Patent Publication Number: US-2020285038-A1

Title: Microscope system

Description:
This is a Continuation of Application No. PCT/JP2017/042685 filed Nov. 28, 2017. The disclosure of the prior application is hereby incorporated by reference herein in its entirety. 
    
    
     TECHNICAL FIELD 
     The technology disclosed herein relates to a microscope system. 
     BACKGROUND ART 
     In a proposed scanning optical microscope device a measurement sample is scanned, and the image obtained thereby is employed to decide the shape of a shape-shifting mirror (Patent Document 1). 
     RELATED ART DOCUMENTS 
     Patent Documents 
     
         
         Patent Document 1: Japanese Patent Application Laid-Open (JP-A) No. 2005-292538. 
       
    
     SUMMARY OF INVENTION 
     A microscope system according to a first aspect of the present disclosure includes a decision section configured to decide an acquisition condition of a second image based on a first image and an acquisition condition of the first image. 
     A microscope system according to a second aspect of the present disclosure includes a decision section configured to decide an acquisition condition of a second image based on a first image and an acquisition condition of the first image, wherein the acquisition condition of the second image includes plural items. 
     A microscope system according to a third aspect of the present disclosure includes an illumination optical system configured to illuminate an object with light emitted from a light source, a detection section configured to detect light from the object, an image generation section configured to generate an image by employing the detected light, and a decision section configured to decide an acquisition condition of a second image based on the first image and an acquisition condition of the first image. 
     A microscope system according to a fourth aspect of the present disclosure includes an illumination optical system configured to illuminate an object with light emitted from a light source, a detection section configured to detect light from the object, an image generation section configured to generate an image by employing the detected light, and a decision section configured to decide an acquisition condition of a second image based on a first image, wherein the second image acquisition condition includes plural items. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a diagram illustrating an example of a microscope system. 
         FIG. 2  is a diagram illustrating an example of a configuration of a microscope device  101 . 
         FIG. 3A  is a block diagram illustrating an example of relevant configuration of an electrical system of the microscope device  101  according to a first exemplary embodiment. 
         FIG. 3B  is a block diagram illustrating an example of relevant configuration of an electrical system of an information processing device  102  according to a first exemplary embodiment. 
         FIG. 3C  is a block diagram illustrating an example of relevant configuration of an electrical system of a server  104  according to a first exemplary embodiment. 
         FIG. 4A  is a functional block diagram illustrating an example of relevant functions of the microscope device  101  according to the first exemplary embodiment. 
         FIG. 4B  is a functional block diagram illustrating an example of relevant functions of the information processing device  102  according to the first exemplary embodiment. 
         FIG. 4C  is a functional block diagram illustrating an example of relevant functions of the server  104  according to the first exemplary embodiment. 
         FIG. 5  is a diagram illustrating an example of a task sequence when an image acquisition processing program is executed by a CPU  302  of a microscope control section  218  of the microscope device  101  and a CPU  402  of the information processing device  102 . 
         FIG. 6  is a diagram illustrating an example of specific content of a model of computation. 
         FIG. 7  is a diagram illustrating an example of a way in which data is exchanged when an image acquisition processing program is executed. 
         FIG. 8A  is a diagram illustrating an example of specific content of a model of computation for a case in which plural second acquisition conditions are determined. 
         FIG. 8B  is a diagram illustrating an example of a model of computation for a case in which a first image  712  is input as is, without being subjected to image processing. 
         FIG. 8C  is a diagram illustrating an example of a model of computation for a case in which an image of only a region of a sample  208  is input to an input layer  701 . 
         FIG. 9  is a diagram illustrating an example of a user interface for generating teaching data, as displayed on a display screen  1800  of a display device  414  of the information processing device  102 . 
         FIG. 10  is a diagram illustrating an example of a task sequence when a teaching data generation processing program is executed by the CPU  302  of the microscope control section  218  of the microscope device  101  and the CPU  402  of the information processing device  102 . 
         FIG. 11  is a diagram illustrating two sets of teaching data. 
         FIG. 12  is a diagram illustrating an example of a user interface for generating teaching data, as displayed on a display screen  2300  of a display device  414  of the information processing device  102 . 
         FIG. 13  is a diagram illustrating an example of a task sequence when a teaching data generation processing program is executed by the CPU  302  of the microscope control section  218  of the microscope device  101  and the CPU  402  of the information processing device  102 . 
         FIG. 14  is a diagram illustrating two sets of teaching data for a case in which teaching data that includes plural setting values is generated. 
         FIG. 15  is a flowchart illustrating an example of a model-of-computation update processing program executed by a CPU  502  of the server  104 . 
         FIG. 16  is a diagram illustrating an example of operation of model-of-computation update processing. 
         FIG. 17  is a diagram illustrating an example of a task sequence when an image acquisition processing program is executed by the CPU  302  of the microscope control section  218  of the microscope device  101  and the CPU  402  of the information processing device  102 . 
         FIG. 18  is a diagram illustrating plural imaging data and plural teaching data corresponding to the respective imaging data. 
         FIG. 19  is flowchart illustrating an example of a model-of-computation update processing program executed by the CPU  502  of the server  104 . 
         FIG. 20  is a diagram illustrating an example of operation of model-of-computation update processing. 
         FIG. 21  is a diagram illustrating an example of a user interface for displaying a first image and a second image on a display screen  3600  of a display device  414  of the information processing device  102  and setting a value of a first acquisition condition. 
         FIG. 22  is a diagram illustrating an example of a task sequence when an image acquisition processing program is executed by the CPU  302  of the microscope control section  218  of the microscope device  101  and the CPU  402  of the information processing device  102 . 
         FIG. 23  is a diagram illustrating an example of a user interface displaying a second acquisition condition value, and a second image obtained with the second acquisition condition value. 
         FIG. 24  is a diagram illustrating an example of a user interface, as displayed on a display screen  4100  of the display device  414  of the information processing device  102 . 
         FIG. 25  is a flowchart illustrating an example of a Live imaging processing program executed by the CPU  402  of the information processing device  102 . 
         FIG. 26  is a diagram illustrating an example of a user interface, as displayed on a display screen  4300  of the display device  414  of the information processing device  102 . 
         FIG. 27  is a flowchart illustrating an example of a time-lapse imaging processing program executed by the CPU  402  of the information processing device  102 . 
         FIG. 28  is a flowchart illustrating an example of a normal imaging processing program executed by the CPU  402  of the information processing device  102  in a sixth modified example to instruct the microscope device  101  to perform normal imaging. 
         FIG. 29  is a flowchart illustrating an example of a model-of-computation update processing program executed by the CPU  502  of the server  104  in the sixth modified example. 
         FIG. 30  is a diagram illustrating an example of operation of model-of-computation update processing in the sixth modified example. 
         FIG. 31  is a diagram illustrating an example of a configuration of a microscope system of a second exemplary embodiment. 
         FIG. 32  is a diagram illustrating an example of a task sequence when a model-of-computation update processing program is executed by a CPU  302  of a microscope control section  218  of the microscope device  101  and a CPU  402  of the information processing device  102 . 
         FIG. 33  is a diagram illustrating an example of a configuration of a microscope system lacking a server  104 . 
         FIG. 34A  is diagram illustrating a configuration of a first modified example of an overall configuration. 
         FIG. 34B  is a diagram illustrating a configuration of a second modified example of an overall configuration. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Description follows regarding examples of exemplary embodiments according to the present invention, with reference to the appended drawings. 
     A first exemplary embodiment will now be described. 
     Configuration 
     A configuration of a microscope system will now be described with reference to  FIG. 1 . As illustrated in  FIG. 1 , a microscope system is equipped with an information processing device  102  and a server device (hereafter referred to as “server”)  104  that are mutually connected over a network  103 , and with a laser scanning microscope (hereafter referred to as microscope)  101  that is connected to the information processing device  102 . 
     Next, description follows regarding a configuration of the microscope device  101  for acquiring images of a sample (cells, for example)  208  placed on a stage  220  and having fluorescent dye pre-added thereto, with reference to  FIG. 2 . As illustrated in  FIG. 2 , the microscope device  101  includes a light source section  209 , an illumination lens  210 , an excitation filter  211 , and a dichromic mirror  212 . The light source section  209  is configured from a light source  209 - 1 , a shutter  209 - 2 , and an acousto-optic element  209 - 3 . A device that emits excitation light at a specific wavelength, such as a laser beam, may be employed as the light source  209 - 1 . The microscope device  101  includes a scanner  213  and an object lens  214  arranged at the sample  208  side of the dichromic mirror  212 . As an example, a galvano scanner or a resonant scanner may be employed as the scanner  213 . The microscope device  101  also includes a fluorescence filter  215 , a light condensing lens  216 , and a detection section  217  arranged at the opposite side of the dichromic mirror  212  to the sample  208  side. A photomultiplier tube may be employed as the detection section  217 . The microscope device  101  includes a stage drive section  221  to move the stage  220  with the sample  208  placed thereon in a horizontal plane. Note that the stage drive section  221  may also be configured to move the stage  220  in an optical axis direction of the object lens  214 . The microscope device  101  also includes a pinhole  230  and a pinhole drive section  231  for adjusting the size of the pinhole  230 . The pinhole  230  is arranged at a light incident side of the detection section  217 , is arranged at a conjugate position to a focal plane of the sample, and, by the diameter of the pinhole  230 , removes light from positions in the optical axis direction displaced from the focal plane by a given displacement or greater. The microscope device  101  also includes a microscope control section  218  to control operation of the plural configuration sections that operate to acquire images, such as, for example, the light source section  209 , the pinhole drive section  231 , the stage drive section  221 , and the scanner  213 . 
