Patent Publication Number: US-2020300768-A1

Title: Determination device, determination method, and determination program

Description:
The contents of the following U.S. provisional application and international application are incorporated herein by reference: 
     No. 62/570,716 filed on Oct. 11, 2017; and 
     No. PCT/JP2018/028753 filed on Jul. 31, 2018 
    
    
     BACKGROUND 
     1. Technical Field 
     The present invention relates to a determination device, a determination method, and a determination program. 
     2. Related Art 
     There is a demand for identifying a primary focus from a specimen of metastatic cancer, and a method is proposed to determine, from the specimen, that the specimen is derived from a stomach cancer (for example, see Patent document 1). 
     Patent document 1 Japanese Patent Application Publication No. 2004-321102 
     The known determination method is a method to check whether or not a specimen is derived from a stomach cancer, and cannot identify a primary focus of the specimen from a plurality of candidates of a primary focus. 
     SUMMARY 
     In the first aspect of the present invention, a determination device is provided including: a determination unit which refers to a learned model generated by learning teacher data including an optical spectrum measured from the cancer tissue whose primary focus is known, and determines a primary focus of the biological specimen according to input data based on the optical spectrum of an unknown biological specimen. 
     In the second aspect of the present invention, a determination method is provided including a step of referring to the learned model generated by learning the teacher data including the optical spectrum measured from the cancer tissue whose primary focus is known and determining the primary focus of the biological specimen according to input data based on the optical spectrum measured from an unknown biological specimen. 
     In the third aspect of the present invention, a determination program is provided to enable a computer to perform a step of referring a learned model generated by learning the teacher data including the optical spectrum measured from the cancer tissue whose primary focus is known, and determining the primary focus of the biological specimen based on input data corresponding to the optical spectrum measured from the unknown biological specimen. 
     The summary clause does not necessarily describe all necessary features of the embodiments of the present invention. The present invention may also be a sub-combination of the features described above. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic view showing a structure of the determination device  100 . 
         FIG. 2  is a flow diagram showing a procedure to generate a learned model. 
         FIG. 3  is a schematic view showing the region of interest  310 . 
         FIG. 4  is an exemplary diagram showing a model of neural network. 
         FIG. 5  is a flow diagram showing a procedure to determine a biological specimen. 
         FIG. 6  is a schematic view showing a structure of the determination unit  210 . 
         FIG. 7  is an exemplary diagram showing an optical spectrum measured from a biological specimen. 
         FIG. 8  is a diagram showing a state in which dimensions are reduced in a case in which a measurement optical spectrum is learned. 
         FIG. 9  is a graph showing an integrated value of light intensities of intrinsic fluorescence. 
         FIG. 10  is a graph showing an integrated value of optical spectrums measured from a biological specimen. 
         FIG. 11  is a graph showing an integrated value of light intensities of intrinsic fluorescence for another biological specimen. 
         FIG. 12  is a schematic view showing the region of interest  310 . 
         FIG. 13  is a diagram showing a process of clustering of optical spectrums. 
         FIG. 14  is a histogram showing a result of clustering of optical spectrums. 
     
    
    
     DESCRIPTION OF EXEMPLARY EMBODIMENTS 
     Hereinafter, some embodiments of the present invention will be described. The following embodiments do not limit the invention according to the claims. In addition, not all combinations of features described in the embodiments are essential for the solution of the invention. 
       FIG. 1  is a schematic view showing a structure of the determination device  100 . The determination device  100  includes the stage  110 , the objective optical system  120 , the light source device  130 , the irradiation optical system  140 , the front detection unit  150 , the rear detection unit  160 , the control unit  170 , and the storage unit  180 . It is noted that in the following description, the “optical spectrum” is simply described as “spectrum”. 
     In the determination device  100 , the stage  110 , the objective optical system  120 , the light source device  130 , the irradiation optical system  140 , the front detection unit  150 , and the rear detection unit  160  constitute the measurement unit to measure a spectrum of the sample  101 . The storage unit  180  stores a learned model described below. The processor  171  of the control unit  170  constitutes a determination unit to determine a primary focus of a cancer tissue together with the storage unit  180 . The processor  171  also performs machine-learning to generate a learned model. 
