotation mechanism 114 rotates the biological sample tube 116, and the rotation speed or acceleration, the rotation angle, the rotation speed, and the timing of the start of rotation are controlled by the rotation control driver 115. By the above, a plurality of images having information of the entire circumference of the biological sample tube 116 can be obtained.

[0072] Figure 4 Step S401: Regarding the imaging method: Supplement 1

[0073] In a case where the user (operator) inserts the biological sample tube 116 into the biological sample analysis device 100 so that the orientation of the biological sample tube 116 held by the holding mechanism 111 is aligned, imaging of the entire circumference of the biological sample tube 116 is not needed, and a plurality of two-dimensional color images imaged while the biological sample tube 116 is rotated can be obtained.

[0074] Figure 4 Step S401: Regarding the imaging method: Supplement 2

[0075] On the other hand, in a case where the user (operator) inserts the biological sample tube 116 into the biological sample analysis device without aligning the orientation of the biological sample tube 116 held by the holding mechanism 111, a plurality of two-dimensional color images are taken while the biological sample tube 116 is rotated so that information of the entire circumference of the biological sample tube 116 can be obtained, and thus the imaging image of the detection target region 117 can be reliably obtained. For example, if the operation of taking an image with the fixed camera 101 every time the biological sample tube 116 is rotated by 10 degrees is repeated to obtain 36 images (36 frames), information of the entire circumference of the biological sample tube 116 can be obtained.

[0076] Figure 4 Step S401: Regarding the imaging method: Supplement 3

[0077] ​​​The rotation angle and the speed or acceleration of rotation can not be fixed, and for example, the speed can be accelerated from 1 frame to 2 frames, decelerated from 31 frames to 32 frames, and the like, so that a total of 32 frames are used to take an image of the entire circumference of the biological sample tube 116. In addition, the shutter speed of the camera, the timing of image taking, and the like can be changed based on the rotation control signal.

[0078] Figure 4 Step S402: Mosaic Synthesis Process

[0079] The image processing section 201 performs each image processing in the region cropping section 301 and the mosaic synthesis section 302, and creates one mosaic from the two-dimensional color image acquired in S401. Details of this step will be described later in the description of the flowchart of Fig. 6. Figure 5

[0080] Figure 4 Step S403: Specimen Information Analysis

[0081] The image processing section 201 performs the processing of the detection target region extraction section 303 and the analysis section 304, extracts the detection target region 117 from the mosaic acquired in S402 (details will be described later), and acquires (calculates) information such as the color and the amount of the detection target acquired in the detection target region 117. For example, color information corresponding to the category of the biological specimen is stored in the memory 107 or the storage device 108 in advance, and the analysis section 304 can determine the category of the biological sample from the color information of the detection target region 117 by comparing the acquired color information with the color information stored in the memory 107 or the like in advance. For example, in the case of serum, there are characteristics in the color of serum depending on the state of the serum (normal, hemolysis, jaundice, chyle). Therefore, by storing the color characteristic amount, the range of the color space used in classification, and the threshold value in the memory 107, and comparing the color information acquired in the analysis section 304, it is possible to determine the state of the serum of the specimen. In addition, it is possible to calculate the amount of the solution of the detection target from the information of the number of pixels in the height direction (the vertical direction of the two-dimensional color image) of the detection target region 117 and the information of the diameter of the biological sample tube 116. For example, the diameter of the biological sample tube 116 is acquired by the user directly inputting information such as the model of the biological sample tube 116 or the diameter of the biological sample tube 116 using the touch panel as the input / output interface 109, or the like.

[0082] Figure 4 Step S403: Specimen Information Analysis: Supplement

[0083] ​​​​For example, by implementing such discrimination of the specimen category before biochemical analysis, a flag can be established in advance for a specimen that has an influence on the precision of biochemical analysis, such as an abnormal serum (hemolysis, jaundice, chyle). Furthermore, by calculating the solution amount of the detection target region 117 in the analysis section 304, it is possible to confirm in advance whether or not a sufficient solution amount for analysis can be ensured. Thus, in the case where the solution amount is insufficient, for example, it is possible to perform a response such as re-blood sampling, and there is an effect of improving the efficiency of biochemical analysis.

[0084] Figure 4 Step S403: Specimen information analysis: Supplement 2)

[0085] For example, with respect to a biological sample tube 116 having a color label 118 of a color similar to the color of the detection target region 117, such as yellow or red, which is the color of the serum, there is a possibility that the color of the color label 118 will be erroneously extracted as the detection target, and thus a false discrimination of the specimen category will be made. Furthermore, due to the erroneous extraction of the region of the color label 118 as the detection target region 117, there is a possibility that the solution amount will be erroneously calculated. Therefore, in the detection target region extraction section 303, the color of the label and the color of the detection target are recognized by the process described later, the color label 118 is removed, and only the detection target region 117 is extracted, whereby it is possible to avoid the false discrimination and the erroneous calculation of the solution amount due to the color label 118, and it is possible to improve the discrimination precision of the specimen category and the measurement precision of the solution amount.

[0086] Figure 4 Step S403: Specimen information analysis: Supplement 3)

[0087] In this step, the color label 118 and the detection target region 117 can be distinguished, for example, by the following process. The information of the color of the label used (distribution of hue, chroma, and lightness values and / or threshold values) is pre-acquired by the label calibration process described later, which is performed by the biological sample tube information acquisition section 203, and is stored in the memory 107 or the storage device 108. A mask of only the region of the color label 118 is extracted from the developed image acquired in S402, based on the pre-stored information of the color of the label, and only the region of the color label 118 is removed from the developed image.

