Patent Publication Number: US-2023142763-A1

Title: Information processing apparatus, information processing method, and non-transitory storage medium

Description:
BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The present disclosure relates to an information processing apparatus that displays other lesions related with a lesion targeted by a user, an information processing method, and a non-transitory storage medium. 
     Description of the Related Art 
     Computer-Aided Detection (CADe) is known to analyze a medical image by using a computer to detect a candidate of a lesion as an abnormality accompanying a disease. With the advance in the Artificial Intelligence (AI) technology, the number of detection target lesions is currently increasing. 
     A medical diagnosis support apparatus discussed in Japanese Patent Application Laid-Open No. 7-37056 divides medical image data into microregions, calculates a feature quantity for each of the microregions, and superimposes image data with different display densities or display colors onto the medical image data according to the feature quantity to display the medical image data. 
     However, the technique discussed in Japanese Patent Application Laid-Open No. 7-37056 discusses the use of a different display form for each lesion portion according to the feature quantity but does not consider the display of other lesions related with a lesion targeted by a user such as a doctor. 
     SUMMARY OF THE INVENTION 
     According to an aspect of the present invention, an information processing apparatus includes a detection result acquisition unit configured to acquire a result of detecting a lesion from medical image data, an instruction reception unit configured to receive an instruction for specifying a target lesion based on the detection result, a related lesion information acquisition unit configured to acquire information about a lesion related with the specified target lesion, the lesion being of a different type from the target lesion, and a display control unit configured to control a display unit to display the information about the related lesion. 
     Further features of the present invention will become apparent from the following description of exemplary embodiments with reference to the attached drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates a configuration of an information processing system according to first to third exemplary embodiments. 
         FIG.  2    illustrates a hardware configuration of an information processing apparatus according to the first to third exemplary embodiments. 
         FIG.  3    illustrates a functional configuration of the information processing apparatus according to the first exemplary embodiment. 
         FIG.  4    illustrates an example of a user interface screen of the information processing apparatus according to the first exemplary embodiment. 
         FIG.  5    is a flowchart illustrating processing of the information processing apparatus according to the first exemplary embodiment. 
         FIG.  6    illustrates a functional configuration of an information processing apparatus according to a second exemplary embodiment. 
         FIG.  7    illustrates an example of a user interface screen of the information processing apparatus according to the second exemplary embodiment. 
         FIG.  8    is a flowchart illustrating processing of the information processing apparatus according to the second exemplary embodiment. 
         FIG.  9 A  illustrates an example of a user interface screen of an information processing apparatus according to a third exemplary embodiment. 
         FIG.  9 B  illustrates another example of the user interface screen of the information processing apparatus according to the third exemplary embodiment. 
         FIG.  10    is a flowchart illustrating processing of the information processing apparatus according to the third exemplary embodiment. 
     
    
    
     DESCRIPTION OF THE EMBODIMENTS 
     The present invention will be described below based on exemplary embodiments with reference to the accompanying drawings. Unless otherwise noted, items described in other exemplary embodiments are assigned the same reference numerals, and redundant descriptions thereof will be omitted. Configurations described in the following exemplary embodiments are to be considered as illustrative, and the present invention is not limited to illustrated configurations. Each of the embodiments of the present invention described below can be implemented solely or as a combination of a plurality of the embodiments or features thereof where necessary or where the combination of elements or features from individual embodiments in a single embodiment is beneficial. 
     A first exemplary embodiment will be described below centering on an information processing apparatus that displays medical image data such as X-ray Computed Tomography (CT) image data and Magnetic Resonance Imaging (MRI) image data. 
     The information processing apparatus according to the first exemplary embodiment includes a lesion detection unit, e.g., Computer-Aided Detection (CADe) for detecting a lesion in the medical image data. When the lesion detection unit detects a candidate of a lesion (hereinafter a candidate of a lesion is simply referred to as a lesion), the information processing apparatus displays the detection result to a user. When the user such as a doctor specifies the detected lesion, the information processing apparatus determines the types of lesions related with the lesion and displays a detection result of CADe for the types of related lesions. Examples of types of lesions to be detected by CADe include a pulmonary nodule, chest wall mass, peritoneal mass, liver mass, pancreatic mass, nephroma mass, large intestine mass, reticulation, honeycomb lung, bronchodilation, pleurisy, pleural effusion, tendovaginitis, bone erosion, osteitis, pancreas hypertrophy, and pancreas necrosis. 
     (System Configuration) 
       FIG.  1    illustrates a configuration of an information processing system including the information processing apparatus according to the present exemplary embodiment. 
     Referring to  FIG.  1   , the information processing system includes a medical case database (hereinafter referred to as a medical case DB)  102 , an information processing apparatus  101 , and a Local Area Network (LAN)  103 . 
     The medical case DB  102  stores medical image data captured by a medical imaging apparatus such as a Computed Tomography (CT) apparatus. The medical case DB  102  further includes a database function for providing the information processing apparatus  101  with medical image data via the LAN  103 . More specifically, the medical case DB  102  according to the present exemplary embodiment is known Picture Archiving and Communication Systems (PACS). 
     (Hardware Configuration) 
       FIG.  2    illustrates a hardware configuration of the information processing apparatus  101  according to the present exemplary embodiment. 
