Patent Publication Number: US-2023141302-A1

Title: Image analysis processing apparatus, endoscope system, operation method of image analysis processing apparatus, and non-transitory computer readable medium

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a Continuation of PCT International Application No. PCT/JP2021/022684 filed on 15 Jun. 2021, which claims priority under 35 U.S.C § 119(a) to Japanese Patent Application No. 2020-120459 filed on 14 Jul. 2020. The above application is hereby expressly incorporated by reference, in its entirety, into the present application. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to an image analysis processing apparatus, an endoscope system, an operation method of an image analysis processing apparatus, and a non-transitory computer readable medium. 
     2. Description of the Related Art 
     In the medical field, diagnosis using an endoscope system that comprises a light source device, an endoscope, and a processor device is widely performed. In diagnosis using the endoscope system, using an image (hereinafter, referred to as an endoscope image) obtained by imaging an observation target with the endoscope through a method called an image enhanced endoscope or image enhanced endoscopy (IEE), a surface structure, a lesion, biological information, or the like of the observation target is enhanced and displayed, and diagnostic support information for a doctor diagnosing the observation target is obtained. 
     A method of obtaining various types of endoscope images, a method of using an endoscope image obtained by picking up an image of an observation target and performing digital image processing thereon, a method of using an endoscope image obtained by illuminating an observation target with specific illumination light and picking up an image of the observation target, or the like is known as IEE. For example, as a doctor selects a specific type of endoscope image, biological information, such as a region of the observation target in which blood vessels are dense and a region where oxygen saturation is low, is determined, and the regions are enhanced and displayed on the display or the like. Such a display is useful as diagnostic support information for the doctor to diagnose the observation target. 
     In addition, computer-aided diagnosis (CAD) technology, in which a disease stage or the like is determined from a range of a region having a probability of being a lesion in an observation target, a degree of inflammation, or the like by performing image analysis on various types of endoscope images through IEE or the like and the obtained determination results are displayed and provided on the display as diagnostic support information, has also been developed. For example, an endoscope system that determines the severity or degree of progression of a disease, such as the stage of ulcerative colitis, with high accuracy using an endoscope image obtained through IEE is known (JP2020-65685A). 
     SUMMARY OF THE INVENTION 
     In IEE, CAD, or the like, in accordance with an instruction from a doctor who is a user of the endoscope, a specific type of endoscope image is displayed on the display, and image analysis results are obtained through image analysis of the endoscope image displayed on the display so as to be used as diagnostic support information. For example, in a case where the doctor desires to observe an observation target in a bright and natural color, an endoscope image obtained by picking up an image of the observation target with illumination light as white light is displayed on the display, image analysis is performed using the endoscope image, and diagnostic support information based on image analysis results is obtained. On the other hand, in a case where the doctor desires to observe a lesion where superficial blood vessels are dense, which is the observation target, in detail, brightness is inferior compared to the endoscope image based on white light, an endoscope image in which superficial blood vessels are enhanced through IEE is displayed on the display, image analysis is performed using the endoscope image, and diagnostic support information based on image analysis results is obtained. 
     As described above, since effects, objects, or applications to be obtained depending on the type of endoscope image obtained through IEE or CAD are different, there can be diagnostic support information that is not necessarily well obtained in image analysis of the type of endoscope image displayed on the display in accordance with a doctor&#39;s instruction or the like. On the other hand, in a different type of endoscope image, the diagnostic support information is well obtained in some cases. Therefore, in a case of using IEE or CAD in observation through the endoscope, for example, although there is diagnostic support information well obtained in image analysis using a different type of endoscope image, the diagnostic support information is not well obtained even in a case where the same image analysis is performed depending on the type of endoscope image. Thus, there is a probability that an abnormality of an observation target such as a lesion is overlooked. 
     An object of the present invention is to provide an image analysis processing apparatus, an endoscope system, an operation method of an image analysis processing apparatus, and a non-transitory computer readable medium that prevent a region-of-interest in an endoscope image from being overlooked. 
     The present invention is an image analysis processing apparatus that performs image analysis based on an image obtained by picking up an image of an observation target using an endoscope and comprises a processor. The processor is configured to acquire a plurality of types of analysis images used in image analysis, perform the image analysis of the analysis image in parallel for each type of the analysis image, acquire a plurality of analysis results through the image analysis, and perform control of displaying, on a display, an analysis result display based on the plurality of analysis results and a display image based on at least one type of the analysis image among the plurality of types of analysis images. 
     It is preferable that the processor is configured to perform the image analysis independently for each type of the analysis image. 
     It is preferable that the processor is configured to acquire association information in which a specific state of the observation target and the analysis image obtained by picking up an image of the observation target including the specific state are associated with each other in advance and obtain the analysis result based on the analysis image and the association information. 
     It is preferable that the processor is configured to acquire the association information for each type of the analysis image and obtain the analysis result based on the analysis image and the association information acquired corresponding to the type of the analysis image. 
     It is preferable that the specific state is at least one of a state where a structure of the observation target is abnormal, a state where the observation target is a specific lesion, or a state where a value of biological information of the observation target is abnormal. 
     It is preferable that the analysis result includes information of a region of the specific state of the observation target. 
     It is preferable that the analysis result includes accuracy related to the analysis result. 
     It is preferable that the processor is configured to select the analysis result having highest accuracy related to the analysis result as an analysis result-of-interest by comparing the plurality of analysis results with each other and create the analysis result display including the analysis result-of-interest. 
     It is preferable that the processor is configured to generate a first analysis image by performing an enhancement process on the image and acquire the first analysis image as one type of the analysis image. 
     It is preferable that the processor is configured to perform a color enhancement process or a structure enhancement process on the image. 
     It is preferable that the processor is configured to acquire the analysis result in association with the type of the analysis image from which the analysis result is obtained and perform control of displaying, on the display, a legend display showing association between the analysis result and the type of the analysis image from which the analysis result is obtained. 
     In addition, the present invention is an endoscope system comprising the image analysis processing apparatus and a light source unit that emits illumination light with which the observation target is irradiated. 
     It is preferable that the processor is configured to acquire the image obtained by picking up an image of the observation target illuminated with each of a plurality of rays of illumination light emitted by the light source unit, which have optical spectra different from each other, as each of different types of the analysis images from each other. 
     It is preferable that the processor is configured to acquire the image obtained by picking up an image of the observation target illuminated with white illumination light emitted by the light source unit as one type of the analysis image. 
     It is preferable that the processor is configured to acquire the image obtained by picking up an image of the observation target illuminated with illumination light, which is emitted by the light source unit and includes narrowband light in a wavelength range set in advance, as one type of the analysis image. 
     It is preferable that the light source unit repeatedly emits each of a plurality of rays of illumination light having optical spectra different from each other in order set in advance. 
     It is preferable that the processor is configured to acquire the analysis result in association with the type of the analysis image from which the analysis result is obtained and perform control of displaying, on the display, a legend display showing association between the analysis result and the type of the analysis image from which the analysis result is obtained. 
     In addition, the present invention is an operation method of an image analysis processing apparatus that performs image analysis based on an image obtained by picking up an image of an observation target using an endoscope, the operation method comprising an analysis image acquisition step of acquiring a plurality of types of analysis images used in image analysis, an image analysis processing step of performing the image analysis on the analysis image in parallel for each type of the analysis image, an analysis result acquisition step of acquiring a plurality of analysis results through the image analysis, and a display control step of performing control of displaying, on a display, a display image including an analysis result display based on the plurality of analysis results and at least one type of the analysis image among the plurality of types of analysis images. 
     In addition, the present invention is a non-transitory computer readable medium for performing image analysis based on an image obtained by picking up an image of an observation target using an endoscope, the computer-executable program causing a computer to execute an analysis image acquisition function of acquiring a plurality of types of analysis images used in image analysis, an image analysis processing function of performing the image analysis on the analysis image in parallel for each type of the analysis image, an analysis result acquisition function of acquiring a plurality of analysis results through the image analysis, and a display control function of performing control of displaying, on a display, a display image including an analysis result display based on the plurality of analysis results and at least one type of the analysis image among the plurality of types of analysis images. 