[0088] <Details of the developed image synthesis process>

[0089] Figure 5 is for explaining Figure 4 ​​the flowchart of the details of S402 (unfolding image synthesis processing). In the following description, the action subject of each step is set to the corresponding processing section, but since each processing section is executed by a program stored in the memory 107 or the storage device 108, the controller (processor) 106 as the execution subject of the program can also be the action subject of each step, and the description will be changed accordingly.

[0090] In order to obtain one unfolding image from two-dimensional color images, the region setting section 202 sets a given region of the two-dimensional color images, and the region cropping section 301 crops the region set by the region setting section 202 from the two-dimensional color images. The unfolding image synthesis section 302 links the cropped images of the plurality of two-dimensional color images cropped by the region cropping section 301 to synthesize one unfolding image.

[0091] Figure 5 Step S501: Given region setting

[0092] The region setting section 202 sets a given region to each of the plurality of two-dimensional color images obtained in S401 in response to a setting input of the given region (cropped region of the two-dimensional color image) by the user or a setting file stored in the memory 107 or the storage device 108. The description content of the setting input by the user or the setting file is, for example, the width, height of the given region, and reference coordinate values within the captured image. Details of this step will be described later. Figures 6A-7

[0093] Figure 5 Step S502: Two-dimensional color image cropping

[0094] The region cropping section 301 crops the given region (cropped region) set in S501 from each of the plurality of two-dimensional color images obtained in S401.

[0095] Figure 5 Step S503: Cropped image linking processing

[0096] The unfolding image synthesis section 302 synthesizes one unfolding image by linking the cropped images of the plurality of two-dimensional color images output in S502. The linking portion can be smoothed by applying a smoothing filter such as a Gaussian filter to the unfolding image.

[0097] Figure 6A is a diagram showing an example of setting the image center as the given region in S501. If the biological sample tube 116 is rotated while being captured by the fixed camera 101, a plurality of two-dimensional color images of the biological sample tube 116 facing different directions are obtained as shown in Figure 6A The region setting section 202 sets a given region to each of the images. Figure 6A ​​​​The example shown in FIG. 9A indicates that the given region is set to be bilaterally symmetrical with respect to the coordinate of the center of each captured image. However, the cutout region is not limited to this. Furthermore, the coordinate and width of the given region can be changed for each image based on information such as the rotation speed or acceleration of the biological sample tube rotating mechanism 114.

[0098] Figure 6B FIG. 10 is an example of a developed image created in the process after S502. In S502, the given region set in S501 is cut out from the image, and in S503, the cut-out images are connected to be synthesized into one developed image.

[0099] The setting method of the given region (cutout region) will be described. Depending on the setting content of the given region, the synthesis accuracy of the developed image obtained in the process after S503, and the color distribution of the detection target region 117 obtained by the detection target region extraction section 303, the deviation changes. First, the effect of the setting of the coordinate and width of the given region on the synthesis accuracy of the developed image will be described.

[0100] When the biological sample tube 116 such as a blood collection tube is imaged and the developed image is synthesized, by setting the coordinate and width of the given region while considering the curvature of the biological sample tube 116, the effect of reducing the deformation of the synthesized developed image and improving the extraction accuracy of the detection target region 117 is obtained. For example, the horizontal coordinate of the center of the captured image is set as the reference coordinate of the given region, and a region bilaterally symmetrical from the reference coordinate is set as the given region, so that the width of the given region is about 8% of the width of the biological sample tube 116 (10 pixels in the case where the region of the biological sample tube 116 is 128 pixels). Thus, the effect of the curvature of the biological sample tube 116 can be reduced. Although the narrower the width of the given region, the smaller the effect of the curvature, in order to obtain a developed image containing information of the entire circumference of the biological sample tube 116, the number of images of the biological sample tube 116 needs to be increased by the amount by which the width of the given region is narrowed.

[0101] Figure 7 FIG. 11 is a graph showing an example of the luminance distribution of the detection target region 117 imaged by the fixed camera 101. The use of the given region set in S501 is indicated by the hatched region in FIG. 11. Figure 7To explain the influence of the setting of the coordinates and the width of the given region on the color distribution of the detection target region 117, if an image is taken in a state in which the biological sample tube 116 is illuminated with the light source 103a and the light source 103b, even in the case where the detection target region is a uniform solution, the center of the detection target region 117 becomes brighter and the ends become dimmer. Therefore, for example, by setting the center region in which the variation in the brightness value along the circumferential direction falls within an allowable range (for example, is fixed) as the given region, only a region in which the brightness value is uniform is cut out from each image, and the unwrapped images are synthesized. As a result, there is an effect of reducing the variation in the brightness value of the detection target region 117 after synthesis and improving the resolution of the color of the detection target region 117. That is, the region setting section 202 decides the given region based on the brightness distribution of the two-dimensional color image so that the variance in the brightness value after synthesis becomes smaller.

[0102] By suppressing the variation in the brightness of the cut-out region, the variation in the brightness value of the color-labeled region 118 after the unwrapped image synthesis can also be reduced with respect to the color of the color-labeled region 118. That is, the overlap of the color distribution of the detection target region 117 and the color-labeled region 118 in the synthesized unwrapped image can be reduced, and the resolution of separating the color of the detection target region 117 and the color-labeled region 118 in the processing performed in the detection target region extraction section 303 can be improved. If the separation / removal accuracy of the color-labeled region 118 is improved, false extraction of the color-labeled region 118 can be avoided, and the resolution of the color and the amount of the detection target can be improved.

