Patent Publication Number: US-2022230308-A1

Title: Bioimage acquiring device, converter generating device, bioimage generating method, converter generating method, and recording medium

Description:
FIELD OF THE INVENTION 
     The present invention relates to a bioimage acquiring device that acquires a positive bioimage, which is a bioimage with few defects, from a negative bioimage, which is a bioimage with defects such as metal artifacts. 
     BACKGROUND ART 
     In X-ray CT, if the imaged object contains a high absorber of X-rays (for example, metal), precise calculation of CT values is hindered, and as a result, an artifact that has a large detrimental effect is produced. 
     For this problem, there is an algorithm called the Metal Artifact Reduction (MAR) algorithm that corrects metal artifacts (see Non-Patent Document 1). 
     There are also other techniques for reducing artifacts using deep learning (see Non-Patent Document 2). 
     PRIOR ART DOCUMENTS 
     Non-Patent Documents 
     
         
         Non-patent document 1: Kenji Ino, “Approach to metal artifacts in CT scans,” [online], [retrieved on Jun. 21, 2019], Internet: [URL: https://www.innervision.co.jp/sp/ad/suite/canonmedical/sup201512/session1-1] 
         Non-patent document 2: Yanbo Zhang, Hengyong Yu, “Convolutional Neural Network Based Metal Artifact Reduction in X-Ray Computed Tomography,” [online], [retrieved on Jul. 3, 2019], Internet: [URL: https://arxiv.org/abs/1709.01581] 
       
    
     SUMMARY OF THE INVENTION 
     Problems to be Solved by the Invention 
     However, in the prior art, it was not possible to precisely obtain a positive bioimage with few defects from a negative bioimage having defects such as metal artifacts. 
     Further, the technique adopted in the above-mentioned prior art is supervised learning, and in such supervised learning, an image having few defects corresponding to an image having defects is required, but generally in bioimages, it is difficult to obtain such an image pair. 
     Means to Solve the Above Problems 
     A bioimage acquiring device according to a first embodiment of the invention comprises a classifier storage unit that stores a classifier that determines whether a bioimage is a negative bioimage or a positive bioimage, wherein the classifier is created using one or more negative bioimages which are defective bioimages and one or more positive bioimages which are non-defective bioimages, a converter storage unit that stores a first converter used for a conversion process to acquire a positive bioimage from a negative bioimage, and a negative bioimage receiving unit that receives negative bioimages, a first conversion unit that performs a first conversion process of converting a negative bioimage received by the negative bioimage receiving unit using a first converter, and acquiring a converted bioimage which is the conversion result, and a classifying unit that performs a first classifying process using the classifier for determining whether the converted bioimage acquired by the first conversion unit is a positive bioimage or a negative bioimage, wherein the first conversion unit performs a learning process using the determination result of the classifying unit and the converted bioimage, and performs an update process for updating the first converter, the negative bioimage receiving unit receives a new negative bioimage, and the first conversion unit converts a new negative bioimage received by the negative bioimage receiving unit using the updated first converter, and acquires a converted bioimage which is the conversion result. 
     According to this configuration, a positive bioimage with few defects can be acquired from a negative bioimage with defects such as metal artifacts, with high precision and without using teacher data. 
     A bioimage acquiring device according to a second embodiment of the invention is identical to that of the first embodiment, but further comprising a bioimage storage unit that stores the one or more negative bioimages and one or more positive bioimages, and a learning unit that generates a classifier used for determining whether the received bioimage is a positive bioimage or a negative bioimage using, in addition to the one or more negative bioimages and one or more positive bioimages of the bioimage storage unit, one or more converted bioimages acquired by the first conversion unit as negative bioimages. 
     According to this configuration, by precisely determining whether the bioimage is a positive bioimage or a negative bioimage, a positive bioimage with few defects can be acquired from a negative bioimage with defects such as metal artifacts, with high precision and without using teacher data. 
     A bioimage acquiring device according to a third embodiment of the invention is identical to that of the first or second embodiment, but further comprising a feature amount vector acquiring unit that acquires an input feature amount vector which is one or more features of the negative bioimage supplied to the first conversion unit and an output feature amount vector which is one or more features of the converted bioimage acquired by the first conversion unit, and a feature amount difference information acquiring unit that acquires feature amount difference information relating to the difference between the input feature amount vector and the output feature amount vector, wherein the first conversion unit performs a learning process so that the feature amount difference information is reduced, and updates the first converter. 
     According to this configuration, a positive bioimage with few defects that retains the features of the negative bioimage can be acquired with high precision from a negative bioimage with defects such as metal artifacts, without using teacher data. 
     A bioimage acquiring device according to a fourth embodiment of the invention is identical to that of any of the first to third embodiments, wherein the converter storage unit further comprises a second conversion unit that stores a second converter used for a conversion process for acquiring a negative bioimage from a positive bioimage that converts the converted bioimage acquired by the first conversion unit using the second converter and performs a second conversion process to acquire a second converted bioimage which is the conversion result, the first conversion unit converts the second converted bioimage acquired by the second conversion unit using the first converter, and the classifying unit performs a second classifying process for determining whether the converted bioimage acquired by the first conversion unit from the second converted bioimage is a positive bioimage or a negative bioimage, further comprising a control unit that performs control such that the first conversion process, the first classifying process, the second conversion process, and the second classifying process are performed once, twice or more. 
     According to this configuration, a positive bioimage with few defects that retains the features of the negative bioimage can be obtained from a negative bioimage with defects such as metal artifacts with high precision, and without using teacher data. 
     The bioimage acquiring device according to a fifth embodiment of the invention is identical to that of the fourth embodiment, but further comprising a feature amount vector acquiring unit that acquires a feature amount vector of at least two of a converted bioimage which is an input to the second conversion unit, a second converted bioimage which is an output of the second conversion unit, and a converted bioimage which is an output for the second converted bioimage from the first conversion unit, and a feature amount difference information acquiring unit that acquires feature amount difference information relating to the difference between at least one pair of two or more feature amount vectors which are acquired by the feature amount vector acquiring unit, wherein the first conversion unit performs a learning process so that the feature amount difference information is reduced, and updates the first converter. 
     According to this configuration, a positive bioimage with few defects that retains the features of the negative bioimage can be obtained from a negative bioimage with defects such as metal artifacts with high precision, and without using teacher data. 
     The bioimage acquiring device according to a sixth embodiment of the invention is identical to that of any of the first to fifth embodiments, wherein the negative bioimage is a set of two or more slice images obtained by cutting a part of a defective image set of an imaged living body into round slices, and the positive bioimage is a set of two or more slice images obtained by cutting a part of a non-defective image set of the imaged living body into round slices. 
     According to this configuration, a three-dimensional positive bioimage with few defects can be obtained from a negative bioimage with defects such as metal artifacts with high precision, and without using teacher data. 
     The bioimage acquiring device according to a seventh embodiment of the invention is identical to that of any of the first to sixth embodiments, wherein the first conversion unit performs a first conversion process only for pixels having a pixel value in a predetermined range, and the classifying unit performs a first classifying process for determining whether the image that is acquired by the first conversion unit is a positive bioimage or a negative bioimage, using a classifier created for only pixels having a pixel value in the predetermined range. 
     According to this configuration, a positive bioimage of a tissue with few defects can be obtained from a negative bioimage of a tissue such as bone with defects such as metal artifacts with high precision, and without using teacher data. 