     With the present invention, a region-of-interest in an endoscope image can be prevented from being overlooked. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is an external view of an endoscope system. 
         FIG.  2    is an external view of an operating part of an endoscope. 
         FIG.  3    is a block diagram showing a function of the endoscope system. 
         FIG.  4    is a graph showing a spectrum of normal light. 
         FIG.  5    is a graph showing a spectrum of special light. 
         FIG.  6    is an explanatory view for describing an illumination light pattern. 
         FIG.  7    is a block diagram showing a function of an image processing unit. 
         FIG.  8    is a block diagram showing a function of an image analysis processing unit. 
         FIG.  9    is an explanatory view for describing a function of an association information acquisition unit. 
         FIG.  10    is a block diagram showing the function of the image analysis processing unit comprising each association information processing unit. 
         FIG.  11    is a block diagram showing a function of a display image generation unit. 
         FIG.  12    is an image view for describing a first analysis result display. 
         FIG.  13    is an image view for describing a second analysis result display. 
         FIG.  14    is an image view of an observation image. 
         FIG.  15    is an image view for describing a display image, in which figures which are two types of analysis result displays are superimposed on the observation image. 
         FIG.  16    is an image view for describing a figure and text which are the second analysis result display. 
         FIG.  17    is a block diagram showing a function of the image processing unit comprising an analysis result-of-interest selection unit. 
         FIG.  18    is an image view for describing a figure or the like showing an analysis result selected by the analysis result-of-interest selection unit. 
         FIG.  19    is an image view for describing a display image in which the figure or the like showing the analysis result selected by the analysis result-of-interest selection unit is superimposed on the observation image. 
         FIG.  20    is an image view for describing a figure and text which are the first analysis result display. 
         FIG.  21    is an image view for describing a display image, in which a figure and text which are analysis result displays are superimposed on the observation image. 
         FIG.  22    is an image view for describing a display image comprising a legend display. 
         FIG.  23    is an image view for describing a display image inconspicuously displaying an analysis result display based on an image which is not displayed. 
         FIG.  24    is an image view for describing a display image displaying, in text, an analysis result display based on the image which is not displayed. 
         FIG.  25    is a graph showing a spectrum of second special light. 
         FIG.  26    is an explanatory view for describing an illumination light pattern including the second special light. 
         FIG.  27    is an image view for describing a display image in which three types of analysis result displays are superimposed on the observation image. 
         FIG.  28    is a flow chart describing a flow of processes of an image analysis processing apparatus. 
         FIG.  29    is an explanatory view showing a diagnosis support device. 
         FIG.  30    is an explanatory view showing a medical service support device. 
     
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     As shown in  FIG.  1   , an endoscope system  10  comprises an endoscope  12 , a light source device  14 , a processor device  16 , a display  18 , and a keyboard  19 . The endoscope  12  images an observation target. The light source device  14  emits illumination light with which the observation target is irradiated. The processor device  16  is an image analysis processing apparatus and performs system control of the endoscope system  10 , an image analysis process of an endoscope image, or the like. The display  18  is a display unit that displays a display image including an endoscope image. The keyboard  19  is an input device that performs setting input or the like into the processor device  16  or the like. 
     The endoscope system  10  includes, as observation modes, three modes including a normal mode, a special mode, and an image analysis mode. In the normal mode, by irradiating an observation target with normal light and picking up an image of the observation target, a normal light image having a natural hue is displayed on the display  18  as a display image. In the special mode, by illuminating an observation target with normal light and special light having a different wavelength range or a different optical spectrum and picking up an image of the observation target, a special light image, in which a specific structure or the like of the observation target is enhanced, is displayed on the display  18  as a display image. In the image analysis mode, a plurality of analysis results are obtained by performing image analysis on a plurality of types of analysis images based on a normal light image, a special light image, or the like in a case of displaying the normal light image or the special light image, which is obtained by irradiating an observation target with normal light or special light and picking up an image of the observation target, on the display  18 . Then, a display image obtained by superimposing an analysis result display based on the plurality of analysis results on an observation image including the normal light image, the special light image, or the like is displayed on the display  18 . In the normal mode or the image analysis mode, insofar as an image has good visibility, even an enhanced image on which an enhancement process or the like is performed may be used in display as a normal light image or a special light image. 
     The type of analysis image used in image analysis is classified by the type of illumination light and the type of enhancement process. Therefore, an endoscope image having the same illumination light is the same type of analysis image, and an endoscope image having different illumination light is a different type of analysis image. In addition, an endoscope image on which an enhancement process is performed through the same method is the same type of analysis image, and an endoscope image on which an enhancement process is performed through a different method is a different type of analysis image. The type of illumination light is classified by an optical spectrum of illumination light. In a case where illumination light is different, an optical spectrum of illumination light is different. Therefore, for example, an endoscope image picked up with white light and an endoscope image picked up with illumination light other than white light are different types of analysis images. In addition, the type of enhancement process is classified by a method of an enhancement process. Therefore, for example, an endoscope image which is picked up with white light and on which a color enhancement process is performed and an endoscope image which is picked up with white light and on which a structure enhancement process is performed are different types of analysis images. 
     The endoscope  12  has an insertion part  12   a  inserted into a subject having an observation target, an operating part  12   b  provided at a proximal end portion of the insertion part  12   a , a bendable part  12   c  provided at a distal end side of the insertion part  12   a , and a distal end part  12   d . The bendable part  12   c  bends by operating an angle knob  12   e  (see  FIG.  2   ) of the operating part  12   b . As a result, the distal end part  12   d  is directed in a desired direction. In addition, as shown in  FIG.  2   , at the operating part  12   b , a treatment tool insertion port  12   f , a scope button  12   g , and a zoom operation part  13  are provided, in addition to the angle knob  12   e . The treatment tool insertion port  12   f  is an entrance into which a treatment tool such as biopsy forceps, a snare, and an electric scalpel is inserted. A treatment tool inserted into the treatment tool insertion port  12   f  protrudes from the distal end part  12   d . Various types of operations can be assigned to the scope button  12   g , and the scope button  12   g  is used in an operation of switching observation modes in the present embodiment. By operating the zoom operation part  13 , an observation target can be imaged while magnified or reduced. 
     As shown in  FIG.  3   , the light source device  14  comprises a light source unit  20  that comprises a light source which emits illumination light and a light source control unit  22  that controls an operation of the light source unit  20 . The light source unit  20  emits illumination light that illuminates an observation target. The illumination light includes emission of light such as excitation light used in order to emit illumination light. The light source unit  20  includes a light source such as a laser diode, a light emitting diode (LED), a xenon lamp, and a halogen lamp and emits at least white illumination light or excitation light used in order to emit white illumination light. The color of white includes a so called color of pseudo-white that is substantially equivalent to white in imaging of an observation target using the endoscope  12 . 
     The light source unit  20  includes, as necessary, a phosphor that emits light by being irradiated with excitation light or an optical filter that adjusts a wavelength range, an optical spectrum, or a light amount of illumination light or excitation light. In addition, the light source unit  20  can emit illumination light consisting of at least light with a narrowband (hereinafter, referred to as narrowband light). In addition, the light source unit  20  can emit a plurality of rays of illumination light having optical spectra different from each other. The illumination light may include narrowband light. In addition, the light source unit  20  can emit, for example, light having a specific wavelength range or a specific optical spectrum, which is necessary in capturing an image used in order to calculate biological information such as oxygen saturation of hemoglobin included in an observation target. 
     The “narrowband” refers to a substantially almost single wavelength range in a relationship of characteristics of an observation target and/or spectral characteristics of a color filter included in an image sensor  45 . For example, in a case where a wavelength range is, for example, approximately ±20 nm or lower (preferably approximately ±10 nm or lower), the light is narrowband light. 