     The sample  208  is an example of an “object” of technology disclosed herein. The light source  209 - 1  is an example of a “light source” of technology disclosed herein. The illumination lens  210 , the excitation filter  211 , the dichromic mirror  212 , the scanner  213 , and the object lens  214  are examples of an “illumination optical system” of technology disclosed herein. The detection section  217  is an example of a “detection section” of technology disclosed herein. The microscope control section  218  is an example of an “image generation section” of technology disclosed herein. 
     Next, description follows regarding relevant configuration of electrical systems of the devices in the microscope system, with reference to  FIG. 3A to 3B . As illustrated in  FIG. 3A , the microscope device  101  is equipped with the microscope control section  218 . The microscope control section  218  is configured by a computer including a central processing unit (CPU)  302 , read only memory (ROM)  304 , random access memory (RAM)  306 , and a secondary storage device  308 . Each of the programs described later (programs of  FIG. 5 ,  FIG. 10 ,  FIG. 13 ,  FIG. 17 , and  FIG. 22  executed by the microscope control section  218 ) are stored in the ROM  304 . The CPU  302  reads each of the programs (programs of  FIG. 5 ,  FIG. 10 ,  FIG. 13 ,  FIG. 17 , and  FIG. 22  executed by the microscope control section  218 ) from the ROM  304 , expands these programs into the RAM  306 , and then executes the programs. The microscope device  101  includes the light source section  209 , the detection section  217 , an input device  310 , a display device  314 , the pinhole drive section  231  for adjusting the size of the pinhole  230 , the stage drive section  221  to move the stage  220 , and a transceiver device  316  for exchanging data with the information processing device  102 . The sections illustrated in  FIG. 3A  of the microscope device  101  are mutually connected together by a bus  320 . 
     As illustrated in  FIG. 3B , the information processing device  102  includes a CPU  402 , ROM  404 , RAM  406 , a secondary storage device  408 , a transceiver device  410  for exchanging data with the server  104  over the network  103 , a transceiver device  416  for exchanging data with the microscope device  101 , an input device  412 , and a display device  414 . Each of the sections illustrated in  FIG. 3B  of the information processing device  102  are mutually connected together by a bus  420 . Each of the programs described later (programs of  FIG. 5 ,  FIG. 10 ,  FIG. 13 ,  FIG. 17 ,  FIG. 22 , and  FIG. 32  executed by the information processing device  102 , and programs of  FIG. 25 ,  FIG. 27 , and  FIG. 28 ) is stored in the ROM  404 . The CPU  402  reads each of the programs (programs of  FIG. 5 ,  FIG. 10 ,  FIG. 13 ,  FIG. 17 ,  FIG. 22 , and  FIG. 32  executed by the information processing device  102 , and programs of FIG.  25 ,  FIG. 27 , and  FIG. 28 ) from the ROM  404 , expands the programs into the RAM  406 , and executes the programs. 
     As illustrated in  FIG. 3C , the server  104  includes a CPU  502 , a ROM  504 , RAM  506 , a secondary storage device  508 , a transceiver device  512  for exchanging data with the information processing device  102  over the network  103 , a display device  514 , and an input device  516 . Each of the sections illustrated in  FIG. 3C  of the server  104  are mutually connected together by a bus  520 . Each of the programs described later (the programs of  FIG. 15 ,  FIG. 19 , and  FIG. 29 , and the program of  FIG. 32  executed by the server  104 ) is stored in the ROM  504 . The CPU  502  reads each of the programs (programs of  FIG. 15 ,  FIG. 19 , and  FIG. 29  and the program of  FIG. 32  executed by the server  104 ) from the ROM  504 , expands the programs into the RAM  506 , and executes the programs. 
     A graphics processing unit (GPU) and field programmable gate arrays (FPGA) may be employed instead of the CPUs  302 ,  402 ,  502 . For example, a hard disk drive (HDD), flexible disk, magneto-optical disk, flash memory or the like may be employed as the secondary storage devices  308 ,  408 ,  508 . 
     Next, description follows regarding functions of each of the devices of the microscope system, with reference to  FIG. 4A  to  FIG. 4C . As illustrated in  FIG. 4A , the microscope device  101  includes a control section  602  and an image construction section  604 . The CPU  302  executes each of the programs (programs of  FIG. 5 ,  FIG. 10 ,  FIG. 13 ,  FIG. 17 , and  FIG. 22  executed by the microscope control section  218 ), and the CPU  302  accordingly functions as the control section  602  and the image construction section  604 . 
     As illustrated in  FIG. 4B , the information processing device  102  includes a setting value computation section  7020  including a model of computation  704 , and a teaching data generation section  706 . The CPU  402  executes each of the programs (programs of  FIG. 5 ,  FIG. 10 ,  FIG. 13 ,  FIG. 17 ,  FIG. 22 , and  FIG. 32  executed by the information processing device  102 , and programs of  FIG. 25 ,  FIG. 27 , and  FIG. 28 ), and the CPU  402  accordingly functions as the setting value computation section  7020  and the teaching data generation section  706 . 
     Note that the teaching data generation section  706  illustrating an example of a “teaching image generation section” of technology disclosed herein. 
     The model of computation  704  is an example of a “model of computation” of technology disclosed herein. The setting value computation section  7020  is an example of a “decision section” of technology disclosed herein. 
     As illustrated in  FIG. 4C , the server  104  is equipped with a machine learning section  802  including a model of computation  804 . Although described in detail later, briefly the server  104  updates the model of computation  804  based on teaching data transmitted from the information processing device  102 , and transmits the updated model of computation  804  to the information processing device  102 . The information processing device  102  stores the model of computation received from the server  104  in the setting value computation section  7020 , substituting for the existing model of computation  704 . The CPU  502  executes each of the programs (programs of  FIG. 15 ,  FIG. 19 , and  FIG. 29  and programs executed by the server  104  of  FIG. 32 ), and the CPU  502  accordingly functions as the machine learning section  802 . 
     The machine learning section  802  is an example of an “updating section” of technology disclosed herein. 
     Operation 
     Description next follows regarding operation of technology disclosed herein. 
     First an operation using the microscope device  101  to acquire an image representing the sample  208  will be described, with reference to  FIG. 2 . Light (excitation light) of a specific wavelength emitted from the light source section  209  passes through the illumination lens  210  and the excitation filter  211  and is incident to the dichromic mirror  212 . 
     The dichromic mirror  212  of the present exemplary embodiment has properties that reflect the excitation light of the specific wavelength, but transmits light of wavelengths other than the specific wavelength of the excitation light. This results in the excitation light incident to the dichromic mirror  212  being reflected by the dichromic mirror  212  and being incident to the scanner  213 . The scanner  213  scans the incident excitation light, and the scanned light is condensed by the object lens  214  onto the sample  208  above the stage  220 . The position of the condensed light is scanned in two dimensions by the scanner  213 . 
     The fluorescent dye has been pre-added to the sample  208 , and so light (fluorescence) is accordingly generated a position on the sample  208  illuminated by the excitation light. The light (fluorescence) emitted from the sample  208  passes through the object lens  214  and the scanner  213 , and is incident to the dichromic mirror  212 . The light (fluorescence) incident to the dichromic mirror  212  has a different wavelength to that of the excitation light, and so is transmitted by the dichromic mirror  212 , passes through the fluorescence filter  215 , and is condensed by the light condensing lens  216 . The condensed light (fluorescence) is incident to the detection section  217 . The detection section  217  performs photoelectric conversion on the incident light, generates digital data with values corresponding to the amount of light (brightness), and transmits the generated digital data to the microscope control section  218 . 
     The microscope control section  218  records this data as single pixel data in the RAM  306  provided to the microscope control section  218 . This recording is synchronized to the timing of the two dimensional scanning of the scanner  213  and the synchronized data is arranged to generate a single image such that an image is acquired thereby. 
     The microscope control section  218  controls the plural configuration sections that operate to acquire images, i.e. the light source section  209 , the pinhole drive section  231 , the stage drive section  221 , and the like. Thus the microscope control section  218  sets, as an image acquisition condition, for example, an intensity of excitation light of the light source section  209 , a size of the pinhole  230 , a scan speed of a spot on the sample  208 , etc. 
     The microscope control section  218  transmits the acquired image and values of the acquisition condition to the information processing device  102 . 
     In the technology disclosed herein, a second image acquisition condition (second acquisition condition) is decided based on the first image representing the sample  208  acquired with the microscope device  101 , and based on a first acquisition condition that is setting information for at least one of the configuration sections of the microscope device  101  (the light source section  209 , the scanner  213 , or the like) when acquiring the first image. 
     The second acquisition condition employed here may include a single item, however, plural specific acquisition states for acquiring a second image may be included, such as, for example, plural items that both affect the brightness of the sample  208  and also generate various different effects. For example, plural items, such as the intensity of excitation light, applied voltage, PMT offset, scan size, scan speed, and pinhole size, etc. may be included in the second image acquisition condition. The applied voltage is a value of voltage applied to the detection section  217 . The PMT offset is a value representing an amount of offset (upward shift) in output current of the detection section  217 . The scan size is a value representing a distance on the sample scanned by the scanner  213  in a given time (sample time). The scan speed is a value of the speed with which light is scanned by the scanner  213 . The pinhole size is the size of the pinhole  230 . The intensity of excitation light, applied voltage, PMT offset, scan size, scan speed, and pinhole size are examples of “items” of technology disclosed herein. 
     For example, the intensity of excitation light, applied voltage, scan speed, and pinhole size are all parameters that raise the brightness of an imaging image. However, at the same time as generating brightness they also cause different effects to occur, as listed in Table 1. 
     