     The bioimage acquiring device according to an eighth embodiment of the invention is identical to the bioimage acquiring device of the seventh embodiment, wherein the pixels having a pixel value in a predetermined range are pixels constituting a bone image. 
     According to this configuration, a positive bioimage of a bone part with few defects can be obtained from a negative bioimage of a bone part with defects such as metal artifacts with high precision, and without using teacher data. 
     The bioimage acquiring device according to a ninth embodiment of the invention is identical to that of any of the first to eighth embodiments, further comprising a bioimage receiving unit that receives two or more bioimages, wherein the classifying unit uses a classifier to determine whether each of the two or more bioimages received by the bioimage receiving unit is a positive bioimage or a negative bioimage, and the negative bioimage receiving unit acquires a bioimage determined to be a negative bioimage by the classifying unit. 
     According to this configuration, since processing is performed only on the negative bioimages among the received bioimages, a set of positive bioimages can be acquired rapidly with high precision and without using teacher data. 
     The converter generating device according to a tenth embodiment of the invention is a device that generates a converter of any of the first to ninth embodiments, comprising a bioimage storage unit that stores the one or more negative bioimages supplied to the bioimage acquiring device, and the one or more converted bioimages acquired by the first conversion unit of the bioimage acquiring device, a learning unit that acquires a converter used to acquire a positive bioimage, which is a converted bioimage, from a negative bioimage using the one or more negative bioimages and the one or more converted bioimages of the bioimage storage unit, and a converter accumulation unit that accumulates the converter acquired by the learning unit. 
     According to this configuration, a highly precise converter for acquiring a positive bioimage from a negative bioimage can be automatically acquired. 
     The bioimage acquiring device according to an eleventh embodiment of the invention is identical to that of the tenth embodiment, but comprising a converter storage unit in which the converter acquired by the converter generating device is stored, a negative bioimage receiving unit that receives a negative bioimage, a conversion unit that converts the negative bioimage received by the negative bioimage receiving unit using the converter of the converter storage unit and acquires a positive bioimage which is the conversion result, and an output unit that outputs the positive bioimage acquired by the conversion unit. 
     According to this configuration, a positive bioimage can be acquired from a negative bioimage with high precision and without using teacher data by using an automatically acquired highly precise converter. 
     Advantageous Effects of the Invention 
     In the bioimage acquiring device according to the present invention, a positive bioimage with few defects can be acquired with high precision from a negative bioimage having defects such as metal artifacts without using teacher data. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of a bioimage acquiring device A according to a first embodiment. 
         FIG. 2  is a flowchart illustrating an operation example of the bioimage acquiring device A according to a first embodiment. 
         FIG. 3  is a flowchart describing an example of a classifying process according to a first embodiment. 
         FIG. 4  is a flowchart describing an example of a converter learning process according to a first embodiment. 
         FIG. 5  is a flowchart describing an example of a classifier learning process according to a first embodiment. 
         FIG. 6  is a figure showing an example of a negative bioimage according to a first embodiment. 
         FIG. 7  is a figure describing the generation of a positive bioimage from a negative bioimage according to a first embodiment. 
         FIG. 8  is a figure describing the generation of a positive bioimage from a negative bioimage according to a first embodiment. 
         FIG. 9  is another example of a block diagram of the bioimage acquiring device A according to a first embodiment. 
         FIG. 10  is a block diagram of a converter generating device B according to a second embodiment. 
         FIG. 11  is a flowchart describing an operation example of the converter generating device B according to a second embodiment. 
         FIG. 12  is a block diagram of a bioimage acquiring device C according to a third embodiment. 
         FIG. 13  is a flowchart illustrating an operation example of the bioimage acquiring device C according to a third embodiment. 
         FIG. 14  is a schematic diagram of a computer system according to the above embodiment. 
         FIG. 15  is a block diagram of the computer system according to the above embodiment. 
     
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Hereinafter, specific embodiments of the bioimage acquiring device and the like will be described referring to the drawings. In the embodiments, components with the same reference signs perform the same function, and their description may not be repeated. 
     Embodiment 1 
     According to the present embodiment, a bioimage acquiring device will be described that generates a positive bioimage, which is a bioimage with few defects, from a negative bioimage which is a defective bioimage. The bioimage may be called a medical image. A bioimage is an image obtained from a living body. A bioimage is usually one, two or more still images obtained by photographing a living body. The part of the living body from which the bioimage can be obtained is not particularly limited. The part is, for example, the oral cavity. The bioimage is, for example, a CT image obtained from a living body such as a human or a horse using X-ray CT, an MRI image obtained from a living body using MRI, or a PET image obtained from a living body using PET. It will be understood that the positive bioimage may be a bioimage without defects. Further, the defective bioimage is, for example, a bioimage having a metal artifact, a bioimage in which a soft tissue region is disturbed, or a bioimage in which a bone tissue is defective. The bioimage in which the soft tissue region is disturbed may be a bioimage in which the CT value of the nearby soft tissue is not correctly configured due to the influence of metal. Moreover, a defective bioimage of bone tissue is, for example, a bioimage including a part of the bone when the CT value of part of the bone is lowered due to infiltration of a tumor, and there is a defect when it is visualized. 
     According to the present embodiment, a bioimage acquiring device will be described comprising a first conversion unit in which a classifier is stored for classifying whether the received bioimage is a positive bioimage or a negative bioimage by using one or more negative bioimages and one or more positive bioimages, that converts the received negative bioimage and which acquires the converted bioimage which is the conversion result, and which determines whether the converted bioimage is a positive bioimage or a negative bioimage using the classifier, wherein the first conversion unit learns the converted bioimage and the determination result, converts the negative bioimage using the first conversion unit after learning, and acquires the converted bioimage. 
     According to the present embodiment, a bioimage acquiring device will be described further comprising a learning unit in which one or more negative bioimages and one or more positive bioimages are stored, which reconfigures the classifier using the converted bioimage acquired by the first conversion unit as a negative bioimage. 
     According to the present embodiment, a bioimage acquiring device will be described wherein information on the difference between one or more feature amounts of the negative bioimage supplied to the first conversion unit and one or more feature amounts of the converted bioimage acquired by the first conversion unit, is acquired, and the first conversion unit learns so that the difference information is reduced. 
     According to the present embodiment, a bioimage acquiring device will be described comprising a second conversion unit which performs an inverse conversion process on the converted bioimage to generate a positive bioimage, and performing a cyclic conversion such that the converted bioimage generated by the second conversion unit is supplied to the first conversion unit as a negative bioimage. 
     According to the present embodiment, a bioimage acquiring device will be described wherein information on the difference between one or more feature amounts of the converted bioimage supplied to the second conversion unit and one or more feature amounts of the converted bioimage generated by the second conversion unit is acquired, and the first conversion unit learns so that the difference information is reduced. 
     According to the present embodiment, a bioimage acquiring device will be described wherein the first conversion unit and the classifying unit perform processing only on pixels having a pixel value in a predetermined range among the pixels of the negative bioimage and the positive bioimage (for example, only the pixels in a bone region). 
     According to the present embodiment, a bioimage acquiring device will be described wherein the image conversion process is performed only on a negative bioimage selected by the user or only an automatically selected negative bioimage among two or more stored bioimages. 
     Further, in the present embodiment, the negative bioimage to be converted once and the generated positive bioimage may be a set of two or more slice images. 
       FIG. 1  is a block diagram of a bioimage acquiring device A according to the present embodiment. The bioimage acquiring device A comprises a storage unit  1 , a receiving unit  2 , a processing unit  3 , and an output unit  4 . 