     In the present embodiment, the light source unit  20  has four colors of LEDs including a V-LED  20   a , a B-LED  20   b , a G-LED  20   c , and an R-LED  20   d . The V-LED  20   a  emits violet light VL having a central wavelength of 405 nm and a wavelength range of 380 to 420 nm. The B-LED  20   b  emits blue light BL having a central wavelength of 460 nm and a wavelength range of 420 to 500 nm. The G-LED  20   c  emits green light GL having a wavelength range of 480 to 600 nm. The R-LED  20   d  emits red light RL having a central wavelength of 620 to 630 nm and a wavelength range of 600 to 650 nm. The central wavelengths of the V-LED  20   a  and the B-LED  20   b  have a range of approximately ±20 nm, preferably approximately ±5 nm to approximately ±10 nm. The violet light VL is light having a small wavelength used in order to detect concentration of superficial blood vessels, intramucosal bleeding, and extramucosal bleeding used in the special mode or the image analysis mode and is preferable to include 410 nm in a central wavelength or a peak wavelength. In addition, it is preferable that the violet light VL is narrowband light. 
     The light source control unit  22  controls turning on or off of each of light sources configuring the light source unit  20 , a timing of shielding, a light emission amount, and the like. As a result, the light source unit  20  can emit a plurality of types of illumination light having different optical spectra in a period set in advance and by a light emission amount set in advance. In the present embodiment, the light source control unit  22  adjusts an optical spectrum of illumination light by inputting each independent control signal for turning on and off of each of the LEDs  20   a  to  20   d , a light emission amount in a case of turning on, insertion or removal of an optical filter, or the like. Accordingly, the light source unit  20  emits white illumination light, a plurality of types of illumination light having different optical spectra, illumination light consisting of at least narrowband light, or the like. Endoscope images obtained by picking up images of an observation target illuminated with a plurality of rays of illumination light respectively having optical spectra different from each other, including illumination light, which is white light, are different types of analysis images. 
     In the present embodiment, as shown in  FIG.  4   , the light source unit  20  emits white illumination light as normal light NL through control of the light source control unit  22 . In a case where the normal mode or the image analysis mode is set, the light source control unit  22  controls each of the LEDs  20   a  to  20   d  such that white light having a ratio of intensity of light between the violet light VL, the blue light BL, the green light GL, and the red light RL of Vc:Bc:Gc:Rc is emitted. The ratio of intensity of light of Vc:Bc:Gc:Rc corresponds to a light amount condition of white illumination light. A normal light image obtained by picking up an image of an observation target illuminated with white illumination light emitted by the light source unit  20  is one type of analysis image. 
     In a case where the special mode or the image analysis mode is set, the light source control unit  22  controls each of the LEDs  20   a  to  20   d  such that special light having a ratio of intensity of light between the violet light VL, the blue light BL, the green light GL, and the red light RL of Vs 1 :Bs 1 :Gs 1 :Rs 1  is emitted as special light. The ratio of intensity of light of Vs 1 :Bs 1 :Gs 1 :Rs 1  corresponds to a light amount condition of special light. It is preferable that special light enhances structures of a superficial blood vessel, a polyp, and the like. For this reason, it is preferable to make special light such that the intensity of the violet light VL is higher than the intensity of the blue light BL. For example, as shown in  FIG.  5   , a ratio between light intensity Vs 1  of the violet light VL and light intensity Bs 1  of the blue light BL is “4:1”. Special light SL of the present embodiment is illumination light including the violet light VL which is narrowband light. A special light image obtained by picking up an image of an observation target illuminated with the special light SL emitted by the light source unit  20  is one type of analysis image. 
     In the present specification, a case where a ratio of intensity of light is such that a ratio of at least one semiconductor light source is 0 (zero) is included. Therefore, a case where any one or two or more of semiconductor light sources are not turned on is included. For example, as in a case where a ratio of intensity of light between the violet light VL, the blue light BL, the green light GL, and the red light RL is 1:0:0:0, also a case where only one semiconductor light source is turned on and other three semiconductor light sources are not turned on has a ratio of intensity of light. 
     It is preferable that the light source unit  20  repeatedly emits each of a plurality of rays of illumination light having optical spectra different from each other in order set in advance. In the present embodiment, the normal light NL and the special light SL are repeatedly emitted in the order set in advance. In the present embodiment, the light source control unit  22  emits, for example, as shown in  FIG.  6   , the normal light NL for consecutively five frames (5FL), next, the special light SL for one frame (1FL), normal light NL again for consecutively five frames (5FL), and the special light SL again for one frame (1FL). An illumination pattern consisting of order in which the normal light NL is emitted for consecutively five frames (5FL) and next, the special light SL is emitted for one frame (1FL) is used as one period (1CY), and this period is repeated. 
     An illumination optical system  30   a  and an imaging optical system  30   b  are provided at the distal end part  12   d  of the endoscope  12  (see  FIG.  3   ). The illumination optical system  30   a  has an illumination lens  42 , and illumination light is emitted toward an observation target via the illumination lens  42 . 
     The imaging optical system  30   b  has an objective lens  43 , a zoom lens  44 , and the image sensor  45 . The image sensor  45  images an observation target using reflected light of illumination light returning from the observation target or the like (including scattered light, fluorescence emitted by the observation target, or fluorescence attributable to drug administered or the like to the observation target, or the like, in addition to the reflected light) via the objective lens  43  and the zoom lens  44 . The zoom lens  44  moves as the zoom operation part  13  operates, and an observation target image is magnified or reduced. 
     The image sensor  45  has one color of color filter among a plurality of colors of color filters, for each pixel. In the present embodiment, the image sensor  45  is a color sensor having a primary color system color filter. Specifically, the image sensor  45  has an R pixel having a red color filter (R filter), a G pixel having a green color filter (G filter), and a B pixel having a blue color filter (B filter). 
     As the image sensor  45 , a charge coupled device (CCD) sensor and a complementary metal oxide semiconductor (CMOS) sensor are usable. In addition, the image sensor  45  of the present embodiment is a primary color system color sensor, but a complementary color system color sensor can also be used. The complementary color system color sensor has, for example, a cyan pixel provided with a cyan color filter, a magenta pixel provided with a magenta color filter, a yellow pixel provided with a yellow color filter, and a green pixel provided with a green color filter. In a case where color conversion of complementary color-primary color is performed on an image obtained from each of the color pixels in a case of using the complementary color system color sensor, the image can be converted into an image which is the same as an image obtained with the primary color system color sensor. The same applies to a case of having one or a plurality of types of pixels having characteristics other than the above, such as a W pixel (a white pixel receiving light in almost the entire wavelength range), in the primary color system sensor or the complementary color system sensor. In addition, the image sensor  45  of the present embodiment is a color sensor, but a monochrome sensor that does not have a color filter may be used. 
     In the processor device  16 , a program related to a process or the like performed by a control unit  51 , an image acquisition unit  52 , an image processing unit  56 , a display control unit  57 , and the like, which are to be described later, is incorporated into a memory (not shown). By operating the program with the control unit  51  composed of a processor included in the processor device  16  functioning as the image analysis processing apparatus, functions of the control unit  51 , the image acquisition unit  52 , the image processing unit  56 , and the display control unit  57  are realized. 
     The control unit  51  performs overall control of the endoscope system  10  such as synchronization control of an irradiation timing of illumination light and an imaging timing. In a case of inputting various types of setting using the keyboard  19  and the like, the control unit  51  inputs the setting into each unit of the endoscope system  10 , such as the light source control unit  22 , the image sensor  45 , and the image processing unit  56 . 
     The image acquisition unit  52  acquires, from the image sensor  45 , a captured image of an observation target using each color pixel, that is, an RAW image. In addition, the RAW image is an image (endoscope image) before performing a demosaicing process. Insofar as an image is an image before performing a demosaicing process, the RAW image includes also an image obtained by performing any process such as a noise reducing process is performed on an image acquired from the image sensor  45 . 
     The image acquisition unit  52  comprises a digital signal processor (DSP)  53 , a noise-reduction section  54 , and a conversion unit  55 , in order to perform various types of processes on an acquired RAW image as necessary. 