     It is noted that, in the illustrated example, the storage unit  180  is provided as a part of the determination device  100 . However, as long as the processor  171  can refer to the learned model stored in the storage unit  180 , the storage unit  180  may be, for example, an online storage or a cloud storage disposed on other locations through a communication line. 
     The stage  110  of the determination device  100  supports the sample  101  which is the target for the determination by the determination device  100 . The sample  101  includes a container or support, and a biological specimen. The container or support in the sample  101  is a container, a board, and the like formed of a material, such as glass, which is transparent for excitation light and Raman scattered light. The biological specimen means a sample which is a small piece including organs, tissues, or cells collected from a human or animal. 
     The stage  110  supports the container at the periphery. The stage  110  also has an opening in a part which does not support the container, and also exposes the container to the stage  110  side. Thereby, the sample  101  placed on the stage  110  can be irradiated with an excitation light from the stage  110  side, and the scattered light produced from the sample  101  can be observed from the stage  110  side. 
     The stage  110  also has the stage scanner  111 . The stage scanner  111  moves the sample  101  in the x-y direction parallel to the surface on which the sample  101  is placed, and the z direction perpendicular thereto, as indicated with arrow x-y-z in the diagram. Thereby, in the determination device  100 , a three-dimensional region in the sample  101  can be a target region for observation or determination while the optical axis of the optical system and the optical path of excitation light are fixed. It is noted that, in the following description, the region in the sample  101  which is the target for observation or determination by the determination device  100  is described as a region of interest. 
     The objective optical system  120  has the front objective lens  121  and the rear objective lens  122  arranged on the opposite sides to each other with respect to the stage  110 . The front objective lens  121  serves to collect lights such as excitation light and illumination light with which the sample  101  is irradiated. 
     The light source device  130  has a plurality of the light source  131  and  132 , and the combiner  139 . The light source  131  and  132  produce irradiation light different from each other. The combiner  139  combines the lights produced by the light source  131  and  132 . Thus, the irradiation light emitted from the light source  131  and  132  becomes a beam passing through a single optical path due to the combiner  139 , and the sample  101  is irradiated with the beam at the same position. 
     The light source  131  produces an excitation light to be used in a case where the Raman spectroscopy of the sample  101  is measured, for example, a laser beam with a wavelength of 532 nm. The light source  132  may also produce an illumination light in a visible light band used in a case where the microscopic image of the sample  101  is observed. Furthermore, Raman scattered light may be produced with CARS process by using the light source  131  and  132  as a light source for pump ray and stokes ray. It is noted that the irradiation light with which the sample  101  is irradiated has preferably long wavelength which is unlikely to invade a living cell regardless of whether the irradiation light is excitation light or illumination light. 
     The irradiation optical system  140  has the galvano scanner  141  and the scan lens  142 . The galvano scanner  141  includes a pair of reflection mirrors to swing around two swing axes which are not parallel to each other. Thereby, the optical path of the light entering the galvano scanner  141  two-dimensionally displaces in the direction crossing the optical axis. 
     The scan lens  142  focuses the excitation light emitted from the galvano scanner  141  on the predetermined primary image plane  143 . Furthermore, the excitation light collimated by the collimator lens  145  arranged in the opposite side of the reflection mirror  144  which bend the optical path of excitation light is collected at the sample  101  by the front objective lens  121 . Thus, any region of interest which is set in the sample  101  is irradiated with the excitation light emitted from the light source device  130 . 
     The front detection unit  150  has the dichroic mirror  151 , the relay lens  152  and  153 , the band pass filter  154 , and the spectrometer  155 . The dichroic mirror  151  transmits the excitation light with which the sample  101  is irradiated from the collimator lens  145  with high efficiency. The dichroic mirror  151  also reflects the scattered light produced by the sample  101  with high efficiency. 
     The dichroic mirror  151  reflects the Raman scattered light produced in the sample  101  irradiated with the excitation light and directs it to the relay lens  152  and  153 . The band pass filter  154  transmits the Raman scattered light produced by the sample  101  and allows it to enter the spectrometer  155 , while absorbing or reflecting excitation light and Rayleigh scattered light. Thereby, the spectrometer  155  efficiently detects the Raman scattered light produced by the sample  101  in the direction of reflection and outputs the spectral image. 