     The storage unit  1  comprises, for example, a bioimage storage unit  11 , a classifier storage unit  12 , and a converter storage unit  13 . The receiving unit  2  comprises, for example, a bioimage receiving unit  21  and a negative bioimage receiving unit  22 . The processing unit  3  comprises, for example, a first conversion unit  31 , a classifying unit  32 , a learning unit  33 , a feature amount vector acquiring unit  34 , a feature amount difference information acquiring unit  35 , a second conversion unit  36 , and a control unit  37 . 
     Various types of information can be stored in the storage unit  1 . The various types of information are, for example, bioimages. The bioimage is a negative bioimage or a positive bioimage described later. Various types of information are, for example, a classifier described later and a converter described later. 
     One, two or more bioimages are stored in the bioimage storage unit  11 . It is preferred that the bioimage storage unit  11  stores one, two or more negative bioimages, and one two or more positive bioimages. 
     The two or more negative bioimages may be a set of two or more slice images obtained by cutting part of a set of bioimages which are the result of photographing a living body, into round slices. Further, the two or more positive bioimages may be a set of two or more slice images obtained by cutting part of a set of bioimages which are the result of photographing a living body, into round slices. Further, the set of two or more slice images may be a set of two or more slice images that are spatially continuous, or a set of two or more slice images that are spatially separated. 
     The classifier is stored in the classifier storage unit  12 . The classifier may be called a learning device. The classifier is information used to determine whether the bioimage to be classified is a negative bioimage or a positive bioimage. The classifier here is a classifier created by using one or more negative bioimages and one or more positive bioimages. Classifiers are usually created by machine learning algorithms. Various image classification algorithms are commonly available for creating classifiers. Specifically, the algorithm used in the machine learning for the process of creating the classifier and the machine learning described later is not particularly limited. For machine learning, for example, deep learning, SVR, random forest, decision tree and the like can be used. Further, in machine learning, in order to configure a classifier, a classifier can for example be obtained by supplying an input information group as an argument to a machine learning function. Machine learning functions comprise, for example, TensorFlow functions, TinySVM, and various Random Forest functions. The input information group is, for example, one or more negative bioimages and information showing negative bioimages, and one or more positive bioimages and information showing positive bioimages. 
     As regards prediction (classification) in machine learning, if a classifier and an information group to be input are supplied to the machine learning function as arguments, predicted information can be obtained. The classifier of the classifier storage unit  12  is, for example, information acquired by the learning unit  33 , described later. The information group to be input is, for example, a bioimage. Further, the predicted information here is information for specifying, for example, whether it is a negative bioimage or a positive bioimage. 
     The first converter is stored in the converter storage unit  13 . The second converter is also stored in the converter storage unit  13 . 
     The first converter is a converter used for a conversion process for acquiring a positive bioimage from a negative bioimage. The conversion process for acquiring a positive bioimage is a process for acquiring a bioimage having few defects or no defects from a defective bioimage. The first converter is, for example, information having a neural network structure acquired by a deep learning algorithm. The neural network structure is, for example, CNN (Convolution Neural Network), AutoEncoder, RNN (Recurrent Neural Network). The first converter can be implemented by, for example, a 2D or 3D convolution function, a pooling function, an activation function, or the like. Further, using a CNN converter, the image after conversion is obtained by performing, for example, the three processes of (1) convolution, (2) pooling, and (3) activation once, twice or more on the received image. 
     The second converter is a converter used for a conversion process for acquiring a negative bioimage from a positive bioimage. The second converter is, for example, information having a neural network structure acquired by a deep learning algorithm. The neural network structure is, for example, CNN (Convolution Neural Network), AutoEncoder, or RNN (Recurrent Neural Network). The second converter can also be implemented by a 2D or 3D convolution function, a pooling function, an activation function, or the like, similarly to the first converter. Using a CNN converter, the image after conversion is obtained by performing the three processes of (1) convolution, (2) pooling, and (3) activation once, twice or more on the received image. Note that the learning process for configuring the first converter and the second converter may be performed by the processing unit  3  or by an external device, not shown. Since the learning process for configuring the first converter and the second converter is a known technique in the art, a detailed description thereof is omitted. 
     The receiving unit  2  receives various instructions, information, and the like. The various instructions, information, and the like are, for example, one, two or more bioimages, or one, two or more negative bioimages. Here, receiving is a concept that comprises the receiving of information input from an input device such as a keyboard, a mouse, or a touch panel, receiving information transmitted via a wired or wireless communication line, and receiving information read from a recording medium such as an optical disk, a magnetic disk, a semiconductor memory, or the like. 
     The input means for inputting various instructions and information and the like may be any means such as a touch panel, a keyboard, a mouse, or a menu screen. 
     The bioimage receiving unit  21  receives two or more bioimages. Such two or more bioimages usually comprise a negative bioimage and a positive bioimage. 
     The negative bioimage receiving unit  22  receives one, two or more negative bioimages. The negative bioimage receiving unit  22  receives, for example, one, two or more new bioimages. The negative bioimage receiving unit  22  acquires, for example, a bioimage determined by the classifying unit  32  to be a negative bioimage. 
     The processing unit  3  performs various processes. The various processes are, for example, processes performed by the first conversion unit  31 , the classifying unit  32 , the learning unit  33 , the feature amount vector acquiring unit  34 , the feature amount difference information acquiring unit  35 , the second conversion unit  36 , and the control unit  37 . 
     The first conversion unit  31  converts the negative bioimage received by the negative bioimage receiving unit  22  using the first converter, and performs the first conversion process to acquire the converted bioimage which is the conversion result. The converted bioimage is usually a bioimage having fewer defects than the negative bioimage to be processed. The converted bioimage acquired by performing the first conversion process a small number of times such as once or twice, may not be a positive bioimage without defects. The first conversion process is, for example, a process using a deep learning algorithm. The first conversion process may, for example, be a process of forward propagation of deep learning. Alternatively, the first conversion process may, for example, be a process of repeating forward propagation and back propagation of deep learning. However, the first conversion process may be a process using an algorithm such as AutoEncoder, U-Net, Res-Net, or style conversion (Adaptive Instance Normalization). 
     The first conversion unit  31  performs a learning process using the determination result obtained by the classifying unit  32  and the converted bioimage, and updates the first converter. Such a process may be called an update process. The update process is, for example, a deep learning back propagation process. The update process may, for example, be a process of repeating forward propagation and back propagation of deep learning. However, the update process may be a process using an algorithm such as gradient descent or steepest descent. 
     The first conversion unit  31  converts a new negative bioimage received by the negative bioimage receiving unit  22  using the updated first converter, and acquires a converted bioimage which is the conversion result, for example. 
     It is preferred that the first conversion unit  31  performs a learning process so that the feature amount difference information is reduced, and updates the first converter, for example. The feature amount difference information is the feature amount difference information acquired by the feature amount difference information acquiring unit  35 , described later. Performing the learning process so that the feature amount difference information is reduced means supplying the feature amount difference information as a loss to the first conversion unit  31 , and performing the update process of the first converter. The first conversion unit  31  adds feature amount difference information as a loss as preprocessing for performing back propagation, and performs back propagation, for example. The first conversion unit  31  performs back propagation by adding feature amount difference information to the loss acquired by forward propagation, for example. 