     The DSP  53  comprises, for example, an offset processing unit, a defect correction processing unit, a demosaicing processing unit, a linear matrix processing unit, a YC conversion processing unit, and the like (none of which are shown). The DSP  53  performs various types of processes on an RAW image using the units or an image generated using the RAW image. 
     The offset processing unit performs an offset process on an RAW image. The offset process is a process in which a dark current component is reduced from the RAW image, and an accurate zero level is set. The offset process is referred to as a clamping process in some cases. The defect correction processing unit performs a defect correction process on the RAW image. The defect correction process is a process of correcting or generating a pixel value of a RAW pixel corresponding to the defective pixel of the image sensor  45  in a case where the image sensor  45  includes a pixel (defective pixel) having a defect attributable to a manufacturing step or a change with time. 
     The demosaicing processing unit performs a demosaicing process on an RAW image having each color corresponding to each color of color filter. The demosaicing process is a process of generating a missing pixel value attributable to arrangement of color filters in the RAW image by interpolation. The linear matrix processing unit performs a linear matrix process on an endoscope image generated by assigning one or a plurality of RAW images to a channel of each color of RGB. The linear matrix process is a process for improving color reproduction of the endoscope image. A YC conversion process performed by the YC conversion processing unit is a process of converting an endoscope image generated by assigning one or a plurality of RAW images to a channel of each color of RGB into an endoscope image having a luminance channel Y, a color difference channel Cb, and a color difference channel Cr. 
     The noise-reduction section  54  performs a noise reducing process on an endoscope image having the luminance channel Y, the color difference channel Cb, and the color difference channel Cr, using, for example, a moving averaging method, a median filter method, or the like. The conversion unit  55  reconverts the luminance channel Y, the color difference channel Cb, and the color difference channel Cr after the noise reducing process into an endoscope image having a channel of each color of BGR again. 
     The image processing unit  56  performs a necessary image process, image analysis, operation, or the like on an endoscope image output by the image acquisition unit  52 . As shown in  FIG.  7   , the image processing unit  56  comprises an enhancement processing unit  61 , an analysis image acquisition unit  62 , an image analysis processing unit  63 , an analysis result acquisition unit  64 , and a display image generation unit  65 . The enhancement processing unit  61  comprises a color enhancement unit  66  and a structure enhancement unit  67 . 
     The enhancement processing unit  61  performs an enhancement process on an endoscope image output by the image acquisition unit  52 . The analysis image acquisition unit  62  acquires a plurality of types of analysis images used in image analysis. The image analysis processing unit  63  performs image analysis of an analysis image in parallel for each type of analysis image. The analysis result acquisition unit  64  acquires a plurality of analysis results through image analysis. The display image generation unit  65  generates an analysis result display and an observation image for displaying on the display  18 . 
     An enhancement process performed by the enhancement processing unit  61  means processing on an endoscope image before an enhancement process such that information of a specific portion is obtained by distinguishing the endoscope image from other tissues, structures, or the like. For example, a process of relatively changing the color or brightness of a portion having a specific feature with respect to other portions (for example, a normal mucous membrane and the like) is an enhancement process. An endoscope image processed by the enhancement processing unit  61  may be a normal light image or a special light image. An endoscope image on which an enhancement process is performed is used as one type of analysis image. 
     The color enhancement unit  66  performs a color enhancement process on an acquired endoscope image to become, for example, an endoscope image in which a boundary between a normal region and an abnormal region in an observation target is clearly represented by a color and chroma saturation. The color enhancement unit  66  performs a color information conversion process on the acquired endoscope image. The color information conversion process is a process of moving each of a plurality of ranges distributed in a color space to a range, which is a conversion destination associated with a range before conversion, in the acquired endoscope image. Since the endoscope image processed by the color enhancement unit  66  has a clear boundary between a normal region and an abnormal region, the endoscope image is an image through which the abnormal region can be more easily and accurately determined as a specific region. The endoscope image on which a color enhancement process is performed is a first analysis image which is one type of analysis image. 
     The structure enhancement unit  67  performs a structure enhancement process on an acquired endoscope image to become, for example, an endoscope image in which a blood vessel in an observation target is enhanced and shown. The structure enhancement unit  67  acquires a shade histogram which is a graph obtained by plotting a pixel value (brightness value) on the lateral axis and a frequency on the vertical axis in the acquired endoscope image and performs gradation correction through a gradation correction table stored in advance in a memory (not shown) of the image processing unit  56  or the like. The gradation correction table has the lateral axis representing an input value and the vertical axis representing an output value, has a gradation correction curve indicating a correspondence relationship between an input value and an output value, and extends the dynamic range of an acquired endoscope image by performing gradation correction based on, for example, a substantially S-shaped gradation correction curve. Accordingly, in an original image before an enhancement process for structural enhancement, a portion having a low density has a lower density and a portion having a high density has a higher density. Thus, for example, a density difference between a blood vessel region and a region where a blood vessel does not exist increases, and contrast of blood vessels improves. Therefore, since contrast of blood vessels is improved, an endoscope image processed by the structure enhancement unit  67  has improved visibility of a blood vessel structure and is, for example, an image through which a region where a degree of density of blood vessels is high can be more easily and accurately determined as a specific region. The endoscope image on which the structure enhancement process is performed is a first analysis image, which is one type of analysis image. 
     The analysis image acquisition unit  62  acquires a plurality of types of analysis images. The analysis image is an image based on a normal light image or a special light image output from the image acquisition unit  52 . A normal light image or a special light image, on which an enhancement process is not performed, and a normal light image or a special light image, on which an enhancement process is performed, are used as analysis images. The type of analysis image is distinguished by the type of illumination light and a method of an enhancement process. In the present embodiment, two types of analysis images including a normal light image obtained by picking up an image of an observation target illuminated with normal light and a special light image obtained by picking up an image of an observation target illuminated with special light are acquired as a plurality of types of analysis images. In the present embodiment, an enhancement process is not performed. 
     The image analysis processing unit  63  performs image analysis on an analysis image acquired by the analysis image acquisition unit  62  in parallel for each type of analysis image. Performing image analysis in parallel means that there are a plurality of image analysis processing units (see  FIG.  8   ) respectively for the types of analysis images and each type of image analysis is performed. Image analysis processing for each type of analysis image may be simultaneously performed or may be performed not simultaneously. Therefore, the image analysis processing unit  63  can independently perform image analysis for each type of analysis image. In the present embodiment, each time an analysis image is acquired, image analysis is performed by a corresponding image analysis processing unit on the acquired analysis image. 
     As shown in  FIG.  8   , the image analysis processing unit  63  comprises a first image analysis unit  71 , a second image analysis unit  72 , and a third image analysis unit  73 , which are provided for respective types of analysis images, and comprises each image analysis unit to an nth image analysis unit  74  corresponding to the number of types of analysis images. As described above, since the image analysis processing unit  63  comprises, for each type of analysis image, an image analysis unit corresponding to each type of analysis image and performs image analysis for each type of analysis image by each image analysis unit, image analysis is performed in parallel for each type of analysis image. In the present embodiment, the first image analysis unit  71  performs image analysis of a normal light image, the second image analysis unit  72  performs image analysis of a special light image, and the first image analysis unit  71  and the second image analysis unit  72  operate in parallel. 
     Image analysis can use a method of image analysis performed in the related art. In the image analysis processing unit  63 , each image analysis unit from the first image analysis unit  71  to the nth image analysis unit  74  may perform image analysis of the same method, and each of the image analysis units may perform image analysis of methods different from each other according to the type of analysis image. In a case of performing image analysis of a method which is different according to the type of analysis image, it is preferable to select and perform a method of image analysis, through which good analysis results are obtained, according to the type of analysis image. 