     A polychromator and the like can be used as the spectrometer  155 . It is noted that in the front detection unit  150 , by placing an image sensor instead of the spectrometer  155 , the determination device  100  can be used as a microscope. 
     The rear detection unit  160  has the reflection mirror  161 , the relay lens  162  and  163 , the band pass filter  164 , and the spectrometer  165 . The reflection mirror  161  reflects the Raman scattered light produced by the sample  101  and directs it to the relay lens  162  and  163 , the band pass filter  164 , and the spectrometer  165 . It is noted that a dichroic mirror to selectively reflect a wavelength of the Raman scattered light may be provided instead of the reflection mirror  161 . 
     The band pass filter  164  transmits the Raman scattered light produced by the sample  101  and allows it to enter the spectrometer  165 , while absorbing or reflecting Rayleigh scattered light and excitation light. Thereby, the spectrometer  165  efficiently detects the Raman spectroscopy due to the transmitted light from the sample  101 . 
     A polychromator and the like can be used as the spectrometer  165 . It is noted that, in the rear detection unit  160 , the determination device  100  can be used as a microscope by arranging an image sensor to detect a light in a visible light band, instead of the spectrometer  165 . 
     In the determination device  100 , a Raman scattered light detected by the front detection unit  150  disposed on the same side as the irradiation optical system  140  with respect to the sample  101  is a backward Raman scattered light reflected by the sample  101 . On the other hand, a Raman scattered light detected by the rear detection unit  160  disposed on the opposite side to the irradiation optical system  140  with respect to the sample  101  is a forward Raman scattered light just like transmitted from the sample  101 . 
     The control unit  170  includes the processor  171 , the mouse  172 , the keyboard  173 , and the display unit  174 . The mouse  172  and the keyboard  173  are connected to the processor  171 , and operated when an instruction from the user is input to the processor  171 . 
     The display unit  174  returns a feedback for the user operation of the mouse  172  and the keyboard  173 , and also displays, for the user, an image or character string generated by the processor  171 . Furthermore, in the determination device  100 , the display unit  174  displays an observation image in which the sample  101  is optically observed, and characters, images or the like representing the determination result. 
     The storage unit  180  stores the learned model  190  (see  FIG. 6 ) to which the determination device  100  refers when performing the determination operation. The learned model  190  may be the learned model  190  generated by means of the machine-learning of the determination device  100  itself, or may be the learned model  190  acquired through a communication line or a storage medium. 
       FIG. 2  is a flow diagram showing a procedure to create a learned model stored in the storage unit  180  of the determination device  100 . When the learned model is created, a biological specimen is first prepared whose attribute to be determined is known (Step S 101 ). 
     As an example of a biological specimen whose attribute to be determined is known, a known cancer tissue is used. Examples of a known cancer tissue include a metastatic cancer tissue whose primary focus is known, and a tissue known to include a cancer tissue. That is, when an attribute to be determined by the determination device  100  is a type of a primary focus of a biological specimen of an unknown metastatic cancer, a biological specimen of a cancer tissue whose primary focus is known is prepared. In addition, when an attribute to be determined by the determination device  100  is whether or not the cancer tissue exists, a biological specimen known to include a cancer tissue and a biological specimen known not to include the cancer tissue are prepared. 
     Then, the sample  101  of the biological specimen prepared in Step S 101  is prepared so that the spectrum can be measured by the determination device  100  (Step S 102 ). The sample  101  can be prepared using well-known various methods to prepare a biological specimen for a histopathologic examination. 
     For example, a biopsy tissue collected with an endoscope and the like is fixed with formalin, embedded with paraffin, and then sliced with a microtome. Furthermore, the obtained slice is placed on a slide glass (support), deparaffinized with xylene, and then dried, completing the sample  101 . After deparaffinization, a cover glass may be placed to make a preparation specimen. 
     Then, all spectrums are measured for each of the prepared sample  101  using the determination device  100  (Step S 103 ). That is, in the determination device  100 , the sample  101  placed on the stage  110  is irradiated with excitation light, and, from the generated Raman scattered light, the all spectrums are measured using at least one of the spectrometer  155  and  165 . Thus, an optical spectrum can be measured from a known cancer tissue. 