     The first conversion unit  31  converts the second converted bioimage acquired by the second conversion unit  36  using the first converter, and acquires the converted bioimage which is the conversion result, for example. 
     The first conversion unit  31  may perform the first conversion process only for pixels having a pixel value in a predetermined range. A pixel having a pixel value in a predetermined range is, for example, a pixel having a pixel value in a specific part or region. Pixels with pixel values in a predetermined range are, for example, pixels that constitute an image of a bone. When the bioimage is a CT image, the pixel values of the pixels constituting the bone image are, for example, 100 to 500. The pixels having a pixel value in a predetermined range are, for example, pixels having a pixel value in a soft tissue region. 
     The classifying unit  32  uses the classifier of the classifier storage unit  12  to perform a first classifying process for determining whether the converted bioimage acquired by the first conversion unit  31  is a positive bioimage or a negative bioimage. 
     The classifying unit  32  may perform a second classifying process for determining whether the converted bioimage acquired by the first conversion unit  31  from the second converted bioimage is a positive bioimage or a negative bioimage. 
     The classifying unit  32  performs a first classifying process to determine whether the converted bioimage acquired by the first conversion unit  31  is a positive bioimage or a negative bioimage using a classifier created for only pixels having a pixel value in a predetermined range, for example. 
     The classifying unit  32  uses the classifier of the classifier storage unit  12  to determine whether each of the two or more bioimages received by the bioimage receiving unit  21  is a positive bioimage or a negative bioimage, for example. 
     The algorithm of the classifying process performed by the classifying unit  32  is not particularly limited. The classifying unit  32  performs classifying processing by an algorithm such as deep learning, SVM, decision tree, or random forest, for example. 
     The classifying unit  32  may use the Wasserstein distance, which is a known technique, to determine whether each of the two or more bioimages received by the bioimage receiving unit  21  is a positive bioimage or a negative bioimage. In this case, the classifying unit  32  precisely measures the distance, and the conversion unit  31  learns to convert a first group of images A (e.g., negative bioimages) to a second group of images (e.g., positive bioimages) by attempting to reduce the distance acquired by the classifying unit  32 . The learning unit  33  generates a classifier used to determine whether the received bioimage is a positive bioimage or a negative bioimage using, in addition to one or more negative bioimages and one or more positive bioimages of the bioimage storage unit  11 , one or more converted bioimages acquired by the first conversion unit  31  as negative bioimages. 
     The learning unit  33  generates the classifier by using the one or more negative bioimages and one or more positive bioimages stored in the bioimage storage unit  11 , for example. The learning unit  33  generates the classifier using one or more negative bioimages and information indicating that they are negative bioimages, and one or more positive bioimages and information indicating that they are positive bioimages, stored in the bioimage storage unit  11 , for example. 
     The process by which the learning unit  33  generates a classifier is usually implemented by a machine learning algorithm. The machine learning algorithm may be deep learning, SVM, decision tree, random forest, or the like. 
     The feature amount vector acquiring unit  34  acquires the feature amount vector of at least two or more of the converted bioimages from among the converted bioimage that is input to the second conversion unit  36 , the second converted bioimage which is the output of the second conversion unit  36 , and the converted bioimage which is the output of the first conversion unit  31  with respect to the second converted bioimage. The feature amount vector is a set of feature amounts of the image. The feature amount of the image is, for example, the pixel value, the difference between the pixel values of the pixels at two predetermined positions, the average value of a predetermined number of pixel values in a predetermined area, and the difference between pixel values at the same position in a spatial direction (z-axis direction) of two bioimages. The content of the feature amount of the image is not particularly limited. Since the process of acquiring the feature amount vector from an image is a known technique in the art, a detailed description thereof is omitted. 
     The feature amount vector acquiring unit  34  acquires an input feature amount vector which is one or more feature amounts of a negative bioimage supplied to the first conversion unit  31 , and an output feature amount vector which is one or more feature amounts of the converted bioimage acquired by the first conversion unit  31 . 
     The feature amount difference information acquiring unit  35  acquires feature amount difference information relating to the difference between one or more pairs of the two or more feature amount vectors acquired by the feature amount vector acquiring unit  34 . The feature amount difference information is, for example, the distance between two or more feature amount vectors. The feature amount difference information is, for example, the absolute value of the difference between two or more feature amount vectors. 
     The feature amount difference information acquiring unit  35  acquires feature amount difference information relating to the difference between the input feature amount vector and the output feature amount vector, for example. 
     The second conversion unit  36  converts the converted bioimage acquired by the first conversion unit  31  using the second converter, and performs a second conversion process to acquire the second converted bioimage which is the conversion result. The second conversion process is, for example, a forward propagation process of deep learning. The second conversion process is, for example, a process of repeating forward propagation and back propagation of deep learning. However, the second conversion process may be a process using an algorithm such as AutoEncoder, U-Net, Res-Net, or style conversion (Adaptive Instance Normalization). 
     The control unit  37  performs control so that the first conversion process, the first classifying process, the second conversion process, and the second classifying process are performed once, twice or more. The control unit  37  performs control to repeat the first conversion process, the first classifying process, the second conversion process, and the second classifying process until a predetermined termination condition is satisfied, for example. The predetermined termination condition is, for example, that the series of processes of the first conversion process, the first classifying process, the second conversion process, and the second classifying process are executed N (N is an integer equal to 1 or more) times. Alternatively, the termination condition is, for example, that a converted bioimage having a ratio equal to or higher than a threshold value (for example, 100%, or 95% or higher) is determined to be a positive bioimage. 
     The output unit  4  outputs various information. The various information is converted bioimages acquired by the first conversion unit  31 , for example. The output unit  4  stores the converted bioimages acquired by the first conversion unit  31  in the storage unit  1 , for example. 
     Here, output means display on a display, projection using a projector, printing by a printer, sound output, transmission to an external device, storage in a recording medium, or transfer of the processing result to another processing device, program, or the like. 
     The storage unit  1 , the bioimage storage unit  11 , the classifier storage unit  12 , and the converter storage unit  13  are preferably non-volatile recording media, but can also be implemented by volatile recording media. 
     The process of storing information in the storage unit  1  or the like is not particularly limited. For example, information may be stored in the storage unit  1  or the like via a recording medium, or information transmitted via a communication line or the like may be stored in the storage unit  1  or the like. Alternatively, information input via an input device may be stored in the storage unit  1  or the like. 
     The receiving unit  2 , the bioimage receiving unit  21  and the negative bioimage receiving unit  22  can be implemented by, for example, a device driver of an input means such as a touch panel or a keyboard, menu screen control software, or the like. 
     The processing unit  3 , the first conversion unit  31 , the classifying unit  32 , the learning unit  33 , the feature amount vector acquiring unit  34 , the feature amount difference information acquiring unit  35 , the second conversion unit  36 , and the control unit  37  are usually implemented by MPUs or memories. The processing procedure of the processing unit  3  and the like is usually implemented by software, and the software is recorded on a recording medium such as a ROM. However, it may be implemented by hardware (a dedicated circuit). 
     The output unit  4  may or may not comprise an output device such as a display or a speaker. The output unit  4  can be implemented by driver software of the output device, or driver software of the output device and the output device, or the like. 
     Next, an operation example of the bioimage acquiring device A will be described referring to the flowchart of  FIG. 2 . 
     (Step S 201 ) The bioimage receiving unit  21  determines whether or not one or more bioimages have been received. If one or more bioimages have been received, the routine proceeds to step S 202 , and if one or more bioimages have not been received, the routine returns to step S 201 . 