     Examples of a method of image analysis include a method of using image processing, a method of using a machine learning technique, or the like. Specifically, examples of the method of image analysis include a method of using values such as a pixel value and/or a brightness value of an image, a method of calculating a value of biological information such as oxygen saturation calculated from an image and a method of using association information in which a specific state of an observation target and each analysis image obtained by picking up an image of the observation target including the specific state are associated with each other in advance. Through the methods of image analysis, the presence or absence of a lesion, a probability of being a lesion, a degree of progression or the like of a lesion, analysis result accuracy, or the like is determined. 
     The image analysis processing unit  63  preferably comprises an association information acquisition unit that acquires association information in which a specific state of an observation target and an analysis image obtained by picking up an image of the observation target including the specific state are associated with each other in advance. The image analysis processing unit  63  preferably obtains analysis results based on the analysis image and the association information. 
     A specific state of an observation target is, for example, a tint state of the observation target, a state where a structure or the like is abnormal, a state where the observation target is a specific lesion, a state where a value of biological information of an observation target is abnormal, a normal or healthy state where a lesion or the like does not exist in the observation target, or the like and is set in advance. Specifically, in a case where the observation target is the large intestine, a specific state is set as, for example, a state having an abnormality such as inflammation, redness, an ulcer, a polyp, and bleeding in the large intestine mucous membrane, a lesion such as a cancer and ulcerative colitis, or an abnormality of biological information such as having an extremely low oxygen saturation. 
     Association information is information in which in a case where an observation target is determined to be in a specific state in advance, an analysis image obtained by picking up an image of the observation target is associated with details of the specific state of the observation target, information of the region, and the like. The association information acquisition unit acquires a plurality of pieces of association information in advance. Therefore, as shown in  FIG.  9   , by inputting an acquired analysis image of which a specific state is unknown into the association information acquisition unit, the association information acquisition unit performs image analysis on the analysis image based on the plurality of pieces of association information and outputs a region, details, or the like related to the specific state of the observation target included in the analysis image. Outputting a region, details, or the like related to the specific state also includes content “there is no region in the specific state”. 
     The association information acquisition unit acquires association information for each type of analysis image, and the image analysis processing unit  63  preferably obtains analysis results based on an analysis image and association information acquired corresponding to the type of analysis image. The association information acquired corresponding to the type of analysis image is information in which in a case where an observation target is determined to be in a specific state in advance, a specific type of analysis image obtained by picking up an image of the observation target is associated with information such as a region, details, and the like related to the specific state of the observation target. In addition, the association information acquisition unit may perform feedback or learning to acquire analysis results after processing by the image analysis processing unit  63  as association information. As shown in  FIG.  10   , in the image analysis processing unit  63 , the first image analysis unit  71  comprises a first association information acquisition unit  75 , the second image analysis unit  72  comprises a second association information acquisition unit  76 , the third image analysis unit  73  comprises a third association information acquisition unit  77 , and the nth image analysis unit  74  comprises an nth association information acquisition unit  78 . 
     A specific state of an observation target with which good results can be obtained through image analysis is different depending on the type of analysis image in some cases. Therefore, the association information acquisition unit is included for each type of analysis image. Thus, for example, in a case where the analysis image is a special light image, brightness is insufficient in image analysis, and it is difficult to detect a lesion from the special light image in a distant view, a lesion which was difficult to be detected in a distant view in the special light image can be easily detected from a normal light image, which is a different type of analysis image on which image analysis is performed in parallel. In addition, for example, since a place where blood vessels of a skin layer or a middle layer of an observation target are dense is easily detected from the special light image and a structure such as a polyp can be easily detected from the normal light image as a specific state of an observation target different from the case of the special light image, different types of regions-of-interest can be detected. 
     The association information acquisition unit is, for example, a trained model in machine learning. Since diagnostic support information, which is analysis results, is more quickly or accurately obtained through image analysis, it is preferable to perform image analysis using the trained model through machine learning as the association information acquisition unit. In the present embodiment, image analysis for determining the presence or absence of a lesion is performed using the trained model in machine learning as each association information acquisition unit. In this case, the trained model uses what is learned for each type of analysis image. Therefore, the first association information acquisition unit  75  and the second association information acquisition unit  76  are preferably trained models different from each other. For example, in the present embodiment, the first association information acquisition unit  75  is a trained model corresponding to a normal light image, and the second association information acquisition unit  76  is a trained model corresponding to a special light image. Each trained model may perform feedback to learn analysis results obtained by the image analysis processing unit  63 . 
     The analysis result acquisition unit  64  acquires analysis results obtained by the image analysis processing unit  63  performing image analysis for each type of analysis image with each analysis processing unit. Since each analysis processing unit obtains each analysis result, the analysis result acquisition unit  64  acquires a plurality of analysis results. The analysis results include content corresponding to a method of performed image analysis. In the present embodiment, since a method of image analysis is determination of the presence or absence of a lesion using a trained model, analysis results are the presence or absence of a lesion in an observation target included in an analysis image on which image analysis is performed and a region in a case where there is a lesion. 
     In the present embodiment, two types of analysis results including a first analysis result obtained by analyzing a normal light image and a second analysis result obtained by analyzing a special light image are obtained. The first analysis result is the presence or absence of a lesion, which is a specific state of an observation target, and a region in a case where there is a lesion, which are obtained based on a normal light image. The second analysis result is the presence or absence of a lesion, which is a specific state of the same observation target, a region in a case where there is a lesion, which are obtained based on a special light image. 
     As shown in  FIG.  11   , the display image generation unit  65  comprises an analysis result display generation unit  79  and an observation image generation unit  80 . The analysis result display generation unit  79  generates an analysis result display based on a plurality of analysis results. The observation image generation unit  80  generates an observation image including an observation target based on at least one type of analysis image among a plurality of types of analysis images. The analysis result display is a display for notifying a user, such as a doctor, of a plurality of analysis results, and any display form may be adopted insofar as the display is a display through which the user can learn the plurality of analysis results. The plurality of analysis results are selected according to conditions set in advance. For example, in the present embodiment, in a case where one frame of special light image in which illumination light is the special light SL is acquired, and five frames of normal light images in which illumination light is the normal light NL are acquired consecutively. In the conditions set in advance in this case, for example, the second analysis result based on one frame of acquired special light image and the first analysis result based on a plurality of specific numbers of frames of normal light images before and after acquiring the special light image are selected as a plurality of analysis results. 
     The plurality of analysis results may be displayed by being distinguished from each other or may be displayed without being distinguished from each other in some cases. Specifically, for example, the plurality of analysis results may be a display by a figure, a color, a pattern, text, or a combination thereof. The size of a figure or the like is preferably adjusted to, for example, the size of a region where an observation target is in a specific state. In a case where the displays of the plurality of analysis results are shown by the same size of figures in the same region, it is preferable to adjust the size of the display of each analysis result and to display the figures such that the figures do not overlap each other. In a case where it is not necessary to display analysis results, such as a case where a specific state, such as a lesion, is not detected, the analysis results are not displayed. 
     In the present embodiment, two analysis results including a first analysis result and a second analysis result are acquired, and the results are distinguished and displayed. In addition, the analysis result display is a display in a form, in which analysis results are the presence or absence of a lesion, which is an observation target, and a region in a case where there is lesion and the first analysis result and the second analysis result can be distinguished. 
     In a case where a lesion is detected as a specific state of an observation target in a first analysis result and a second analysis result, as shown in  FIG.  12   , a first analysis result display  81  showing the first analysis result shows a region of a lesion in the observation target by surrounding with a square  FIG.  82   . In addition, as shown in  FIG.  13   , a second analysis result display  83  showing the second analysis result shows a region of a lesion in the observation target by surrounding with a square  FIG.  84   . The  FIG.  82    and the  FIG.  84    are represented by different colors. In  FIGS.  11  and  12   , diagonal lines in different directions indicate different colors. 
     The display image generation unit  65  also generates an observation image based on at least one type of analysis image among a plurality of types of analysis images. The observation image is an analysis image selected to be displayed on the display  18  by a user. The display image generation unit  65  generates an observation image by performing image processing on the analysis image selected for display in a case where it is necessary for displaying on the display  18 . In the present embodiment, the observation image is continuously displayed until a display switching instruction is given. 