     Herein, the all spectrums means spectrums corresponding to the entire region of the regions of interest in the sample  101 . The region of interest herein is a region which is the target for the measurement for a Raman spectrum from the biological specimen for the purpose of determination, and is set by a user of the determination device  100 . 
     When a target for determination by the determination device  100  is a cancer tissue of a biological specimen, the biological specimen needs to be identified by the level of the cell size. Therefore, the regions of interest which is the target for the determination preferably contains a size equal to the size of a cancer cell, for example, equal to or larger than 5 μm 2  and equal to or less than 30 μm 2 , or equal to or larger than 10 μm 2  and equal to or less than 20 μm 2 . In addition, the regions of interest are set in a region on a biological specimen which is likely to include a cancer tissue. 
       FIG. 3  is a diagram showing one example of a measuring method for all spectrums in the region of interest  310  of the sample  101 . The region of interest  310  is divided into a plurality of the unit regions  320  which are even smaller regions, and each of the unit regions  320  is irradiated with excitation light to measure a light intensity of the Raman scattered light. 
     Herein, the unit region  320  is set in regions larger than the cell  300  included in the biological specimen, or the cell nucleus  301  of the cell  300  included in the biological specimen. More specifically, for example, the region of interest  310  with the size of 10 μm×10 μm is divided to unit regions  320 , the size of which is each equal to or less than 4 μm 2 , and the Raman spectrum is measured by irradiating each of the plurality of unit regions  320  at least once with excitation light. Thereby, the spectrums of the entire region of interest  310 , that is, the all spectrums are ultimately measured. 
     In addition, for example, a region of interest with a rectangular shape with a size of 1 μm 2  is set, and 121 spectrums are measured by irradiating 121 lattice-like points formed by dividing each side with 10 division lines with excitation light having wavelength of 532 nm. The all spectrums of the region of interest  310  are also measured in this manner. 
     It is noted that when it is already known that a biological specimen includes a cancer cell and it is to be determined which organ the cancer derives from, that is, when an organ in which a primary focus of a biological specimen exists is determined, or when it is known that the biological specimen includes a metastatic cancer cell and the primary focus is to be determined, the identification may be at the tissue or organ level. Therefore, one region of interest may be set to be a larger region. 
     In addition, when the Raman spectrum is measured, after reducing the intrinsic fluorescence with photobleaching, the excitation light for measuring the Raman spectrum may be used for irradiation. Thereby, the intrinsic fluorescence of a biological specimen is significantly reduced, improving the S/N ratio of the measured spectrum. Furthermore, the shape of the regions of interest is not limited to the rectangular shape, and may be a geometrical shape such as a circle, an ellipse, and the like, as well as an irregular shape along the outline of a cell and the like. 
     Referring again to  FIG. 2 , a teacher data including information corresponding to a spectrum measured as described above is then generated (the spectrum  104 ). When a target for the determination by the determination device  100  is a primary focus of the cancer tissue included in a biological specimen, a teacher data including information related to the measured spectrum and the primary focus of the cancer tissue is generated. In addition, when a target for the determination by the determination device  100  is whether or not a cancer tissue exists in a biological specimen, a teacher data including information related to the measured spectrum and the presence or absence of the cancer tissue is generated. Then, the processor  171  of the determination device  100  learns the generated teacher data and generate the learned model  190  (Step S 105 ). 
       FIG. 4  is a diagram showing one example of the neural network  200  which can be used in a case where the learned model  190  stored in the storage unit  180  is generated. The neural network  200  emulating a brain neural circuit in human is formed, for example, in the processor  171  and has the input layer  201 , the hidden layer  202 , and the output layer  203 . 
     The input layer  201  adjusts the weight of the activation function when an input signal is passed to the hidden layer  202 . Then, after the adjustment of weight is repeated depending on the number of layers of the hidden layer  202 , the signal passed to the ultimate output layer  203  is output. The output layer  203  outputs, for example, a probability that the input signal corresponds to any of the option prepared in advance. 