[0103] Figure 8 is a diagram showing an example in which two linear reflection regions appear in the center of the biological sample tube 116. If imaging is performed in a state in which the given region is illuminated with the light source 103a and the light source 103b, there is a case in which a reflection component of the light from the light source 103a and the light source 103b is reflected on the surface of the biological sample tube 116. The region of the reflection component is usually high in brightness value and becomes white, and thus, in the case where the reflection component is present in the detection target region 117, the resolution processing of the specimen information becomes noise. Therefore, the region setting section 202 can remove the region of the reflection component from the detection target region 117 after the unwrapped image synthesis by setting the setting coordinates of the given region in a region in which the reflection of the light is not present, and can improve the resolution of the color of the detection target region 117.

[0104] Further, by removing the region of the reflection component, false detection of the boundary between the white reflection component and the detection target region 117 as a label can be avoided, and there is an effect of improving the extraction accuracy of the detection target region 117 and improving the resolution of the amount of the detection target. For example, in the case where the reflection component is present in the detection target region 117, the region setting section 202 can set the given region so that the reflection component is not present in the detection target region 117 after the unwrapped image synthesis. Figure 8In the case of such two linear reflection regions, by setting the region between the reflection regions (the center of the biological sample tube 116) as the given region, or by setting the region outside the reflection regions as the given region, the reflection component can be prevented from being reflected.

[0105] The setting method of the width of the given region taking into account the number of images of the biological sample tube 116 by the fixed camera 101 and the rotation speed of the biological sample tube rotation mechanism 114 will be described. As described above, by setting the coordinates and the width of the given region, the effects of improvement of the synthesis accuracy of the unwrapping image and reduction of the color distribution deviation of the detection target region 117 can be obtained, but in order to obtain an unwrapping image including information of the entire circumference of the biological sample tube 116, the width of the given region needs to be set taking into account the rotation speed of the biological sample tube rotation mechanism 114 and the imaging interval of the fixed camera 101 so that there is no region that is not included after the unwrapping image is synthesized.

[0106] For example, if the biological sample tube 116 (blood collection tube) is rotated once and 36 images are obtained at a fixed imaging interval in a state where the rotation speed of the biological sample tube rotation mechanism 114 is fixed, the biological sample tube 116 is rotated by 10° between the frames, but in the case where an unwrapping image of the entire circumference of the biological sample tube 116 is obtained from the 36 images, if the radius of the biological sample tube 116 is set to r [mm], the width of the given region needs to be at least (2πr / 36) [mm] at the minimum.

[0107] Further, the rotation speed of the biological sample tube rotation mechanism 114 is not fixed, and the rotation control driver 115 is controlled so that the biological sample tube 116 is accelerated at the beginning of rotation and decelerated after reaching a fixed speed to reach the end of rotation, and by setting the width of the given region corresponding to the acceleration of the biological sample tube rotation mechanism 114 each time an image is captured, an unwrapping image of the entire circumference of the biological sample tube 116 can be obtained without omission, with overlapping of regions, and with high efficiency.

[0108] Further, the imaging timing of the fixed camera 101 can be controlled based on the control signal of the rotation control driver 115, and the width of the given region corresponding to the rotation speed of the biological sample tube rotation mechanism 114 and the imaging timing of the fixed camera 101 can be set.

[0109] In addition to the above effects, by setting the coordinates and the width of the given region in the vertical direction, an unwrapping image of only the region of interest (for example, the detection target region 117) is obtained, and thus there is an effect of reducing the image size of the unwrapping image output in the subsequent processing and reducing the computational processing. Further, by setting the given region, a region such as a region where only the background plate is imaged in the captured image, which is not needed for analysis, is excluded, and there is an effect of removing noise in the specimen information analysis processing and improving the analysis accuracy.

[0110] <Details of the specimen information analysis processing>

[0111] Figure 9 is a flowchart for explaining details of S403 (specimen information analysis processing). In the following explanation, the action subject of each step is set to the corresponding processing section, but since each processing section is realized by a program stored in the memory 107 or the storage device 108, the controller (processor) 106 as the execution subject of the program can also be the action subject of each step, and the explanation will be changed accordingly.

[0112] Regarding the specimen information analysis processing of the present embodiment, in the detection target region extraction section 303 and the analysis section 304, the detection target region 117 is extracted from the development drawing generated by the development drawing synthesis section 302, and the discrimination of the state of the detection target and the measurement processing of the liquid amount are performed according to the color of the extracted region and the size of the region.

[0113] ( Figure 9 : Step S901: Label region determination / removal)

[0114] The detection target region extraction section 303 performs processing of determining a label region unnecessary for the analysis of the specimen information from the development drawing output in S402. Regarding the specific processing of the present step, it will be described later.

[0115] ( Figure 9 : Step S902: Horizontal direction position determination)

[0116] The detection target region extraction section 303 removes the label region determined in S901 as a region unnecessary for the analysis of the specimen information from the development drawing output in the development drawing synthesis processing in S402. The detection target region extraction section 303 further generates a mask of a horizontal direction region which is analyzed in the processing after S903 is determined. Alternatively, processing of generating an image of only the horizontal direction region which is necessary for the analysis is performed. The horizontal direction region is set, for example, based on the coordinates of both ends of the label region determined in S901.

[0117] ( Figure 9 : Step S903: Vertical direction position determination)

[0118] The detection target region extraction section 303 performs edge detection processing or the like on the mask of the horizontal direction region output in S902 or the image of only the horizontal direction region. For example, by recognizing the edges of each region of the specimen, the detection target region 117 and the region of the separating agent or the like are separated, and the detection target region 117 is extracted.