     As shown in  FIG.  14   , in the present embodiment, since a user selects a normal light image to be displayed on the display  18 , the display image generation unit  65  performs necessary image processing in order to display the normal light image on the display  18  and uses the normal light image as an observation image  85 . Therefore, the normal light image in which an observation target is picked up is continuously displayed on the display. In the present embodiment, a special light image is acquired at a ratio of one frame to six frames, but the special light image is not displayed on the display  18 . Instead of displaying one frame of acquired special light image, one frame of normal light image, which is one frame ahead of the acquired special light image, is continuously displayed or the like. Through the setting by the user, the observation image  85  can be switched to the special light image or the like. In this case, the normal light image is acquired at a ratio of one frame to six frames, and the normal light image is not displayed on the display  18 . 
     The display control unit  57  performs control of displaying, on the display  18 , an analysis result display based on a plurality of analysis results and the observation image  85  based on at least one type of analysis image among a plurality of types of analysis images. Specifically, the display control unit  57  performs control of superimposing a plurality of analysis result displays on the observation image  85  after aligning the positions to form a display image and displaying the display image on the display  18 . The plurality of analysis result displays are superimposed on the observation image  85  in a form in which each analysis result display can be distinguished and recognized. In the present embodiment, the  FIG.  82    displaying a first analysis result and the  FIG.  84    displaying a second analysis result are displayed in colors different from each other. As shown in  FIG.  15   , the display control unit  57  performs control of superimposing the  FIG.  82    displaying the first analysis result and the  FIG.  84    displaying the second analysis result on the observation image  85  in different colors. 
     As described above, in the processor device  16 , which is the image analysis processing apparatus, or the endoscope system  10 , by performing image analysis for each type of endoscope image, for example, not only image analysis results based on one type of endoscope image selected to be displayed on the display but also image analysis results based on a different type of endoscope image can be quickly obtained. In addition, in a case analysis results obtained well are different depending on the type of endoscope image, even an analysis result which has not been detected in one endoscope image is well detected in the other endoscope image in some cases, and a region-of-interest, such as a lesion, can be prevented from being overlooked. 
     In the present embodiment, since a normal light image and a special light image are used as analysis images, the normal light image has sufficient brightness, and a lesion is well detected in a distant view through image analysis by the first image analysis unit  71 . Even in a case where the special light image has insufficient brightness and the lesion cannot be detected in a distant view through the image analysis by the second image analysis unit  72 , the  FIG.  82    is superimposed on the observation image  85  and is displayed on the display  18 . In particular, in a case where the observation image  85  is mainly observed in a near view as the special light image, a lesion or the like that is darkly displayed on the display  18  in a distant view is displayed as the  FIG.  82   . Thus, even in a case where the special light image is continuously displayed on the display  18 , a region-of-interest, such as a lesion that is difficult to be visually determined and is in a region darkly displayed, can be prevented from being overlooked. 
     It is preferable that an analysis result includes accuracy related to the analysis result. The accuracy related to the analysis result is a degree to which the analysis result is accurate or a degree to which an error is not included. In a case where the presence or absence of a specific state is used as an analysis result, accuracy indicates a degree to which an error, noise, or the like is included when detecting information of a region in image analysis together with detecting information of a region of the specific state, which is the analysis result. The accuracy related to the analysis result is specifically indicated by a numerical value. For example, in a case where a specific lesion is a cancer, since there can be a type of analysis image through which the cancer can be well detected and a type of analysis image through which the cancer cannot be well detected, depending on the type of analysis image, accuracy related to an analysis result related to detection of the cancer is different. In addition, depending on the situation of analysis image, for example, depending on a state of an image or the like, in which blurring has occurred, accuracy related to an analysis result is different in some cases even in a case where the cancer is detected as an analysis result from the same type of analysis image. Therefore, it is preferable to show the analysis result accuracy in a numerical value according to the situation of each image analysis. 
     By performing setting such that accuracy related to an analysis result is also displayed as the analysis result through image analysis on a selected analysis image by the image analysis processing unit  63 , the accuracy related to the analysis result may be displayed in numerical values and/or text, together with the presence or absence of a lesion and a region in a case where there is a lesion, which are analysis results. In a case of showing the accuracy in a numerical value, it is preferable to show the accuracy through a percentage or the like such that the accuracy increases as the numerical value increases. For example, a case of using a trained model through machine learning in image analysis is preferable since accuracy related an analysis result, which is an analysis result, can be output in numerical values. 
     As shown in  FIG.  16   , in a case where an analysis result includes accuracy related to the analysis result, the second analysis result display  83  includes the  FIG.  84    showing a region of a lesion, which is an analysis result, and an accuracy display  86  showing accuracy related to the analysis result when displaying a second analysis result in the present embodiment. 
     As an analysis result includes accuracy related to the analysis result, details of the analysis result can be learned at a glance. For example, in a case where a region of a lesion is displayed in a figure based on the analysis result, since accuracy related to the analysis result is also displayed, a user can learn diagnostic support information in detail. 
     In addition, an analysis result-of-interest selection unit that selects an analysis result having the highest analysis result accuracy as an analysis result-of-interest by comparing a plurality of analysis results with each other is included, and it is preferable for the display image generation unit  65  to create an analysis result display including the analysis result-of-interest. As shown in  FIG.  17   , in this case, the image processing unit  56  comprises an analysis result-of-interest selection unit  91 . 
     The analysis result-of-interest selection unit  91  compares a plurality of analysis results with each other. In a case of comparing, when the plurality of analysis results include the same region in an observation target, the analysis results are compared with each other. Then, among the plurality of analysis results, an analysis result having the highest analysis result accuracy included in the analysis results is selected as an analysis result-of-interest. After performing a region determination process on a plurality of analysis images that are sources of the plurality of analysis results in order to determine that the plurality of analysis results include the same region in the observation target, the analysis result-of-interest selection unit  91  may compare the plurality of analysis results with each other. 
     As shown in  FIG.  18   , in the present embodiment, in a case where a first analysis result is detected to be a lesion even in the same region as the  FIG.  84    showing a second analysis result and the first analysis result display  81  includes a  FIG.  87    and an accuracy display  88 , the analysis result-of-interest selection unit  91  compares accuracy of the second analysis result shown in the  FIG.  84    of 80% (see  FIG.  16   ) and accuracy of the first analysis result shown in the  FIG.  87    of 60% with each other. Then, a second analysis result which is an analysis result having high analysis result accuracy is selected as an analysis result-of-interest. 
     The analysis result display generation unit  79  (see  FIG.  11   ) creates an analysis result display to include an analysis result-of-interest. As shown in  FIG.  19   , in the present embodiment, in a case where the analysis result-of-interest selection unit  91  has selected a second analysis result as an analysis result-of-interest, for a lesion in a region shown by the  FIG.  84    and the  FIG.  87    in an observation target, an analysis result display shown by the  FIG.  84    and the accuracy display  86  caused by the second analysis result without showing the  FIG.  87    and the accuracy display  88  caused by a first analysis result is generated. The display control unit  57  superimposes the analysis result display on the observation image  85  and displays the analysis result display on the display  18 . 
     With the analysis result-of-interest selection unit  91 , analysis results can be displayed with higher accuracy among a plurality of analysis results. Therefore, the accuracy or reliability of image analysis can be improved. 
     An analysis result may include a probability that a region in an observation target is in a specific state. A probability of being in a specific state is a numerical value indicating the certainty that an observation state is in a specific state set in advance. Specifically, for example, in a case where a specific lesion is a cancer, as it is certain that the specific lesion is a cancer in the observation target, a higher numerical value is displayed as a probability, and as it is not certain that the specific lesion is a cancer while there is a probability of being a cancer, a lower numerical value is displayed as a probability. In particular, in image analysis using a trained model through machine learning, a region, which is a specific state set in advance, in an image and a probability thereof can be output as analysis results. Therefore, by performing setting a method of image analysis on a selected analysis image by an image analysis unit in the image analysis processing unit  63  such that for example, a region of a cancer and a probability thereof are detected, the region of the cancer and the probability thereof are displayed. 