     The output probability is examined, and the adjustment of the weight is repeated until an appropriate output signal is output. Thus, the learned model  190  is generated which includes an activation function having a weight ultimately adjusted. The learned model  190  which is thus generated is stored in the storage unit  180  of the determination device  100  (Step S 106 ). In addition, by being stored in the storage unit  180 , the learned model  190  can be referred to from the processor  171 . Therefore, the determination device  100  can refer to the learned model and perform a determination process. 
     It is noted that, for the generation of the learned model, at least one machine-learning method may be performed which is selected form the group including neural network, support vector machine, decision tree, Bayesian network, linear regression, multivariate analysis, logistic regression analysis, and determination analysis. In addition, there is no particular limit to the number of layers of the hidden layer  202 . 
     Furthermore, the teacher data used in a case where a learned model is generated may use a representative spectrum which is processed to be suitable to a machine-learning from all spectrums. The representative spectrum is a single spectrum which represents individual regions of interest, and may be, for example, a sum of all spectrums of the regions of interest in the sample  101 , or an arithmetic mean calculated by dividing the sum by the number of the spectrums. 
     Still further, although the determination device  100  itself is used to generate a teacher data in the above-mentioned example, another device may be used to generate a teacher data. In addition, when a plurality of the determination device  100  exists, a teacher data generated by one determination device  100  may be used in another determination device  100 . Still further, the learned model  190  to be stored in the storage unit  180  may use the learned model  190  generated by one determination device  100  in a plurality of other determination device. 
       FIG. 5  is a flow diagram showing a determination procedure for a biological specimen by the determination device  100 . When the determination device  100  determines an unknown biological specimen, a biological specimen which is the target for determination is first prepared (Step S 201 ). Then, corresponding to Step S 102  of a process to generate the learned model  190  shown in  FIG. 2  (Step S 202 ), after the prepared biological specimen is processed into the sample  101 , the spectrum of a biological specimen is measured in a similar manner to the above-mentioned process (Step S 203 ). 
     The spectrum is thus measured from the biological specimen, and the determination device  100  which generated the input data based on the spectrum of the sample  101  refers to the learned model  190  and performs the determination process to make a determination for an unknown biological specimen (Step S 204 ). Herein, the determination by the determination device  100  is, for example, whether or not a biological specimen includes a cancer tissue, which is determined by the learned model  190  which learned the teacher data. Alternatively, the determination by the determination device  100  is, for example, a type of the primary focus of the cancer tissue included in a biological specimen, which is determined by the learned model  190  which learned the teacher data through learning. 
       FIG. 6  is the block diagram showing the configuration of the determination unit  210  which is formed in the determination device  100  and performs the determination process. The determination unit  210  is formed such that it includes the processor  171  which acquires the input data from the measurement unit including at least one of the front detection unit  150  and the rear detection unit  160 , and the storage unit  180  which stored the learned model  190 . 
     At least one of the front detection unit  150  and the rear detection unit  160  measures, as the measurement unit, a spectrum from a biological specimen placed on the stage  110  as the sample  101 . The processor  171  performs a determination process using data which is obtained by performing a process such as removing noise components or standardizing a signal on the measured spectrum data, as the input data. 
     The learned model  190  stored in the storage unit  180  is referred to in a case where the processor  171  as the determination unit  210  performs the determination process. The storage unit  180  may incorporate in the processor  171 , or may be a storage medium connected to the processor  171 . Alternatively, the storage unit  180  may be a storage medium to which the processor  171  refers through a communication line and which is disposed outside. 
     In the determination unit  210 , when input data measured from the biological specimen is input through the processor  171 , the learned model  190  returns, to the processor  171 , a determination result likely to correspond to the information. The result of the determination process by the learned model  190  is output to the user through the processor  171  as a determination result of the cancer tissue for the input data. 
     Then, a process is described which is performed for a spectrum measured in at least one of the front detection unit  150  and the rear detection unit  160  when the teacher data is generated, and when input data is determined based on an unknown biological specimen. 
       FIG. 7  is a diagram showing one example of a spectrum measured from the sample  101 . When the illustrated spectrum is learned as a representative spectrum, the number of dimensions is 756. However, when this data is used for a machine-learning or a determination with a learned model, it is preferable to reduce the dimensions. 