     Referring to  FIG.  2   , the information processing apparatus  101  includes a storage medium  201 , a Read Only Memory (ROM)  202 , a Central Processing Unit (CPU)  203 , and a Random Access Memory (RAM)  204 . 
     The information processing apparatus  101  further includes a LAN interface  205 , an input interface  208 , a display interface  206 , and an internal bus  211 . 
     The storage medium  201  is, for example, a Hard Disk Drive (HDD) that stores an Operating System (OS), processing programs for performing various kinds of processing according to the present exemplary embodiment, and various kinds of information. The ROM  202  stores a program such as a Basic Input Output System (BIOS) that initializes the hardware and activates the OS. The CPU  203  performs calculation processing when executing the BIOS, the OS, and the processing programs. The RAM  204  temporarily stores information used when the CPU  203  executes a program. The LAN interface  205  conforming to the Institute of Electrical and Electronics Engineers (IEEE) 802.3ab enables the information processing apparatus  101  to perform communication via the LAN  103 . A display  207  displays user interface screens. The display interface  206  converts screen information to be displayed on the display  207  into a signal and outputs the signal to the display  207 . A keyboard  209  inputs key entries. A mouse  210  specifies a coordinate position on the screen and inputs a button operation. The input interface  208  receives signals from the keyboard  209  and the mouse  210 . The internal bus  211  transmits signals in communication between blocks. 
     (Functional Configuration) 
       FIG.  3    illustrates a function configuration of the information processing apparatus  101  according to the present exemplary embodiment. 
     Referring to  FIG.  3   , the information processing apparatus  101  includes an image data acquisition unit  311 , a lesion detection unit  312 , a detection result acquisition unit  313 , an instruction reception unit  314 , a related lesion information acquisition unit  315 , and a display control unit  316 . 
     Referring to  FIG.  3   , the medical case DB  102  stores medical image data  321 - i  (i=1, 2, 3, . . . ) and provides the information processing apparatus  101  with the medical image data  321 - i  (i=1, 2, 3, . . . ) via the LAN  103 . The medical image data  321 - i  (i=1, 2, 3, . . . ) is, for example, a Digital Imaging and Communications in Medicine (DICOM) file. 
     The image data acquisition unit  311  acquires the medical image data  321 - i  (i=1, 2, 3, . . . ) to be subjected to inspection from the medical case DB  102  via the LAN  103 . According to the present exemplary embodiment, the acquisition of the medical image data  321 - i  (i=1, 2, 3, . . . ) conforms to DICOM. 
     The lesion detection unit  312  detects a plurality of types of lesions from the acquired medical image data  321 - i  (i=1, 2, 3, . . . ). A detector where a Convolutional Neural Network (CNN) as one of deep learning models has been subjected to learning is used for lesion detection. In the learning of the detector, a set of medical image data and ground truth data indicating lesion regions in the medical image data is used as teacher data. The medical image data of the teacher data is input to the CNN. CNN parameters are adjusted to minimize the difference between the output value of the CNN and the data indicating the lesion region. The lesion detection unit  312  may be configured to detect one lesion by using one CNN or detect a plurality of lesions by using one CNN. The CNN has been described above as an example of a detector, the CNN may be a detector based on machine learning and deep learning or a detector that detects a lesion by using an image processing technique based on the feature quantity. 
     The detection result acquisition unit  313  acquires a result of the lesion detection by the lesion detection unit  312 . The detection result acquisition unit  313  may acquire one lesion or a plurality of lesions depending on the number of lesions detected by the detector. The detection result acquired by the detection result acquisition unit  313  is information about the type of the detected lesion and information that identifies the position of the lesion. The information about the type of the detected lesion is, for example, an identifier (ID) uniquely assigned to each type of lesion. The information that identifies the position of the lesion is, for example, a mask image that can be displayed by superimposing on coordinate information and medical image data. The information that identifies the position of the lesion may be different for each type of lesion. 
     The instruction reception unit  314  receives from the user a specification for at least one lesion from the result of the lesion detection acquired by the detection result acquisition unit  313 . The result of the lesion detection acquired by the detection result acquisition unit  313  is displayed in a list manner on the display control unit  316 . The instruction reception unit  314  receives a lesion specification instruction through a left-click operation of the mouse  210  by the user. The instruction reception unit  314  may receive a user instruction for changing the highlight position in the detection result with the arrow keys and the TAB key on the keyboard  209  and a specification instruction with a combination of the ENTER key and the space key. 