     As shown in  FIG.  20   , in a case where analysis results include a probability that a region in an observation target is in a specific state, the first analysis result display  81  includes the  FIG.  82    showing a region of a lesion and a probability display  89  in which the region in the observation target is in a specific state, which are analysis results, when displaying a first analysis result in the present embodiment. In the probability display  89 , for example, “a suspected cancer probability of 50%” or the like is shown. 
     As analysis results include a probability that a region in an observation target is in a specific state, the analysis results can be learned in detail at a glance. For example, in a case where a region of a lesion is displayed in a figure according to the analysis results, since a probability that the region is in a specific state is also displayed, a user can easily learn diagnostic support information in detail, and a lesion or the like can be better prevented from being overlooked. 
     Even a case where an analysis result includes a probability that a region in an observation target is in a specific state can be the same as a case where an analysis result includes accuracy related to the analysis result. For example, in a case where different probabilities are output for the same region of the observation target in a plurality of analysis results, an analysis result display including an analysis result having the highest probability among the probabilities is used. As shown in  FIG.  21   , in the present embodiment, for a lesion in a region shown by the  FIG.  84    or the  FIG.  87    (see  FIG.  18   ) in the observation target, an analysis result display shown by the  FIG.  84    and a probability display  90  from a second analysis result having the highest probability, without showing the  FIG.  87    from a first analysis result and a probability display for a probability that the region in the observation target is in a specific state, which is included in the analysis result, is superimposed on the observation image  85  and is displayed on the display  18 . Then, for the region of the  FIG.  82    in the first analysis result display  81 , since the second analysis result does not include an analysis result of being a lesion, the region of the  FIG.  82    shows the  FIG.  82    of the first analysis result display  81  and the probability display  89  as they are. As described above, a region of the observation target in which at least one of a plurality of analysis results is a lesion is preferably included an analysis result display and is displayed on a display image. 
     As analysis results include a probability that a region in an observation target is in a specific state after selecting an analysis result-of-interest from a plurality of analysis results, the accuracy or reliability of image analysis can be further improved, and a region-of-interest, such as a lesion, can be better prevented from being overlooked. 
     It is preferable for the analysis result acquisition unit  64  to acquire an analysis result in association with the type of analysis image from which the analysis result is obtained, and it is preferable for the display control unit  57  to perform control of displaying, on the display  18 , a legend display showing association between an analysis result and the type of analysis image from which the analysis result is obtained. The legend display is a display in an analysis result display and a display showing the type of analysis image in a form of the same color or the like such that the display in the analysis result display is an analysis result of which the type of analysis image can be learned at a glance. Since based on which type of analysis image the analysis result display is obtained can be quickly and easily learned through the legend display, for example, the reliability or the like of the analysis result display can also be learned. 
     As shown in  FIG.  22   , in the present embodiment, the  FIG.  82    showing a first analysis result is an endoscope image obtained using normal light as an analysis image, and the  FIG.  84    showing a second analysis result is an endoscope image obtained using special light as an analysis image. Therefore, “normal light” indicating that the endoscope image is obtained with normal light in the same color as the  FIG.  82    and “special light” indicating that the endoscope image is obtained with special light in the same color as the  FIG.  84    are displayed as a legend display  95  at the lower left of the observation image  85 . 
     In a case where a figure showing an analysis result or the like is displayed by displaying which analysis image the analysis result has been obtained from as a legend, through which image, through which a lesion or the like is well detected, an analysis result has been obtained can be easily learned at a glance. In addition, even in a case where a lesion or the like does not exist in an observation target and no figure showing an analysis result or the like is displayed when a display image is a normal light image, which type of analysis image has been currently acquired, that is, which IEE has been performed can be learned through the legend display  95 . For example, illumination light based on which type of special light is used in one period ( FIG.  6   , 1CY) of illumination light can be learned. Therefore, the legend display  95  is also useful as a display showing which IEE that has been performed at the time point in endoscopy. 
     In addition, an analysis result based on an analysis image of a type other than the observation image  85  displayed on the display  18  may be an analysis result display distinguished from an analysis result based on an analysis image that is a source of the observation image  85 . As shown in  FIG.  23   , the  FIG.  82    and the probability display  89 , which are analysis result displays based on a normal light image which is a source of the observation image  85 , may be conspicuously shown in a dark color, and the  FIG.  84    and the accuracy display  86 , which are analysis result displays based on a special light image which is not displayed as in the observation image  85 , may be displayed in a light color, a dotted line, or the like so as not to be conspicuous. In  FIG.  23   , a figure or a display having wide intervals between diagonal lines is shown in a light color or a dotted line. 
     In addition, the  FIG.  84    and the accuracy display  86 , which are analysis result displays based on a special light image which is not displayed as in the observation image  85 , may be shown in a method other than being superimposed on the observation image  85 . For example, as shown in  FIG.  24   , analysis results based on the special light image can be shown in a sentence together with the legend display  95 . In addition, the second analysis result display  83  may be shown as a sub-screen outside the observation image  85 . 
     A plurality of types of analysis images may be three or more types without being limited to two types. For example, in an analysis image, an endoscope image obtained by illuminating with normal light is used as a first analysis image, special light caused by the optical spectrum shown in  FIG.  5    is used as first special light, an endoscope image obtained by illuminating with the first special light is used as a second type of analysis image, and an endoscope image obtained by illuminating with second special light different from the first special light is used as a third type of analysis image. 
     The light source control unit  22  controls each of the LEDs  20   a  to  20   d  such that special light having a ratio of intensity of light between the violet light VL, the blue light BL, the green light GL, and the red light RL of Vs 2 :Bs 2 :Gs 2 :Rs 2  is emitted as second special light. The ratio of intensity of light of Vs 2 :Bs 2 :Gs 2 :Rs 2  corresponds to a light amount condition of second special light. The second special light preferably enhances a superficial blood vessel. For this reason, the second special light emits only the violet light VL which is narrowband light having a small wavelength with a ratio of intensity of light as 1:0:0:0 as shown in  FIG.  25   . 
     In the present embodiment, the light source control unit  22  emits, for example, as shown in  FIG.  26   , the normal light NL for consecutively five frames (5FL), next, first special light  1 SL for one frame (1FL), normal light NL again for consecutively five frames (5FL), and next second special light  2 SL for one frame (1FL). With an illumination pattern consisting of this order as one period (1CY), the period is repeated. 
     As in the embodiment, the third image analysis unit  73  (see  FIG.  10   ) performs image analysis on an analysis image based on second special light, and a third analysis result is sent to the analysis result acquisition unit  64 . The analysis result acquisition unit  64  acquires a first analysis result of an analysis image based on normal light, a second analysis result of an analysis image based on first special light, and a third analysis result of an analysis image based on second special light. As shown in  FIG.  27   , the third analysis result is displayed in a  FIG.  96   . 
     As shown in  FIG.  27   , in some cases, the  FIG.  96   , which is a detection result included in a third analysis result, overlaps the  FIG.  84   , which is a detection result included in a second analysis result. Since an endoscope image based on first special light shows the structures of a superficial blood vessel, a polyp, and the like well and an endoscope image based on second special light shows an enhanced superficial blood vessel, a region where a region of the  FIG.  84    included in a second analysis result and a region of the  FIG.  96    included in a third analysis result overlap each other is a region having a high probability that there is an abnormality in the structure of the superficial blood vessel or the like, and an alert display  98  that particularly calls attention of a user is included as an analysis result. 
     In addition, in the image analysis processing unit  63 , in a case where analysis results of a plurality of specific analysis images include the same region, the analysis results with respect to the region may include an observation target being in a specific state. As in the present embodiment, in a case where a second analysis result and a third analysis result include the same region, the analysis results may be different from a case where a specific region is included in only the second analysis result or only the third analysis result. In a case where each of the second analysis result and the third analysis result includes a probability of a specific lesion as a different analysis result, both of the second analysis result and the third analysis result include a region displaying a depth direction of the specific lesion or the like. For example, by combining being able to detect even in a case where the specific lesion such as a cancer is mild, accurately showing a region of the specific lesion in the depth direction or the like, or the like through image analysis using machine learning, analysis results with better accuracy can be included. 