     In other words, the ultimate determination accuracy can be improved by removing unnecessary data, components to which the spectrum of a biological specimen is not reflected from the above-mentioned representative spectrum, and the like, and then reducing the dimensions of spectrum data for a determination process to make the data suitable to the process such as machine-learning. When these processes are performed, a standardization process may be performed in advance so that an integrated value (an area of a region enclosed by the spectrum and the horizontal axis) of the representative spectrum becomes a predetermined value (for example 1). 
     As one process for the representative spectrum, a spectrum which is derived from those other than the biological specimen included in the sample  101  may be removed. In this case, those other than the biological specimen include, for example, a spectrum of reagent and the like used in a case where the sample  101  is prepared, a spectrum of glass which forms a container to support the biological specimen in the sample  101 , a spectrum of an intrinsic fluorescence produced in the biological specimen, and the like. 
     In the above-mentioned example, it is assumed that removing the representative spectrums at both end of the measured region among the representative spectrums does not affect the determination. Thus, the dimensions of the spectrum data can be reduced by removing regions of, for example, 500-600 cm −1  or 1750-1798 cm −1 . In this manner, the learned model  190  may be generated with the machine-learning of the spectrums in a band whose wavelength is equal to or higher than 1750 cm −1  and equal to or lower than 600 cm −1 . Similarly, the spectrum which is the target for determination by the determination device  100  may be limited to the wavelength which is equal to or higher than 1750 cm −1  and equal to or lower than 600 cm −1 . 
     One example of the spectrums which is clearly of no-biological specimen includes a spectrum of the paraffin in a preparation process of the biological specimen. With the process to remove a peak region of the paraffin from the representative spectrum, the dimensions in the learning can be reduced, while the S/N ratio of the spectrum data can be improved. 
     As an example, in the spectrum shown in  FIG. 7 , the spectrum data which has initially 756 dimensions can be reduced to 641 dimensions by removing 87 spectrums positioned at both ends of the spectrum, and 28 spectrums mentioned below corresponding to the peak region of the paraffin used to prepare the sample  101 . 
     For 888 cm −1 , four spectrums positioned at 886-891 cm −1    
     For 1061 cm −1 , five spectrums positioned at 1057-1064 cm −1    
     For 1131 cm −1 , five spectrums positioned at 1128-1135 cm −1    
     For 1293 cm −1 , nine spectrums positioned at 1287-1301 cm −1    
     For 1366 cm −1 , five spectrums positioned at 1363-1370 cm −1    
     In this manner, the ultimate determination accuracy can be improved by removing spectrums other than those of the biological specimen. In addition, by reducing the dimensions of the spectrum data which is the target for determination, the processing load of the processor  171  for the determination can be alleviated, while the processing speed can be improved. 
       FIG. 8  shows an example in which the dimensions are reduced one tenth by averaging each 10 representative spectrums shown in  FIG. 7  in the wave number direction. As illustrated, the spectrum reduced to 641 dimensions at the above-mentioned step is further reduced to 64 dimensions. 
     Furthermore, the representative spectrum obtained in the above-mentioned manner may include data improper for determination. Examples for improper spectrums include data in which the spectrum of the intrinsic fluorescence intensity is too large. In addition, other examples of improper spectrums include data in which the spectrum derived from the biological specimen is too small. 
       FIG. 9  is a diagram showing an example in which the spectrum of the intrinsic fluorescence intensity is too large. In the illustrated example, 125 spectrums are measured for five types of biological specimen including breast cancer, lung cancer  1 , lung cancer  2 , colon cancer  1 , and colon cancer  2 . For the region of 500-1800 cm −1 , the integrated value of the spectrum of the intrinsic fluorescence is obtained, the integrated value 180000 is defined as a boundary as shown as the dotted line A, and the higher representative spectrum is removed. 
       FIG. 10  is a diagram showing the example in which the spectrum of the Raman scattered light produced from the biological specimen is too small. In the illustrated example, 125 spectrums are measured for five types of biological specimen including breast cancer, lung cancer  1 , lung cancer  2 , colon cancer  1 , and colon cancer  2 . For the region of 500-1800 cm −1 , the integrated value of the spectrum derived from the biological specimen is obtained, the integrated value 10000 is defined as a boundary as shown as the dotted line B, and the lower representative spectrum is removed. With this process, the dimensions of the representative spectrum data can be reduced to 333. 