     The related lesion information acquisition unit  315  identifies related lesions as lesions related with the target lesion specified by the user via the instruction reception unit  314 , the lesions being of different types from the target lesion. For example, the related lesion information acquisition unit  315  maintains the relations between the lesion type and the related lesion types as tabular format information, and identifies the related lesions based on the table. When a plurality of lesions is specified by the user, the instruction reception unit  314  ORs or ANDs related lesion types obtained from the table for each specified lesion type. Which of OR and AND is to be used may be preselected by the information processing apparatus  101  as setting information, selected by the user as required via the instruction reception unit  314 , or selected by the information processing apparatus  101  according to the combination of specified lesions. The related lesion information acquisition unit  315  may maintain combinations of a plurality of lesion types and the corresponding related lesion types as tabular format information and identify related lesions by using the information. The related lesion information acquisition unit  315  may also identify related lesions from a combination of a plurality of lesion types based on a set rule. The table and rule used by the related lesion information acquisition unit  315  to acquire related lesions may be pre-generated based on medical knowledge. Examples of medical knowledge include relations between a primary focus and metastatic focuses, relations between complications, and relations that define types of lesions related to the evaluation of the severity risk. Examples of relations between a primary focus and metastatic focuses will be described below. For a pulmonary nodule suspected to be a primary lung cancer, related lesions include a tumor mass of a chest wall, peritoneum, liver, and pancreas that are considered as metastatic positions of a primary lung cancer. For a pulmonary nodule suspected to be a metastatic lung cancer, related lesions include a tumor mass of a large intestine, kidney, and mammary gland that are considered as primary focuses. For a lung reticulation that is possibly a complication of an articular rheumatism, related lesions include tendovaginitis, bone erosion, and osteitis for the discrimination. For a pancreatitis, related lesions include pancreas hypertrophy and pancreas necrosis for the determination of the severity risk. 
     The related lesion information acquisition unit  315  acquires information about the operating state of the detection processing for detecting a specified lesion. The operating state acquired by the related lesion information acquisition unit  315  is information about whether the lesion detection unit  312  has implemented the lesion detection processing for each lesion type. The operating state acquired by the related lesion information acquisition unit  315  may include the implementation of the lesion detection processing, i.e., a state where the lesion detection processing has been started but has not been ended. The related lesion information acquisition unit  315  acquires the operating state of the detection processing for detecting a specified lesion, detects the specified lesion, and determines whether related lesions are identified. For example, if the detection processing for detecting a specified lesion is operating, the related lesion information acquisition unit  315  can grasp whether related lesions are detected as a result of the detection processing. If the detection processing for detecting a specified lesion is not operating, the related lesion information acquisition unit  315  can grasp that the detection processing is not performed. Since the reason why the detection processing is not operating is displayed (described below), the user can introduce a detector that performs the detection processing and issue an instruction for processing and editing medical data to perform the detection processing. 
     When the lesion detection unit  312  is currently implementing the lesion detection processing, the related lesion information acquisition unit  315  may acquire the progress and remaining time of the detection processing together. The related lesion information acquisition unit  315  may acquire the operating state for all lesion types, or may acquire only the operating state of the detection processing for the related lesions related with the lesion specified by the user. The related lesion information acquisition unit  315  may acquire results for the related lesions. In this case, the display control unit  316  (described below) controls the display unit to display detection results for the related lesions. 
     The display control unit  316  controls the display unit to display information about the related lesions acquired by the related lesion information acquisition unit  315 . For example, the display control unit  316  controls the display unit (the display  207 ) to display the information about the related lesions, for example, by displaying the result of the related lesion detection and the operating state of the detection processing in an associated way. 
     The display control unit  316  also displays the medical image data  321 - i  (i=1, 2, 3, . . . ) acquired by the image data acquisition unit  311  and the result of the related lesion detection acquired by the related lesion information acquisition unit  315 , on the display unit (display  207 ). The display control unit  316  displays the result of detecting the related lesions related with the lesion specified by the user so as to be distinguished from the results of detecting other lesions. The present exemplary embodiment will be described below centering on a form in which the display control unit  316  displays the related lesions and other lesions in different display areas. For example, these lesions may be displayed with different characters, different background colors, or different icon images. The display control unit  316  controls the display unit to display the operating state of the detection processing so that the operating state for the related lesions can be distinguished. The display control unit  316  may also identifiably display the medical image data, the detection result, and the result of the related lesions. The present exemplary embodiment will be described below centering on a form in which the display control unit  316  controls the display unit to perform display in a different display area, similarly to that of the detection result. However, display may be performed, for example, with different characters, different background colors, or different icon images. 
     (User Interface Screens) 
       FIG.  4    illustrates an example of a user interface screen displayed on the display unit by the display control unit  316  of the information processing apparatus  101  according to the present exemplary embodiment. The user interface screen is displayed on the display unit (the display  207 ), and various operations by the user are input via the keyboard  209  and the mouse  210 . 
     Referring to  FIG.  4   , a user interface screen  400  includes a medical image data display area  401 , a lesion detection result display area  402 , and a related lesion detection result display area  403 . 
     The display control unit  316  controls the display unit to display the medical image data acquired by the image data acquisition unit  311 , in the medical image data display area  401 . In the medical image data display area  401 , the user changes Window Level/Window Width (WL/WW), slice position, and magnification of the image in response to operations via the keyboard  209  and the mouse  210 . The display control unit  316  controls the display unit to display an annotation  411  indicating the position of the specified lesion based on the result of the lesion detection acquired by the detection result acquisition unit  313 , in the medical image data display area  401 . The display control unit  316  may display an overlay image that highlights the lesion region corresponding to the lesion position, in the medical image data display area  401 . 