       
         
           
               
               
               
               
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                   
                 S/N 
                   
                 Fluorescent 
                 Imaging 
               
               
                   
                 Brightness 
                 Ratio 
                 Resolution 
                 Dye Fade 
                 Time 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 Intensity of 
                 Increase 
                 Increase 
                 — 
                 Increase 
                 — 
               
               
                 Excitation 
               
               
                 Light 
               
               
                 (Increase) 
               
               
                 Applied 
                 Increase 
                 Decrease 
                 — 
                 — 
                 — 
               
               
                 Voltage 
               
               
                 (Increase) 
               
               
                 Scan Speed 
                 Increase 
                 — 
                 — 
                 — 
                 Increase 
               
               
                 (Decrease) 
               
               
                 Pinhole Size 
                 Increase 
                 Increase 
                 Decrease 
                 — 
                 — 
               
               
                 (Increase) 
               
               
                   
               
            
           
         
       
     
     More specifically, as the intensity of excitation light is raised, the brightness and the S/N ratio also rises, however a rise in the intensity of excitation light also causes the progression of fluorescent dye fading in the cells of the sample  208 . As the applied voltage is raised the brightness also rises, however the S/N ratio decreases. As the scan speed is lowered the brightness also rises, however the imaging time is lengthened thereby. As the pinhole size is raised the brightness and the S/N ratio also rise, however the resolution falls. 
     There are accordingly various disadvantages inherent in the parameters to raise brightness, making it necessary to decide on the values to set for each item by making a comprehensive determination for various evaluation items, rather than independently optimizing each single setting item. 
     Although described in detail later, briefly in the present exemplary embodiment a model of computation is employed to decide an acquisition condition for a second image, and the model of computation is updated (e.g. trained). 
     The second acquisition condition, i.e. the items mentioned above, are not limited to the intensity of excitation light, applied voltage, PMT offset, scan size, scan speed, and pinhole size. For example, the following items are also be applicable. Namely, an object lens Z coordinate, an object lens type, a line average, a line integral, and a line skip may be included in the items mentioned above. 
     The object lens Z coordinate is a coordinate of the object lens  214  along the optical axis direction. The object lens  214  is attached to a revolver  219  that is capable of moving along the optical axis direction, and the object lens  214  is moved in the optical axis direction by the revolver  219  being moved in the optical axis direction by the microscope control section  218 . The optical axis direction coordinate of the object lens  214  can be ascertained from a position of the object lens  214  as measured by an encoder or the like. 
     Description follows regarding the object lens type. In the example illustrated in  FIG. 2  there is a single object lens  214  provided. However, some laser scanning microscopes are laser scanning microscopes having object lenses of plural types, i.e. different magnification or the like, attached to the revolver  219 . An object lens selected from out of the plural object lenses is then set on the optical axis by the revolver  219 . The object lens type is determined by the magnification of the lens or the like. 
     Description follows regarding line average and line integral. 
     A line average is the number of scans performed in a case in which plural scans are performed at the same position (line) and values obtained by averaging the acquired data is employed for pixel values of that position (line). For example, in a case in which the line average is two, then scanning is performed twice at the same position (line), and the average values thereof are employed as the pixel values. A line integral is a number of scans performed in a case in which plural scans are performed at the same position (line) and values obtained by summing the acquired data are employed as pixel values for that position (line). In a case in which the line integral is two, then the sum of two scans is employed for the pixel values. These correspond to binning for a camera. Note that although description has been given here for a line shape, a task of scanning and generating an image may be performed plural times for an entire plane, and averaging or integration performed using plural sets of two dimensional data. 
     A line skip is a sampling width of a line unit. In a case in which the line skip is two then detection is performed every other line. 
     Next, description follows regarding image acquisition processing of an image acquisition processing program executed by the CPU  302  of the microscope control section  218  of the microscope device  101  and the CPU  402  of the information processing device  102 , with reference to  FIG. 5 .  FIG. 7  illustrates an example of a way in which data is exchanged when an image acquisition processing program is executed. 
     When a user operates the input device  412  of the information processing device  102  and an instruction to acquire a first image representing the sample  208  is input to the information processing device  102 , an instruction from the information processing device  102  is input to the microscope control section  218  of the microscope device  101 , and the image acquisition processing of  FIG. 5  executed by the information processing device  102  is started. 
     When the image acquisition processing is started, at step  900  the setting value computation section  7020  of the information processing device  102  transmits, to the microscope device  101 , the first condition acquisition command data to command the microscope device  101  so as to acquire the first acquisition condition i.e. setting information for the plural configuration sections of the microscope device  101  when acquiring the first image. The control section  602  of the microscope device  101  receives the first condition acquisition command data. 
     At step  902 , the control section  602  of the microscope device  101  acquires the values of the first acquisition condition. Examples of values of the first acquisition condition include: 10, for example, as an excitation light intensity value v 1 ; 100, for example, as an applied voltage value v 2 ; 1, for example, as a scan speed value v 3 ; and 30, for example, as a pinhole size value v 4 . At step  904 , the first acquisition condition is transmitted to the information processing device  102 , and the information processing device  102  receives the first acquisition condition. 
     At step  906 , the setting value computation section  7020  of the information processing device  102  transmits, to the microscope device  101 , first image imaging command data to command the microscope device  101  so as to image the first image with the first acquisition condition. The control section  602  of the microscope device  101  receives the first image imaging command data. 
     At step  908 , the image construction section  604  of the microscope device  101  controls plural configuration sections (the light source section  209 , the scanner  213 , etc.) so as to acquire the first image. The first image is thereby acquired by operating to acquire an image representing the sample  208 . 
     At step  910 , the control section  602  of the microscope device  101  transmits the first image to the information processing device  102 , and the information processing device  102  receives the first image. 
     While this will be described in detail later, briefly, at step  912 , the setting value computation section  7020  of the information processing device  102  employs a model of computation  704  held in the setting value computation section  7020  (more specifically, stored in the secondary storage device  408 ) built from the first image and the respective values of the first acquisition condition to compute values of a second acquisition condition of the microscope device  101 . 
     At step  914 , the setting value computation section  7020  of the information processing device  102  transmits, to the microscope device  101 , second condition setting command data to command the microscope device  101  so as to set the values of the second acquisition condition in the microscope device  101 . The control section  602  of the microscope device  101  receives the second acquisition condition. 
     At step  916 , the control section  602  of the microscope device  101  sets the second acquisition condition. For example, the microscope control section  218  sets the light source  209 - 1  such that the intensity of excitation light from the light source  209 - 1  is a value as instructed in the second acquisition condition. Note that the microscope control section  218  may be configured so as to set the pinhole drive section  231  such that the size of the pinhole  230  is a value as instructed in the second acquisition condition. 
     At step  918 , the setting value computation section  7020  of the information processing device  102  transmits second image imaging command data commanding the microscope device  101  to image the second image with the second acquisition condition, and the control section  602  of the microscope device  101  receives the second image imaging command data. 
     At step  920 , the image construction section  604  of the microscope device  101  acquires the second image under the second acquisition condition. At step  924  the image construction section  604  transmits the second image to the information processing device  102 , and the information processing device  102  receives the second image and displays the second image on the display device  414 . 
     By transmitting the second acquisition condition to the microscope device  101  in the manner described above, the microscope device  101  can be caused to acquire the second image under the second acquisition condition. 
     Next, description follows regarding processing of step  912  of  FIG. 5  to compute the second acquisition condition, with reference to  FIG. 6 . 
     A multilayer neural network may be employed as an example of the model of computation  704 , such as, for example, a convolutional neural network as illustrated in  FIG. 6 .  FIG. 6  illustrates an example of a case in which a single first image  710  and four first acquisition condition values v=(v 1 , v 2 , v 3 , v 4 ) are employed when computing a single second acquisition condition. For example, as described above, the first acquisition condition v is set with 10 for the excitation light intensity value v 1 , 100 for the applied voltage value v 2 , 1 for the scan speed value v 3 , and 30 for the pinhole size value v 4 . As illustrated in  FIG. 6 , the convolutional neural network includes an input layer  701  input with data related to the first image  710 , a convolutional layer  702  to perform a convolutional operation on the data related to the first image  710  that has been input to the input layer  701 , and an output layer  703  to output a second image acquisition condition y from the output of the convolutional layer  702  and the respective values of the first image acquisition condition v. 
     The model of computation referred to here is a system for accepting one or more input signal and deciding one or more output signal. Although a neural network as described above corresponds to such a model of computation, there is no limitation to a neural network, and, for example, a linear regression model or a support vector machine may be employed therefor. 
     Moreover, a neural network is a model of computation for accepting one or more input signal, and employs a weight and an activation function for each of the input signals to decide the one or more output signals. 
     The convolutional neural network illustrated in  FIG. 6  is an example of a “multilayer neural network” of technology disclosed herein. A multilayer neural network employing a fully connected layer instead of the convolutional layer  702  may also be employed. The number of layers in the neural network is also not limited to three layers, and may be plural layers including three or more layers. 
     In the setting value computation section  7020 , the input layer  701  accepts values resulting from image processing performed on the input first image  710 , for example, values x 1  resulting from down sampling brightness values of the first image  710 . 
     The down sampling referred to here is, for example, performed by dividing the plural pixels of the first image  710  into plural (for example 9) individual blocks, and finding an average value, maximum value, minimum value, or the like for the brightness value of pixels in each of the blocks. In the example illustrated in  FIG. 6 , average values x 1  to x 9  are found for the brightness values of the pixels in the nine blocks. 
     The setting value computation section  7020  then uses the convolutional layer  702  to scan with a filter (for example a 2×2 filter) to perform a convolutional operation thereon. The filter has weights ω 1 , ω 2 , ω 3 , ω 4 , and the same weights are used in the convolutional layer  702  when scanning the filter. Thereby, for example, outputs z 1  to z 4  for the input layer  701  are computed as: 
         z   1   =f ( x   1 ×ω 1   +x   2 ×ω 2   +x   4 ×ω 3   +x   5 ×ω 4   +b   1 )
 
         z   2   =f ( x   2 ×ω 1   +x   3 ×ω 2   +X   5 ×ω 3   +X   6 ×ω 4   +b   1 )
 
         z   3   =f ( x   4 ×ω 1   +x   5 ×ω 2   +x   7 ×ω 3   +x   8 ×ω 4   +b   1 )
 
         z   4   =f ( x   5 ×ω 1   +x   6 ×ω 2   +x   8 ×ω 3   +x   9 ×ω 4   +b   1 )
 
     wherein ω i  (i=1, 2, 3, 4) indicates the weights, b 1  indicates a bias of the convolutional layer  702 , and f indicates an activation function. 
     The setting value computation section  7020  employs the output layer  703  to accept the outputs z 1  to z 4  of the convolutional layer  702  and the first acquisition condition values v=(v 1 , v 2 , v 3 , v 4 ), and to compute values of the second acquisition condition y according to the following equation. 
         y=f ( z   1 ×ω 5   +z   2 ×ω 6   +z   3 ×ω 7   +z   4 ×ω 8   +v   1 ×ω 9   +v   2 ×ω 10   +v   3 ×ω 11   +v   4 ×ω 12   +b   2 )
 