     (Step S 202 ) The classifying unit  32  performs a classifying process on the one or more bioimages received in step S 201 . An example of the classifying process will be described referring to the flowchart of  FIG. 3 . 
     (Step S 203 ) The first conversion unit  31  and the like perform a converter learning process. An example of the converter learning process will be described referring to the flowchart of  FIG. 4 . The converter learning process is a process of updating the first converter stored in the converter storage unit  13 . The converter learning process may be considered as a tuning process for improving the precision of the first converter. 
     (Step S 204 ) The learning unit  33  and the like perform a classifier learning process. An example of the classifier learning process will be described referring to the flowchart of  FIG. 5 . The classifier learning process is a process of updating the classifier stored in the classifier storage unit  12 . The classifier learning process may be considered as a tuning process for improving the precision of the classifier. 
     (Step S 205 ) The control unit  37  determines whether or not to terminate the loop process that repeats the processes of steps S 203  and S 204  based on a predetermined termination condition. If it terminates, the routine proceeds to step S 206 , and if it does not terminate, the routine returns to step S 203 . The termination condition is, for example, that the processes of steps S 203  and S 204  are executed N (N is an integer equal to 1 or more) times. Alternatively, as the termination condition, for example in step S 405  in the converter learning process, it may be determined that a converted bioimage equal to or higher than a threshold value (for example, 100%, or 95% or more) is a positive bioimage. Specifically, it may be determined that a converted bioimage equal to or higher than a threshold value (for example, 100%, or 95% or more) is a positive bioimage by a classifier that can precisely classify the presence or absence of defects provided in advance, or the like. 
     (Step S 206 ) The first conversion unit  31  substitutes 1 in a counter i. 
     (Step S 207 ) The first conversion unit  31  determines whether or not an i-th negative bioimage exists in the bioimage storage unit  11 . If the i-th negative bioimage exists, the routine proceeds to step S 208 , and if the i-th negative bioimage does not exist, the routine proceeds to step S 212 . 
     (Step S 208 ) The first conversion unit  31  acquires the i-th negative bioimage from the bioimage storage unit  11 . 
     (Step S 209 ) The first conversion unit  31  performs the first conversion process on the i-th negative bioimage acquired in step S 208  by using the updated first converter, and acquires an i-th positive bioimage. Here, the i-th positive bioimage is preferably an image wherein defects such as metal artifacts are completely removed, but may also be an image wherein defects are reduced. 
     (Step S 210 ) The output unit  4  stores the i-th positive bioimage in the storage unit  1 . The output unit  4  may associate the i-th positive bioimage with the i-th negative bioimage and store it in the storage unit  1 , or overwrite the i-th negative bioimage, and store the i-th positive bioimage in the storage unit  1 . 
     (Step S 211 ) The first conversion unit  31  increments the counter i by 1. The routine returns to step S 207 . 
     (Step S 212 ) The control unit  37  determines whether or not all the learning processes have been completed. When all the learning processes have been completed, the routine is terminated, and when all the learning processes have not been completed, the routine returns to step S 201 . The control unit  37  determines that all the learning processes have been completed when, for example, any of the following termination conditions is met. The termination condition is, for example, that the processes shown in steps S 201  to S 211  are executed N (N is an integer equal to 1 or more) times. Alternatively, as the termination condition, for example in step S 405  in the converter learning process, it may be determined that a converted bioimage equal to or higher than a threshold value (for example, 100%, or 95% or more) is a positive bioimage. Specifically, it may be determined that a converted bioimage equal to or higher than the threshold value (for example, 100%, or 95% or more) is a positive bioimage by a classifier that can precisely classify the presence or absence of defects provided in advance, or the like. 
     In the flowchart of  FIG. 2 , in step S 202 , the process of classifying bioimages into negative bioimages and positive bioimages may be performed by a person. When a person performs the classifying process, the receiving unit  2  receives the judgment result of the person (information indicating whether the image is a positive bioimage or a negative bioimage), and the processing unit  3  associates the bioimage with the judgment result. 
     In the flowchart of  FIG. 2 , the process may be performed only for pixels of the bioimage having pixel values in a predetermined range. For example, when the bioimage is a CT image, only pixels having a pixel value of “100 to 500” may be acquired, and the first conversion unit  31  may perform the first conversion process and the classifying unit  32  may perform the first classifying process only for a bioimage of a bone region. In this case, for example, the processing unit  3  inspects the pixel value of each pixel of the bioimage to be processed, and leaves only the pixels having pixel values in the predetermined range (for example, it sets the values of other pixels to 0 or a maximum value, or the like). 
     Next, an example of the classifying process in step S 202  will be described referring to the flowchart of  FIG. 3 . 
     (Step S 301 ) The classifying unit  32  substitutes 1 in the counter i. 
     (Step S 302 ) The classifying unit  32  determines whether or not the i-th bioimage exists in the bioimages received in step S 201 . If the i-th bioimage exists, the routine proceeds to step S 303 , and if the i-th bioimage does not exist, the routine returns to the preceding process. 
     (Step S 303 ) The classifying unit  32  acquires the i-th bioimage. 
     (Step S 304 ) The classifying unit  32  determines whether the i-th bioimage is a positive bioimage or a negative bioimage using the classifier of the storage unit  1 , and acquires the determination result. 
     (Step S 305 ) The classifying unit  32  associates the determination result acquired in step S 304  with the i-th bioimage. 
     (Step S 306 ) The classifying unit  32  increments the counter i by 1. The routine returns to step S 302 . 
     Next, an example of the converter learning process in step S 203  will be described referring to the flowchart of  FIG. 4 . 
     (Step S 401 ) The first conversion unit  31  substitutes 1 in the counter i. 
     (Step S 402 ) The first conversion unit  31  determines whether or not the i-th negative bioimage exists. If the i-th negative bioimage exists, the routine proceeds to step S 403 , and if the i-th negative bioimage does not exist, the routine proceeds to step S 411 . 
     The i-th negative bioimage is, for example, the i-th negative bioimage among the bioimages determined to be negative bioimages in the classifying process of step S 202 . 
     Alternatively, the i-th negative bioimage may, for example, be the i-th negative bioimage among bioimages determined to be negative bioimages in the classifying process of step S 202 , and one or more converted bioimages acquired in the immediately preceding converter learning process. 
     (Step S 403 ) The first conversion unit  31  acquires the i-th negative bioimage. 
     (Step S 404 ) The first conversion unit  31  performs the first conversion process on the i-th negative bioimage using the first converter of the converter storage unit  13 , acquires the i-th converted bioimage, and temporarily stores it in at least a buffer, not shown. 
     (Step S 405 ) The classifying unit  32  performs the first classifying process on the i-th converted bioimage acquired in step S 404 , determines whether the i-th converted bioimage is a positive bioimage or a negative bioimage, and acquires the determination result. 
     (Step S 406 ) The feature amount vector acquiring unit  34  acquires an input feature amount vector which is a set of two or more feature amounts of the i-th negative bioimage. Alternatively, the feature amount vector acquiring unit  34  acquires an output feature amount vector which is a set of two or more feature amounts of the i-th converted bioimage. 
     (Step S 407 ) The feature amount difference information acquiring unit  35  acquires feature amount difference information relating to the difference between the input feature amount vector and the output feature amount vector acquired in step S 406 . 
     (Step S 408 ) The first conversion unit  31  adds the feature amount difference information acquired in step S 407  as a loss, and updates the loss relating to the first converter. 