     The display control unit  316  controls the display unit to display the result of the lesion detection (lesion detection result)  421 - i  (i=1, 2, 3, 4, . . . ) acquired by the detection result acquisition unit  313 , in the lesion detection result display area  402 . The display unit (the display  207 ) displays only the lesion detected as the lesion detection result  421 - i  (i=1, 2, 3, 4, . . . ) in the lesion detection result display area  402 . The lesion detection result  421 - 1  indicates that the lesion of “lesion type  1 - 1 ” is detected. In the lesion detection result display area  402 , the user can specify the result of the lesion detection by left-clicking on the result of the lesion detection with the mouse  210 . For example, the display control unit  316  controls the display unit to highlight the frame line and background for the specified detection result, as illustrated in the lesion detection result  421 - 2 . The display control unit  316  also updates the annotation  411  according to the specification of the result of the lesion detection based on the corresponding lesion detection position. The display control unit  316  also updates the contents of the related lesion detection result display area  403  based on the result of the related lesion detection acquired by the related lesion information acquisition unit  315 . 
     The display control unit  316  controls the display unit to display the result of the related lesion detection (related lesion detection result)  431 - i  (i=1, 2, 3, 4, . . . ) acquired by the related lesion information acquisition unit  315 , in the related lesion detection result display area  403 . The display control unit  316  displays the lesion types of the related lesions as the related lesion detection result  431 - i  (i=1, 2, 3, 4, . . . ). When no related lesion is detected, the display control unit  316  controls the display unit to display that no related lesion is detected, in the related lesion detection result display area  403 , as illustrated by the related lesion detection result  431 - 1 . The display control unit  316  controls the display unit to display also the operating state of the lesion detection processing acquired by the related lesion information acquisition unit  315 , in the related lesion detection result  431 - i  (i=1, 2, 3, 4, . . . ). The related lesion detection result  431 - 3  indicates that the lesion detection is not implemented as the operating state. 
     The display of the related lesion detection results  431 - 1  and  431 - 2  enables the user to grasp the result of detection of the related lesions as lesions related with the lesion targeted by the user, the related lesions being of different types from the target lesion. The display of the related lesion detection result  431 - 3  enables the user to confirm such a status that the processing for detecting the related lesions is not implemented. 
     (Processing Flow) 
       FIG.  5    is a flowchart illustrating processing of the information processing apparatus  101  according to the present exemplary embodiment. After the activation of the information processing apparatus  101 , the information processing apparatus  101  starts report generation processing based on an instruction from other systems or the user. When starting processing, a medical case to be processed is specified. 
     In step S 501 , the image data acquisition unit  311  acquires the medical image data  321 - i  (i=1, 2, 3, . . . ) of the medical case specified at the time of the activation from the medical case DB  102  via the LAN  103 . Then, the processing proceeds to the next step. 
     In step S 502 , the lesion detection unit  312  detects a lesion from the medical image data  321 - i  (i=1, 2, 3, . . . ) acquired in step S 501 . The lesion detection unit  312  performs the detection processing by using a detector that is subjected to learning and generated based on machine learning and deep learning. The detector to be used for the detection processing may be preset. When the lesion detection unit  312  subjects the medical image data to the lesion detection processing, the processing proceeds to the next step. 
     In step S 503 , the display control unit  316  controls the display unit to display the medical image data  321 - i  (i=1, 2, 3, . . . ) acquired by the image data acquisition unit  311  in step S 501 , in the medical image data display area  401  of the user interface screen  400 . The user changes the WL/WW, slice position, and magnification of the image to be displayed in response to operations via the keyboard  209  and the mouse  210 . 
     In step S 504 , the detection result acquisition unit  313  acquires the result of the lesion detection from the lesion detection unit  312 . The result of the lesion detection includes information about the types of the detected lesions and information about the lesion position in the medical image data  321 - i  (i=1, 2, 3, . . . ). 
     In step S 505 , the display control unit  316  controls the display unit to display the lesion detection result  421 - i  (i=1, 2, 3, 4, . . . ) based on the result of the lesion detection acquired in step S 504 , in the lesion detection result display area  402  of the user interface screen  400 . 
     In step S 506 , the instruction reception unit  314  receives an instruction for specifying the user&#39;s target lesion for the detected lesions based on inputs from the keyboard  209  and the mouse  210 . When the specification of the user&#39;s target lesion is detected (YES in step S 506 ), the processing proceeds to step S 511 . On the other hand, when the specification of the user&#39;s target lesion is not detected (NO in step S 506 ), the processing proceeds to step S 507 . 
     In step S 507 , the OS (not illustrated) detects the end processing of the information processing apparatus  101 . The end processing includes the OS shutdown operation, power off operation, window closing operation, and process deactivation. When the end processing is detected (YES in step S 507 ), the processing exits the flowchart. On the other hand, when the end processing is not detected (NO in step S 507 ), the processing returns to step S 503 . Then, the information processing apparatus  101  repeats the processing. 
     In step S 511 , based on an instruction for specifying the user&#39;s target lesion for the detected lesion detection result in step S 506 , the related lesion information acquisition unit  315  identifies related lesions as lesions related with the lesion by referring to the table that defines the relation between lesion types. 
     In step S 512 , the related lesion information acquisition unit  315  acquires from the detection result acquisition unit  313  the result of detection of the related lesion identified in step S 511 . 
     In step S 513 , the display control unit  316  displays the related lesion detection result  431 - i  (i=1, 2, 3, . . . ) in the related lesion detection result display area  403  of the user interface screen  400  based on the result of the related lesion detection acquired by the related lesion information acquisition unit  315  in step S 512 . 