     The above-mentioned process can delete the measurement data which is difficult to separate the spectrum derived from the biological specimen and the noise and does not contribute to improve the determination accuracy. It is noted that the all spectrums may be measured again by setting another region of interest instead of the deleted spectrum. 
     The process for the measurement spectrum as described above is similarly performed in a case where the teacher data which is used to generate the learned model is create and in a case where the sample  101  including the biological specimen is determined. Thus, by generating a learned model using a cancer tissue known to be a cancer or a cancer tissue whose primary focus is known, and storing it in the storage unit  180  of the determination device  100 , when the sample  101  including the biological specimen is provided for the determination, the determination device  100  determines whether or not the biological specimen included in the sample  101  is cancer, or the primary focus of the cancer tissue. 
     EXPERIMENTAL EXAMPLE 1 
     As shown in  FIG. 8 , by using the spectrum data whose dimensions are reduced by averaging each 10 spectrums, and the spectrum data whose dimensions are still not reduced, the determination result of the biological specimen whose primary focus is the colon cancer is compared for the two biological specimens. As described below, it is shown that the change in the determination rate due to the reduction of dimensions is small. It is noted that the determination rate indicates the ratio of the number of correct determination result to the number of all determinations. 
     Biological specimen 1; 
     Before averaging: 84.5% 
     (Peaks: 1665 cm −1 , 1406 cm −1 , 1581 cm −1 , 1004 cm −1 ) 
     After averaging: 85.5% 
     (Peaks: 1350 cm −1 , 1614 cm −1 , 1367 cm −1 , 1598 cm −1 ) 
     Biological specimen 2; 
     Before averaging: 94% 
     (Peaks: 1036 cm −1 , 1282 cm −1 , 1430 cm −1 , 878 cm −1 ) 
     After averaging: 95% 
     (Peaks: 1450 cm −1 , 1266 cm −1 , 721 cm −1 , 1434 cm −1 ) 
     It is noted that the above-mentioned “Peaks” indicates the value of intensity at a position of each spectrum. In the above-mentioned example, two intensity ratios are made by using four spectrum intensities of 1665 cm −1 , 1406 cm −1 , 1581 cm −1 , 1004 cm −1 , and a linear discriminant analysis is performed by creating a two-dimensional scatter plot. 
     EXPERIMENTAL EXAMPLE 2 
     For the sample  101  known to include five types of biological specimen including breast cancer, lung cancer  1 , lung cancer  2 , colon cancer  1 , colon cancer  2 ,  125  representative spectrums of the regions of interest are obtained in total. The size of the individual region of interest is 10 μm×10 μm. In the regions of interest, 121 spots with the interval of 1 μm are irradiated with excitation light for five seconds, and 121 spectrums are obtained for each region of interest. Furthermore, for each region of interest, the sum of the spectrum is divided by 121 to obtain the representative spectrum of the regions of interest. Thus, as shown in  FIGS. 9 and 10 , 125 representative spectrums are obtained. 
     Furthermore, after standardizing so that the integrated value of each representative spectrum is 1, by removing the regions of 500-600 cm −1  and 1750-1798 cm −1  positioned at both ends of the spectrum and the peak region of the paraffin, the dimensions of the spectrum data is reduced from 756 dimensions to 641 dimensions. Furthermore, the dimensions are reduced to 64 dimensions by averaging each 10 data. 
     Then, as shown in  FIG. 9 , for each of 125 representative spectrums, the integrated value for the region of 500-1800 cm −1  of the intrinsic fluorescence spectrum is obtained and the representative spectrum equal to or higher than 180000 is removed. Furthermore, as shown in  FIG. 10 , the integrated value for the region of 500-1800 cm −1  of the spectrum derived from the biological specimen is obtained and the representative spectrum equal to or lower than 10000 is removed. With these processes, the representative spectrum is reduced to 333. 
     Among these 333 representative spectrums, 283 representative spectrums are learned by the neural network shown in  FIG. 4  as teacher data including information about a primary focus, and the learned model  190  is generated. The learned model  190  which is generated is stored in the storage unit  180 , and the determination device  100  is enabled to perform the determination for the other 50 representative spectrum. As a result, the primary focus determination rate was 92%. 