     Wherein ω p  (p=5 to 12) indicate weights, b 2  indicates a bias of the output layer  703 , and f indicates an activation function. 
     The first acquisition condition values v=(v 1 , v 2 , v 3 , v 4 ) are respective values for the excitation light intensity, applied voltage, scan speed, and pinhole size, as described above. The second acquisition condition y is a value of excitation light intensity. 
     In the present exemplary embodiment as described above, the second image acquisition condition (second acquisition condition) is decided based on the first image and based on the first image acquisition condition (first acquisition condition). When comparing the present exemplary embodiment to an example in which the second acquisition condition is decided from the first image, the present exemplary embodiment is different therefrom in that the second acquisition condition is decided by also considering the first acquisition condition. The present exemplary embodiment enables acquisition of a second image of higher quality than a second image of the example not considering the first acquisition condition. 
     This thereby enables setting of an acquisition condition to acquire the second image of higher image quality even without a user having a thorough operational knowledge of the microscope device  101 . 
     Moreover, the present exemplary embodiment also enables the excitation light intensity that is an acquisition condition to be set automatically, a feat that has not hitherto been achieved automatically. 
     Modified Examples 
     Description next follows regarding a first modified example to a sixth modified example of the first exemplary embodiment. The first modified example to the sixth modified example are substantially the same as the first exemplary embodiment, and the first modified example to the sixth modified example will now be described with reference to portions thereof that differ from the first exemplary embodiment. 
     First Modified Example 
     In the first exemplary embodiment, the second acquisition condition value y is one of the values in the first acquisition condition values v=(v 1 , v 2 , v 3 , v 4 ). In the technology disclosed herein, the second acquisition condition value y may also be a value other than the first acquisition condition values v=(v 1 , v 2 , v 3 , v 4 ). In the example described above, for example, a value such as scan size or the like may be employed therefor. 
     Second Modified Example 
     In the first exemplary embodiment down sampling is performed as the image processing performed on the first image  710  input to the input layer  701 . However, technology disclosed herein is not limited thereto, and, for example, another type of image processing may be applied, such as image normalization, image reflection, or the like. 
     Third Modified Example 
     In the first exemplary embodiment, a single value (for example excitation light intensity value) in the second acquisition condition is decided based on the first image and the first acquisition condition values. However, technology disclosed herein is not limited thereto, and, for example, plural values in the second acquisition condition may be decided based on the first image and the first acquisition condition values. 
     As illustrated above in Table 1, plural second acquisition conditions may include plural items that not only affect the respective specific acquisition state (for example, the brightness of the sample  208 ) when acquiring the second image but also generate various different effects. 
     For example, plural items from out of the first acquisition condition may be applied as plural items in the second image acquisition condition. Moreover, plural items other than those of the first acquisition condition may be applied as plural items of the second acquisition condition. Furthermore, one or more items from out of the first acquisition conditions and one or more items from out of the plural items other than those of the first acquisition condition may be applied therefor. 
     Description next follows regarding specific content of a model of computation for a case in which plural second acquisition condition values are decided, with reference to  FIG. 8A . The input layer  701  and the convolutional layer  702  of  FIG. 8A  are similar to those of the example illustrated in  FIG. 6 . However, the output layer  703  of  FIG. 8A  decides values for plural second acquisition conditions, for example, computing four values of an acquisition condition in the following manner. 
         y   1   =f ( z   1 ×ω 5,1   +z   2 ×ω 6,1   +z   3 ×ω 7,1   +z   4 ×ω 8,1   +v   1 ×ω 9,1   +v   2 ×ω 10,1   +v   3 ×ω 11,1   +v   4 ×ω 12,1   +b   2 )
 
         y   2   =f ( z   1 ×ω 5,2   +z   2 ×ω 6,2   +z   3 ×ω 7,2   +z   4 ×ω 8,2   +v   1 ×ω 9,2   +v   2 ×ω 10,2   +v   3 ×ω 11,2   +v   4 ×ω 12,2   +b   2 )
 
         y   3   =f ( z   1 ×ω 5,3   +z   2 ×ω 6,3   +z   3 ×ω 7,3   +z   4 ×ω 8,3   +v   1 ×ω 9,3   +v   2 ×ω 10,3   +v   3 ×ω 11,3   +v   4 ×ω 12,3   +b   2 )
 
         y   4   =f ( z   1 ×ω 5,4   +z   2 ×ω 6,4   +z   3 ×ω 7,4   +z   4 ×ω 8,4   +v   1 ×ω 9,4   +v   2 ×ω 10,4   +v   3 ×ω 11,4   +v   4 ×ω 12,4   +b   2 )
         wherein ω s, t  (s=5, 6, 7, 8; t=1, 2, 3, 4) are weights, b 2  is a bias of the output layer  703 , and f indicates an activation function.       

