Biological sample analysis system, information processing device, information processing method, and biological sample analysis method

Abstract

A biological sample analysis system according to an embodiment includes: an emission unit for emitting light onto biological particles in a biological sample; a detection unit provided with a plurality of event pixels, each of which detects, as an event, a change in brightness of light radiated from the biological particles due to the light emitted from the irradiation unit, the event pixels being arranged in a two-dimensional grid form, and the detection unit outputting event data, the event data includes address information of each event pixel and information related to a time at which an event is detected by each event pixel; a generation unit that corrects time information of a portion of the plurality of pieces of event data using each of a plurality of correction amounts, and generates image data relating to the biological particles for each correction amount using the plurality of pieces of event data including the event data having the corrected time information; an evaluation unit for evaluating each of the plurality of pieces of image data; and a processing unit that generates biological particle information related to the biological particles on the basis of the image data having the highest evaluation from the evaluation unit among the plurality of pieces of image data.

Description

Technical Field

[0001] The present disclosure relates to a biological sample analysis system, an information processing device, an information processing method, and a biological sample analysis method. Background Art

[0002] So far, flow cytometry has been used as a method for analyzing (or studying (analysis includes the study in the present disclosure)) proteins of biologically relevant microparticles such as cells, microorganisms, and liposomes. An apparatus for flow cytometry is called a flow cytometer (FCM). In a flow cytometer, a laser with a specific wavelength is used to irradiate microparticles flowing in a column in a flow channel, and light (such as fluorescence, forward scattered light, and side scattered light) emitted from each microparticle is converted into an electrical signal by a photodetector and quantified, and the results are statistically studied to determine the type, size, structure, etc. of each microparticle.

[0003] Furthermore, in recent years, a so-called imaging flow cytometer (IFCM) has been developed that acquires a two-dimensional image of fluorescence emitted from microparticles by an image sensor.

[0004] Reference List

[0005] Patent Literature

[0006] Patent Document 1: US 6,211,955 Summary of the invention

[0007] Technical issues

[0008] However, since the fluorescence emitted from the microparticles is very weak, the imaging flow cytometer according to the related art cannot acquire a clear image with sufficient brightness, and thus, it is difficult to improve the accuracy of microparticle analysis.

[0009] In addition, in recent years, a so-called time delay integration (TDI)-charge coupled device (CCD) imaging flow cytometer has been developed that transmits and integrates the exposure value of the CCD to an adjacent row in synchronization with the speed of the cells (for example, see Patent Document 1). However, in this method, the speed of the cells flowing in the flow channel cannot be accurately specified, and there is a possibility that the image quality deteriorates.

[0010] Therefore, the present disclosure proposes a biological sample analysis system, an information processing apparatus, an information processing method, and a biological sample analysis method that are capable of suppressing degradation of image quality.

[0011] Technical solution to the problem

[0012] In order to solve the above-mentioned problems, according to an embodiment of the present disclosure, a biological sample analysis system includes: an irradiation unit, which irradiates biological particles in a biological sample with light; a detection unit, which detects a brightness change of light emitted from the biological particles by irradiating with light as an event, the detection unit includes a plurality of event pixels arranged in a two-dimensional grid pattern and outputs event data, the event data including address information of each event pixel and time information of the event detected in each event pixel; a generation unit, which corrects the time information of a part of event data in a plurality of event data by using each of a plurality of correction amounts, and generates image data about the biological particles for each correction amount by using a plurality of event data including the event data with the corrected time information; an evaluation unit, which evaluates each of a plurality of image data; and a processing unit, which generates biological particle information about the biological particles based on the image data with the highest evaluation by the evaluation unit in the plurality of image data. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a schematic diagram showing a configuration example of a biological sample analysis device according to the present disclosure.

[0014] Figure 2 : is a block diagram showing a more specific configuration example of the biological sample analysis device according to the first embodiment of the present disclosure.

[0015] Figure 3 : is a block diagram showing a schematic configuration example of an event-based vision sensor (EVS) device according to the first embodiment.

[0016] Figure 4 is a diagram for describing an event detected for each position in an image of a biological particle in the first embodiment.

[0017] Figure 5 : is a diagram for describing an operation example of a time delay integration (TDI)-EVS method according to the first embodiment (Part 1).

[0018] Figure 6 is a diagram for describing an operation example of the TDI-EVS method according to the first embodiment (Part 2).

[0019] Figure 7 is a diagram for describing an operation example of the TDI-EVS method according to the first embodiment (Part 3).

[0020] Figure 8 is a diagram for describing an operation example of the TDI-EVS method according to the first embodiment (Part 4).

[0021] Fig. 9 is a diagram illustrating an example of the relationship between an event detection threshold and noise.

[0022] Fig.10 is a block diagram showing a more specific configuration example of the event data processing unit according to the first embodiment.

[0023] Fig.11 : is a diagram showing an example of evaluation values ​​calculated by the evaluation function according to the first embodiment.

[0024] Fig.12 is a flowchart showing an example of the operation flow of the event data processing unit according to the first embodiment.

[0025] Fig.13 : is a diagram for describing a specific example of evaluation value calculation according to the first embodiment.

[0026] Fig.14 is a flowchart showing an operation example according to the first embodiment.

[0027] Fig.15 It is shown in Fig.14 A flowchart of a more specific operation example of the event stream acquisition operation shown in step S105 of FIG.

[0028] Fig.16 : is a block diagram showing a schematic configuration example of part of a detection unit according to a second embodiment of the present disclosure.

[0029] Fig.17 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device according to the present disclosure. DETAILED DESCRIPTION

[0030] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Note that in the following embodiments, the same reference numerals denote the same parts, and repeated descriptions will be omitted.

[0031] Furthermore, the present disclosure will be described in the following order.

[0032] 1. Introduction

[0033] 2. System configuration example of biological sample analysis device

[0034] 3. First Implementation

[0035] 3.1 System Configuration Example

[0036] 3.2 Schematic Configuration Example of Event-Based Vision Sensor (EVS) Device

[0037] 3.3 Example of the Time Delay Integration (TDI)-EVS Method

[0038] 3.4 Relationship between event detection threshold and noise

[0039] 3.5 Specific configuration example of event data processing unit

[0040] 3.6 Evaluation value calculation method

[0041] 3.7 Variation of the evaluation function

[0042] 3.8 Example of operation flow of event data processing unit

[0043] 3.9 Specific example of evaluation value calculation

[0044] 3.10 Operation flow example

[0045] 3.11 Example of the process of event stream acquisition operation

[0046] 3.12 Conclusion

[0047] 4. Second Implementation

[0048] 5. Third Implementation

[0049] 6. Hardware Configuration

[0050] 1. Introduction

[0051] In a TDI-charge coupled device (CCD) imaging flow cytometer, the TDI-CCD imaging flow cytometer transmits and integrates the exposure value of the CCD to adjacent rows in synchronization with the speed of the cells, for example, the light reception amount of all pixels on the row orthogonal to the moving direction of the cells in the image sensor is obtained, and the light reception amount is added (i.e., integrated) for each row at a timing synchronized with the speed of the cells, so that a good cell image without jitter caused by the movement of the cells can be obtained, and random noise unrelated to the speed of the cells is reduced. The necessary data transmission amount is (the number of pixels per row) × (the number of bits per pixel) × (row rate).

[0052] Here, when the number of pixels and pixel depth per row are 128 pixels and 8 bits, respectively, which are the minimum values ​​required for cell analysis, the data transfer amount per row is 128×8=1024 bits of the row rate. Therefore, the row rate is limited to about 1 / 1000 of the data transfer bandwidth of the image sensor.

[0053] For example, the maximum row rate of an image sensor with a data transmission bandwidth of 40 Gbps (gigabits per second) is about 40 kHz (kilohertz), and when the resolution of the cell's moving direction is the minimum 0.5 μm (micrometer) required for cell analysis, the maximum cell speed that can be imaged is (5×10 -7 m)×40kHz=0.02m / s (meter / second). The maximum cell velocity is 1 / 50 to 1 / 250 of the cell velocity of 1m / s to 5m / s used in a conventional flow cytometer that analyzes using the fluorescence intensity or scattered light intensity of the cell without using imaging, and therefore cannot be considered sufficient. Although the TDI-CCD method is shown here, the same applies to a TDI complementary metal oxide semiconductor (CMOS) method that uses a CMOS image sensor as an image sensor.

[0054] In the following embodiment, an event-based vision sensor (EVS) is used as a sensor (detection unit) for acquiring information about biological particles (hereinafter, also referred to as biological particle information), and the event-based vision sensor (EVS) outputs the coordinates of the pixel where the brightness (also referred to as light intensity) change has been detected, the direction (polarity) of the brightness change, and the time (event data) in a synchronous or asynchronous manner. This makes it possible to greatly reduce the data size of the biological particle information to be transmitted, so that the restriction on the number of samples that can be observed per unit time is greatly relaxed, and as a result, the redundancy of the analysis time can be suppressed.

[0055] In addition, the biological particle information may include at least one of the following: image data of biological particles reconstructed based on event data (the biological particle image described later); feature quantities of biological particles such as shape, size or color extracted from event data or image data of biological particles; the speed (or relative speed) of biological particles generated based on event data, image data of biological particles, feature quantities of biological particles, etc., attribute information indicating normal / abnormal, etc.

[0056] In addition, in the following embodiments, an imaging method similar to the TDI method is implemented using EVS. Hereinafter, the method is also referred to as the TDI-EVS method. By using EVS in an imaging method similar to the TDI method, biological particle information can be acquired with reduced noise, etc. For example, a clear image of biological particles with sufficient brightness (hereinafter, also referred to as a biological particle image) can be acquired. As a result, the analysis accuracy can be improved while suppressing the redundancy of the analysis time.