     (Step S 409 ) The first conversion unit  31  performs a learning process on the first converter of the converter storage unit  13  using the loss updated in step S 408 , and updates the first converter. Note that this process is a learning process of changing the first converter so that the feature amount difference information is reduced. 
     (Step S 410 ) The first conversion unit  31  increments the counter i by 1. The routine returns to step S 402 . 
     (Step S 411 ) The second conversion unit  36  substitutes 1 in the counter i. 
     (Step S 412 ) The second conversion unit  36  determines whether or not the i-th converted bioimage exists among the converted bioimages temporarily stored in step S 404 . If the i-th converted bioimage exists, the routine proceeds to step S 413 , and if the i-th converted bioimage does not exist, the routine returns to the preceding process. 
     (Step S 413 ) The second conversion unit  36  acquires the i-th converted bioimage from a buffer, not shown. 
     (Step S 414 ) The second conversion unit  36  performs a second conversion process on the i-th converted bioimage acquired in step S 413  using the second converter of the converter storage unit  13 , and acquires an i-th converted bioimage. The converted bioimage acquired here may be referred to as a second converted bioimage. 
     (Step S 415 ) The classifying unit  32  performs a classifying process on the i-th second converted bioimage acquired in step S 414 , determines whether the second converted bioimage is a positive bioimage or a negative bioimage, and acquires the determination result. Note that this process is a second classifying process. 
     (Step S 416 ) The feature amount vector acquiring unit  34  acquires the feature amount vector of each of a pair of bioimages. The two paired bioimages are, for example, the i-th converted bioimage and the i-th second converted bioimage, or the i-th negative bioimage and the i-th second converted bioimage. 
     (Step S 417 ) The feature amount difference information acquiring unit  35  acquires the feature amount difference information of the two feature amount vectors acquired in step S 416 . 
     (Step S 418 ) The first conversion unit  31  updates the loss of the first converter using the feature amount difference information acquired in step S 417 . 
     (Step S 419 ) The second conversion unit  36  performs a learning process using the loss acquired in step S 418 , and updates the first converter. Alternatively, the second conversion unit  36  performs a learning process on the second converter, and updates the second converter. 
     (Step S 420 ) The second conversion unit  36  increments the counter i by 1. The routine returns to step S 412 . 
     Note that, in the flowchart of  FIG. 4 , steps S 411  to S 420  may be omitted. 
     In the flowchart of  FIG. 4 , it is not necessary to add the feature amount difference information acquired in step S 417  to the loss used for the update process of the first converter. Adding to the loss means, for example, a join process such as adding a feature difference to the loss obtained by forward propagation of the first converter by linear sum. 
     In the flowchart of  FIG. 4 , the update process of the first converter may be performed in combination with known techniques such as norm error, Gradient Penalty, and Wasserstein distance. Further, in the flowchart of  FIG. 4 , as described above, in the second and subsequent converter learning processes, one or more converted bioimages acquired in the immediately preceding converter learning process may also suitably be used as the negative bioimage. 
     Next, an example of the classifier learning process in step S 204  will be described referring to the flowchart of  FIG. 5 . 
     (Step S 501 ) The learning unit  33  acquires one or more positive bioimages from the bioimage storage unit  11 . 
     (Step S 502 ) The learning unit  33  acquires one or more converted bioimages acquired by the first conversion unit  31  as negative bioimages. 
     (Step S 503 ) The learning unit  33  acquires one or more negative bioimages from the bioimage storage unit  11 . 
     (Step S 504 ) The learning unit  33  generates a classifier to classify images as positive or negative bioimages by a machine learning algorithm using the one or more positive bioimages acquired in step S 501 , and two or more negative bioimages acquired in steps S 502  and S 503 . Here, the learning unit  33  may, instead of using the two or more negative bioimages acquired in step S 503 , generate a classifier to classify bioimages as positive bioimages or negative bioimages by a machine learning algorithm using the negative bioimages acquired in step S 502 . 
     (Step S 505 ) The learning unit  33  stores the classifier generated in step S 504  in the classifier storage unit  12 . The routine returns to the preceding process. Note that the classifier is updated by this process. 
     Hereinafter, the specific operation of the bioimage acquiring device A according to the present embodiment will be described. In this specific example, the bioimage is, for example, a CT image. Negative bioimages are images with metal artifacts (e.g.,  FIG. 6 ).  FIG. 6  is an image of the oral cavity of a human, but metal artifacts are produced by implants and dentures in the oral cavity, which make diagnosis and surgery by medical staff difficult. In the image of  FIG. 6 , a part which should be a soft tissue is partially hollow due to the detrimental effect of the metal artifacts. 
     In such a situation, two examples will be described. In Example 1, the bioimage is a slice image. In Example 2, the bioimage is a three-dimensional image which is a set of slice images. 
     Example 1 
     It will be assumed that the bioimage storage unit  11  of the bioimage acquiring device A stores negative bioimages such as (a), (b), and (c) of  FIG. 7 , and a large number of positive bioimages, not shown. 
     It will be assumed that the classifier storage unit  12  stores a classifier used for determining whether the bioimage is a negative bioimage or a positive bioimage. 
     It will be assumed that the converter storage unit  13  stores a first converter used for a conversion process for acquiring a positive bioimage from a negative bioimage. It will further be assumed that the converter storage unit  13  stores a second converter used for a conversion process for acquiring a negative bioimage from a positive bioimage. 
     In this situation, when the bioimage acquiring device A receives a processing start instruction, a classifying process is performed on the bioimages of the bioimage storage unit  11  using the classifier to classify them as positive bioimages and negative bioimages. It will be assumed that the bioimage acquiring device A has determined that bioimages such as (a), (b), and (c) in  FIG. 7  are negative bioimages. 
     Next, the bioimage acquiring device A converts the negative bioimages of  FIGS. 7 ( a ), ( b ), and ( c )  to  FIGS. 7 ( d ), ( e ), and ( f ) , respectively, by the processes described referring to the flowcharts of  FIGS. 2 to 5 , to obtain positive bioimages. 
     Note that the image of  FIG. 7 ( d )  has few or no metal artifacts as compared with the image of  FIG. 7 ( a ) , and is filled with soft tissue, and the shape features of the oral cavity in  FIG. 7( a )  are retained. The image of  FIG. 7 ( e )  has few or no metal artifacts as compared with the image of  FIG. 7 ( b ) , and the shape features of the oral cavity in the image of  FIG. 7 ( b )  are retained. Further, the image of  FIG. 7 ( f )  has few or no metal artifacts as compared to the image of  FIG. 7 ( c ) , is filled with soft tissue, and the shape features of the oral cavity in the image of  FIG. 7 ( c )  are retained. 
     Example 2 
     The bioimage storage unit  11  of the bioimage acquiring device A stores three-dimensional images including negative bioimages such as (a), (b), and (c) of  FIG. 8 , and a large number of positive bioimages, not shown.  FIGS. 8 ( a ), ( b ), and ( c )  are three-dimensional images having a plurality of bioimages including still images having metal artifacts or the like. Further,  FIGS. 8 ( a ), ( b ), and ( c )  are images wherein only pixels having a pixel value (100 to 500) in the range of a bone are represented. 
     The classifier storage unit  12  stores a classifier used for determining whether a set of one, two or more bioimages are negative bioimages or positive bioimages. 