     As described above, in a third modified example, based on the model of computation, values of a second acquisition condition are decided for plural items that affect a specific acquisition state when acquiring respective second images and that also generate various different effects. Thus the respective values of the second acquisition condition for the plural items consider the effects on other second acquisition conditions, and can be decided in a comprehensive manner. 
     Moreover, the third modified example enables the acquisition condition to be decided in a comprehensive manner in consideration of the effect on other acquisition conditions, a feat that has not hitherto been achieved automatically. 
     Fourth Modified Example 
     In the first exemplary embodiment, down sampling, image normalization, image reflection, or other image processing is performed on the first image  710  input to the input layer  701 . However, the technology disclosed herein is not limited thereto, and the first image  710  may be input as is, without the image processing described above being performed thereon. 
       FIG. 8B  illustrates an example of a case in which the first image  710  is input as is, without performing the image processing described above thereon. 
     For example, the example illustrated in  FIG. 6  illustrates an example of a case in which a 5×5 region is down sampled to x 1 . In contrast thereto, in  FIG. 8B  a single pixel value of the first image  712  (3×3) corresponds to x 1  as is. 
     Fifth Modified Example 
     In the first exemplary embodiment, all of the pixels of the first image are utilized. However, the technology disclosed herein is not limited thereto, and a configuration may be adopted in which an image  714  of merely a region (ROI) of the first image  710  is extracted, as illustrated in  FIG. 8C , so as to be input to the input layer  701 . Note that the image  714  may be subjected to the image processing described above. 
     A sixth modified example will be described later. 
     Teaching Data Generation Processing, Model-of-Computation Update Processing Employing Teaching Data 
     Teaching Data Generation Processing 
     Next teaching data generation processing and model-of-computation update processing employing teaching data will be described for the first exemplary embodiment. First description follows regarding the teaching data generation processing. 
     Firstly, description follows regarding a first example of user mediated teaching data generation that includes a single setting value (self-determined a right by a user) using a user interface. 
     A user interface will now be described, with reference to  FIG. 9 . The user interface is displayed on a display screen  1800  of the display device  414  of the information processing device  102  and is employed for generating the teaching data. As illustrated in  FIG. 9 , a Live image display section  1801 , an imaging image display section  1802 , a setting value control section  1803 , and a teaching data generation instruction section  1810  are included on the user interface screen, as displayed on the display screen  1800  of the display device  414  of the information processing device  102 . The setting value control section  1803  includes plural slider bars  1803 - i  for changing input numerical values by sliding a knob to set respective values for plural first acquisition conditions, and includes an auto setting button  1804 . A setting start button  1811  and a setting complete button  1812  are also included in the teaching data generation instruction section  1810 . 
     Next, description follows regarding a teaching data generation processing program executed by the CPU  302  of the microscope control section  218  of the microscope device  101  and by the CPU  402  of the information processing device  102 , with reference to  FIG. 10 . When the teaching data generation processing is started, at step  1002  the teaching data generation section  706  of the information processing device  102  transmits, to the microscope device  101 , first acquisition condition setting command data to command the microscope device  101  so as to set plural (for example, four) acquisition conditions for acquiring the first image. More specifically, the user manipulates knobs on the slider bars  1803 - i  of the setting value control section  1803 . For example, the knobs on the slider bars  1803 - i  are moved so that the excitation light intensity is 10, the applied voltage is 100, the scan speed is 1, and the pinhole size is 20. These values are then input to the teaching data generation section  706  through the input device  412 , and stored in the RAM  406 . Four individual acquisition conditions are accordingly set thereby. The first acquisition condition setting command data including these acquisition condition values (excitation light intensity value of 10; applied voltage value of 100; scan speed value of 1, and pinhole size value of 20) are transmitted to the microscope device  101  to command the microscope device  101  so as to set these values. The control section  602  of the microscope device  101  receives the first acquisition condition setting command data. 
     At step  1004 , the control section  602  of the microscope device  101  sets the values of the first acquisition condition. More specifically, the control section  602  controls each of the sections ( 209 ,  217 ,  213 ,  231 ) so as to set the excitation light intensity value 10, the applied voltage value 100, the scan speed value 1, and the pinhole size value 20. 
     When a user presses the “setting start” button  1811  of the display screen  1800 , as illustrated in  FIG. 9 , of the display device  414  of the information processing device  102 , at step  1006  the teaching data generation section  706  of the information processing device  102  detects pressing of the “setting start” button  1811 . 
     At step  1008 , the teaching data generation section  706  transmits, to the microscope device  101 , the first image imaging command data to command the microscope device  101  so as to image the first image at the first acquisition condition values, and the control section  602  of the microscope device  101  receives the first image imaging command data. 
     At step  1010 , the image construction section  604  of the microscope device  101  acquires the first image under the first acquisition condition values, and at step  1012 , the first image is transmitted to the information processing device  102 , and the information processing device  102  receives the first image. 
     At step  1014 , the teaching data generation section  706  of the information processing device  102  transmits, to the microscope device  101 , second acquisition condition setting command data to set the second acquisition condition. More specifically, the first image is displayed in the Live image display section  1801  of  FIG. 9 , and the user sets a value of the second acquisition condition of the microscope device  101  by moving one of the knobs of the setting value control section  1803  while viewing the Live image. The value represented by the knob is transmitted to the microscope device  101  as the knob is moved. The above processing is repeated while the knob is being moved. The user decides, by their own determination, the most appropriate value (for example, an excitation light intensity of 8) for the second image acquisition condition while viewing the first image displayed on the Live image display section  1801 . The second acquisition condition setting command data including the excitation light intensity value of 8, applied voltage value of 100, scan speed value of 1, and pinhole size value of 20 is transmitted, and the second acquisition condition setting command data is received by the microscope. 
     The user presses the “setting complete” button  1812 . At step  1018 , the teaching data generation section  706  of the information processing device  102  detects the pressing of the “setting complete” button  1812 , and at step  1020  the teaching data generation section  706  transmits, to the microscope device  101 , the second image imaging command data to command acquisition of the second image under the second acquisition condition, and the control section  602  of the microscope device  101  receives the second image imaging command data. 
     At step  1022 , the image construction section  604  of the microscope device  101  acquires the second image under the values of the second acquisition condition (excitation light intensity value of 8; applied voltage value of 100; scan speed value of 1; and pinhole size value of 20), and at step  1024  transmits the second image to the information processing device  102 , and the information processing device  102  receives the second image. 
     At step  1025 , the teaching data generation section  706  generates teaching data, and saves the teaching data in the secondary storage device  408 . More specifically, as illustrated in  FIG. 11 , two sets of teaching data {image (input), acquisition condition, setting value (right)} are generated and stored in the secondary storage device  408 . The two sets of teaching data include first teaching data  2201  including {a first image A, a first acquisition condition A, and an excitation light intensity of 8 (one setting value: right)}, and second teaching data  2202  configured by {a second image B, a second acquisition condition B, and an excitation light intensity of 8 (one setting value: right)}. 
     Note that the first image A and the second image B are examples of “teaching images” of technology disclosed herein. 
     At step  1026 , the teaching data generation section  706  of the information processing device  102  transmits the teaching data (two sets of teaching data  2201 ,  2202 ) to the server  104 . Note that although described in detail later, briefly the server  104  utilizes the teaching data to update the model of computation (by utilization at step  2802  of  FIG. 15 ). 
     Description next follows regarding a second example of user mediated generation of teaching data that includes a single setting value (self-determined as right by a user) using a user interface. 
     Description follows regarding a user interface for generating teaching data, with reference to  FIG. 12 , as displayed on a display screen  2300  of the display device  414  of the information processing device  102 . As illustrated in  FIG. 12 , the user interface screen displayed on the display screen  2300  of the display device  414  of the information processing device  102  includes a Live image display section  1801 , an imaging image display section  1802 , and a setting value control section  1803  similar to those of the first example. The teaching data generation instruction section  1810  is not included in the second example of a user interface screen. However, a capture button  2310 , and a stage manipulation section  2320  for inputting an instruction to move the stage  220  with the sample  208  placed thereon (see  FIG. 2 ) are included therein. 
     Description next follows regarding teaching data generation processing executed by the CPU  302  of the microscope control section  218  of the microscope device  101  and by the CPU  402  of the information processing device  102 , with reference to  FIG. 13 . When the teaching data generation processing is started, similarly to in the first example, at step  1502  the teaching data generation section  706  of the information processing device  102  transmits, to the microscope device  101 , first acquisition condition setting command data to command the microscope device  101  so as to set a first acquisition condition (excitation light intensity of 10, applied voltage of 100, scan speed of 1, pinhole size of 20), and the control section  602  of the microscope device  101  receives the first acquisition condition setting command data. At step  1504 , the teaching data generation section  706  of the information processing device  102  transmits, to the microscope device  101 , movement instruction command data to instruct the microscope device  101  to move the position of the stage  220  such that the observation target of the sample  208  (cells) reaches the optical axis center of the object lens  214 , and the control section  602  of the microscope device  101  receives the movement instruction command data. 
     At step  1506 , while moving the position of the stage  220  as instructed by the movement instruction command data, the control section  602  of the microscope device  101  periodically (for example, every second) transmits coordinates of the stage  220  to the information processing device  102  at step  1508 , and stops transmitting the coordinates of the stage  220  when the observation target sample  208  (cells) have reached the optical axis center. 
     When the coordinates of the stage are being transmitted, the teaching data generation section  706  of the information processing device  102  detects that the stage  220  has become stationary at step  1510  by detecting the cessation of transmission of the stage coordinates. 
     At step  1512 , the teaching data generation section  706  of the information processing device  102  transmits, to the microscope device  101 , first image imaging command data to command the microscope device  101  so as to acquire the first image under the values of the first acquisition condition (excitation light intensity value of 10; applied voltage value of 100; scan speed value of 1; and pinhole size value of 20), and the control section  602  of the microscope device  101  receives the first image imaging command data. 
     The control section  602  of the microscope device  101  sets the first acquisition condition at step  1514 , the image construction section  604  acquires the first image under the first acquisition condition values at step  1516 , transmits the first image to the information processing device  102  at step  1518 , and the teaching data generation section  706  of the information processing device  102  receives the first image. 
     The teaching data generation section  706  of the information processing device  102  executes second acquisition condition adjustment processing at step  1520 . More specifically, the first image is displayed in the Live image display section  1801 , and the user sets a single first acquisition condition of the microscope device  101  by moving a knob of the setting value control section  1803  while viewing the Live image. The value represented by the knob is transmitted to the microscope device  101  as the knob is moved. By repeating the processing described above, the user decides by self-determination the most appropriate value (for example, an excitation light intensity of 8) in the second image acquisition condition while viewing the first image displayed on the Live image display section  1801 , and the user presses the “Capture” button  2310 . At step  1522 , the teaching data generation section  706  of the information processing device  102  detects pressing of the “Capture” button  2310 , and at step  1524  the teaching data generation section  706  transmits, to the microscope device  101 , second acquisition condition setting command data to command the microscope device  101  so as to set the values of the second acquisition condition (excitation light intensity value of 8; applied voltage value of 100; scan speed value of 1; and pinhole size value of 20), and the control section  602  of the microscope device  101  receives the second acquisition condition setting command data. The control section  602  of the microscope device  101  sets the second acquisition condition values at step  1526 . 
     At step  1528  the teaching data generation section  706  of the information processing device  102  transmits the second image imaging command data to command the microscope device  101  to image the second image under the second acquisition condition values, and the control section  602  of the microscope device  101  receives the second image imaging command data. At step  1530  the image construction section  604  acquires the second image under the second acquisition condition values, and at step  1532  the second image is transmitted to the information processing device  102  and the teaching data generation section  706  of the information processing device  102  receives the second image. 
     At step  1534 , the teaching data generation section  706  generates teaching data as illustrated in  FIG. 11 , and saves the teaching data in the secondary storage device  408 . Specifically, two sets of teaching data {image (input), acquisition condition, setting value (right)} are generated and stored in the RAM  406 . More specifically, the two sets of teaching data include firstly first teaching data  2201  configured from {a first image A, a first acquisition condition A, excitation light intensity 8 (one setting value)}, and secondly second teaching data  2202  configured from {a second image B, a second acquisition condition B, excitation light intensity 8 (one setting value)}. 
     At step  1536  the teaching data generation section  706  of the information processing device  102  transmits the teaching data (the two sets of teaching data  2201 ,  2202 ) to the server  104 . The server  104  utilizes the teaching data to update the model of computation (by utilization at step  2802  of  FIG. 15 ). 
     The second example of a case in which teaching data is generated by user mediation as described above omits the “setting start” button  1811  and the “setting complete” button  1812  of the first example (see  FIG. 9 ). In the second example, detecting the stage  220  as being stationary and detecting the pressing of the “Capture” button  2310 ″ are substituted for the tasks of pressing the “setting start” button  1811  and pressing the “setting complete” button  1812 . Thus in the second example the teaching data can be generated even without the explicit intent of a user to generate teaching data. 
     Next, description follows regarding a case in which teaching data including plural setting values (self-determined as right by a user) are generated through user mediation. The current case is similar to the first example and the second example of cases in which the teaching data including a single setting value is generated through user mediation, and so detailed description thereof will be omitted, and only the different configuration therefrom will be described. A case in which the teaching data including plural setting values (self-determined as right by a user) are generated will be described with reference to the first example of a case in which the teaching data including a single setting value is generated through user mediation. 
     In the first example the excitation light intensity 8 is set as the most appropriate value in the second image acquisition condition. In contrast thereto, the present case in which teaching data including plural setting values is generated differs in that plural (for example, four) values are set, such as excitation light intensity of 8, applied voltage of 180, scan speed of 1/16, pinhole size of 40. 
     Two sets of teaching data will now be described for a case in which teaching data including plural setting values are generated, with reference to  FIG. 14 . As illustrated in  FIG. 14 , the two sets of teaching data are configured by the first teaching data  2701  and the second teaching data  2702 . 
     The first teaching data  2701  includes {first image A, first acquisition condition A (excitation light intensity of 10, applied voltage of 100, scan speed of 1, pinhole size of 20), and second acquisition condition B (excitation light intensity of 8, applied voltage of 180, scan speed of 1/16, pinhole size of 40). The second teaching data  2702  includes (second image B, second acquisition condition B). Note that excitation light intensity of 8, applied voltage of 180, scan speed of 1/16, pinhole size of 40 are accurate setting values (right setting values). The teaching data are transmitted to the server  104 , and the server  104  utilizes the teaching data to update the model of computation (by utilization at step  2802  of  FIG. 15 ). 
     Model-of-Computation Update Processing Using Teaching Data 
     Next, description follows regarding a first update processing of a model of computation using teaching data. 
     Firstly, with reference to  FIG. 15  and  FIG. 16 , input of the first image and the first acquisition condition values, output of a single setting value that is a value in the second acquisition condition, and first update processing to update the model of computation will be described. 
     When the model-of-computation update processing of  FIG. 15  is started, at step  2802  the machine learning section  802  inputs a first image A of the teaching data to the input layer  701  of the model of computation, as data that has been image processed (for example, by down sampling), and inputs the first acquisition condition to the output layer  703 , as illustrated in  FIG. 16 . Note that an image of merely a region (ROI) of the sample  208  may be input to the input layer  701 . 
     The teaching data employed at step  2802  is the teaching data transmitted at step  1026  of  FIG. 10  and at step  1536  of  FIG. 13 . 
     At step  2804 , the machine learning section  802  outputs from the model of computation, via the convolutional layer  702  and the output layer  703 , a setting value (for example: excitation light intensity y=9.6) that is a value of a second acquisition condition. 
     At step  2806 , the machine learning section  802  computes a square error between the output setting value (for example: excitation light intensity y=9.6) and the right value (for example: excitation light intensity y=8 (setting value)) corresponding to the input image A (see also  FIG. 16 ). 
     At step  2808 , based on the square error, the machine learning section  802  updates weights co and bias b of the model of computation by a gradient descent method employing the following equation. 
       ω i ←ω i −ρ×(∂(square error))/(∂ω i ))
 
         b   j   ←b   j −ρ×((∂(square error))/(∂ b   j ))
 