[0057] However, not only in the TDI-EVS method but also in the imaging method adopting the TDI method, in order to obtain a captured image with high image quality, it is necessary to accurately synchronize the row rate with the speed at which the object image moves on the image sensor. However, it is difficult to accurately specify the speed of the object (such as cells flowing through a flow channel in an imaging flow cytometer). Therefore, blurring, etc. may occur in the captured image obtained by the imaging method adopting the TDI method, thereby reducing the image quality.

[0058] Therefore, in the following embodiments, a biological sample analysis system, an information processing apparatus, an information processing method, and a biological sample analysis method capable of suppressing degradation of image quality will be described by way of example.

[0059] 2. System configuration example of biological sample analysis device

[0060] Figure 1 A configuration example of a biological sample analysis device according to the present invention is shown. Figure 1 The biological sample analysis device 100 shown in FIG. 1 includes: a light irradiation unit 101 that irradiates a biological sample S flowing through a flow channel C with light; a detection unit 102 that detects the light generated by the irradiation; and an information processing unit 103 that processes information about the light detected by the detection unit 102. Examples of the biological sample analysis device 100 include a flow cytometer and an imaging flow cytometer. The biological sample analysis device 100 may include a sorting unit 104 that sorts specific biological particles P in the biological sample S. Examples of the biological sample analysis device 100 including the sorting unit 104 include a cell sorter.

[0061] (Biological Samples)

[0062] The biological sample S can be a liquid sample containing biological particles P. The biological particles P are, for example, cellular biological particles or non-cellular biological particles. The cells can be living cells, and more specific examples of cells include blood cells (such as red blood cells or white blood cells) and reproductive cells (such as sperm or fertilized eggs). The cells can be collected directly from a sample such as whole blood, or can be cultured cells obtained after culture. Examples of non-cellular biological particles include extracellular vesicles, specifically exosomes and microvesicles. The biological particles P can be labeled with one or more marker substances (for example, dyes (specifically fluorescent dyes) and fluorescent dye-labeled antibodies). It should be noted that the biological sample analysis device 100 of the present disclosure can analyze particles other than biological particles, or can analyze microspheres used for calibration, etc.

[0063] (Flow channel)

[0064] The flow channel C can be configured to make the biological sample S flow, specifically, to form a flow in which the biological particles P contained in the biological sample S are basically arranged in a row. The flow channel structure including the flow channel C can be designed so as to form a laminar flow, and specifically, to be designed so as to form a laminar flow in which the flow of the biological sample S (sample flow) is surrounded by the flow of the sheath liquid. The design of the flow channel structure can be appropriately selected by those skilled in the art, and a known flow channel structure can be adopted. The flow channel C can be formed in a flow channel structure such as a microchip (a chip with a micrometer-level flow channel) or a flow chamber. The width of the flow channel C is 1 mm (millimeter) or less, and can be specifically 10 μm (micrometer) or more and 1 mm or less. The flow channel C and the flow channel structure including the flow channel C can be formed of materials such as plastic or glass.

[0065] The apparatus of the present disclosure may be configured so that the biological sample S flowing in the flow channel C, specifically, the biological particles P in the biological sample S, are irradiated with light from the light irradiation unit 101. The apparatus of the present disclosure may be configured so that the light interrogation point relative to the biological sample S is in the flow channel structure in which the flow channel C is formed, or may be configured so that the light interrogation point is outside the flow channel structure. Examples of the former case may include a configuration in which the flow channel C in a microchip or a flow cell is irradiated with light. In the latter case, the biological particles P after leaving the flow channel structure (specifically, the nozzle portion of the flow channel structure) may be irradiated with light, and examples of this case include a flow cytometer of the air jet method.

[0066] (Light irradiation unit)

[0067] The light irradiation unit 101 includes a light source unit that emits light and a light-guiding optical system that guides the light to the flow channel C. The light source unit includes one or more light sources. Examples of the type of light source include laser light sources and light-emitting diodes (LEDs). The wavelength of light emitted from each light source can be any wavelength of ultraviolet light, visible light, or infrared light. For example, the light-guiding optical system includes optical components such as a beam splitter group, a mirror group, or an optical fiber. In addition, the light-guiding optical system may include a lens group for collecting light, and may include, for example, an objective lens. The biological sample S can be irradiated with light at one or more interrogation points. The light irradiation unit 101 can be configured to collect light emitted from one or more different light sources relative to an interrogation point.

[0068] (Detection unit)

[0069] The detection unit 102 includes at least one light detector that detects light generated by irradiating particles with light by the light irradiation unit 101. For example, the light to be detected is fluorescence, scattered light (for example, any one or more of forward scattered light, backscattered light, and side scattered light), transmitted light, or reflected light. Each light detector includes one or more light receiving elements, for example, a light receiving element array. Each light detector may include one or more photodiodes (such as photomultiplier tubes (PMTs) and / or avalanche photodiodes (APDs) and multi-pixel photon counters (MPPCs)) as light receiving elements. The light detector includes, for example, a PMT array in which a plurality of PMTs are arranged in a one-dimensional direction. In addition, the detection unit 102 may include an imaging element such as a charge coupled device (CCD) or a complementary metal oxide semiconductor (CMOS). The detection unit 102 may obtain biological particle information about the biological particles P through the imaging element.

[0070] As described above, the biological particle information may include at least one of a biological particle image of the biological particle, a feature amount of the biological particle, attribute information of the biological particle, etc. In addition, the biological particle image of the biological particle may include, for example, a bright field image, a dark field image, a fluorescent image, etc.

[0071] The detection unit 102 includes a detection optical system that allows light having a predetermined detection wavelength to reach a corresponding light detector. The detection optical system includes a spectroscopic unit such as a prism or a diffraction grating, or a wavelength separation unit such as a dichroic mirror or a filter. The detection optical system can be configured, for example, to spectrally separate the light from the biological particles P, and detect light in different wavelength ranges through a plurality of light detectors greater than the number of fluorescent dyes. A flow cytometer including such a detection optical system is called a spectral flow cytometer. In addition, for example, the detection optical system can be configured to separate light corresponding to the fluorescent wavelength range of the fluorescent dye from the light from the biological particles P, and cause the corresponding light detector to detect the separated light.

[0072] In addition, the detection unit 102 may include a signal processing unit that converts the electrical signal obtained by the light detector into a digital signal. The signal processing unit may include an A / D converter as a device that performs the conversion. The digital signal obtained by the conversion of the signal processing unit may be sent to the information processing unit 103. The information processing unit 103 may process the digital signal into data about light (hereinafter also referred to as "light data"). The light data may be, for example, light data including fluorescence data. More specifically, the light data may be light intensity data, and the light intensity may be light intensity data of light including fluorescence (which may include feature quantities such as area, height, and width).

[0073] (Information Processing Unit)

[0074] For example, the information processing unit 103 includes a processing unit that performs processing of various data (e.g., light data) and a storage unit that stores various data. When light data corresponding to a fluorescent dye is acquired from the detection unit 102, the processing unit may perform fluorescence leakage correction (compensation processing) on ​​the light intensity data. In addition, in the case of a spectral flow cytometer, the processing unit performs fluorescence separation processing on the light data and acquires light intensity data corresponding to the fluorescent dye.

[0075] The fluorescence separation process may be performed according to the unmixing method described in, for example, JP 2011-232259 A. In the case where the detection unit 102 includes an imaging element, the processing unit may acquire morphological information of the biological particle P based on an image acquired by the imaging element. The storage unit may be configured to be able to store the acquired light data. The storage unit may further be configured to be able to store spectral reference data used in the unmixing process.

[0076] In the case where the biological sample analysis apparatus 100 includes the sorting unit 104 described below, the information processing unit 103 may determine whether to sort the biological particles P based on the light data and / or the morphology information. Then, the information processing unit 103 controls the sorting unit 104 based on the determination result, and the biological particles P may be sorted by the sorting unit 104.

[0077] The information processing unit 103 may be configured to be able to output various data (e.g., light data and images). For example, the information processing unit 103 may output various data generated based on the light data (e.g., two-dimensional charts and spectrum charts). In addition, the information processing unit 103 may be configured to be able to receive various data, and, for example, receive a gating process performed by a user on a chart. The information processing unit 103 may include an output unit (e.g., a display) or an input unit (e.g., a keyboard) for output or input.

[0078] The information processing unit 103 may be configured as a general-purpose computer, and may be configured as an information processing device including, for example, a central processing unit (CPU), a random access memory (RAM), and a read-only memory (ROM). The information processing unit 103 may be provided in a housing provided with the light irradiation unit 101 and the detection unit 102, or may be outside the housing. In addition, various processes or functions performed by the information processing unit 103 may be implemented by a server computer or a cloud connected via a network.

[0079] (Sorting unit)

[0080] The sorting unit 104 can, for example, sort the biological particles P according to the determination result of the information processing unit 103. The method for sorting can be the following method: droplets containing biological particles P are generated by vibration, electric charges are applied to the droplets to be sorted, and the travel direction of the droplets is controlled by electrodes. The method for sorting can be a method of sorting by controlling the travel direction of the biological particles P in the flow channel structure. For example, a control mechanism using pressure (injection or suction) or electric charge is set in the flow channel structure. Examples of flow channel structures include chips having a flow channel structure in which a flow channel C branches into a recovery flow channel and a waste liquid flow channel on the downstream side of the flow channel C (for example, a chip described in JP2020-76736A), and specific biological particles P are recovered into the recovery flow channel.