     In step S 514 , the related lesion information acquisition unit  315  acquires the operating state of the related lesion detection processing for the related lesions determined in step S 511 . According to the present exemplary embodiment, the operating state has information about the implementation of the lesion detection processing. 
     In step S 515 , based on the operating state of the related lesion detection acquired in step S 514 , the display control unit  316  controls the display unit to display the operating state in the related lesion detection result  431 - i  (i=1, 2, 3, . . . ) in the related lesion detection result display area  403  of the user interface screen  400 . When the processing in step S 515  is completed, the processing returns to step S 507 . 
     As described above, the information processing apparatus  101  of the exemplary embodiment of the present invention includes the display control unit  316  that controls the display unit to display the result of detecting at least one lesion from the medical image data, and information about related lesions related with the target lesion selected for at least one lesion in response to an instruction from the user. The information processing apparatus  101  of the exemplary embodiment of the present invention includes the detection result acquisition unit  313  that acquires the result of detecting a lesion from the medical image data, the instruction reception unit  314  that receives an instruction for specifying a target lesion for the detected lesion, the related lesion information acquisition unit  315  that acquires information about related lesions as lesions related with the specified target lesion, the lesions being of different types from the target lesion, and the display control unit  316  that controls the display unit such as the display  207  to display the information about the related lesions. In this configuration, when the user specifies a target lesion for the lesions detected by the lesion detection unit  312  via the instruction reception unit  314 , the related lesion information acquisition unit  315  automatically determines the types of the lesions related with the user&#39;s target lesion, and the display control unit  316  displays the result of the related lesion detection on the display unit. This enables efficiently grasping other lesions related with the target lesion of the user such as a doctor. Even when the number of detection target lesions increases, the user is able to easily find the presence or absence of the detection result of other related lesions. Further, the display control unit  316  controls the display unit to display the operating state of the lesion detection processing. When no lesion is detected, this configuration makes it easier to distinguish between a state where no lesion is detected with CADe performed and a state where no lesion is detected with CADe not performed. 
     (Modification of First Exemplary Embodiment) 
     The information processing apparatus  101  may be an image processing workstation, an integrated viewer for integrally displaying electronic charts and information from a plurality of types of apparatuses, and an apparatus for capturing medical images such as an ultrasonic diagnostic apparatus. 
     The lesion detection unit  312  may exist on another apparatus such as an image processing server connected with the information processing apparatus  101  via a network. The lesion detection unit  312  may detect a lesion at the timing when the medical image data  321 - i  (i=1, 2, 3, . . . ) is captured, at the timing when the data is stored in the medical case DB  102 , or during other background processing, and store the detection result in a storage device such as the medical case DB  102 . In this case, the detection result acquisition unit  313  acquires the detection result from the storage device. 
     The lesion detection unit  312  as a detector may detect a lesion by using a method other than CNN, such as Support Vector Machine (SVM) as one of machine learning techniques. 
     The detection result acquisition unit  313  may acquire whether a lesion is detected, the position of the detected lesion, and other information related to the detection result, such as the progress of the processing and the remaining time till the end of the processing. 
     The related lesion information acquisition unit  315  also extracts medical knowledge by subjecting past radiogram interpretation reports, theses, and medical care guidelines to language processing to generate tables and rules to be used to determine related lesions. 
     An information processing apparatus  101  according to a second exemplary embodiment includes the information processing apparatus  101  according to the first exemplary embodiment, and additionally includes functions of acquiring and displaying information about the feasibility of the detection processing when the operating state of the related lesion detection is “Not Implemented”. The information processing apparatus  101  receives an instruction for implementing the lesion detection when the detection processing is not implemented and feasible. In the present exemplary embodiment, the system configuration of the information processing apparatus  101  is similar to that according to the first exemplary embodiment described above with reference to  FIG.  1   , the hardware configuration is similar to that according to the first exemplary embodiment described above with reference to  FIG.  2   , and redundant descriptions thereof will be omitted. 
     (Functional Blocks) 
       FIG.  6    illustrates a functional configuration of the information processing apparatus  101  according to the present exemplary embodiment. Function blocks identical to the function blocks according to the first exemplary embodiment described above with reference to  FIG.  3    are assigned the same reference numerals, and redundant descriptions thereof will be omitted. 
     Referring to  FIG.  6   , a instruction reception unit  614  of the information processing apparatus  101  according to the present exemplary embodiment receives an instruction for implementing the detection processing. The instruction reception unit  614  receives an additional instruction from the user according to the operating state acquired by the related lesion information acquisition unit  315 . According to the present exemplary embodiment, when the operating state of the processing for detecting a predetermined lesion acquired by the related lesion information acquisition unit  315  is “Not Implemented”, and the operating state for the feasibility of the detection processing is “Feasible”, the instruction reception unit  614  receives an instruction for the processing for detecting the lesion by the lesion detection unit  312  from the user. Upon reception of an instruction for implementing the additional lesion detection processing from the user, the instruction reception unit  614  instructs the lesion detection unit  312  to implement the additional lesion detection processing. According to the present exemplary embodiment, the additional lesion detection processing is implemented upon reception of a user instruction by the instruction reception unit  614 . However, the related lesion information acquisition unit  315  may implement the additional detection processing without receiving a confirmation input from the user. 