     EXPERIMENTAL EXAMPLE 3 
     By using a biological specimen including a colon cancer, and, among slices cut out to create the biological specimen, a biological specimen of the normal tissue adjacent to the region including a cancer cell, the training data and the test data are prepared in a procedure similar to experimental example 2. Corresponding to the case shown in  FIG. 9 ,  FIG. 11  is a diagram showing a result of obtaining the integrated value for the region of 500-1800 cm −1  of the spectrum of the intrinsic fluorescence for each of 500 representative spectrums. The representative spectrum whose integrated value is equal to or higher than 180000 is removed from the illustrated data. Furthermore, corresponding to the case shown in  FIG. 10 , the integrated value for the region of 500-1800 cm −1  of the spectrum derived from the biological specimen is obtained and the representative spectrum equal to or lower than 10000 is removed. 
     The result of determining 50 spectrums as test data among the data which is thus reduced shows that the determination rate is 90.2%. In this manner, the determination device  100  can also determine whether or not the biological specimen includes a cancer tissue. 
       FIG. 12  is a diagram to exemplify another method to measure the all spectrums from the sample  101 . This method is a method to measure the Raman spectrum by irradiating the entire region of the regions of interest with the excitation light with uniform intensity. That the irradiated regions are uniformly distributed means that the irradiated regions are distributed substantially without bias. Whether or not it is uniformly distributed can be confirmed with a well-known method which evaluates the uniformity of distribution. 
     For example, it refers to the case in which, when the regions of interest is divided into n regions with equal areas (n is any integer equal to or more than 2), the number or area of the irradiated regions included in each divided region is substantially equal. For example, the number of all spectrums which is acquired may be, for each region of interest, equal to or more than 50, 60, 70, 80, 90, or 100, depending on the area of the regions of interest. In addition, the measurement result may be obtained as a total of the all measurement result in the region of interest. Furthermore, the average spectrum obtained by dividing the total by the number of irradiation may be used. 
     In addition, as a method to reduce the dimensions of the spectrum data, another well-known method such as clustering can be used.  FIG. 13  and  FIG. 14  indicate the reduction in the dimensions of the representative spectrum data with the clustering.  FIG. 13  shows 756 peaks in the representative spectrum. By classifying this to 50 clusters and replacing each cluster with one peak, the spectrum can be reduced to 50 dimensions as shown in  FIG. 14 . The clustering can reduce more spectrums than a simple averaging while reflecting the property of the biological specimen, since it averages the peaks, as a cluster, with the same characteristics. 
     While the embodiments of the present invention have been described, the technical scope of the invention is not limited to the above described embodiments. It is apparent to persons skilled in the art that various alterations and improvements can be added to the above-described embodiments. It is also apparent from the scope of the claims that the embodiments added with such alterations or improvements can be included in the technical scope of the invention. 
     The operations, procedures, steps, and stages of each process performed by an device, system, program, and method shown in the claims, embodiments, or diagrams can be performed in any order as long as the order is not indicated by “prior to,” “before,” or the like and as long as the output from a previous process is not used in a later process. Even if the process flow is described using phrases such as “first” or “next” in the claims, embodiments, or diagrams, it does not necessarily mean that the process must be performed in this order. 
     EXPLANATION OF REFERENCES 
       100  determination device,  101  sample,  110  stage,  111  stage scanner,  120  objective optical system,  121  front objective lens,  122  rear objective lens,  130  light source device,  131 ,  132  light source,  139  combiner,  140  irradiation optical system,  141  galvano scanner,  142  scan lens,  143  primary image plane,  144  reflection mirror,  145  collimator lens,  150  front detection unit,  151  dichroic mirror,  152 ,  153 ,  162 ,  163  relay lens,  154 ,  164  band pass filter,  155 ,  165  spectrometer,  160  rear detection unit,  161  reflection mirror,  170  control unit,  171  processor,  172  mouse,  173  keyboard,  174  display unit,  180  storage unit,  190  learned model,  200  neural network,  201  input layer,  202  hidden layer,  203  output layer,  210  determination unit,  300  cell,  301  cell nucleus,  310  region of interest,  320  unit region