[0081] 3. First Implementation

[0082] Next, a biological sample analysis system, an information processing apparatus, an information processing method, and a biological sample analysis method according to a first embodiment of the present disclosure will be described in detail with reference to the accompanying drawings.

[0083] 3.1 System Configuration Example

[0084] Figure 2 : is a block diagram showing a more specific configuration example of the biological sample analysis device according to the present embodiment. In the present embodiment and the following embodiments, the biological sample analysis device may be configured as a system in which a plurality of devices are combined.

[0085] like Figure 2 As shown, the biological sample analysis device 100 according to the present embodiment includes: a light source unit 111 and a light guide optical system 112 included in a light irradiation unit 101; a detection optical system 121, an EVS device 122, an event data processing unit 123, and a speed measurement unit 124 included in a detection unit 102; an information processing unit 103; and a sorting unit 104, and observes an image of fluorescence, reflected light, and / or transmitted light emitted from biological particles P in a biological sample S flowing through a flow channel C. It should be noted that the light source unit 111, the light guide optical system 112, the detection optical system 121, the information processing unit 103, and the sorting unit 104 may be the same as those described above with reference to Figure 1 Similar to those described.

[0086] More specifically, light output from the light source unit 111 (hereinafter, also referred to as excitation light) is collected by the light guide optical system 112. The collected light is applied to the biological particles P flowing at a high speed in the flow channel C through which the biological sample S in which the biological particles P float flows. An image of reflected light or transmitted light and / or fluorescence emitted from the biological particles P irradiated with light is formed on a light receiving surface of the EVS device 122 by the detection optical system 121.

[0087] (EVS)

[0088] The EVS device 122, which will be described in detail later, includes pixels (hereinafter referred to as event pixels) arranged in a two-dimensional grid pattern, for example. Each event pixel detects an event based on a change in the brightness of incident light. The EVS device 122 outputs event data including position information (X address and Y address) of a pixel that has detected an event, polarity information (positive event / negative event) of the detected event, time information (time stamp) at which the event has been detected, and the like.

[0089] A series of event data (hereinafter, also referred to as event stream) generated in each pixel corresponding to the image of the biological particles P moving on the light receiving surface of the EVS device 122 is sent to the event data processing unit 123 .

[0090] (Speed ​​measurement unit)

[0091] The velocity measurement unit 124 measures, for example, a relative velocity of the biological particles P flowing through the flow channel C with respect to the velocity measurement unit 124. In this example, a case where the velocity measurement unit 124 is stationary with respect to the flow channel C is exemplified. Therefore, in the following description, the velocity measurement unit 124 is regarded as measuring the velocity of the biological particles P.

[0092] For the velocity measurement unit 124 , various detection methods such as an electrostatic method and an optical method can be employed that can detect the velocity of the biological particle P. The velocity of the biological particle P detected by the velocity measurement unit 124 is sent to the event data processing unit 123 as needed.

[0093] In the case where the speed of the biological particles P is known, for example, in the case where the speed of the biological particles P flowing through the flow channel C is controlled to be maintained at a desired speed by controlling a pump system that transports the biological sample S, the speed measuring unit 124 may be omitted. However, even in the case where the speed of the biological particles P is known, the speed of the biological particles P may fluctuate due to changes in ambient temperature, resistance of the liquid transport system, etc. Therefore, the speed measuring unit 124 may be used to actually measure the speed of the biological particles P.

[0094] (Event Data Processing Unit)

[0095] The event data processing unit 123 reconstructs frame data of an image of the biological particle P from the event stream input from the EVS device 122 and the velocity of the biological particle P, and transmits the reconstructed frame data to the information processing unit 103 .

[0096] The velocity variation of the biological particle P is sufficiently gentle compared to the arrival frequency of the biological particle P. Therefore, the velocity of the biological particle P used to reconstruct the frame data is not limited to the velocity of the biological particle P itself included in the frame data to be reconstructed, and may be the velocity of the biological particle P arriving before and / or after the biological particle P, an average value, or the like.

[0097] (Information Processing Device)

[0098] The information processing unit 103 analyzes the frame data input from the event data processing unit 123, and performs correction to offset the rotation of the biological particles P moving in the flow channel C, extracts the feature amount of the biological particles P, distinguishes the type of the biological particles P, etc. In addition, the information processing unit 103 may include a display unit, and may present the biological particle information for analysis and the feature amount, statistical data, type distinction results, etc. based on the analysis results to the user. In addition, the information processing unit 103 may sort and collect a specific type of biological particles P by controlling the sorting unit 104 based on the type distinction results of the biological particles P.

[0099] 3.2 Schematic Configuration Example of Event-Based Vision Sensor (EVS) Device

[0100] Next, a schematic configuration example of the EVS device 122 will be described. Figure 3 is a block diagram showing a schematic configuration example of an EVS device according to the present embodiment. Figure 3 As shown, the EVS device 122 includes a pixel array unit 201 , an X arbitrator 202 , a Y arbitrator 203 , an event signal processing circuit 204 , a system control circuit 205 , and an output interface (I / F) 206 .

[0101] The pixel array unit 201 has a configuration in which a plurality of event pixels 20 are arranged in a two-dimensional grid pattern, each event pixel 20 detecting an event based on a change in brightness of incident light. In the following description, the row direction refers to the arrangement direction of pixels in a pixel row (lateral direction in the drawings), and the column direction refers to the arrangement direction of pixels in a pixel column (vertical direction in the drawings).

[0102] Each event pixel 20 includes a photoelectric conversion element that generates electric charge according to the brightness of incident light. When detecting a change in the brightness of incident light based on a photocurrent flowing from the photoelectric conversion element, the event pixel outputs a request to read from itself to the X arbitrator 202 and the Y arbitrator 203, and outputs an event signal indicating that an event has been detected according to the coordination of the X arbitrator 202 and the Y arbitrator 203.

[0103] Each event pixel 20 detects the presence or absence of an event based on whether a change exceeding a predetermined threshold occurs in the photocurrent according to the brightness of the incident light. For example, each event pixel 20 detects a brightness change exceeding a predetermined threshold (positive event) or a brightness change below a predetermined threshold (negative event) as an event.

[0104] When an event has been detected, the event pixel 20 outputs a request for permission to output an event signal indicating the occurrence of the event to each of the X arbitrator 202 and the Y arbitrator 203. Next, when a response indicating permission to output the event signal is received from each of the X arbitrator 202 and the Y arbitrator 203, the event pixel 20 outputs the event signal to the event signal processing circuit 204.

[0105] The X arbitrator 202 and the Y arbitrator 203 coordinate a request to output an event signal supplied from each of the plurality of event pixels 20, and transmit a response based on the coordination result (permission / non-permission of output of the event signal) and a reset signal for resetting event detection to the event pixel 20 that has output the request.

[0106] The event signal processing circuit 204 generates and outputs event data by performing predetermined signal processing on the event signal input from the event pixel 20 .

[0107] As described above, the change in the photocurrent generated in the event pixel 20 can also be considered as a change in the amount of light (brightness change) of light incident on the photoelectric conversion unit of the event pixel 20. Therefore, it can also be said that an event is a change in the amount of light (brightness change) of the event pixel 20 that exceeds a predetermined threshold. The event data indicating the occurrence of an event includes at least position information, for example, the coordinates indicating the position of the pixel 20 where the change in the amount of light as an event has occurred. In addition to the position information, the event data may also include the polarity of the change in the amount of light.

[0108] For a series of event data output from the event pixel 20 when an event has occurred, as long as the interval between multiple pieces of event data when the event occurred is maintained, it can be said that the event data implicitly includes time information indicating the relative time when the event occurred.

[0109] However, when the interval between multiple pieces of event data when an event occurs is not maintained because the event data is stored in a memory, etc., the time information implicitly included in the event data is lost. Therefore, before not maintaining the interval between multiple pieces of event data when an event occurs, the event signal processing circuit 204 may include time information indicating the relative time when the event occurred, such as a timestamp, in the event data.

[0110] (Other configurations)

[0111] The system control circuit 205 includes a timing generator that generates various timing signals, and performs drive control of the X arbitrator 202 , the Y arbitrator 203 , the event signal processing circuit 204 , and the like based on the various timings generated by the timing generator.

[0112] The output I / F 206 sequentially outputs a plurality of pieces of event data output in line units from the event signal processing circuit 204 to the event data processing unit 123 as an event stream.

[0113] 3.3 Example of TDI-EVS method

[0114] Next, an operation example of the TDI-EVS method according to the present example will be described. Figure 4 is a diagram for describing an event detected for each position in an image of a biological particle, and Figures 5 to 8 is a diagram for describing an operation example of the TDI-EVS method according to the present embodiment.

[0115] In the following description, an example is given in which the moving direction of the biological particle P is consistent with the column direction, but the present disclosure is not limited thereto, and the moving direction of the biological particle P may be consistent with the row direction. In this case, the pixel column in the following description is replaced by the pixel row, and the column direction is replaced by the row direction. Note that the pixel column may be a sequence of event pixels 20 arranged in the column direction, and the pixel row may be a sequence of event pixels 20 arranged in the row direction.

[0116] exist Figure 4 In the figure, (a) shows an image P_img of a biological particle P formed on a light receiving surface of the EVS device 122 and a moving direction D of the biological particle P, (b) shows the brightness of the image P_img along a line segment A1-A2, and (c) shows an example of an event detected along the line segment A1-A2 when the image P_img moves in the moving direction.