     The converter storage unit  13  stores a first converter used for a conversion process for acquiring one, two or more positive bioimages from one, two or more negative bioimages. The converter storage unit  13  also stores a second converter used for a conversion process for acquiring one, two or more negative bioimages from one, two or more positive bioimages. 
     In this situation, when the bioimage acquiring device A receives a processing start instruction, it performs a classifying process on a set of two or more slice images constituting a three-dimensional bioimage in the bioimage storage unit  11  using a classifier, and performs a process to classify them into positive bioimages and negative bioimages. It will be assumed that the bioimage acquiring device A has determined, for example, that the set of two or more slice images constituting (a), (b), and (c) of  FIG. 8  are negative bioimages. 
     Next, the bioimage acquiring device A converts the set of two or more slice images constituting (a), (b) and (c) of  FIG. 8  by the processes described referring to the flowcharts of  FIGS. 2 to 5 , and acquires a set of two or more slice images without defects such as metal artifacts. The bioimage acquiring device A then constructs a three-dimensional image using the set of two or more acquired slice images. These three-dimensional images are (d), (e) and (f) of  FIG. 8 . 
     In the image of  FIG. 8 ( d ) , metal artifacts are reduced or eliminated as compared with the image of  FIG. 8 ( a ) , and the shape features of the oral cavity or the like in the image of  FIG. 8 ( a )  are retained. In the image of  FIG. 8 ( e ) , metal artifacts are reduced or eliminated as compared with the image of  FIG. 8 ( b ) , and the shape features of the oral cavity, shoulder or the like in the image of  FIG. 8 ( b )  are retained. Further, in the image of  FIG. 8 ( f ) , metal artifacts are reduced or eliminated as compared with the image of  FIG. 8 ( c ) , and the shape features of the oral cavity or the like in the image of  FIG. 8 ( c )  are retained. 
     As described above, according to the present embodiment, a positive bioimage with few defects can for example be automatically acquired from a negative bioimage having defects such as metal artifacts without using teacher data. 
     According to the present embodiment, a positive bioimage with few defects can for example be automatically acquired from a negative bioimage having defects such as metal artifacts with high precision and without using teacher data. 
     According to the present embodiment, a positive bioimage having few defects that retains the features of the negative bioimage, can for example be automatically acquired from a negative bioimage having defects such as metal artifacts without using teacher data. 
     According to the present embodiment, a positive bioimage having few defects that retains the features of the negative bioimage, can for example be automatically acquired from a negative bioimage having defects such as metal artifacts with high precision, and without using teacher data. 
     According to the present embodiment, a three-dimensional positive bioimage with few defects can for example be automatically acquired from a three-dimensional negative bioimage having defects such as metal artifacts without using teacher data. 
     According to the present embodiment, a positive bioimage of a bone part having few defects can for example be automatically acquired from a negative bioimage which is a bioimage having defects such as metal artifacts, and which is a bioimage of a bone part, without using teacher data. 
     According to the present embodiment, the bioimage acquiring device A may have the minimum configuration as shown in  FIG. 9 . Specifically, the bioimage acquiring device A comprises a storage unit  1 , a receiving unit  2 , a processing unit  3 , and an output unit  4 . The storage unit  1  comprises a classifier storage unit  12  and a converter storage unit  13 . The receiving unit  2  comprises a negative bioimage receiving unit  22 . Further, the processing unit  3  comprises a first conversion unit  31  and a classifying unit  32 . 
     The processing in the present embodiment may be implemented by software. This software may be distributed by software download or the like. Alternatively, this software may be recorded on a recording medium such as a CD-ROM, and distributed. Note that this also applies to the other embodiments herein. The software that implements the bioimage acquiring device A according to the present embodiment is the program described below. Specifically, the program causes a computer that can access a classifier storage unit that stores a classifier created by using one or more negative bioimages that are defective bioimages and one or more positive bioimages that are non-defective bioimages to determine whether the image is a negative bioimage or a positive bioimage, and a converter storage unit that stores a first converter used for a conversion process that attempts to acquire a positive bioimage from a negative bioimage, to function as a negative bioimage receiving unit that receives a negative bioimage, a first conversion unit that performs a first conversion process that acquires the converted bioimage which is the conversion result, and as a classifying unit that performs a first classifying process for determining whether the converted bioimage acquired by the first conversion unit is a positive bioimage or a negative bioimage, wherein the first conversion unit performs a learning process using the determination result of the classifying unit and the converted bioimage, and an update process for updating the first converter, the negative bioimage receiving unit receives a new negative bioimage, and the first conversion unit converts the new negative bioimage received by the negative bioimage receiving unit using the updated first converter, and acquires the converted bioimage which is the conversion result. 
     Embodiment 2 
     According to this embodiment, a converter component device that constitutes a first converter that performs a conversion process will be described wherein the one or more negative bioimages supplied to the bioimage acquiring device A described in the first embodiment are input, and one or more converted bioimages finally acquired by the bioimage acquiring device A are output. 
       FIG. 10  is a block diagram of a converter generating device B according to the present embodiment. The converter generating device B comprises a storage unit  5  and a processing unit  6 . The storage unit  5  comprises a bioimage storage unit  11 . The processing unit  6  comprises a learning unit  61  and a converter accumulation unit  62 . 
     Various information is stored in the storage unit  5 . The various information is, for example, one or more bioimages. 
     The bioimage storage unit  11  stores one or more negative bioimages supplied to the bioimage acquiring device A, and one or more converted bioimages acquired by the first conversion unit  31  of the bioimage acquiring device A. The one or more converted bioimages are converted bioimages finally acquired by the bioimage acquiring device A. It will be assumed that each of the one or more converted bioimages corresponds to a negative bioimage before conversion. 
     The processing unit  6  performs various processes. The various processes are, for example, processes performed by the learning unit  61  and the converter accumulation unit  62 . 
     The learning unit  61  acquires a converter used to acquire a positive bioimage which is a converted bioimage from a negative bioimage by using the one or more negative bioimages and one or more converted bioimages of the bioimage storage unit  11 . Since such a learning process is a known technique in the art, a detailed description thereof is omitted. The learning unit  61  usually acquires the converter by a machine learning algorithm. The machine learning algorithm is, for example, deep learning, but it is not particularly limited. 
     The converter storage unit  62  accumulates the converter acquired by the learning unit  61 . The converter accumulation unit  62  accumulates the converter in the storage unit  5 , for example. 
     The storage unit  5  and the bioimage storage unit  11  may suitably use non-volatile recording media, but volatile recording media can also be used. 
     The process of storing information in the storage unit  5  or the like is not particularly limited. Information may for example be stored in the storage unit  5  or the like via a recording medium, or information transmitted via a communication line or the like may be stored in the storage unit  5  or the like. Alternatively, information input via an input device may be stored in the storage unit  5  or the like. 
     The processing unit  6 , the learning unit  61 , and the converter accumulation unit  62  can usually be implemented by an MPU, a memory, or the like. The processing procedure of the processing unit  6  and the like is usually implemented by software, and the software is recorded on a recording medium such as a ROM. However, it may be implemented by hardware (a dedicated circuit). 
     Next, an operation example of the converter generating device B will be described referring to the flowchart of  FIG. 11 . 
     (Step S 1101 ) The learning unit  61  acquires one or more sets of negative bioimages and converted bioimages from the bioimage storage unit  11 . 
     (Step S 1102 ) The learning unit  61  generates a converter having a negative bioimage as input and a converted bioimage as output using the one or more pairs of bioimages acquired in step S 1101 . 