     (User Interface Screen) 
       FIG.  7    illustrates an example of a user interface screen displayed by the display control unit  316  in the information processing apparatus  101  according to the present exemplary embodiment. Elements identical to the elements of the user interface according to the first exemplary embodiment described above with reference to  FIG.  4    are assigned the same reference numerals, and redundant descriptions thereof will be omitted. 
     A user interface screen  700  displays the information in the user interface screen  400  according to the first exemplary embodiment and, when the operating state of the detection processing is “Not Implemented”, additionally displays information about the feasibility of the detection processing in the related lesion detection result  431 - i  (i=1, 2, 3, . . . ). The display control unit  316  displays a lesion detection instruction check window  404  that is associated with the instruction reception unit  614 . 
     The lesion detection instruction check window  404  is used by the instruction reception unit  614  to receive an instruction for implementing the lesion detection processing from the user. The display control unit  316  controls the display unit to display the lesion detection instruction check window  404 , and a lesion detection result (the related lesion detection result  431 - 4  in  FIG.  7   ) when the operating state of the detection processing regarding the implementation of the detection processing is “Not Implemented”, and the operating state of the detection processing regarding the feasibility of the detection processing is “Feasible”. When the instruction reception unit  614  receives a user instruction for specifying “Yes” in the lesion detection instruction check window  404 , the lesion detection processing is implemented. On the other hand, when the instruction reception unit  614  receives a user instruction for specifying “No”, the lesion detection processing is not implemented. The instruction reception unit  614  detects a user&#39;s specification instruction based on inputs from the mouse  210  and the keyboard  209 . 
     (Processing Flow) 
       FIG.  8    is a flowchart illustrating processing of the information processing apparatus  101  according to the present exemplary embodiment. Steps identical to the steps of the flowchart according to the first exemplary embodiment described above with reference to  FIG.  5    are assigned the same reference numerals, and redundant descriptions thereof will be omitted. 
     In step S 516 , the related lesion information acquisition unit  315  determines whether the operating state of the detection processing regarding the lesion specified by a user operation via the instruction reception unit  614  is “Not Implemented” and “Feasible”. When the operating state is “Not Implemented” and “Feasible” (YES in step S 516 ), the processing proceeds to step S 521 . On the other hand, when the operating state is “Not Implemented” and “Infeasible” (NO in step S 516 ), the processing returns to step S 507 . According to the present exemplary embodiment, the operation information acquired by the related lesion information acquisition unit  315  includes information about the implementation of the detection processing and information about the feasibility of the detection processing. 
     In step S 521 , the instruction reception unit  614  receives an instruction for performing the lesion detection processing determined to be “Not Implemented” and “Feasible” by the related lesion information acquisition unit  315  in step S 516 . 
     According to the present exemplary embodiment, when the instruction reception unit  614  receives an instruction, the display control unit  316  displays the lesion detection instruction check window  404  illustrated in  FIG.  7    and receives a confirmation input from the user. Upon reception of a specification “Yes” from the user in the lesion detection instruction check window  404 , the instruction reception unit  614  instructs the lesion detection unit  312  to perform the lesion detection. On the other hand, upon reception of a specification “No”, the instruction reception unit  614  ends the processing of this step without instructing the lesion detection unit  312  to perform the lesion detection. 
     As described above, according to the present exemplary embodiment, when the user specifies the lesion detected by the lesion detection unit  312 , the related lesion information acquisition unit  315  automatically determines the types of the related lesions related with the user&#39;s target lesion, and the display control unit  316  displays the result of the related lesion detection. Therefore, even when the number of detection target lesions increases, the user is able to easily find the presence or absence of the detection result of other lesions related with the user&#39;s target lesion. 
     Further, the display control unit  316  controls the display unit to display the operating state of the lesion detection processing. This makes it easier for the user to distinguish between a state where no lesion is detected with CADe performed and a state where no lesion is detected with CADe not performed. 
     When the lesion detection is “Not Implemented” and “Feasible”, the instruction reception unit  614  receives an instruction for performing the additional lesion detection processing from the user, making it easier to implement the lesion detection processing not implemented. 
     (Modification of Second Exemplary Embodiment) 
     The present exemplary embodiment has been described above centering on a case where the instruction reception unit  614  receives an instruction for the lesion detection processing, and the lesion detection unit  312  performs the additional lesion detection based on a user instruction. However, the instruction reception unit  614  does not need to receive a user instruction. In this case, the related lesion information acquisition unit  315  may instruct the lesion detection unit  312  to perform the detection processing not implemented. 
     An information processing apparatus  101  according to a third exemplary embodiment includes the information processing apparatus  101  according to the second exemplary embodiment, and additionally includes a function of the display control unit  316  to control the display unit to display information about the reason of infeasibility when the operating state for the related lesion detection is “Infeasible”. When the detection processing is infeasible because the detection processing function is not installed, the related lesion information acquisition unit  315  receives an instruction for installing the detection processing function via the instruction reception unit  614 . When the detection processing is infeasible because the medical image data is out of the implementation condition for the detection processing, the related lesion information acquisition unit  315  receives an instruction for acquiring the medical image data that satisfies the implementation condition. The system configuration and the hardware configuration of the information processing apparatus  101  according to the present exemplary embodiment are similar to those according to the first exemplary embodiment described above with reference to  FIGS.  1  and  2   , respectively, and the function block according to the present exemplary embodiment is similar to that according to the second exemplary embodiment described above with reference to  FIG.  6   , and redundant descriptions thereof will be omitted. 