[0117] In this example, assume that Figure 4The brightness distribution along the line segment A1-A2 in the image P_img of the biological particle P shown in (a) is a distribution in which the brightness of the region R1 of the central core is the highest, the brightness of the region R2 outside the region R1 is slightly lower, and the brightness of the region R3 of the membrane in the peripheral part is slightly higher. In this case, when the image P_img moves in the moving direction to pass through the event pixel 20 located at the point A3 on the line segment A1-A2, as shown in (b), Figure 4 As shown in (c), the event pixel 20 located at point A3 on the line segment A1-A2 detects a positive event at a position where the brightness on the line segment A1-A2 increases above a predetermined threshold (hereinafter, referred to as the event detection threshold), and detects a negative event at a position where the brightness on the line segment A1-A2 decreases above the event detection threshold.

[0118] Figure 5 Shows Figure 4 The detection of the event queue shown in is applied to the case where pixel columns are arranged in a column direction parallel to the moving direction of the image P_img. Figure 5 Shown in Figure 4 1 is an example of an event queue detected in each of the event pixels 20_0 to 20_N of the pixel column corresponding to the line segment A1-A2, in the case where the image P_img of the biological particle P shown in (a) is moved in the column direction of the pixel array unit 201. Note that the pixel column is not limited to an array of more than two event pixels 20, and may be one event pixel 20.

[0119] like Figure 5 As shown in FIG. 1 , in each of the event pixels 20_0 to 20_N in the pixel column corresponding to the line segment A1-A2, Figure 4 However, in each event queue, a false event (the occurrence of an event is falsely detected even if the event has not occurred) or an event loss (the event is not detected even if the event has originally occurred) is included as noise. Generally, such a false event or event loss occurs randomly.

[0120] Therefore, in this embodiment, if Figure 6 As shown, the time of the event stream as a queue of event data (the queue of event data is obtained by converting the event queue detected by the event pixels 20_0 to 20_N into data) is aligned, and the event stream with the aligned time is added (also called superimposed) on the time axis. In this case, as Figure 7 As shown, the correct positive events and the correct negative events detected according to the brightness change are accumulated, and the random error events and event losses are offset and not accumulated. As a result, Figure 8 As shown, the brightness changes can be reconstructed based on the correct positive and negative events.

[0121] In the reconstruction of the brightness change, for example, the brightness change along the line segment A1-A2 of the biological particle P can be reconstructed by adding the brightness value in the positive event and dividing by the brightness value in the negative event in the time series. However, the present disclosure is not limited thereto, and for example, various methods may be used, such as a method of obtaining a brightness gradient between events based on the occurrence interval of the events in the time series and reconstructing the brightness change along the line segment A1-A2 of the biological particle P using the brightness gradient, and a method of performing machine learning using an event stream before or after the addition as an input and using the brightness change as an output.

[0122] Furthermore, for example, the arrival of the image P_img of the biological particle P at the event pixel 20 (hereinafter, referred to as the head pixel) of the pixel column first can be detected by monitoring the event stream of the head pixel output from the EVS device 122. For example, the arrival of the image P_img at the head pixel can be detected from the occurrence frequency, occurrence pattern, etc. of events in the event stream of the head pixel.

[0123] Furthermore, a time offset (also referred to as a correction amount) for aligning the times of the event streams may be determined based on, for example, the speed of the biological particle P measured by the speed measurement unit 124 and the interval of the event pixels 20 in the moving direction of the biological particle P. That is, the movement time between pixels of the image P_img of the biological particle P is calculated according to the speed of the biological particle P and the interval of the event pixels 20, and the time stamp in the event data of each event stream is adjusted using the movement time as an offset, whereby the times of the respective event streams may be aligned.

[0124] However, the alignment of the time of the event stream is not limited to the above method, and various modifications can be made. For example, a method of detecting the arrival of the image P_img of the biological particle P in each event pixel 20 in the same pixel column and using the difference between the detected times as an offset can be used. In this case, the offset (e.g., the initial value of the offset) can be calculated by calculation based on accurate timestamp information, and thus an effect such as a reduction in signal processing cost can be expected.

[0125] Furthermore, the moving speed of the image P_img of the biological particle P is considered to be equal in two or more pixel columns. Therefore, for example, the offset amount can be determined based on the average value of the difference in the time at which the arrival of the image P_img is detected in each of the two or more pixel columns.

[0126] 3.4 Relationship between event detection threshold and noise

[0127] As described above, in the present embodiment, the brightness change is reconstructed based on the positive events and the negative events. Therefore, it is necessary to set the event occurrence threshold to be small enough to obtain the grayscale number (brightness resolution) for satisfactorily reproducing the image P_img of the biological particle P. However, as Fig. 9 As shown, the event detection threshold and noise are in a trade-off relationship. Therefore, when the event detection threshold is reduced, noise (such as false events or event loss that are not related to the brightness change of the image P_img) increases. For example, the event detection threshold can be determined based on at least one of the type of biological sample S, the type of fluorescent dye marking the biological particles P, or the intensity of the light (laser) output from the light source unit 111.

[0128] However, as in the present embodiment, in the case where the time of the event streams detected by the plurality of event pixels 20 in the same pixel column is respectively aligned and then the event streams are added together, the correct positive events and negative events are enhanced, but the random false events and event losses are not enhanced. Therefore, the S / N ratio can be improved. As a result, even in the case where the grayscale number is increased by lowering the event detection threshold, highly accurate biological particle information about the biological particle P can be generated. For example, a biological particle image that well reproduces the image P_img of the biological particle P can be reconstructed.

[0129] 3.5 Specific configuration example of event data processing unit

[0130] Next, a specific configuration example of the event data processing unit 123 according to the present embodiment will be described with reference to the drawings. Fig.10 : is a block diagram showing a more specific configuration example of the event data processing unit according to the present embodiment.

[0131] like Fig.10 As shown, the event data processing unit 123 includes a TDI circuit 11 , an evaluation unit 12 , a selection unit 13 , a buffer 14 and a reconstruction unit 15 .

[0132] (TDI circuit 11)

[0133] The TDI circuit (also referred to as a generation unit) 11 generates event image data by aligning the time of event data output from the EVS device 122 as an event stream for each event pixel 20 and adding (superimposing) the polarity of the event data. The event image data is image data generated using an event stream input during a specific frame period, and may be, for example, two-dimensional data (also referred to as event information) including, as shown in FIG. Figures 4 to 8 A collection of event data described for each pixel count.

[0134] Here, the TDI circuit 11 according to the present embodiment generates a plurality of event image data from an event stream input during a specific frame period by changing the offset amount (i.e., the movement time between pixels of the image P_img of the biological particle P corresponding to the speed of the biological particle P) when the event image data is generated by TDI processing.

[0135] The change in offset (hereinafter, also referred to as offset candidate value) can be various values, such as a plurality of pre-registered values, two or more values ​​narrowed down from a plurality of values ​​based on an optimal offset specified in a previous frame (an initial value in the case of a first frame), and two or more values ​​set at predetermined intervals before and / or after an offset specified in a previous frame serving as a benchmark.

[0136] A plurality of pieces of event image data having different feature amounts generated using a plurality of offset amount candidate values ​​are input in the order of generation to the evaluation unit 12 and the selection unit 13. The offset amount used for generation may be added to the event image data as meta information.

[0137] (Evaluation Unit 12)

[0138] The evaluation unit 12 calculates the evaluation value of the input event image data and outputs the calculated evaluation value to the selection unit 13. Here, the evaluation value may be a value corresponding to the image quality of the frame data reconstructed from the event image data. The method of calculating the evaluation value by the evaluation unit 12 will be described in detail later.

[0139] (Selection Unit 13)

[0140] The selection unit 13 inputs one piece of event image data sequentially input from the TDI circuit 11 to the reconstruction unit 15. For example, the selection unit 13 inputs the event image data having the highest evaluation value to the reconstruction unit 15.

[0141] Specifically, for example, the selection unit 13 stores the event image data initially input for a specific frame in the buffer 14. At this time, the selection unit 13 may save the evaluation value for the event image data stored in the buffer 14 input from the evaluation unit 12 in a register (not shown) or the like. In addition, the buffer 14 may be, for example, a frame buffer or the like.

[0142] Next, in the case where the evaluation value of the subsequently input event image data is higher than the evaluation value of the event image data stored in the buffer 14, the selection unit 13 updates the event image data in the buffer 14 to the event image data with the higher evaluation value. At this time, the register or the like may be updated with the evaluation value of the updated event image data.

[0143] Thereafter, when the updating of the buffer 14 is completed based on all the event image data related to the specific frame, the selection unit 13 inputs the event image data stored in the buffer 14 to the reconstruction unit 15 .

[0144] (Reconstruction Unit 15)

[0145] As mentioned above Figure 8 As described above, the reconstruction unit (also referred to as the processing unit) 15 reconstructs the frame data as image data by reconstructing the brightness change based on the polarity of the event data counted for each pixel of the event image data. The reconstructed frame data is input to the information processing unit 103 (see Figure 2 ).

[0146] 3.6 Evaluation value calculation method

[0147] Next, description will be given of a method of calculating the evaluation value by the evaluation unit 12. The evaluation value of event image data is calculated such that, for example, the lower the image quality degradation such as blur or shake in frame data reconstructed from the event image data, the higher the evaluation value.