     Next, the series of flows of processes related to image analysis performed by the processor device  16 , which is the image analysis processing apparatus, or the endoscope system  10  will be described with reference to a flowchart shown in  FIG.  28   . In a case where observation starts, illumination light is emitted in order set in advance in accordance with a predetermined illumination light pattern (see  FIG.  6   ). First, illumination light is normal light, and a normal light image is acquired (Step ST 110 ). Next, illumination light is special light, and a special light image is acquired (Step ST 120 ). Normal light image analysis (Step ST 130 ) and special light image analysis (Step ST 140 ) are performed in parallel. A first analysis result caused by the normal light image analysis and a second analysis result caused by the special light image analysis are acquired by the analysis result acquisition unit (Step ST 150 ). In a case where a plurality of analysis results include an analysis result of an observation target with respect to the same region, the analysis result-of-interest selection unit selects an analysis result-of-interest (Step ST 160 ). An analysis result display is generated in accordance with the selected analysis result-of-interest, and as set in advance, a display image based on one type of a plurality of analysis images is generated (Step ST 170 ). The display control unit  57  displays the analysis result display and the display image on the display  18  (Step ST 180 ). 
     Although the processor device  16  functions as the image analysis processing apparatus in the embodiment, a modification example, and the like, the image analysis processing apparatus including the image processing unit  56  may be provided separately from the processor device  16 . In addition, as shown in  FIG.  29   , the image processing unit  56  can be provided at a diagnosis support device  911  that acquires an RAW image captured by the endoscope  12 , for example, directly from the endoscope system  10  or indirectly from picture archiving and communication systems (PACS)  910 . In addition, as shown in  FIG.  30   , the image processing unit  56  can be provided at a medical service support device  930  that is connected to various types of examination devices including a first examination device  921 , a second examination device  922 , . . . , and a Kth examination device  923 , including the endoscope system  10 , via a network  926 . 
     Each of the embodiments and the modification examples can be performed in any combination of some or all of the embodiments and the modification examples. In addition, although the endoscope  12  uses a so-called soft endoscope having the flexible insertion part  12   a  in each of the embodiments and the modification examples, the present invention is suitable also in a case of using a capsule-type endoscope used by swallowing an observation target or a hard endoscope (laparoscope) using a surgical operation or the like. 
     The embodiment, the modification example, and the like include an operation method of an image analysis processing apparatus that comprises a processor and performs image analysis based on an image obtained by picking up an image of an observation target using an endoscope, the operation method of an image analysis processing apparatus comprising an analysis image acquisition step of acquiring a plurality of types of analysis images used in image analysis, an image analysis processing step of performing image analysis on an analysis image in parallel for each type of analysis image, an analysis result acquisition step of acquiring a plurality of analysis results through image analysis, and a display control step of performing control of displaying, on a display, a display image including an analysis result display based on the plurality of analysis results and at least one type of analysis image among the plurality of types of analysis images. 
     In addition, the embodiment, the modification example, and the like include a program for an image analysis processing apparatus installed in an image analysis processing apparatus that comprises a processor and performs image analysis based on an image of an observation target using an endoscope, the program for an image analysis processing apparatus for causing a computer to realize an analysis image acquisition function of acquiring a plurality of types of analysis images used in image analysis, an image analysis processing function of performing image analysis on an analysis image in parallel for each type of analysis image, an analysis result acquisition function of acquiring a plurality of analysis results through image analysis, and a display control function of performing control of displaying, on a display, a display image including an analysis result display based on the plurality of analysis results and at least one type of analysis image among the plurality of types of analysis images. 
     In the embodiment, hardware structures of processing units that perform various types of processes, including the control unit  51 , the image acquisition unit  52 , the DSP  53 , the noise-reduction section  54 , the conversion unit  55 , the image processing unit  56 , and the display control unit  57  included in the processor device  16 , which is the image analysis processing apparatus, are various types of processors shown below. The various types of processors include a central processing unit (CPU) that is a general-purpose processor functioning as various types of processing units, which execute software (program) and function as various types of processing units, a programmable logic device (PLD) that is a processor which can change a circuit configuration after manufacturing, such as a field programmable gate array (FPGA), and a dedicated electric circuit that is a processor which has a circuit configuration exclusively designed for executing various types of processes. 
     One processing unit may be composed of one of the various types of processors or may be composed of the same type or different types of two or more processors (for example, a plurality of FPGAs or a combination of the CPU and the FPGA). In addition, one processor may configure a plurality of processing units. As an example of configuring a plurality of processing units with one processor, first, there is a form in which one processor is configured by a combination of one or more CPUs and software and the processor functions as the plurality of processing units, as represented by a computer such as a client and a server. Second, there is a form in which a processor that realizes functions of the entire system including a plurality of processing units with one integrated circuit (IC) chip is used, as represented by a system on chip (SoC) or the like. 
     As described above, the various types of processing units are composed of one or more of the various types of processors used as a hardware structure. 
     Further, the hardware structures of the various types of processors are, more specifically, an electric circuit (circuitry) in a form in which circuit elements such as semiconductor elements are combined. 
     The present invention can be useful also in a system, a device, or the like that acquires a medical image (including a motion picture) other than an endoscope image, in addition to an endoscope system that acquires an endoscope image or the like, a processor device, other related devices, and the like. For example, the present invention can be applied to an ultrasonic examination device, an X-ray image imaging device (including a computed tomography (CT) examination device and a mammography device), a magnetic resonance imaging (MRI) device, and the like. 
     EXPLANATION OF REFERENCES 
     
         
         
           
               10 : endoscope system 
               12 : endoscope 
               12   a : insertion part 
               12   b : operating part 
               12   c : bendable part 
               12   d : distal end part 
               12   e : angle knob 
               12   f : treatment tool insertion port 
               12   g : scope button 
               13 : zoom operation part 
               14 : light source device 
               16 : processor device 
               18 : display 
               19 : keyboard 
               20 : light source unit 
               20   a : V-LED 
               20   b : B-LED 
               20   c : G-LED 
               20   d : R-LED 
               22 : light source control unit 
               30   a : illumination optical system 
               30   b : imaging optical system 
               41 : light guide 
               42 : illumination lens 
               43 : objective lens 
               44 : zoom lens 
               45 : image sensor 
               51 : control unit 
               52 : image acquisition unit 
               53 : DSP 
               54 : noise-reduction section 
               55 : conversion unit 
               56 : image processing unit 
               57 : display control unit 
               61 : enhancement processing unit 
               62 : analysis image acquisition unit 
               63 : image analysis processing unit 
               64 : analysis result acquisition unit 
               65 : display image generation unit 
               66 : color enhancement unit 
               67 : structure enhancement unit 
               71 : first image analysis unit 
               72 : second image analysis unit 
               73 : third image analysis unit 
               74 : nth image analysis unit 
               75 : first association information acquisition unit 
               76 : second association information acquisition unit 
               77 : third association information acquisition unit 
               78 : nth association information acquisition unit 
               79 : analysis result display generation unit 
               80 : observation image generation unit 
               81 : first analysis result display 
               82 ,  84 ,  87 ,  96 : FIG. 
               83 : second analysis result display 
               85 : observation image 
               86 ,  88 : accuracy display 
               89 ,  97 : probability display 
               91 : analysis result-of-interest selection unit 
               95 : legend display 
               98 : alert display 
               910 : PACS 
               911 : diagnosis support device 
               921 : first examination device 
               922 : second examination device 
               923 : Kth examination device 
               926 : network 
               930 : medical service support device 
             NL: normal light 
             SL: special light 
               1 SL: first special light 
               2 SL: second special light 
             ST 110  to ST 180 : Step