     (User Interface Screens) 
       FIGS.  9 A and  9 B  are examples of user interface screens displayed by the display control unit  316  of the information processing apparatus  101  according to the present exemplary embodiment. Elements identical to the elements of the user interfaces according to the first and second exemplary embodiments described above with reference to  FIGS.  4  and  7    are assigned the same reference numerals, and redundant descriptions thereof will be omitted. 
     A user interface screen  900  displayed on the display unit by the display control unit  316  includes the user interface screen  700  according to the second exemplary embodiment. In addition, when the information about the operating state (feasibility of the detection processing) is “Infeasible”, the user interface screen  900  displays the reason of the infeasibility. In this case, the instruction reception unit  614  receives an additional instruction from the user. The user interface screen  900  displayed on the display unit by the display control unit  316  includes a lesion detection installation instruction check window  405  and an implementation condition image acquisition instruction check window  406 . 
     In the user interface screen  900  displayed on the display unit by the display control unit  316 , a related lesion detection result  431 - 5  indicates that the detection processing is infeasible because the detection processing function is “Not Installed” and a related lesion detection result  431 - 6  indicates that the detection processing is infeasible because the medical image data to be subjected to the lesion detection processing is “Out of Applicable Condition” for the detection processing. 
     The lesion detection installation instruction check window  405  displayed on the display unit such as the display  207  by the display control unit  316  is used by the instruction reception unit  614  to check whether the user issues an instruction for installing the lesion detection function. The display control unit  316  controls the display unit to display the lesion detection installation instruction check window  405  when the user specifies a lesion for which the detection processing is infeasible because the detection processing function is not installed (the related lesion detection result  431 - 5  in  FIG.  9 A ). When the user specifies “Yes” in the lesion detection installation instruction check window  405 , the instruction reception unit  614  instructs the lesion detection unit  312  to install the lesion detection processing function. On the other hand, when the user specifies “No”, the instruction reception unit  614  does not instruct the lesion detection unit  312  to install the lesion detection processing function. The instruction reception unit  614  detects a user&#39;s specification instruction based on inputs from the mouse  210  and the keyboard  209 . The instruction for installing the detection processing function via the instruction reception unit  614  is, for example, key entries for software installation and software activation. 
     The implementation condition image acquisition instruction check window  406  displayed on the display unit by the display control unit  316  is used by the instruction reception unit  614  to check whether the user issues an instruction for acquiring the medical image data that satisfies the implementation condition for the lesion detection processing. The implementation condition image acquisition instruction check window  406  is displayed when the user specifies a lesion for which the detection processing is infeasible because the medical image data is out of the applicable condition for the lesion detection processing (the related lesion detection result  431 - 6  in  FIG.  9 B ). When the user specifies “Yes” in the implementation condition image acquisition instruction check window  406 , the instruction reception unit  614  instructs the image data acquisition unit  311  to acquire the medical image data. On the other hand, when the user specifies “No”, the instruction reception unit  614  does not instruct the image data acquisition unit  311  to acquire the medical image data. The instruction reception unit  614  detects a user&#39;s specification instruction based on inputs from the mouse  210  and the keyboard  209 . The implementation condition image acquisition instruction check window  406  includes a user interface for issuing instructions for adding and changing condition specifications. The applicable condition for the lesion detection processing includes the modality that captures images, reconfiguration function, radiographic contrasting condition, time phase, and imaging range. An instruction for acquiring medical image data includes condition-based image reconfiguration and an image capturing order to an ordering system. 
     (Processing Flow) 
       FIG.  10    illustrates a processing flow of the information processing apparatus  101  according to the present exemplary embodiment. Steps identical to the steps of the flowcharts according to the first and second exemplary embodiments described above with reference to  FIGS.  5  and  8   , respectively, are assigned the same reference numerals, and redundant descriptions thereof will be omitted. 
     In step S 517 , the related lesion information acquisition unit  315  determines whether the operating state of the detection processing regarding the lesion specified by a user operation is “Infeasible” because the detection processing function is not installed. When the detection processing is infeasible because the detection processing function is not installed (YES in step S 517 ), the processing proceeds to step S 531 . On the other hand, when the detection processing is infeasible not because the detection processing function is not installed (NO in step S 517 ), the processing proceeds to step S 518 . According to the present exemplary embodiment, the operation information acquired by the related lesion information acquisition unit  315  includes information about the implementation of the detection processing, information about the feasibility of the detection processing, and the reason why the detection processing is infeasible. 