[0148] Therefore, an evaluation function such as that shown in the following equation (1) can be used to calculate an evaluation value e(i) that becomes higher as the image quality deteriorates. In equation (1), n_p represents the number of pixels in the event image data to which only positive events are assigned, n_n represents the number of pixels to which only negative events are assigned, and n_pn is the number of pixels to which both positive and negative events are assigned. In addition, n_p_e(i) represents the number of positive events of the i-th pixel (i is a natural number) among the pixels in which only positive events are detected, and n_n_e(i) represents the number of negative events of the i-th pixel among the pixels in which only negative events are detected. In addition, w() is a weighting coefficient. The weighting coefficient w() can be adjusted within a range of, for example, 0 to 1 according to imaging conditions, etc. However, when weighting is not performed, w() can be omitted. The following equations including equation (1) are based on the assumption that the denominator is not zero. That is, it is assumed that when the denominator is a sum, at least one of n_p or n_n (or n_pn) is 1 or more, and when the denominator is a product, both n_p and n_n (and n_pn) are 1 or more.

[0149]

[0150] As described above, in the evaluation function shown in equation (1), for event image data with offset candidate value = i, the value obtained by dividing the sum (or weighted sum) of the total number of positive events in pixels assigned only positive events (Σ(n_p_e(i))) and the total number of negative events in pixels assigned only negative events (Σ(n_n_e(i))) by the sum of the number of pixels assigned only positive events n_p, the number of pixels assigned only negative events n_n, and the number of pixels assigned both positive and negative events n_pn is calculated as the evaluation value e(i).

[0151] The evaluation value e(i) is calculated by using the evaluation function as described above, such as Fig.11 As shown, the evaluation value e(i) can be obtained as the image quality degradation in the reconstructed frame data is lower. Fig.11 : is a diagram showing an example of evaluation values ​​calculated by the evaluation function according to the present embodiment, and is a diagram showing the result of plotting the evaluation value e(i) obtained at each offset candidate value based on event image data acquired under specific imaging conditions, when the offset candidate value (=interval of event pixels 20 (also referred to as pixel pitch) / speed of biological particles P) is set in increments of 1 within the range of -180 to -120. Fig.11 In the example shown in , the highest evaluation value e(i) is obtained when the offset = -151. Therefore, it is predicted that the image quality of the frame data reconstructed from the event image data generated with the offset = -151 is the highest.

[0152] The polarity of the event in the pixel where the event has occurred should be positive or negative. Therefore, there is a high possibility that the pixel assigned with both the positive event and the negative event is a pixel (hereinafter, also referred to as an error pixel) including an error (also referred to as noise). Therefore, by including the number n_pn of pixels assigned with both the positive event and the negative event in the denominator of the evaluation function as in equation (1), the calculated evaluation value e(i) can be reduced as the number of error pixels increases. As a result, the selection unit 13 can select event image data with few error pixels, and thus the image quality of the frame data to be reconstructed in the reconstruction unit 15 can be improved.

[0153] 3.7 Variation of the evaluation function

[0154] The evaluation function used to calculate the evaluation value e(i) is not limited to the above equation (1), and various evaluation functions can be adopted. The following equations (2) to (18) show some variations of the evaluation function used to calculate the evaluation value e(i).

[0155] For example, the following equations (2) and (3) each show an evaluation function for calculating the evaluation value e(i) for only one of a positive event and a negative event.

[0156]

[0157] In the following equations (4) to (6), examples are given of the case where the numerator in each of the above equations (1) to (3) is set to "1". As described above, even in the case where the sum (or weighted sum) of the total number of positive events in pixels to which only positive events are assigned (Σ(n_p_e(i)) and the total number of negative events in pixels to which only negative events are assigned (Σ(n_n_e(i))) is a fixed value ("1" in this example), similar to equations (1) to (3), the evaluation value e(i) of event image data in which blurring, shaking, etc. may occur in the frame data to be reconstructed can be reduced.

[0158]

[0159] In the following equations (7) to (12), the case where the denominator n_pn is omitted from the above equations (1) to (6) is exemplified. That is, the denominator n_pn in the evaluation function is not essential and can be omitted.

[0160]

[0161] Furthermore, as shown in the following equation (13), event image data may also be evaluated based only on error pixels.

[0162]

[0163] In addition, in the above equations (1) to (10), the case where the denominator and / or the numerator are both the sum of numbers has been exemplified, but the present disclosure is not limited thereto, and at least one of the denominator or the numerator may be a product as exemplified in the following equations (14) to (18). Since there is a possibility that the number of error pixels n_pn is zero, an evaluation function in which n_pn is not included in the denominator is exemplified below.

[0164]

[0165] In addition, for example, in the above equations (1) to (18), the denominator and the numerator may be interchanged. In this case, an evaluation value e(i) having a lower value may be calculated for event image data that is evaluated to have a better image quality than the reconstructed frame data. The weighting coefficient w() may be adjusted to a value where the denominator is not zero.

[0166] Furthermore, each value of n_p, n_n, and n_pn, a total value of two or more values ​​of n_p, n_n, and n_pn, and the like may be obtained as an evaluation value. Such an evaluation value utilizes the fact that the number of pixels assigned only positive events, the number of pixels assigned only negative events, and the number of pixels assigned both positive and negative events tend to decrease as the offset approaches the velocity of the biological particle P. In this case, the smaller the evaluation value, the higher the evaluation.

[0167] In addition, for event image data, the sum of the number of pixels most frequently assigned with n_p_e(i) (hereinafter, also referred to as the most frequent pixels) and the number of pixels most frequently assigned with n_n_e(i) (most frequent pixels) can be used as an evaluation value. Such an evaluation value utilizes the fact that the most frequent pixels in each of the positive event pixels and the negative event pixels tend to increase as the offset approaches the velocity of the biological particle P. In this case, the larger the evaluation value, the higher the evaluation. In the case where the evaluation values ​​of a plurality of offset candidate values ​​are the same, the offset candidate value having a larger most frequent value can be adopted.

[0168] 3.8 Example of operation flow of event data processing unit

[0169] Next, the operation flow of the above-mentioned event data processing unit 123 will be described using an example. Fig.12 : is a flowchart showing an example of the operation flow of the event data processing unit according to the present embodiment.

[0170] like Fig.12 As shown, in operation, first, a variable i (i=1) is initialized in the TDI circuit 11 and the evaluation unit 12, and an evaluation value e_max (e_max=0) in a register is initialized in, for example, the selection unit 13 (step S1).

[0171] Next, the TDI circuit 11 performs TDI processing TDI(v(i)) using the i-th offset candidate value v(i) of the event stream input from the EVS device 122 within the frame period, thereby generating event image data ev_img(i) (step S2). The generated event image data ev_img(i) is input to the evaluation unit 12 and the selection unit 13. It is assumed that there are n+1 (n is an integer greater than 1) offset candidate values ​​v(i), and the offset candidate values ​​v(i) are assigned numbers from 0 to n (corresponding to i).

[0172] Next, the evaluation unit 12 analyzes the input event image data ev_img(i) to specify necessary parameters among n_p, n_n, n_pn, n_p_e(i) and n_n_e(i), and substitutes the specified parameters into the evaluation function fe(ev_img(i)) to calculate the evaluation value e(i) (step S3). The calculated evaluation value e(i) is input to the selection unit 13.

[0173] Next, the selection unit 13 determines whether the evaluation value e(i) input in step S3 is higher than the evaluation value e_max stored in the register or the like (step S4). In the case where the evaluation value e(i) is equal to or lower than the evaluation value e_max (No in step S4), the operation proceeds to step S6.

[0174] On the other hand, when the evaluation value e(i) is higher than the evaluation value e_max (yes in step S4), the selection unit 13 updates the evaluation value e_max managed by a register, etc. with the new evaluation value e(i) (e_max=e(i)), and updates the event image data ev_img stored in the buffer 14 with the new event image data ev_img(i) (ev_img=ev_img(i)) (step S5), and the operation proceeds to step S6.

[0175] In step S6 , the variable i is incremented by 1 (i++) in the TDI circuit 11 and the evaluation unit 12 .

[0176] Next, the control unit that controls the TDI circuit 11, the evaluation unit 12, the selection unit 13, or the event data processing unit 123 determines whether the incremented variable i is greater than the number n of the offset candidate values ​​v(i) (i.e., whether the processing of all the offset candidate values ​​v(i) has been completed) (step S7). In the case where i is equal to or less than n (No in step S7), the operation returns to step S2, and the subsequent operation is repeated.

[0177] On the other hand, in the case where i is greater than n (yes in step S7), the event image data ev_img in the buffer 14 is read by the selection unit 13 and the event image data ev_img is input to the reconstruction unit 15. The reconstruction unit 15 generates frame data img by performing a process (event_to_image (ev_img)) of reconstructing the input event image data ev_img (step S8). The generated frame data img is input to the information processing unit 103.

[0178] In step S8, the selection unit 13 may input the value of the variable i to the TDI circuit 11 and the evaluation unit 12 when calculating the evaluation value e_max (=ev_img(i)) finally held in the register or the like. On the other hand, the TDI circuit 11 and the evaluation unit 12 may also perform processing such as changing the initial value (which may be a reference value) of the offset candidate value v(i) to be used in the next frame or limiting the offset candidate value v(i) based on the variable i input from the selection unit 13. The speed measurement unit 124 (see Figure 2 ) is used as the initial value or reference value of the offset candidate value v(i).

[0179] Thereafter, for example, the control unit of the control event data processing unit 123 determines whether to end the operation (step S9), and in the case where the control unit determines to end the operation (yes in step S9), the operation ends. On the other hand, in the case where the control unit determines not to end the operation (no in step S9), the operation returns to step S1, and the subsequent operation is performed.