[0119] ( Figure 9 : Step S904: Specimen information analysis processing)

[0120] The analysis section 304 performs the state determination of the specimen and the calculation of the liquid amount based on the color information and the information of the region (coordinates and the number of pixels) of the detection target region 117 determined in the process of S903. For example, in the case where the detection target is serum, since there is a feature that the color of the serum changes in correspondence with the state of the serum (normal, hemolysis, jaundice, chyle), by storing the color feature amount, the range of the color space used for classification, and the threshold value in the memory 107, and comparing the color information obtained in the detection target region 117, the state of the serum of the specimen can be determined. Further, the solution amount of the detection target is calculated based on the information of the region (coordinates and the number of pixels) of the detection target region 117 and the diameter information of the biological sample tube 116 obtained in the biological sample tube information obtaining section 203.

[0121] ( Figure 9 : Step S904: Specimen information analysis process: Supplement

[0122] In the specimen information analysis process of S904, as an advantage of extracting the detection target region 117 from the expanded image instead of extracting one captured image of the biological sample tube 116, the following is presented: (a) By the given region setting method in S402, the color deviation of the detection target region 117 is reduced, and the determination accuracy of the state can be improved. (b) In the biological sample tube 116 to which the color label 118 is attached, the removal accuracy of the color label 118 is improved, and the false extraction of the detection target region 117 caused by the color label 118 can be avoided. Further, by the given region setting method, since the extraction process of the detection target region 117 can be performed based on the removal of the unnecessary region such as the reflection component in the captured image of the biological sample tube 116 from the expanded image, the false extraction of the detection target region 117, the false analysis of the color of the detection target, and the false determination caused by the noise can be avoided.

[0123] An example of a method of determining a label region without a colored portion (e.g., a white label and a black label with only a barcode and printing) in S901 is described. The determination of a label region without a colored portion is determined by image processing such as detection of a barcode region and / or detection of white. The detection of a barcode region is, for example, a method of extraction based on a black color threshold, a method of extraction based on image processing such as edge extraction. In the case of detection based on edge extraction, a fine line of a barcode can also be detected by combining morphological processing. The detection of a white region sets a threshold value of a color (e.g., hue, chroma, lightness) and extracts a region within the range that meets the threshold value as a label region. Furthermore, it is also possible to initially determine a barcode region, determine and remove the range of the label based on the information of the size of the label and the like acquired by the biological sample tube information acquisition unit 203, with the detected barcode region as a reference coordinate.

[0124] An example of a method of determining a region of a colored label 118 similar to the color of the detection target in S901 is described. As a combination that meets the detection target and the colored label 118, for example, there are yellow serum and a yellow colored label 118, red serum and a red colored label 118, and the like. Furthermore, in the case where the color of the detection target is red, yellow, and the like and differs for each measurement (specimen), the combination of yellow serum and a red colored label 118, red serum and a yellow colored label 118 also meets.

[0125] The colored label 118, as shown in the expanded view of FIG. 6, there are labels and the like with a colored portion in a part of the end of a white label, a colored portion in the upper and lower positions of a barcode region. In the case where the colored portion is close in color to the detection target region 117, there is a possibility that the colored portion will be erroneously extracted as the detection target region 117, thereby reducing the resolution accuracy of the specimen information. By determining and removing the region of the colored label 118 from the expanded view before the extraction of the detection target region 117, there is an effect of avoiding erroneous extraction of the colored portion as the detection target and improving the resolution accuracy.

[0126] In the label calibration described later, as a method of determining a region of a colored label 118 similar in color to the detection target region, there are the following methods: (Method 1) a method of determining a colored portion based on color information of the colored label 118 acquired by the biological sample tube information acquisition unit 203; (Method 2) a method of determining based on information of the size of the colored portion and / or the position of the colored portion in the label (the right end, the left end, the upper and lower positions, and the like in the label) acquired by the biological sample tube information acquisition unit 203.

[0127] Explanation (Method 1) A method of determining the colored portion based on color information. The biological sample tube information acquisition section 203 acquires information on the color of the label used (distribution of hue, chroma, lightness value and / or threshold value) in advance, for example, by executing the label calibration process described later, and stores the information on the color of the label used in the storage 107 or the storage device 108. In S901, the color information of the colored label 118 stored in the storage 107 or the storage device 108 is acquired, the color threshold (hue, chroma, lightness, etc.) of the colored label 118 is set, a mask for extracting only the colored portion of the colored label 118 from the expanded view is created, and the area of the colored portion is determined.

[0128] (Method 1: Supplement) The color information of the colored label 118 used can be input by the user using, for example, a touch panel or the like as the input / output interface 109, and stored in the storage 107 or the storage device 108. The biological sample tube information acquisition section 203 determines the area of the colored label 118 by searching in the color space based on the color specified by the user. Thus, the color matching the colored label 118 can be quickly found.

[0129] Explanation (Method 2) A method of determining the colored portion based on the information on the size of the colored portion and / or the position of the colored portion in the label (the right end, left end, upper and lower positions, etc.) acquired by the biological sample tube information acquisition section 203. The biological sample tube information acquisition section 203 acquires the size of the colored label 118 and / or the position of the colored portion in the label, for example, by executing the label calibration process described later, and stores it in the storage 107 or the storage device 108. Alternatively, the user can input it using, for example, a touch panel or the like as the input / output interface 109, and store it in the storage 107 or the like.