     (Step S 1103 ) The converter accumulation unit  62  accumulates the converter generated in step S 1102  in the storage unit  5 . The routine is then terminated. 
     As described above, according to this embodiment, a highly precise converter for acquiring a positive bioimage from a negative bioimage can be automatically acquired. 
     In the present embodiment, the converter generating device B may have part or all of the functions of the bioimage acquiring device A. 
     The software that implements the converter generating device B in the present embodiment is the program described below. Specifically, the program causes a computer that can access a bioimage storage unit that stores one or more negative bioimages supplied to the bioimage acquiring device A and one or more converted bioimages acquired by the first conversion unit of the bioimage acquiring device A, to function as a learning unit for acquiring a converter used to acquire a positive bioimage, which is a converted bioimage, from a negative bioimage using the one or more negative bioimages and one or more converted bioimages of the bioimage storage unit, and as a converter accumulation unit for accumulating the converter acquired by the learning unit. 
     Embodiment 3 
     In this embodiment, a bioimage acquiring device for acquiring a positive bioimage using the converter generated by the converter generating device B will be described. 
       FIG. 12  is a block diagram of a bioimage acquiring device C according to the present embodiment. The bioimage acquiring device C comprises a storage unit  7 , a receiving unit  2 , a processing unit  8 , and an output unit  9 . The storage unit  7  comprises a converter storage unit  71 . The receiving unit  2  comprises a negative bioimage receiving unit  22 . The processing unit  8  comprises a conversion unit  81 . 
     Various types of information are stored in the storage unit  7 . The various types of information are, for example, a converter and a bioimage. 
     The converter storage unit  71  stores the converter acquired by the converter generating device B. 
     The processing unit  8  performs various processes. The various processes are, for example, processes performed by the conversion unit  81 . The processing unit  8  may comprise a classifying unit  32 . 
     The conversion unit  81  converts the negative bioimage received by the negative bioimage receiving unit  22  using the converter of the converter storage unit  71 , and acquires a positive bioimage which is the conversion result. Since the processing of the conversion unit  81  is a known technique in the art, a detailed description thereof is omitted. The conversion unit  81  may be identical to the first conversion unit  31 . 
     The output unit  9  outputs the positive bioimage acquired by the conversion unit  81 . The output unit  9  usually stores positive bioimages acquired by the conversion unit  81 . The storage location for positive bioimages is, for example, the storage unit  7 , but it is not particularly limited. 
     The storage unit  7  and the converter storage unit  71  may suitably use non-volatile recording media, but volatile recording media can also be used. 
     The process of storing information in the storage unit  7  or the like is not particularly limited. Information may for example be stored in the storage unit  7  or the like via a recording medium, or information transmitted via a communication line or the like may be stored in the storage unit  7  or the like. Alternatively, information input via an input device may be stored in the storage unit  7  or the like. 
     The processing unit  8 , the conversion unit  81 , and the output unit  9  are usually implemented by an MPU, a memory, or the like. The processing procedure of the processing unit  8  and the like is usually implemented by software, and the software is recorded on a recording medium such as a ROM. However, it may be implemented by hardware (a dedicated circuit). 
     Next, an operation example of the bioimage acquiring device C will be described referring to the flowchart of  FIG. 13 . 
     (Step S 1301 ) It is determined whether or not the negative bioimage receiving unit  22  has received a negative bioimage. If a negative bioimage has been received, the routine proceeds to step S 1302 , and if a negative bioimage has not been received, the routine returns to step S 1301 . 
     (Step S 1302 ) The conversion unit  81  acquires a converter from the converter storage unit  71 . 
     (Step S 1303 ) The conversion unit  81  converts the negative bioimage received in step S 1301  using the converter acquired in step S 1302 , and acquires the positive bioimage which is the conversion result. 
     (Step S 1304 ) The output unit  9  stores the positive bioimages acquired in step S 1303 . The routine returns to step S 1301 . 
     Note that in the flowchart of  FIG. 13 , when the power is turned off or a process terminate interrupt is issued, the process terminates. 
     As described above, according to the present embodiment, a positive bioimage can be acquired from a negative bioimage using an automatically acquired, highly precise converter. 
     The software that implements the bioimage acquiring device C in the present embodiment is the program described below. Specifically, the program causes a computer that can access the converter storage unit that stores the converter acquired by the converter generating device B, to function as a conversion unit that converts a negative bioimage received by the negative bioimage receiving unit using the converter stored in the converter storage unit, and acquires a positive bioimage which is the conversion result, and as an output unit that outputs the positive bioimage acquired by the conversion unit. 
       FIG. 14  shows the external appearance of a computer that executes the programs described in the present specification to implement the various embodiments described above (for example, the bioimage acquiring device A). The above embodiments can be implemented by computer hardware, and by computer programs running on it.  FIG. 14  is a schematic view of a computer system  300 , and  FIG. 15  is a block diagram of the system  300 . 
     In  FIG. 14 , the computer system  300  comprises a computer  301  having a CD-ROM drive, a keyboard  302 , a mouse  303 , and a monitor  304 . 
     In  FIG. 15 , the computer  301 , in addition to a CD-ROM drive  3012 , comprises an MPU  3013 , a bus  3014  connected to the MPU  3013  and the CD-ROM drive  3012 , a ROM  3015  for storing a program such as a boot-up program, a RAM  3016  connected to the MPU  3013  for temporarily storing instructions of an application program and providing temporary storage space, and a hard disk  3017  for storing the application program, the system program, and data. Although not shown here, the computer  301  may further comprise a network card that provides a connection to a LAN. 
     A program for causing the computer system  300  to execute the functions of the bioimage acquiring device A and the like according to the above-described embodiments may be stored in the CD-ROM  3101 , inserted into the CD-ROM drive  3012 , and further transferred to the hard disk  3017 . Alternatively, the program may be transmitted to the computer  301  via a network, not shown, and stored on the hard disk  3017 . The program is loaded into the RAM  3016  at runtime. The program may be loaded directly from the CD-ROM  3101  or a network. 
     The program does not necessarily comprise an operating system (OS) that causes the computer  301  to execute the functions of the bioimage acquiring device A or the like according to the above-described embodiments, or a third-party program or the like. The program needs only comprise part of the instructions that call the appropriate functions (modules) in a controlled manner to obtain the desired result. It is well known how the computer system  300  works, and a detailed description thereof is omitted. 
     In the above program, in the step of transmitting information and the step of receiving information, processing performed by hardware, for example, processing performed by a modem or interface card in the transmission step (processing performed only by hardware), is not included. 
     The number of computers that execute the above program may be singular or plural. Specifically, centralized processing may be performed, or distributed processing may be performed. 
     In each of the above embodiments, the above program may be singular or plural. Specifically, as a known technique for processing by AI, a process (ensemble process) may be performed wherein a plurality of AIs are trained simultaneously, and the average value or linear combination sum of the determination results is adopted as the final result. 
     In each of the above embodiments, it will be understood that two or more communication means existing in one device may be physically implemented by one medium. 
     It will be understood that the present invention is not particularly limited to the above embodiments, various modifications being possible which are also comprised within the scope of the present invention. 
     INDUSTRIAL APPLICABILITY 
     As described above, the bioimage acquiring device according to the present invention has the advantageous effect of being able to precisely acquire a positive bioimage with few defects from a negative bioimage having defects such as metal artifacts, and is useful as a bioimage acquiring device or the like.