[0180] 3.9 Specific example of evaluation value calculation

[0181] Next, a specific calculation example of the evaluation value will be described below. Fig.13 : is a diagram for describing a specific example of evaluation value calculation according to the present embodiment, wherein (A) shows a case where the offset is "-149", (B) shows a case where the offset is "-150", and (C) shows a case where the offset is "-151". Hereinafter, in order to simplify the description, the following case will be exemplified: the offset candidate value (= interval of event pixels 20 / speed of biological particle P) is set in increments of 1 within the range of -180 to -120, three offset candidate values ​​are used (for example, three offset candidate values ​​of -149 to -151), and the above equation (1) is used as the evaluation function.

[0182] First, if Fig.13 As shown in (A), in the case where the offset is "-149" (offset candidate value v(0) = -149), the number n_p of pixels assigned only positive events is 2, the number n_n of pixels assigned only negative events is 3, and the number n_pn of pixels assigned both positive and negative events is 1. In addition, the total number n_p_e of positive events among pixels assigned only positive events is 3, and the total number n_n_e of positive events among pixels assigned only negative events is 3. Therefore, the evaluation value e(i = 0) obtained by equation (1) is 1.00.

[0183] Furthermore, as shown in (B), in the case where the offset is "-150" (offset candidate value v(1) = -150), the number n_p of pixels to which only positive events are assigned is 3, the number n_n of pixels to which only negative events are assigned is 4, and the number n_pn of pixels to which both positive and negative events are assigned is 0. Furthermore, the total number n_p_e of positive events among pixels to which only positive events are assigned is 5, and the total number n_n_e of positive events among pixels to which only negative events are assigned is 4. Therefore, the evaluation value e(i = 1) obtained by equation (1) is 1.29.

[0184] Furthermore, as shown in (C), in the case where the offset is "-151" (offset candidate value v(2) = -151), the number n_p of pixels to which only positive events are assigned is 3, the number n_n of pixels to which only negative events are assigned is 3, and the number n_pn of pixels to which both positive and negative events are assigned is 0. Furthermore, the total number n_p_e of positive events among pixels to which only positive events are assigned is 5, and the total number n_n_e of positive events among pixels to which only negative events are assigned is 4. Therefore, the evaluation value e(i = 2) obtained by equation (1) is 1.50.

[0185] Therefore, in this example, the evaluation value e(i=2) in the case where the offset amount is “−151” is the maximum value, and the event image data ev_img(i=2) generated in the case where the offset amount is “−151” is output from the selection unit 13 .

[0186] 3.10 Operation Process Example

[0187] Next, an example of an operation performed by the biological sample analysis apparatus 100 according to the present embodiment will be described. Fig.14 Execution of the operation described below can be controlled by a control unit (not shown) or the like that controls the biological sample analysis apparatus 100 .

[0188] like Fig.14 As shown, in operation, first, the EVS device 122 is activated (step S101), delivery of the biological sample S to the flow channel C is started (step S102), and output of light from the light irradiation unit 101 is started (step S103). The order of performing steps S101 to S103 may be changed.

[0189] As described above, when delivery of the biological sample S to the flow channel C is started, in operation, the velocity measurement unit 124 measures the velocity of the biological particles P flowing in the flow channel C (step S104 ). The measured velocity of the biological particles P is input to the event data processing unit 123 .

[0190] A specific point on the flow channel C is irradiated with light from the light irradiation unit 101. Therefore, when a biological particle P contained in a biological sample S delivered to the flow channel C passes through the point, fluorescence or scattered light is emitted from the point, and transmitted light and reflected light are emitted. The light emitted from the point is incident on the light receiving surface of the EVS device 122 via the detection optical system 121. Therefore, each event pixel 20 in the EVS device 122 detects the brightness change caused by the image of the light emitted when the biological particle P passes through the point as a positive event and a negative event. The event data detected for each event pixel 20 is output from the EVS device 122 to the event data processing unit 123 at any time (i.e., in an asynchronous manner).

[0191] Since the event data including the polarity and time stamp of the event occurring only in the event pixel 20 where the event occurred is output as a stream (event stream) from the EVS device 122 to the event data processing unit 123, the amount of data transmission can be significantly reduced compared with the TDI-CCD method, the TDI-CMOS method, etc., which output the light receiving amount of all pixels. As a result, even when the data transmission band connecting the EVS device 122 and the event data processing unit 123 is the same bandwidth, imaging can be performed at a higher speed of the biological particles P.

[0192] The event data processing unit 123 generates an event stream for each biological particle P based on the event data for each event pixel 20 output from the EVS device 122 (step S105 ). The event stream for each biological particle P may be a collection of event streams for each event pixel 20 .

[0193] Then, the event data processing unit 123 generates biological particle information (frame data in this example) of the biological particle P passing through the point on the flow channel C based on the event stream generated for each biological particle P (step S106), and outputs the reconstructed biological particle information to the information processing unit 103 (step S107).

[0194] Thereafter, it is determined whether to end the operation (step S108), and in the case where it is determined that the operation is not to be ended (No in step S108), the operation returns to step S104, and the subsequent operation is performed. On the other hand, in the case where it is determined that the operation is to be ended (Yes in step S108), the output of light from the light irradiation unit 101 is stopped (step S109), the delivery of the biological sample S to the flow channel C is stopped (step S110), and the operation ends.

[0195] 3.11 Example of the process of event stream acquisition operation

[0196] Next, we will refer to Fig.15 Described in more detail in Fig.14The operation of obtaining the event stream is described in step S105. Fig.15 It is shown in Fig.14 A flowchart of a more specific operation example of the event stream acquisition operation shown in step S105 of FIG.

[0197] like Fig.15 As shown, in Fig.14 In the event stream acquisition operation shown in step S105, first, the event data processing unit 123 monitors the queue of event data (event stream) input from the EVS device 122 to determine whether the biological particle P has reached a point on the flow channel C (step S121). Fig.14 After the operations shown in , the event data processing unit 123 can continuously monitor the event stream.

[0198] When the biological particle P arrives at a point on the flow channel C (Yes in step S121), the event data processing unit 123 starts collecting event data including event data indicating the arrival of the biological particle P after the arrival of the biological particle P (step S122). In step S123, all event data input from the EVS device 122 may be accumulated in a predetermined storage area separately from the collection of the event data.

[0199] Thereafter, the event data processing unit 123 monitors the queue (event stream) of event data input from the EVS device 122 to determine whether the biological particle P has passed through the point on the flow channel C (step S123). Then, when the biological particle P has finished passing through the point on the flow channel C (yes in step S123), the event data processing unit 123 stops collecting event data (step S124), and generates an event stream of each biological particle P that has passed through the point on the flow channel C based on the collected event data for each event pixel 20 (step S125). Thereafter, the operation returns to Fig.14 The operations shown in .

[0200] 3.12 Conclusion

[0201] As described above, according to the present embodiment, event image data generated using each of a plurality of candidate offset values ​​is evaluated, and therefore, the event image data processed by TDI can be selected using the most appropriate offset based on the measured value. Then, since the frame data is reconstructed using the event image data having the highest evaluation value, the image quality degradation of the reconstructed frame data can be effectively suppressed.

[0202] 4. Second Implementation

[0203] Next, a biological sample analysis system, an information processing apparatus, an information processing method, and a biological sample analysis method according to a second embodiment of the present disclosure will be described. In the following description, the same configuration as that of the above-described embodiment is cited, and repeated description is omitted.

[0204] Fig.16 is a block diagram showing a schematic configuration example of part of a detection unit according to the present embodiment. Fig.16 1 shows a schematic configuration example including an EVS device 122 and an event data processing unit 123, and shows the same configuration as that described in the first embodiment. Fig.10 The configuration described corresponds to the configuration.

[0205] like Fig.16 As shown, in the present embodiment, the recording unit 125 is added to the output of the EVS device 122 , and the event stream output from the EVS device 122 is accumulated in the recording unit 125 .

[0206] In this way, the event stream output from the EVS device 122 is accumulated, and frame data can be generated from the accumulated event stream, and therefore, frame data can be generated without being limited to real-time requirements. As a result, the variation of the offset candidate value can be increased, and therefore, frame data with higher image quality can be generated.

[0207] Other configurations, operations, and effects may be similar to those of the above-described embodiment, and thus detailed descriptions thereof are omitted here.

[0208] 5. Third Implementation

[0209] Next, a biological sample analysis system, an information processing apparatus, an information processing method, and a biological sample analysis method according to a third embodiment of the present disclosure will be described. In the following description, the same configuration as that of the above-described embodiment is cited, and repeated description is omitted.

[0210] In the above-described embodiment, the offset candidate value when generating event image data with the highest evaluation value among a plurality of offset candidate values ​​is designated as the optimal offset, but the method of designating the optimal offset is not limited thereto and may be variously modified.

[0211] For example, an optimal offset can be specified using an approximate curve obtained by fitting a Gaussian function or the like to the evaluation value of event image data generated using three or more offset candidate values. Then, event image data can be generated from the event stream using the optimal offset specified by the approximate curve.

[0212] In this way, by specifying the optimal offset from the approximate curve obtained by fitting, a more accurate offset can be specified, and therefore, the degradation of the image quality of the frame data to be reconstructed can be further suppressed. In addition, since the number of times the TDI circuit 11 performs TDI processing (i.e., the number of times event image data is generated) can be reduced, the processing time can be shortened and power consumption can be reduced.

[0213] In addition, in the processing of the next frame and subsequent frames, the range of candidate offset values ​​to be searched can be narrowed down to the vicinity of the optimal offset specified according to the approximate curve, so that the processing time can be further shortened and the power consumption can be further reduced.

[0214] Other configurations, operations, and effects may be similar to those of the above-described example, and thus detailed descriptions thereof are omitted here.