[0130] (Method 2: Specific Process) The biological sample tube information acquisition section 203 determines the barcode area and the white label area by the above-described example of the method of determining the area of the label without the colored portion, etc. Based on the coordinates of the determined barcode area or white area, the area of the colored label 118 is determined based on the size of the colored label 118 and / or the configuration information of the colored portion in the label. For example, in the label having the colored portion at the end of the label as in the expanded view of FIG. 6, after detecting the edge coordinates of the white label area or the barcode area, the area outside the edge by the corresponding amount of the size of the colored portion (the left side of the barcode area in FIG. 6) is determined as the area of the colored label 118.

[0131] <Details of Label Calibration Process>

[0132] Figure 10is a flowchart for explaining details of the label calibration. In the following explanation, the action subject of each step is set to the corresponding each processing section, but each processing section is realized by a program stored in the memory 107 or the storage device 108, and thus the controller (processor) 106 as the execution subject of the program can be set as the action subject of each step, and the explanation will be changed.

[0133] The label calibration processing performed by the biological sample tube information acquisition section 203 is processing for acquiring color information of the colored label 118 and information such as size from the captured image of the empty biological sample tube 116 (hereinafter referred to as a calibrator) to which the colored label 118 of the used color is attached before the specimen analysis processing. The present flowchart can be implemented as a part of S901.

[0134] As a separation method of the color of the colored label 118, a method of acquiring color distribution of hue, chroma, lightness, and the like from the captured image or the developed image of the biological sample tube 116 and setting a threshold value at which the both can be separated is considered, but even in the case of a uniform solution, the color of the detection target region 117 can be color-differentiated depending on the presence or absence of the label on the back and the reflection component of the surface of the biological sample tube 116, and color-differentiation of the detection target region 117 can be detected as a false detection of color-differentiation caused by the colored label 118. Therefore, by the label calibration processing of the present flowchart, by the user input, information of the used label is input in advance, there is an effect of avoiding false extraction of color-differentiation of the detection target region 117 as the colored label 118 and improving the removal precision of the colored label 118.

[0135] ( Figure 10 : Step S1001: Calibrator two-dimensional image acquisition)

[0136] The biological sample tube information acquisition section 203 acquires a plurality of two-dimensional color images of the calibrator captured by the fixed camera 101. The acquisition method of the images is the same as S401.

[0137] ( Figure 10 : Step S1002: Developed image synthesis processing)

[0138] The biological sample tube information acquisition section 203 performs the same processing as the developed image synthesis processing of Figure 4 from the two-dimensional color images of the calibrator acquired in S1001.

[0139] ( Figure 10 : Step S1003: Colored label extraction)

[0140] The biological sample tube information acquisition section 203 determines the region of the colored label 118 from the color distribution of the developed image output in S1002.

[0141] Figure 10 Step S1003: Color label area extraction: specific example 1

[0142] As a method of determining the color label area 118, there is a method of comparing a color distribution of an unwrapped image (reference image) created from an image taken of an empty biological sample tube 116 to which no color label 118 is attached and a color distribution of the unwrapped image of the calibrator, extracting a color difference between the two as the color of the color label 118, and determining an area of a color that matches from the unwrapped image as the color label area.

[0143] Figure 10 Step S1003: Color label area extraction: specific example 2

[0144] There is also a method in which the user inputs color information of the color label 118 via the input / output interface 109 or the like, and extracts a color that matches from the unwrapped image of the calibrator. For example, a palette of multiple colors is displayed on a screen of the touch panel or the like, and the user selects the color of the color label 118. The biological sample tube information acquisition section 203 acquires information of a color threshold value corresponding to the palette stored in advance in the storage 107 or the storage device 108 or the like, and sets the color threshold value of the color label 118 in accordance with the palette selected by the user. A color that matches the acquired color threshold value is extracted from the unwrapped image, and is determined as the color label area. Alternatively, the unwrapped image of the calibrator can be displayed on a screen of the touch panel or the like, and the user can select the area of the color label 118 directly or using a cursor or the like on the screen.

[0145] Figure 10 Step S1004: Color label color information acquisition

[0146] The biological sample tube information acquisition section 203 acquires a color distribution of the area of the color label 118 determined in step 902, sets a color threshold value that defines the color of the label of the color label 118 in accordance with the acquired color information, and stores it in the storage 107 or the storage device 108 or the like.

[0147] Figure 10 Step S1004: Color label color information acquisition: specific example

[0148] As a method of setting the threshold value in this step, there is a process such as the following. For example, the biological sample tube information acquisition section 203 can acquire a histogram of hue, chroma, lightness, or the like in the color distribution of the area of the color label 118 determined in step 902, and set a range of, for example, ±5% centered on each of the mode, the average, or the like as the threshold value.

[0149] <Embodiment 1: Summary> ​​​​

[0150] The biological sample analysis device 100 of Embodiment 1 performs a process of taking two-dimensional color images of the biological sample tube while rotating the biological sample tube by the biological sample tube rotation mechanism and synthesizing one spread image from a plurality of two-dimensional color images, and a sample information analysis process. In the process of synthesizing the spread image, a given region setting process, a two-dimensional color image cropping process, a two-dimensional color image cropping process, and a cropped image linking process are performed, and in the sample information analysis process, a label region determination process, a horizontal direction detection position determination process, and a vertical direction detection position determination process are performed. In this way, by removing the label region from the spread image synthesized from a plurality of two-dimensional color images and extracting a detection target region (for example, a serum region), it is possible to separate the color label and the detection target region with high precision and analyze the liquid contained in the detection target region.