     At step  2810 , determination is made as to whether or not the processing described above (steps  2802  to  2810 ) has been executed for all the teaching data. 
     In cases in which determination has been made that the processing described above (steps  2802  to  2810 ) has not been executed for all the teaching data, processing returns to step  2802 , and the processing described above (steps  2802  to  2810 ) is executed for the remaining teaching data. 
     However, in cases in which determination has been made that the processing described above (steps  2802  to  2810 ) has been executed for all the teaching data, then the current processing is ended. 
     The server  104  transmits to the information processing device  102  the model of computation updated as described above. The information processing device  102  stores the model of computation received from the server  104  in the setting value computation section  7020  as a substitute for the existing model of computation  704 . 
     Teaching Data Generation Processing 
     Next, description follows regarding a case in which the teaching data is generated without user mediation. 
     The teaching data generation processing executed by the CPU  402  of the information processing device  102  will now be described, with reference to  FIG. 17 . When the teaching data generation processing of  FIG. 17  is started, at step  1802 , the teaching data generation section  706  shifts the excitation light intensity serving as a value of the first acquisition condition in a specific range, for example, a fixed interval of from 0 to 80 in, for example, four steps (of 20 values), and transmits the first image imaging command data instructing acquisition of first images under each of these respective first acquisition condition values, and the microscope device  101  accordingly receives the first image imaging command data. 
     The control section  602  of the microscope device  101  sets respective excitation light intensities X i  (X 1  to X 20 ) at step  1804 NN, the image construction section  604  acquires first images A i  (A 1  to A 20 ) at the respective excitation light intensities at steps  1804   n   1  to  1804   nn  and saves the respective first acquisition conditions E 1  (E 1  to E 20 ) in the secondary storage device  308 . At steps  1804   n   1  to  1804   nn  the image construction section  604  transmits the respective first images A i  (A 1  to A 20 ) and the first acquisition condition values E i  (E 1  to E 20 ) to the information processing device  102 , and the teaching data generation section  706  of the information processing device  102  receives each of the first images and the first acquisition condition values. 
     At step  1806   n   1  to  1806   nn , the teaching data generation section  706  computes a score representing the image quality for each of the first images A i  (A 1  to A 20 ). Examples of the score include computing a number of saturation pixels, an S/N ratio etc. 
     The number of saturation pixels is the number of pixels from the detection section  217  having a pixel brightness value of a specific value or greater. 
     The S/N ratio is computed using the following procedure from a single image composed of collected single pixel data obtained from the detection section  217 . 
     Firstly, (1) each of the pixels of the image is classified into three classes according to brightness value. For example, a noise class of brightness values from 0 to 29, an exclude class of from 30 to 99, and a signal class of from 100 to 255. 
     Next, (2) a brightness average is computed for pixels classified as noise, and then, (3) a brightness average is computed for the pixels classified as signal. 
     Finally, (4) the S/N ratio is computed as signal brightness average/noise brightness average. 
     At step  1808 , the teaching data generation section  706  selects as a setting value (right value) an excitation light intensity (for example, x 2 =8) corresponding to the image with the maximum score (for example, A 2 ). 
     At step  1810 , the teaching data generation section  706  generates teaching data and saves the teaching data in the RAM  406 . The teaching data includes plural sets in which each set includes an image, first acquisition condition values, and a setting value (right). More specifically, as illustrated in  FIG. 18 , the teaching data includes 20 sets of (A 1 , E 1 , x 2 ), (A 2 , E 2 , x 2 ), (A 3 , E 3 , x 2 ), . . . (A 20 , E 20 , x 2 ). 
     At step  1812 , the teaching data generation section  706  transmits the teaching data to the server  104 . The server  104  utilizes the teaching data to update the model of computation ( FIG. 19 ). 
     Model-of-Computation Update Processing Using Teaching Data 
     Next second update processing will be described for updating a model of computation through input of a first image and first image acquisition condition values and output of a single setting value using batch units. 
     Whereas in the first update processing the model of computation is updated using each of the plural teaching data, the second update processing differs therefrom in that the model of computation is updated by single batch units, with plural teaching data in a single batch unit. 
       FIG. 19  illustrates a flowchart of an example of a model-of-computation first update processing program. The same processing is executed at step  3402  to step  3406  of  FIG. 19  as the processing of step  2802  to step  2806  of  FIG. 15 . Note that the teaching data employed at step  3402  is, as described above, teaching data transmitted at step  1812  of  FIG. 17 . 
     At step  3408 , the machine learning section  802  determines whether or not the above processing (i.e. steps  3402  to  3408 ) has been executed for all the teaching data. 
     Processing returns to step  3402  in cases in which determination is made that the above processing (i.e. steps  3402  to  3408 ) has not been executed for all the teaching data, and the above processing (i.e. steps  3402  to  3408 ) is executed for remaining teaching data. 
     In cases in which determination is made that the above processing (i.e. steps  3402  to  3408 ) has been executed for all the teaching data, then at step  3410  the average value of square errors for 100 sets of teaching data is computed, and at step  3412  the weights and bias of the model of computation are updated based on the average square errors. 
     Next description follows regarding third update processing in which a first image and first acquisition condition values are input, plural setting values are output, and the model of computation is updated.  FIG. 20  illustrates an example of operation of model-of-computation update processing. The third update processing is substantially the same as the first update processing and the second update processing, and so detailed description thereof will be omitted, and only different configuration thereto will be described. Description follows regarding differences to the first update processing, with reference to  FIG. 20 . 
     In the first update processing, a single setting value (for example, excitation light intensity) is employed, however, in the third update processing, as illustrated in  FIG. 20 , plural, for example four, setting values are employed: excitation light intensity 30, applied voltage  140 , scan speed ⅛, and pinhole size 20. 
     Specifically, four setting values (for example: excitation light intensity 30, applied voltage  140 , scan speed ⅛, and pinhole size 20) are output from the model of computation via the convolutional layer  702  and the output layer  703  (step  3404  of  FIG. 19 ). 
     Then the average square error is computed between the output setting values (excitation light intensity 30, applied voltage  140 , scan speed ⅛, and pinhole size 20) and the right values that are the first acquisition condition (for example: excitation light intensity 20, applied voltage  180 , scan speed 1/16, and pinhole size 40) (step  3406  of  FIG. 19 ). 
     Based on the square errors, the above equations are employed to update the model of computation weights w and bias b using a gradient descent method. 
     During Normal Imaging 
     Next, description follows regarding, during normal imaging processing to display an image obtained by imaging the sample  208  once, processing to acquire a second image with values of a second acquisition condition decided based on the first image representing the sample  208 , and the first acquisition condition values of the microscope device  101  when acquiring the first image. 
     With reference to  FIG. 21 , an example is illustrated of a user interface displayed on the display screen  3600  of the display device  414  of the information processing device  102  when a non-illustrated icon (on a screen of the display device  414  of the information processing device  102 ) is manipulated to instruct normal imaging. The user interface is for the first image or the second image, and for setting first acquisition condition values. As illustrated in  FIG. 21 , the user interface includes a display section  3602  for displaying the first image or the second image, and a setting value control section  3604  for setting the first acquisition condition values. In order to set each of the plural first acquisition condition values, the setting value control section  3604  includes plural slider bars  3608 - i  with knobs that are slid to change input values and an “Auto Setting” button  3606 . 
     Next, description follows regarding image acquisition processing executed by the CPU  302  of the microscope control section  218  of the microscope device  101  and the CPU  402  of the information processing device  102 , with reference to  FIG. 22 . 
     To instruct normal imaging the user presses the “Auto Setting” button  3606 . At step  3802  the setting value computation section  7020  of the information processing device  102  accordingly detects the pressing of the “Auto Setting” button  3606 . At step  3804 , the setting value computation section  7020  transmits first acquisition condition transmission command data to the information processing device  102  to command the information processing device  102  so as to transmit the first acquisition condition values. 
     The control section  602  of the microscope device  101  accordingly receives the first acquisition condition transmission command data at step  3702 . The control section  602  acquires the setting values (first acquisition condition) from each of the configuration sections of the microscope device  101  at step  3704 . For example, an excitation light intensity value of 10, an applied voltage value of 100, a scan speed value of 1, and a pinhole size value of 30 are acquired. The control section  602  transmits the first acquisition condition values to the information processing device  102 . 
     The setting value computation section  7020  of the information processing device  102  accordingly receives the first acquisition condition at step  3806 . At step  3808  the setting value computation section  7020  transmits the first image imaging command data to the microscope device  101  to command the microscope device  101  to image the first image. 
     The control section  602  of the microscope device  101  accordingly receives the first image imaging command data at step  3706 . At step  3708  the image construction section  604  acquires the first image under the current setting values (the first acquisition condition) of each of the configuration sections. At step  3710  the control section  602  transmits the first image to the information processing device  102 . 
     The setting value computation section  7020  of the information processing device  102  accordingly receives the first image at step  3810 . 
     At step  3812  the setting value computation section  7020  employs the model of computation  704  held in the setting value computation section  7020  (more specifically, stored in the secondary storage device  408 ) to compute the second acquisition condition values of the microscope device  101  from the first acquisition condition values and the first image. For example, 8 is computed as the excitation light intensity value. At step  3814  the value of the excitation light intensity is changed to 8. Thus although up to the execution of step  3814  the excitation light intensity has been 10 as represented by  3608 -L 1  in the slider bars  3608 - i  of the setting value control section  3604  of the user interface of  FIG. 21 , this is now changed to 8 as represented by excitation light intensity  3608 -L 2  as illustrated in  FIG. 23 . The setting value computation section  7020  transmits the excitation light intensity value=8 (second acquisition condition value) to the microscope device  101  at step  3816 . 
     The control section  602  of the microscope device  101  accordingly receives the excitation light intensity value=8 (second acquisition condition value) at step  3712 , and sets the light source section  209 - 1  so as to achieve the excitation light intensity value of 8 at step  3714 . 
     At step  3818  the setting value computation section  7020  of the information processing device  102  transmits second image imaging command data to command the microscope device  101  so as to acquire the second image under the second acquisition condition values. 
     The control section  602  of the microscope device  101  accordingly receives the second image imaging command data at step  3716 , and the image construction section  604  acquires the second image at step  3718  in a state in which an excitation light intensity value is 8, an applied voltage value is 100, a scan speed value is 1, and a pinhole size value is 30. The control section  602  transmits the second image to the information processing device  102  at step  3720 . 
     The setting value computation section  7020  of the information processing device  102  accordingly receives the second image at step  3820 , and displays the second image on the display section  3602  at step  3822 . 
     During Live Imaging 
     Next, description follows regarding processing performed each time during Live imaging to display images successively imaging the sample  208 , to acquire the second image under the second acquisition condition values, as decided based on the first image representing the sample  208  and based on the first acquisition condition values of the microscope device  101  when the first image was acquired. 
     Description follows, with reference to  FIG. 24 , regarding a user interface displayed on the display screen  4100  of the display device  414  of the information processing device  102  when an non-illustrated icon (on a screen of the display device  414  of the information processing device  102 ) is manipulated to instruct Live imaging. As illustrated in  FIG. 24 , the user interface includes a display section  4102  to display the first image or the second image, a setting value control section  4104  to set the first acquisition condition values, a “Live start” button  4108  and a “Live end” button  4110 . The setting value control section  4104  includes plural slider bars  4108 - i  with knobs that are slid to change input values and an “Auto Setting” check box  4106  in order to set each of the plural first acquisition condition values. 
     Next, description follows regarding Live imaging processing executed by the CPU  402  of the information processing device  102 , with reference to  FIG. 25 . To instruct Live imaging the user presses and activates the “Auto Setting” check box  4106 . At step  4202  of  FIG. 25 , the setting value computation section  7020  of the information processing device  102  accordingly detects that the “Auto Setting” check box  4106  has been activated. The user continues by pressing the “Live start” button  4108 . At step  4204  the setting value computation section  7020  accordingly detects that the “Live start” button  4108  has been pressed. 
     Next, at step  4206 , steps  3804  to  3822  of  FIG. 22  are executed. Normal image processing of  FIG. 22  is accordingly executed as these steps are performed. 
     At the next step  4208 , the setting value computation section  7020  determines whether or not the “Live end” button  4110  has been pressed. Processing returns to step  4206  in cases in which determination is made that the “Live end” button  4110  has not been pressed, and steps  3804  to  3822  of  FIG. 22  are executed so as to execute the normal image processing of  FIG. 22 . 
     However, the user presses the “Live end” button  4110  when instructing Live imaging to end. Thus in such cases affirmative determination is made at step  4208 , and the Live imaging processing is ended. 
     During Time-Lapse Imaging 
     Next, description follows regarding processing to acquire the second image under the second acquisition condition values decided based on the first image representing the sample  208  and based on the first acquisition condition values of the microscope device  101  when the first image was acquired. This processing is performed from the start of time-lapse imaging of the sample  208  until a specific time (an overall imaging time) has elapsed, and is performed each time time-lapse imaging is performed to display images being successively imaged at a fixed time (thinning imaging time) interval. 
     With reference to  FIG. 26 , description follows regarding a user interface displayed on a display screen  4300  of the display device  414  of the information processing device  102  when a non-illustrated icon (on a screen of the display device  414  of the information processing device  102 ) is manipulated to instruct time-lapse imaging. 