[0215] 6. Hardware Configuration

[0216] For example, by having Fig.17 The computer 1000 of the configuration shown in embodies the event data processing unit 123 and / or the information processing unit 103 according to the above-described embodiments, modifications, and application examples. Fig.17 10 is a hardware configuration diagram showing an example of a computer 1000 that implements the functions of the event data processing unit 123 and / or the information processing unit 103. The computer 1000 includes a CPU 1100, a RAM 1200, a ROM 1300, a hard disk drive (HDD) 1400, a communication interface 1500, and an input / output interface 1600. Each component of the computer 1000 is connected by a bus 1050.

[0217] The CPU 1100 operates based on the programs stored in the ROM 1300 or the HDD 1400 and controls the respective components. For example, the CPU 1100 loads the programs stored in the ROM 1300 or the HDD 1400 onto the RAM 1200 and executes processing corresponding to the various programs.

[0218] The ROM 1300 stores a boot program such as a basic input output system (BIOS) executed by the CPU 1100 when the computer 1000 is started, a program depending on the hardware of the computer 1000 , and the like.

[0219] HDD 1400 is a computer-readable recording medium in which programs executed by CPU 1100, data used by the programs, etc. are non-temporarily recorded. Specifically, HDD 1400 is a recording medium in which a program for realizing various operations according to the present disclosure is recorded as an example of program data 1450.

[0220] The communication interface 1500 is an interface for connecting the computer 1000 to an external network 1550 (eg, the Internet). For example, the CPU 1100 receives data from another device via the communication interface 1500 or transmits data generated by the CPU 1100 to another device.

[0221] The input / output interface 1600 has a configuration including the above-mentioned I / F unit 18, and is an interface for connecting the input / output device 1650 and the computer 1000. For example, the CPU 1100 receives data from an input device such as a keyboard or a mouse via the input / output interface 1600. In addition, the CPU 1100 transmits data to an output device such as a display, a speaker, or a printer via the input / output interface 1600. In addition, the input / output interface 1600 can be used as a medium interface for reading a program recorded in a predetermined recording medium, etc. Examples of the medium include an optical recording medium such as a digital versatile disk (DVD) or a phase change rewritable disk (PD), a magneto-optical recording medium such as a magneto-optical disk (MO), a magnetic tape medium, a magnetic recording medium, and a semiconductor memory.

[0222] For example, in the case where the computer 1000 is used as the event data processing unit 123 and / or the information processing unit 103 according to the above-described embodiment, the CPU 1100 of the computer 1000 implements the functions of the event data processing unit 123 and / or the information processing unit 103 by executing the program loaded on the RAM 1200. In addition, the HDD 1400 stores the program according to the present disclosure, etc. The CPU 1100 reads the program data 1450 from the HDD 1400 and executes the program data 1450, but as another example, these programs may be acquired from another device via the external network 1550.

[0223] Although the embodiments of the present disclosure have been described above, the technical scope of the present disclosure is not limited to the above-described embodiments, and various modifications may be made without departing from the scope of the present disclosure. In addition, components of different embodiments and modifications may be appropriately combined.

[0224] In addition, the effects in each embodiment described in this specification are merely examples. The effects of the present disclosure are not limited thereto, and other effects may be obtained.

[0225] It should be noted that the present technology may also have the following configurations.

[0226] (1) A biological sample analysis system comprising:

[0227] an irradiation unit, wherein the irradiation unit irradiates biological particles in the biological sample with light;

[0228] a detection unit that detects a change in brightness of light emitted from a biological particle by irradiation with light as an event, the detection unit including a plurality of event pixels arranged in a two-dimensional grid pattern and outputting event data, the event data including address information of each event pixel and time information at which the event was detected in each event pixel;

[0229] a generating unit that corrects time information of a part of the plurality of pieces of event data by using each of a plurality of correction amounts, and generates image data about biological particles for each correction amount by using the plurality of pieces of event data including the event data of the corrected time information;

[0230] an evaluation unit that evaluates each of the plurality of pieces of image data; and

[0231] A processing unit generates biological particle information about the biological particles based on the image data that is most highly evaluated by the evaluation unit among the plurality of pieces of image data.

[0232] (2) The biological sample analysis system according to (1), wherein:

[0233] The biological particle moves along a first direction relative to the detection unit,

[0234] The plurality of event pixels include two or more event pixels arranged adjacent to or apart from each other in a second direction corresponding to the first direction, and

[0235] The generating unit corrects time information of first event data based on an event detected in a first event pixel located downstream in the second direction among two or more event pixels arranged in the second direction by using each correction amount among multiple correction amounts, and superimposes second event data based on an event detected in a second event pixel located upstream in the second direction with each first event data corrected by using each correction amount, thereby generating image data for each correction amount.

[0236] (3) The biological sample analysis system according to (2), wherein:

[0237] The first event data and the second event data each include a polarity of a corresponding event, and

[0238] The generation unit generates image data by superimposing the polarity of the first event data and the polarity of the second event data.

[0239] (4) A biological sample analysis system according to any one of (1) to (3), wherein the evaluation unit evaluates the image data based on at least one of the number of pixels to which event data is assigned among the pixels included in the image data or the number of pieces of event data included in the image data.

[0240] (5) The biological sample analysis system according to (4), wherein:

[0241] The event data includes the polarity of the corresponding event, and

[0242] The evaluation unit counts at least one of the number of pixels assigned with event data or the number of pieces of event data included in the image data for each polarity included in the event data, and evaluates the image data based on at least one of the number of pixels counted or the number of pieces of event data counted for each polarity.

[0243] (6) The biological sample analysis system according to any one of (1) to (5), wherein the evaluation unit evaluates the image data based on equation (19):

[0244]

[0245] Among them, the evaluation value of the image data is e(i) (i is a natural number), n_p represents the number of pixels in the image data to which only positive events are assigned, n_p represents the number of pixels in the image data to which only positive events are assigned, n_n represents the number of pixels to which only negative events are assigned, n_pn represents the number of pixels to which both positive and negative events are assigned, n_p_e(i) represents the number of positive events for the i-th pixel among the pixels where only positive events are detected, n_n_e(i) represents the number of negative events for the i-th pixel among the pixels where only negative events are detected, and w() represents the weighting coefficient.

[0246] (7) The biological sample analysis system according to any one of (1) to (6), wherein the plurality of correction amounts are values ​​determined based on a velocity of the biological particle and an interval between event pixels in a moving direction of the biological particle.

[0247] (8) The biological sample analysis system according to any one of (1) to (7), wherein the generation unit generates the plurality of pieces of image data based on event data output from the detection unit during a predetermined frame period.

[0248] (9) A biological sample analysis system according to (8), wherein the generation unit limits the correction amount used to generate multiple image data based on event data output from the detection unit during a subsequent frame period from multiple correction amounts based on the correction amount used when generating image data with the highest evaluation by the evaluation unit among multiple image data generated based on event data output from the detection unit during a previous frame period.

[0249] (10) A biological sample analysis system according to (8) or (9), wherein the generation unit sets the correction amount used when generating image data with the highest evaluation by the evaluation unit among multiple image data generated based on event data output from the detection unit during a previous frame period to an initial value of the correction amount used to generate multiple image data based on event data output from the detection unit during a subsequent frame period.

[0250] (11) The biological sample analysis system according to any one of (1) to (10), wherein:

[0251] an evaluation unit specifies an optimum correction amount based on an evaluation result for each of the plurality of pieces of image data,

[0252] The generating unit corrects time information of a part of the plurality of pieces of event data by using the optimal correction amount specified by the evaluating unit, and generates image data having the optimal correction amount by using the plurality of pieces of event data including the event data of the corrected time information, and

[0253] The processing unit generates biological particle information based on the image data having the optimal correction amount.

[0254] (12) The biological sample analysis system according to any one of (1) to (11), wherein the biological particle information is image data.

[0255] (13) The biological sample analysis system according to any one of (1) to (12), wherein the biological particles are cellular biological particles or non-cellular biological particles.

[0256] (14) The biological sample analysis system according to any one of (1) to (13), wherein:

[0257] The irradiation unit irradiates a predetermined point on the flow channel with light, and

[0258] The biological particles move in the flow channel so as to pass through the predetermined point.

[0259] (15) The biological sample analysis system according to any one of (1) to (14), further comprising a spectral optical system for spectrally separating light emitted from biological particles, wherein:

[0260] The bioparticles are labeled with one or more fluorescent dyes.

[0261] an irradiation unit irradiates the biological particles with light including excitation light in one or more wavelength ranges, and

[0262] The detection unit detects the brightness change of each light ray spectrally separated by the spectral optical system as an event.

[0263] (16) The biological sample analysis system according to (15), wherein the detection unit includes a plurality of detection units, and the plurality of detection units are arranged in a one-to-one correspondence with each light ray spectrally separated by the spectral optical system.

[0264] (17) An information processing device comprising:

[0265] a generating unit that corrects time information of a part of a plurality of pieces of event data output by a detecting unit by using each of a plurality of correction amounts, the detecting unit detecting a change in brightness of light emitted from biological particles in a biological sample by irradiating the biological particles with light as an event, the detecting unit including a plurality of event pixels arranged in a two-dimensional grid pattern and outputting event data, the event data including address information of each event pixel and time information of when the event was detected in each event pixel, and generates image data about the biological particles for each correction amount by using the plurality of pieces of event data including the event data after the corrected time information;

[0266] an evaluation unit that evaluates each of the plurality of pieces of image data; and

[0267] A processing unit generates biological particle information about the biological particles based on the image data that is most highly evaluated by the evaluation unit among the plurality of pieces of image data.