[0151] The biological sample analysis device 100 according to Embodiment 1 crops a given region and synthesizes it in order to remove noise such as a reflection component reflected in the captured image (see Figure 8 ). Furthermore, a portion in which the variation of the luminance value along the circumferential direction of the biological sample tube 116 falls within an allowable range is cropped, and a spread image is created by linking the cropped images (see Figure 7 ). Thus, it is possible to expect an improvement in the analysis precision of the sample information.

[0152] The biological sample analysis device 100 according to Embodiment 1 performs label calibration by comparing a label-free biological sample tube and a labeled biological sample tube. By label calibration, information of the label is obtained in advance, and the region with the color label is determined and removed based on the obtained information. Since label calibration is performed in advance, the color label and the detection target region are not mixed together, and thus it is possible to avoid misanalysis in which the color of the color label is erroneously extracted as the color of the detection target, and it is possible to expect a more correct analysis result.

[0153] <Embodiment 2>

[0154] Figure 11 is a flowchart showing a spread image synthesis process in Embodiment 2 of the present disclosure. In Embodiment 2, instead of cropping the captured image according to a given region determined in advance, the inclination of the biological sample tube 116 within the captured image and the deviation from the center of the image are detected for each captured image, and the detected deviation is set as the rotation axis of the biological sample tube 116 (S1101). A given region is set for each image with reference to the detected rotation axis (S1102 to S1104). The structure other than the spread image synthesis process is the same as in Embodiment 1.

[0155] Figure 12is a diagram showing an example of the rotation axis of the biological sample tube 116. The biological sample tube 116 held by the holding mechanism 111 and rotated by the biological sample tube rotation mechanism 114, although also has Figure 12 the rotation axis coincides with the center of the image as in the left, but sometimes also deviates laterally as in the Figure 12 center, and the rotation axis remains inclined as in the Figure 12 right. In the case where the characteristics of the rotation axis cannot be fixed between measurements and between apparatuses, by not setting the given region in advance according to a fixed value, but dynamically setting the given region based on the rotation axis of the biological sample tube 116, there is an effect of improving the synthesis accuracy of the developed diagram.

[0156] Figure 13 is a flowchart showing an example of the rotation axis detection process (S1101) of the biological sample tube 116. The region setting section 202 performs the rotation axis (regression straight line) detection process of the biological sample tube 116 in accordance with this flowchart. This flowchart utilizes the left-right symmetry of the biological sample tube 116, and detects the inclination and the positional deviation from the central axis of the biological sample tube 116 from the distance in the RGB space between the captured image and the flipped image of the captured image.

[0157] Figure 13 : Step S1301: Two-dimensional color image acquisition

[0158] The region setting section 202 acquires a two-dimensional color image of the biological sample tube 116 captured by the fixed camera 101. The method of acquiring the image is the same as in S401.

[0159] Figure 13 : Step S1302: Analysis region extraction process

[0160] The region setting section 202 extracts an analysis region from the two-dimensional color image acquired in step 1301. Since the analysis is performed on the premise of the left-right symmetry of the biological sample tube 116, the region of the captured image of the biological sample tube 116 to which a label is not attached is extracted as the analysis region. For example, if it is a blood specimen after centrifugal separation, the region near the bottom of the biological sample tube 116 in which a blood clot is photographed is selected. The analysis region size is, for example, set to the order of the diameter of the biological sample tube 116 in the horizontal direction, and to 50 pixels or the like in the vertical direction. If it is a captured image of the same specimen, the set value of the analysis region need not be changed for each captured image, and can be a fixed value, but in the case of measuring biological sample tubes 116 of different lengths, the analysis region needs to be set in correspondence with the length of the biological sample tube 116. At this time, a plurality of set values are stored in the memory 107 or the storage device 108, or the like, and the set value is changed in accordance with the length information of the biological sample tube 116 or the like acquired in the biological sample tube information acquisition section 203.​​

[0161] Figure 13 Step S1303: Analysis region flipped image acquisition

[0162] The region setting section 202 acquires or generates a left-right flipped image of the analysis region set for the two-dimensional color image.

[0163] Figure 13 Step S1304: RGB space distance acquisition

[0164] The region setting section 202 acquires the distance in the RGB color space between the image of the analysis region and the flipped image for each pixel of the analysis region.

[0165] Figure 13 Step S1305: RGB color space distance minimum coordinate extraction processing

[0166] The region setting section 202 compares the distance in the RGB color space acquired in S1304 for each vertical direction coordinate of the analysis region, acquires the minimum value for each vertical direction coordinate and the coordinate at which the minimum value is obtained.

[0167] Figure 13 Step S1306: Regression straight line acquisition processing

[0168] The region setting section 202 extracts the center coordinate of the analysis region by connecting the coordinates of the minimum values detected in S1305 in the vertical direction, and sets a first regression straight line based on the extracted coordinates.

[0169] The region setting section 202 sets the given region based on the rotation axis detected by the present flowchart, and thus even in the case where the rotation axis of the biological sample tube 116 is inclined or laterally offset at the time of measurement, the desired region can be set as the given region, and thus the spread image synthesis accuracy is improved.

[0170] <Embodiment 2: Summary>

[0171] The biological specimen analysis apparatus 100 according to the present embodiment 2 crops and synthesizes the given region set based on the rotation speed information of the biological sample tube rotation mechanism, the inclination of the biological sample tube, and the lateral offset information in the spread image synthesis processing. Thus, an improvement in the synthesis accuracy of the spread image used in the analysis of the specimen information can be expected.