     As illustrated in  FIG. 26 , the user interface includes a display section  4302  to display the first image or the second image, a setting value control section  4304  to set the first acquisition condition values, and a time-lapse setting screen  4306 . The setting value control section  4304  includes plural slider bars  4308 - i  with knobs that are slid to change input values in order to set each of the plural first acquisition condition values. The time-lapse setting screen  4306  includes a time-lapse setting section  4816 , an “Auto Setting” check box  4810 , a “Run” button  4812 , and a “Finish” button  4814 . The time-lapse setting section  4816  includes a thinning imaging time setting section  4818  to set the thinning imaging time, and a overall imaging time setting section  4820  to set the overall imaging time. 
     The thinning imaging time represents a time from a given imaging to the next imaging. The overall imaging time represents the overall time from the start of time-lapse imaging to the end of the final imaging. Namely, in cases in which the thinning imaging time is set to 1 second and the overall imaging time is set to 10 seconds, then eleven images are imaged from when time-lapse imaging is started, at 0 seconds, 1 second, 2 seconds, . . . 10 seconds. 
     Next, description follows regarding the time-lapse imaging processing executed by the CPU  402  of the information processing device  102 , with reference to  FIG. 27 . 
     In order to instruct time-lapse imaging a user sets the thinning imaging time on the thinning imaging time setting section  4818  of the time-lapse setting section  4816 , and sets the overall imaging time on the overall imaging time setting section  4820  thereof (step  4400  of  FIG. 27 ). 
     Then the user presses and activates the “Auto Setting” check box  4810 . At step  4402  of  FIG. 27 , the setting value computation section  7020  of the information processing device  102  accordingly detects that the “Auto Setting” check box  4810  has been activated. The user then continues by pressing the “Run” button  4812 . The setting value computation section  7020  accordingly detects at step  4404  that the “Run” button  4812  has been pressed. 
     Next, at step  4406 , the steps  3804  to  3822  of  FIG. 22  are executed. The normal image processing of  FIG. 22  is accordingly executed as these steps are performed. 
     At the next step  4408 , the setting value computation section  7020  determines whether or not the overall imaging time set at step  4400  has elapsed since the time-lapse imaging was started. In cases in which determination has been made that that the overall imaging time has not yet elapsed, the setting value computation section  7020  determines whether or not the thinning imaging time set at step  4400  has elapsed since step  4406  was executed. If the thinning imaging time has not yet elapsed then the setting value computation section  7020  waits until the thinning imaging time elapses. In cases in which the thinning imaging time has elapsed, the time-lapse imaging instruction processing returns to step  4406 . The steps  3804  to  3822  of  FIG. 22  are executed at step  4406 , and the normal image processing of  FIG. 22  is accordingly re-executed. 
     However, the time-lapse imaging processing is ended in cases in which determination is made that the overall imaging time set at step  4400  has elapsed since the time-lapse imaging was started at step  4408 . 
     Note that the overall imaging time setting section  4820  may be set to unlimited. In such cases, after the start of time-lapse imaging, the time-lapse imaging is ended when the user presses a “Finish” button  4814 . 
     Although, as illustrated in the example of  FIG. 27 , the information processing device  102  determines that the specific time has elapsed at step  4410 , the determination as to whether or not the specific time has elapsed may be performed on the microscope device  101  side. Namely, together with executing of the step  4406  of  FIG. 27 , the microscope device  101  side may be configured to wait for the specific time each time the normal imaging processing of  FIG. 22  is executed. 
     Sixth Modified Example 
     Next, description follows regarding a sixth modified example in addition to the first modified example to the fifth modified example described above. Points of difference of the sixth modified example to the first exemplary embodiment will now be described. 
     In the first exemplary embodiment described above, irrespective of the type of the sample  208 , the information processing device  102  holds the model of computation  704  (see  FIG. 4B ), and the server  104  holds the model of computation  804  (see  FIG. 4C ). However, technology disclosed herein is not limited thereto, and models of computation dependent on the type (class) of the sample  208  may be employed. 
     The class here refers, for example, to cell type (nerve cells, myocardial cells, iPS cells, or the like) of the sample  208 . 
     The shape and size of cells differ according to the cell type, and consequently the most appropriate values for the second acquisition condition are also different. This is because a significant factor is the effect of differences in appearance. 
     Thus in the sixth modified example, although described in detail later, briefly a model of computation corresponding to the class of the sample  208  is employed to compute the second acquisition condition ( FIG. 28 ), and training is performed on the model of computation corresponding to the class of the sample  208  ( FIG. 29 ,  FIG. 30 ). 
     In a case in which the cell type examples described above are present, the models of computation include a “nerve cell model of computation”, a “myocardial cell model of computation”, and an “iPS cell model of computation”. Although the structure of multilayer neural network is common to these models of computation, the values of weights co and bias b are values that correspond to each type of cell. Namely, although the structure of models of computation is common across cell types, parameters held in the models of computation vary according to type of cell. 
     The precision of automatic setting of the second acquisition condition can be improved by employing the model of computation corresponding to the class of the sample  208  in this manner to compute the second acquisition condition, and to train the model of computation according to class. 
     Description follows regarding normal imaging processing in the sixth modified example, executed by the CPU  402  of the information processing device  102 , to instruct the microscope device  101  to perform normal imaging, with reference to  FIG. 28 . 
     In the sixth modified example, a “nerve cell model of computation”, a “myocardial cell model of computation”, and an “iPS cell model of computation” are stored in the secondary storage device  408  of the information processing device  102  and in the secondary storage device  508  of the server  104 , stored in association with an ID for the cell type. 
     To perform normal imaging of the sample  208 , a user inputs an ID of the type of the sample  208  through the input device  412  of the information processing device  102 . 
     In the normal imaging processing illustrated in  FIG. 28 , the steps  4502  to  4510  and the steps  4514  to  4522 , are common from the point of view of execution to the steps  3802  to  3810  and the steps  3814  to  3822  of  FIG. 22 . However, there is a difference in that in the normal imaging instruction processing of  FIG. 28  the step  4511  is executed between step  4510  and step  4512 , and in that the model of computation employed at step  4512  is a model of computation corresponding to the class of the sample  208 . 
     At step  4511 , the setting value computation section  7020  identifies the class that the first image has been classified into based on the input ID. At step  4512 , the model of computation corresponding to the classified class of step  4511  is employed to compute the excitation light intensity L from the first acquisition condition and the first image. 
     Next, description follows regarding model-of-computation update processing, with reference to  FIG. 29 . In the sixth modified example, in the teaching data generation processing ( FIG. 10 ), a user inputs an ID of the type of sample  208  through the input device  412  of the information processing device  102 . When generating the teaching data (step  1024 ), the ID of the sample  208  is appended to the teaching data. 
     At step  4600  of  FIG. 29 , the machine learning section  802  of the server  104  identifies the class the image A of the teaching data has been classified into based on the ID appended to the teaching data (see also  FIG. 30 ). At the next step  4602 , the images A and the first acquisition condition values are input to the model of computation corresponding to the classified class. 
     Following on from this, the processing of steps  2804  to  2810  of  FIG. 15  is executed, at steps  4604  to  4610 . 
     Next, description follows regarding a second exemplary embodiment. 
       FIG. 31  illustrates an example of a microscope system of a second exemplary embodiment. As illustrated in  FIG. 31 , the microscope system of the second exemplary embodiment is equipped with plural sets of a microscope and an information processing device, and a server  104 , mutually connected over a network  103 . As illustrated in  FIG. 31 , each of the plural sets includes plural microscopes  101 - 1 ,  101 - 2 , . . . and plural information processing devices  102 - 1 ,  102 - 2 , . . . , which are each configured respectively similar to the microscope device  101  and the information processing device  102  of the first exemplary embodiment. 
     The operation of the second exemplary embodiment is substantially the same as the operation of the first exemplary embodiment, and so only configuration that differs therefrom will now be described. 
     Description follows regarding a model-of-computation update processing program executed by each of the CPUs of the plural information processing devices  102 - 1 ,  102 - 2 , . . . , and by the CPU  502  of the server  104 , with reference to  FIG. 32 . The model of computation modification processing will now be explained below with reference to an example of the information processing device  102 - 1  as representative of the plural information processing devices  102 - 1 ,  102 - 2 , . . . . At step  4902 , the teaching data generation section  706  of the information processing device  102 - 1  transmits the teaching data to the server  104 . 
     In the server  104  the teaching data has been transmitted to, the machine learning section  802  receives the teaching data at step S 002 , the machine learning section  802  updates the model of computation at step S 004  based on the teaching data, and the machine learning section  802  transmits the updated model of computation to the information processing device  102  at step S 006 . 
     In the information processing devices  102 - 1 , the setting value computation section  7020  receives the updated model of computation at step  4904 , and the setting value computation section  7020  stores the updated model of computation at step  4906 , substituted for the existing model of computation  704 . 
     In the second exemplary embodiment as described above, the server  104  individually updates the model of computation in each of the plural information processing devices  102 - 1 ,  102 - 2 , . . . . The technology disclosed herein is not limited thereto. For example, the server  104  may receive teaching data from all of the plural information processing devices  102 - 1 ,  102 - 2  . . . at step  4902 , and may update all of the models of computation in the plural information processing devices  102 - 1 ,  102 - 2 , . . . at step  4904 . 
     Moreover, the microscope system of the second exemplary embodiment as described above is also equipped with the server  104 . However, the technology disclosed herein is not limited thereto, and the server  104  may be omitted.  FIG. 33  illustrates an example of a configuration of a microscope system in which the server  104  is omitted. As illustrated in  FIG. 33 , this microscope system is equipped with plural sets that each include a microscope and an information processing device, mutually connected together over a network  106 . As illustrated in  FIG. 33 , each of the plural sets includes plural respective microscopes  101 - 1 ,  101 - 2 , . . . and plural respective information processing devices  102 - 1 ,  102 - 2 , . . . having configurations respectively similar to those of the microscope device  101  and the information processing device  102  in the first exemplary embodiment. 
     Note that in such a case, one out of the plural information processing devices  102 - 1 ,  102 - 2  . . . , for example the information processing device  102 - 1 , may include the functions of each of the information processing device  102  and the server  104 . 
     Modified Examples 
     Overall Configuration of Modified Examples 
     Firstly, description follows regarding a modified example of an overall configuration of a system.  FIG. 34A  illustrates a configuration of a first modified example of an overall configuration.  FIG. 34B  illustrates a configuration of a second modified example of an overall configuration. The microscope system of the first exemplary embodiment is equipped with the microscope device  101 , the information processing device  102 , and the server  104 , as illustrated in  FIG. 1 . However, the technology disclosed herein is not limited thereto, and a configuration may be adopted in which the server  104  is connected to a microscope system  110  equipped with a microscope device  101  and an information processing device  102 , as illustrated in  FIG. 34A . Moreover, in the technology disclosed herein, a configuration may be adopted in which the information processing device  102  is provided in the microscope device  101 , as illustrated in  FIG. 34B . 
     Image Acquisition Device Other than Laser Scanning Microscope 
     In each of the examples described above, examples have been described in which the second image acquisition condition is decided for the microscope device  101  that is a laser scanning microscope. However, the technology disclosed herein is not limited thereto, and application may be made to a microscope other than a laser scanning microscope. There are various microscopes that may be employed as a microscope other than a laser scanning microscope, such as, for example, an upright microscope, an inverted microscope, or the like. In such cases the second image acquisition condition includes the above object lens Z coordinate, the above object lens type, illumination light intensity, Z stack range, and filter type. The illumination light intensity referred to here is a brightness intensity of light from a halogen lamp when performing bright field microscopy with an upright microscope or an inverted microscope, or is a brightness intensity of light from a mercury lamp when performing fluoroscopy. Furthermore, in the technology disclosed herein, not only can application be made to a laser scanning microscope and to a microscope other than a laser scanning microscope, but application can also be made to deciding a second image acquisition condition of a camera connected to a microscope. The second image acquisition condition in such a case includes the exposure time, gain, binning, and white balance. The binning referred to here is a number of elements lumped together and treated as a single pixel when plural adjacent elements on a CCD chip are lumped together to raise the detected brightness. For example, in a case in which binning is 4, then a total of 4 pixels, configured from 2 adjacent rows added together both vertically and horizontally, are treated as a single pixel. The white balance is a value to correct for changes in the color of an imaging subject due to a color temperature of the light source. 
     Note that in technology disclosed herein there is no limitation to the above listed acquisition conditions for a laser scanning microscope, a microscope other than a laser scanning microscope, or a camera, and information about the device environment such as the temperature, humidity, or the like, or the temperature, humidity, or the like of the measurement subject, may be applied therefor. 
     Moreover, in each of the above examples a second image acquisition condition is decided based on the first image and the first image acquisition condition. However, the technology disclosed herein may be configured so as to decide plural second acquisition conditions from a single first image. For example, a configuration may be adopted in which, for example, the excitation light intensity, applied voltage, scan speed, and pinhole size are decided from a single first image. 
     Moreover, each processing of the examples described above is merely an example. Thus obviously steps not required may be omitted, new steps may be added, and the processing sequence may be swapped around within a scope not departing from the spirit of technology disclosed herein. Moreover, each processing may be implemented by a hardware configuration alone, such as by FPGAs, ASICs, or the like, or may be implemented by a combination of a hardware configuration and a software configuration using a computer. 
     Other Modified Examples 
     Although in the examples described above a model of computation was employed to decide the second image acquisition condition, the technology disclosed herein is not limited thereto, and the second acquisition condition may be decided without employing a model of computation, as in the following first approach to third approach. 
     First Approach 
     Data obtained in each of the examples described above may be converted into a database as described below. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 2 
               