[0268] (18) An information processing method comprising:

[0269] correcting time information of a portion of a plurality of pieces of event data output from a detection unit by using each of a plurality of correction amounts, the detection unit detecting a change in brightness of light emitted from biological particles in a biological sample by irradiating the biological particles with light as an event, the detection unit including a plurality of event pixels arranged in a two-dimensional grid pattern and outputting event data, the event data including address information of each event pixel and time information of when the event was detected in each event pixel;

[0270] generating image data about biological particles for each correction amount by using a plurality of pieces of event data including event data of corrected time information;

[0271] evaluating each of the plurality of pieces of image data; and

[0272] Biological particle information about biological particles is generated based on the highest-evaluated image data among the plurality of pieces of image data.

[0273] (19) A biological sample analysis method comprising:

[0274] irradiating biological particles in the biological sample with light by an irradiation unit;

[0275] outputting event data through a detection unit, the detection unit detecting a change in brightness of light emitted from biological particles by irradiation with light as an event, and the detection unit including a plurality of event pixels arranged in a two-dimensional grid pattern, the event data including address information of each event pixel and time information at which the event was detected in each event pixel;

[0276] correcting time information of a portion of event data among a plurality of pieces of event data by using each of a plurality of correction amounts, and generating image data about biological particles for each correction amount by using the plurality of pieces of event data including the event data of the corrected time information;

[0277] evaluating each of the plurality of pieces of image data; and

[0278] Biological particle information about biological particles is generated based on the highest-evaluated image data among the plurality of pieces of image data.

[0279] Reference numerals list

[0280] 11 TDI circuit

[0281] 12 Evaluation Units

[0282] 13 Select Unit

[0283] 14 Buffer

[0284] 15 Reconstruction Unit

[0285] 20 event pixels

[0286] 100 Biological sample analysis device

[0287] 101 Light irradiation unit

[0288] 102 Detection Unit

[0289] 103 Information Processing Unit

[0290] 104 Sorting Units

[0291] 111 Light source unit

[0292] 112 Light-guiding optical system

[0293] 121 Detection optical system

[0294] 122 EVS device

[0295] 123 Event Data Processing Unit

[0296] 124 Speed ​​measurement unit

[0297] 125 Recording Units

[0298] 201 pixel array unit

[0299] 202 X Arbitrator

[0300] 203 Y Arbitrator

[0301] 204 event signal processing circuit

[0302] 205 System Control Circuit

[0303] 206 output I / F.

Claims

1. A biological sample analysis system, comprising: an irradiation unit, the irradiation unit irradiating biological particles in the biological sample with light; a detection unit that detects a change in brightness of light emitted from the bioparticles by irradiation with light as an event, the detection unit including a plurality of event pixels arranged in a two-dimensional grid pattern and outputting event data including address information of each of the event pixels and time information at which the event was detected in each of the event pixels; a generating unit that corrects time information of a part of the plurality of pieces of the event data by using each of a plurality of correction amounts, and generates image data about the biological particle for each correction amount by using the plurality of pieces of the event data including the event data of the corrected time information; an evaluation unit, the evaluation unit evaluating each piece of image data among the plurality of pieces of image data; as well as A processing unit generates biological particle information about the biological particles based on the image data that is most highly evaluated by the evaluation unit among the plurality of pieces of image data.

2. The biological sample analysis system according to claim 1, wherein: The biological particle moves along a first direction relative to the detection unit, The plurality of event pixels include two or more event pixels arranged adjacent to or apart from each other in a second direction corresponding to the first direction, and The generating unit corrects time information of first event data based on an event detected in a first event pixel located downstream in the second direction among two or more event pixels arranged in the second direction by using each of the multiple correction amounts, and superimposes second event data based on an event detected in a second event pixel located upstream in the second direction with each of the first event data corrected by using each of the correction amounts, thereby generating the image data for each correction amount.

3. The biological sample analysis system according to claim 2, wherein: The first event data and the second event data each include a polarity of a corresponding event, and The generation unit generates the image data by superimposing the polarity of the first event data and the polarity of the second event data.

4. The biological sample analysis system according to claim 1, wherein: The evaluation unit evaluates the image data based on at least one of the number of pixels to which the event data is assigned among the pixels included in the image data or the number of pieces of the event data included in the image data.

5. The biological sample analysis system according to claim 4, wherein: The event data includes the polarity of the corresponding event, and The evaluation unit counts, for each polarity included in the event data, at least one of the number of pixels assigned with the event data or the number of pieces of event data included in the image data, and evaluates the image data based on at least one of the counted number of pixels or the counted number of pieces of event data for each polarity.

6. The biological sample analysis system according to claim 1, wherein: The evaluation unit evaluates the image data based on equation (1): Among them, the evaluation value of the image data is e(i) (i is a natural number), n_p represents the number of pixels in the image data to which only positive events are assigned, n_n represents the number of pixels to which only negative events are assigned, n_pn represents the number of pixels to which both the positive events and the negative events are assigned, n_p_e(i) represents the number of positive events for the i-th pixel among the pixels in which only the positive events are detected, n_n_e(i) represents the number of negative events for the i-th pixel among the pixels in which only the negative events are detected, and w() represents a weighting coefficient.

7. The biological sample analysis system according to claim 1, wherein: The plurality of correction amounts are values ​​determined based on the velocity of the biological particle and the interval between the event pixels in the moving direction of the biological particle.

8. The biological sample analysis system according to claim 1, wherein: The generation unit generates the plurality of pieces of the image data based on the event data output from the detection unit during a predetermined frame period.

9. The biological sample analysis system according to claim 8, wherein: The generation unit limits the correction amount used to generate multiple image data based on the event data output from the detection unit during a subsequent frame cycle from multiple correction amounts, based on the correction amount used when generating image data highest evaluated by the evaluation unit among multiple image data generated based on the event data output from the detection unit during a previous frame cycle.

10. The biological sample analysis system according to claim 8, wherein: The generation unit sets the correction amount used when generating image data highest evaluated by the evaluation unit among multiple image data generated based on event data output from the detection unit during a previous frame cycle as an initial value of the correction amount used to generate multiple image data based on event data output from the detection unit during a subsequent frame cycle.

11. The biological sample analysis system according to claim 1, wherein: the evaluation unit specifies an optimum correction amount based on an evaluation result for each of the plurality of pieces of image data, the generating unit corrects the time information of a part of the plurality of pieces of the event data by using the optimal correction amount specified by the evaluating unit, and generates image data having the optimal correction amount by using the plurality of pieces of the event data including the event data of the corrected time information, and The processing unit generates the biological particle information based on the image data having the optimal correction amount.

12. The biological sample analysis system according to claim 1, wherein: The biological particle information is image data.

13. The biological sample analysis system according to claim 1, wherein: The biological particles are cellular biological particles or non-cellular biological particles.

14. The biological sample analysis system according to claim 1, wherein: The irradiation unit irradiates a predetermined point on the flow channel with light, and The biological particles move in the flow channel so as to pass through the predetermined point.

15. The biological sample analysis system according to claim 1, further comprising a spectral optical system for spectrally separating the light emitted from the biological particles, wherein: The biological particles are labeled with one or more fluorescent dyes, The irradiation unit irradiates the biological particles with light including excitation light in one or more wavelength ranges, and The detection unit detects the brightness change of each light beam spectrally separated by the spectral optical system as the event.

16. The biological sample analysis system according to claim 15, wherein: The detection unit includes a plurality of detection units, and the plurality of detection units are arranged in a one-to-one correspondence with each light beam subjected to spectral separation by the spectral optical system.

17. An information processing device, comprising: a generating unit that corrects time information of a part of a plurality of pieces of event data output by a detecting unit that detects, as an event, a change in brightness of light emitted from biological particles in a biological sample by irradiating the biological particles with light, by using each of a plurality of correction amounts, the detecting unit including a plurality of event pixels arranged in a two-dimensional grid pattern and outputting event data including address information of each of the event pixels and time information of when the event was detected in each of the event pixels, and generates image data about the biological particles for each correction amount by using the plurality of pieces of event data including the corrected time information; an evaluation unit, the evaluation unit evaluating each of the plurality of image data; as well as A processing unit generates biological particle information about the biological particles based on the image data that is most highly evaluated by the evaluation unit among the plurality of pieces of image data.

18. An information processing method, comprising: correcting time information of a portion of a plurality of pieces of event data output from a detection unit that detects, as an event, a change in brightness of light emitted from biological particles in a biological sample by irradiating the biological particles with light, the detection unit including a plurality of event pixels arranged in a two-dimensional grid pattern and outputting event data, the event data including address information of each of the event pixels and time information at which the event is detected in each of the event pixels, by using each of a plurality of correction amounts; generating image data about the biological particle for each correction amount by using a plurality of pieces of the event data including the event data of the corrected time information; evaluating each of the plurality of image data; as well as Biological particle information about the biological particles is generated based on the highest-evaluated image data among the plurality of pieces of the image data.

19. A biological sample analysis method, comprising: irradiating biological particles in the biological sample with light by an irradiation unit; outputting event data through a detection unit that detects a change in brightness of light emitted from the biological particle by irradiation with light as an event, and the detection unit includes a plurality of event pixels arranged in a two-dimensional grid pattern, the event data including address information of each of the event pixels and time information at which the event is detected in each of the event pixels; correcting time information of a part of the plurality of pieces of event data by using each of a plurality of correction amounts, and generating image data about the biological particle for each correction amount by using the plurality of pieces of event data including the corrected time information; evaluating each of the plurality of image data; as well as Biological particle information about the biological particles is generated based on the highest-evaluated image data among the plurality of pieces of the image data.

Images