     In step S 531 , the CPU  203  instructs the lesion detection unit  312  to perform the lesion detection function installation specified by the user via the instruction reception unit  614 . According to the present exemplary embodiment, when the instruction reception unit  614  instructs the lesion detection unit  312  to an install the detection function, the display control unit  316  controls the display unit of the display  207  to display the lesion detection installation instruction check window  405  illustrated in  FIG.  9 A . Then, the instruction reception unit  614  receives a confirmation input from the user. Upon reception of a specification “Yes” in the lesion detection installation instruction check window  405 , the instruction reception unit  614  instructs the lesion detection unit  312  to install the lesion detection processing function. On the other hand, upon reception of a specification “No”, the instruction reception unit  614  ends the processing of this step without instructing the lesion detection unit  312  to install the lesion detection processing function. 
     In step S 518 , the related lesion information acquisition unit  315  determines whether the operating state of the detection processing regarding the lesion specified by a user operation is “Infeasible” because the medical image data is out of the implementation condition for the lesion detection processing. When the lesion detection processing is infeasible because the medical image data is out of the implementation condition for the lesion detection processing (YES in step S 518 ), the processing proceeds to step S 541 . On the other hand, when the lesion detection processing is infeasible not because the medical image data is out of the implementation condition for the lesion detection processing (NO in step S 518 ), the processing returns to step S 507 . 
     In step S 541  (when the related lesion information acquisition unit  315  determines that the implementation of the detection processing is infeasible because the medical image data is out of the implementation condition in step S 518 ), the related lesion information acquisition unit  315  instructs the image data acquisition unit  311  to acquire the medical image data that satisfies the implementation condition for the lesion detection processing corresponding to the lesion detection result. According to the present exemplary embodiment, the display control unit  316  controls the display unit of the display  207  to display the implementation condition image acquisition instruction check window  406  illustrated in  FIG.  9 B  and receives an input of a confirmation for the implementation of the processing for acquiring medical image data that satisfies the implementation condition from the user. When the instruction reception unit  614  detects a specification “Yes” in the implementation condition image acquisition instruction check window  406 , the related lesion information acquisition unit  315  instructs the image data acquisition unit  311  to acquire the medical image data that satisfies the implementation condition. On the other hand, when the instruction reception unit  614  detects a specification “No”, the related lesion information acquisition unit  315  ends the processing of this step without instructing the image data acquisition unit  311  to acquire the medical image data. The image data acquisition unit  311  may generate the medical image data that satisfies the implementation condition based on the obtained medical image data, acquire the medical image data from the outside, or transmit an acquisition order to an ordering system. 
     As described above, according to the present exemplary embodiment, when the user specifies the lesion detected by the lesion detection unit  312 , the related lesion information acquisition unit  315  automatically determines the types of the related lesions, and the display control unit  316  displays the result of detecting the related lesions related with the user&#39;s target lesion. Therefore, even when the number of detection target lesions increases, the user is able to easily find the presence or absence of the detection result of other lesions related with the user&#39;s target lesion. 
     The display control unit  316  controls the display unit to display the operating state of the lesion detection processing. This makes it easier for the user to distinguish between a state where no lesion is detected with CADe performed and a state where no lesion is detected with CADe not performed. 
     When the lesion detection processing is “Not Implemented” and “Feasible”, the related lesion information acquisition unit  315  issues an instruction for the lesion detection processing, making it easier to implement the lesion detection not implemented. 
     When the lesion detection processing is infeasible because the detection processing function is not installed, the related lesion information acquisition unit  315  issues an instruction for installing the lesion detection processing, making it easier to install the required lesion detection function. 
     When the lesion detection processing is infeasible because the medical image data is out of the implementation condition, the related lesion information acquisition unit  315  issues an instruction for acquiring the medical image data that satisfies the implementation condition, making it easier to acquire the required medical image data. 
     (Other Exemplary Embodiments) 
     The present invention is implemented also by performing the following processing. More specifically, software (program) for implementing the functions of the above-described exemplary embodiments is supplied to a system or apparatus via a network or various types of storage media, and a computer (CPU or micro processing unit (MPU)) of the system or apparatus reads and executes the program. 
     Other Embodiments 
     Embodiment(s) of the present invention can also be realized by a computer of a system or apparatus that reads out and executes computer executable instructions (e.g., one or more programs) recorded on a storage medium (which may also be referred to more fully as a ‘non-transitory computer-readable storage medium’) to perform the functions of one or more of the above-described embodiment(s) and/or that includes one or more circuits (e.g., application specific integrated circuit (ASIC)) for performing the functions of one or more of the above-described embodiment(s), and by a method performed by the computer of the system or apparatus by, for example, reading out and executing the computer executable instructions from the storage medium to perform the functions of one or more of the above-described embodiment(s) and/or controlling the one or more circuits to perform the functions of one or more of the above-described embodiment(s). The computer may comprise one or more processors (e.g., central processing unit (CPU), micro processing unit (MPU)) and may include a network of separate computers or separate processors to read out and execute the computer executable instructions. The computer executable instructions may be provided to the computer, for example, from a network or the storage medium. The storage medium may include, for example, one or more of a hard disk, a random-access memory (RAM), a read only memory (ROM), a storage of distributed computing systems, an optical disk (such as a compact disc (CD), digital versatile disc (DVD), or Blu-ray Disc (BD)™), a flash memory device, a memory card, and the like. 
     While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions. 
     This application claims the benefit of Japanese Patent Application No. 2021-184189, filed Nov. 11, 2021, which is hereby incorporated by reference herein in its entirety.