[0172] <Embodiment 3>

[0173] Figure 14 ​​​​is a flowchart showing the unwrapped image synthesis process in Embodiment 3 of the present disclosure. Embodiment 3 concatenates the cut-out two-dimensional color images after performing the curvature correction process on the cut-out two-dimensional color images in the unwrapped image synthesis process, and outputs the unwrapped image. By performing the curvature correction process, for example, in the case where the reference coordinates of the given region are shifted from the center of the biological sample tube 116, the effect of reducing the distortion of the synthesized unwrapped image is obtained. Furthermore, in the case where there is noise such as a reflection region in the central region of the biological sample tube 116 as in the example of Figure 8 the case where the reference coordinates of the given region are shifted from the center of the biological sample tube 116, the unwrapped image without distortion can be obtained by shifting the given region, and thus the effect of improving the resolution of the detection target region 117 is obtained. The structures other than the unwrapped image synthesis process are the same as in Embodiment 1. Figure 8

[0174] S1401 corresponds to S501, S1402 corresponds to S502, and S1404 corresponds to S504. In S1403, the region setting unit 202 calculates the correction coefficient considering the curvature of the biological sample tube 116 from the information on the diameter of the biological sample tube 116 acquired by the biological sample tube information acquisition unit 203 and the information on the given region set in S1401, and corrects the cut-out images, for example, by affine transformation or the like.

[0175] Figure 15 is an example in which the cut-out images are distorted due to the curvature of the biological sample tube 116. For example, if the given region is set in a region shifted from the center of the biological sample tube 116 and the unwrapped images are synthesized in Embodiment 1, the color-labeled 118 is distorted as on the left in Figure 8 Figure 15 Specifically, if a rectangular (long and thin) region is cut out as the given region without considering the curvature of the biological sample tube 116, distortion occurs in the synthesized image as on the left in Figure 15 Figure 15 the right shows the color-labeled 118 in the case where the synthesis process is performed after the distortion correction of the images in the cut-out given regions according to the flowchart of Figure 14 By correcting the distortion for each cut-out image considering the size and shape (curvature) of the biological sample tube 116, the effect of reducing the distortion of the synthesized unwrapped image is obtained.

[0176] <Embodiment 4>

[0177] Figure 16 ​​​is a flowchart showing detailed processing of S403 in Embodiment 4 of the present disclosure. In S403 of Embodiment 1, it is explained that the state discrimination of the detection object and the liquid amount measurement are performed in accordance with the information of the color and the size of the region of the detection target region 117. In this Embodiment 4, the examination information of the examinee, the specimen, and the like is acquired from the information of the detected label, the color labeled label 118. The other structures are the same as those of Embodiment 1.

[0178] As the information acquired from the label, the color labeled label 118, there are a bar code and color information of the color labeled portion. With regard to the information of the bar code, by accessing a database that associates the read content (for example, ID number and the like) and the information of the examinee stored in an external host server or the like, it is possible to acquire the information of the examinee and the like in the biological specimen analysis apparatus of this Embodiment.

[0179] Further, for example, by storing in the external server or the storage 107 or the storage device 108 a database that associates the information of the kind of the biochemical analysis, the kind of the specimen (serum, whole blood, urine, and the like) and the color of the color labeled label 118, it is possible to acquire the analysis content, the kind of the specimen from the acquired color information of the color labeled label 118.

[0180] The biological specimen analysis apparatus 100 of this Embodiment stores in advance the color information corresponding to the kind of the biological sample in the storage 107 or the storage device 108, and compares the acquired color information of the detection target region 117 and the color information stored in advance in the storage 705 or the like by the analysis section 304, thereby discriminating the kind of the biological sample. At this time, by storing the data for comparison for each kind of the specimen, the data for comparison is selected from the kind of the specimen acquired from the color information of the color labeled label 118, thereby it is possible to perform the analysis of the biological sample of a plurality of kinds.

[0181] <About Modified Examples of the Present Disclosure>

[0182] The present disclosure is not limited to the aforementioned embodiments, but includes various modified examples. For example, the above-described embodiments are described in detail in order to easily understand the present application, but are not necessarily limited to having all the structures described. Further, a part of the structure of a certain embodiment can be replaced with the structure of another embodiment, and further, the structure of another embodiment can be added to the structure of a certain embodiment. Further, a part of the structure of each embodiment can be added, deleted, or replaced with another structure.

[0183] In the above-described embodiments, after the cutting region is decided by S501, the coordinates and the size of the cutting region can be changed by user designation. Specifically, the user designates the coordinates and the size of the cutting region via a user interface, and the controller 106 sets the cutting region in accordance with the designation.

[0184] The functions of the embodiments of the present disclosure can also be implemented by program codes of software. In this case, a storage medium storing the program codes is provided to a system or an apparatus, and a computer (or CPU, MPU) of the system or the apparatus reads out the program codes stored in the storage medium. In this case, the computer (or CPU, MPU) itself reads out and executes the program codes stored in the storage medium to function according to the functions of the above-described embodiments. The program codes themselves and the storage medium storing the program codes constitute the present disclosure. As the storage medium for supplying such a program code, a floppy® disk, a CD-ROM, a DVD-ROM, a hard disk, an optical disk, a magneto-optical disk, a CD-R, a magnetic tape, a non-volatile memory card, a ROM, and the like are used, for example.

[0185] Further, a part or all of the actual processing performed by an OS (operating system) or the like operating on a computer can be performed based on instructions of the program codes, and the functions of the above-described embodiments can be realized by the processing. Furthermore, a part or all of the actual processing performed by a CPU or the like of the computer can be performed based on instructions of the program codes after the program codes read out from the storage medium are written into a memory on the computer, and the functions of the above-described embodiments can be realized by the processing.