             
            
               
                   
                   
               
               
                   
                 First Acquisition Condition 
                 Second Acquisition Condition 
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
               
            
               
                 First 
                 First 
                 Second 
                 Third 
                 Fourth 
                 First 
                 Second 
                 Third 
                 Fourth 
                 Second 
               
               
                 Image 
                 Item 
                 Item 
                 Item 
                 Item 
                 Item 
                 Item 
                 Item 
                 Item 
                 Image 
               
               
                   
               
               
                 G1 
                 G1V11 
                 G1V12 
                 G1V13 
                 G1V14 
                 G1V21 
                 G1V22 
                 G1V23 
                 G1V24 
                 G12 
               
               
                 G2 
                 G2V11 
                 G2V12 
                 G2V13 
                 G2V14 
                 G2V21 
                 G2V22 
                 G2V23 
                 G2V24 
                 G22 
               
               
                 G3 
                 G3V11 
                 G3V12 
                 G3V13 
                 G3V14 
                 G3V21 
                 G3V22 
                 G3V23 
                 G3V24 
                 G32 
               
               
                 . 
                 . 
                 . 
                 . 
                 . 
                 . 
                 . 
                 . 
                 . 
                 . 
               
               
                 . 
                 . 
                 . 
                 . 
                 . 
                 . 
                 . 
                 . 
                 . 
                 . 
               
               
                 . 
                 . 
                 . 
                 . 
                 . 
                 . 
                 . 
                 . 
                 . 
                 . 
               
               
                   
               
            
           
         
       
     
     After converting into a database, the second acquisition condition is decided in the following specific manner. 
     (1) as in the first exemplary embodiment, the first condition and the first image are acquired. 
     (2) (a) a distance L is computed between the first condition values (v 1 , v 2 , v 3 , v 4 ) acquired at (1) and each of the values of plural first conditions in the database. 
         L =√(( G 1 V 11− V 1) 2 +( G 1 V 12− V 2) 2 +( G 1 V 13− V 3) 2 +( G 1 V 14− V 4) 2 )
 
     (b) first images corresponding to plural first conditions are selected in sequence of increasing distance from the first condition having the shortest distance in the database. 
     (3) a closest image to the first image acquired at (1) is extracted from out of the plural images selected at (2)(b). 
     The “closest image” may be obtained by (a) selecting the image having the highest computed likeness in pattern matching, or 
     (b) by selecting the same cell type (nerve cell, myocardial cell, iPS cell etc.). 
     (4) a second condition (G1V21, G1V22, G1V23, G1V24) corresponding to the closest image (for example, G1) extracted at (3) is decided as the final second acquisition condition. 
     (5) the second image is acquired under the second acquisition condition decided at (4). 
     (6) the first image obtained at (1), the first condition, the second condition obtained at (4), and the second image obtained at (5) are added to the database (Table 2). 
     Second Approach 
     The second approach also employs the database of the first approach. 
     (1) the first condition and the first image are acquired as in the first exemplary embodiment. 
     (2) The “closest image” (similarly to (3) of the first approach) to the first image acquired at (1) is acquired from out of the database. 
     (4), (5), (6) of the first approach are executed as (3), (4), (5). 
     Third Approach 
     The second condition decided by a model of computation as in the first exemplary embodiment and the second exemplary embodiment is taken as a first mode, the first approach is taken as a second mode, and a user selects either the first mode or the second mode. 
     Other Inventions 
     Next, description follows regarding other inventions. In each of the examples described above, an acquisition condition is decided for the second image based on the first image and the acquisition condition for the first image. In contrast thereto, in other inventions an acquisition condition is decided for the second image based on the first image alone. This is more specifically as described below. 
     The data obtained in each of the examples described above are associated with each of plural images as described below (g1, g2, g3 . . . ) and a table (database) of most appropriate second acquisition conditions is prepared. 
     
       
         
           
               
               
               
               
               
               
             
               
                 TABLE 3 
               
               
                   
               
               
                   
                 Second 
                 Second 
                 Second 
                 Second 
                   
               
               
                 First 
                 Acquisition 
                 Acquisition 
                 Acquisition 
                 Acquisition 
               
               
                 Image 
                 Condition 
                 Condition 
                 Condition 
                 Condition 
                 . . . 
               
               
                   
               
             
            
               
                 g1 
                 j11 
                 j21 
                 j31 
                 j41 
                 . . . 
               
               
                 g2 
                 j12 
                 j22 
                 j32 
                 j42 
                 . . . 
               
               
                 g3 
                 j13 
                 j23 
                 j33 
                 j43 
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     The information processing device  102  acquires the current first image from the microscope device  101 , and selects as a second acquisition condition for acquiring a second acquisition condition set (j12, j22, j32, j42, . . . ) corresponding to the “closest image” to the first image (similarly to in the first approach) (for example, g2). 
     Although detailed description has been given above with reference to the drawings regarding exemplary embodiments of the present invention, specific configurations do not limited the exemplary embodiments, the present invention includes design etc. within a range not departing from the spirit of the present invention. 
     All publications, patent applications and technical standards mentioned in the present specification are incorporated by reference in the present specification to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. 
     REFERENCE NUMERALS 
     
         
           101  microscope 
           102  information processing device 
           104  server 
           209  light source section 
           213  scanner 
           231  pinhole drive section 
           704 ,  804  model of computation 
           7020  setting value computation section 
           602  control section