[0186] Furthermore, the program codes of software implementing the functions of the embodiments can be distributed via a network, and stored in a storage unit or a storage medium such as a hard disk, a memory, or a CD-RW, CD-R of a system or an apparatus, and a computer (or CPU, MPU) of the system or the apparatus reads out and executes the program codes stored in the storage unit or the storage medium at the time of use.

[0187] In the above-described embodiments, control lines and information lines considered to be necessary for explanation are shown, but all of the control lines and information lines are not necessarily shown on products. All of the structures can be connected to each other.

[0188] Explanation of Reference Numerals

[0189] 100 biological body sample analysis apparatus

[0190] 101 fixed camera

[0191] 102 background plate

[0192] 103a and 103b light source

[0193] 104a and 104b light source driver

[0194] 105 biological body sample tube holder

[0195] 106 controller

[0196] 107 memory

[0197] 108 storage

[0198] 109 input / output interface

[0199] 110 data bus

[0200] 111 gripping mechanism

[0201] 112 moving mechanism

[0202] 113 up / down control driver

[0203] 114 biological sample tube rotating mechanism

[0204] 115 rotation control driver

[0205] 116 biological sample tube

[0206] 117 detection target region

[0207] 118 color label

[0208] 201 image processing section

[0209] 202 region setting section

[0210] 203 biological sample tube information acquisition section

[0211] 301 region cropping section

[0212] 302 spread image synthesizing section

[0213] 303 detection target region extraction section

[0214] 304 analysis section

Claims

1. A biological sample analysis apparatus that analyzes a biological sample housed in a biological sample tube to which a label is attached, the biological sample analysis apparatus characterized by comprising: a rotating mechanism that holds the biological sample tube and rotates it; a camera that acquires a two-dimensional color image by imaging the biological sample tube; and a processor that processes the two-dimensional color image, the processor crops a partial region in the two-dimensional color image, the processor synthesizes an unfolded view of the biological sample tube by joining the images of the cropped partial regions along the circumferential direction of the biological sample tube, the processor extracts a detection target region that is set as a detection target from among the two-dimensional color image from the unfolded view, the processor detects the position of a rotation axis when the rotating mechanism rotates the biological sample tube in the two-dimensional color image, the processor determines the region that is cropped as the partial region by using a direction orthogonal to the detected rotation axis as the circumferential direction of the biological sample tube, the processor generates a flipped image that flips the two-dimensional color image left and right, the processor calculates a distance in a color space between the two-dimensional color image and the flipped image, the processor determines the rotation axis by joining the coordinates at which the distance is the smallest along the vertical direction.

2. The biological sample analysis apparatus according to claim 1, characterized in that the processor identifies an image of the label contained in the two-dimensional color image based on information that characterizes features of the image of the label, the processor extracts the detection target region from a portion of the two-dimensional color image other than the identified image of the label.

3. The biological sample analysis apparatus according to claim 2, characterized in that the processor uses information obtained by comparing the two-dimensional color image of the biological sample tube to which the label is attached and the two-dimensional color image of the biological sample tube to which the label is not attached as the information that characterizes features of the image of the label.

4. The biological sample analysis apparatus according to claim 2, characterized in that the biological sample analysis apparatus further comprises an interface that accepts an instruction input that specifies at least any one of a color of the label, a position of the label, a shape of the label, or a size of the label, the processor decides the information that characterizes features of the image of the label in accordance with the instruction input accepted by the interface.

5. The biological sample analysis apparatus according to claim 2, characterized in that the biological sample analysis apparatus further comprises a storage device that stores data that describes at least any one of a color of the label, a position of the label, a shape of the label, or a size of the label, the processor uses information described by the data stored in the storage device as the information that characterizes features of the image of the label.

6. The biological sample analysis apparatus according to claim 1, characterized in that The processor crops a part region from the two-dimensional color image, as a region in which a variation in luminance along a circumferential direction of the biological sample tube falls within a given range.

7. The biological sample analysis apparatus according to claim 1, wherein The biological sample analysis apparatus further includes a light source that irradiates light to the biological sample tube, The processor crops a part region from the two-dimensional color image, as a region in which a noise amount of reflected light noise generated by reflection of light irradiated by the light source on a surface of the biological sample tube is below a threshold value.

8. The biological sample analysis apparatus according to claim 1, wherein The processor controls a rotation speed of the rotation mechanism, an imaging timing of the two-dimensional color image, and a number of images of the two-dimensional color image.

9. The biological sample analysis apparatus according to claim 1, wherein The processor acquires a color of the detection target region from the unwinding image, The processor determines a category of the biological sample contained in the detection target region by comparing the acquired color of the detection target region with data describing a color of the biological sample, and outputs the determined category.

10. The biological sample analysis apparatus according to claim 1, wherein The processor calculates an amount of the biological sample contained in the detection target region using information describing coordinates of the detection target region, a number of pixels of the detection target region, and a size of the biological sample tube.

11. The biological sample analysis apparatus according to claim 1, wherein The processor corrects a distortion of the two-dimensional color image using a curvature of the biological sample tube, The processor synthesizes the unwinding image using the two-dimensional color image whose distortion is corrected.

12. The biological sample analysis apparatus according to claim 1, wherein The biological sample analysis apparatus further includes an interface that accepts an instruction input that specifies a position and a size of the part region, The processor determines the position and the size of the part region in accordance with the instruction input accepted by the interface.

13. The biological sample analysis apparatus according to claim 1, wherein The processor recognizes an image of the label contained in the two-dimensional color image, The processor determines an attribute of the biological sample by extracting information describing the attribute of the biological sample from the recognized image of the label, and analyzes the biological sample in accordance with the determined attribute.

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