Processing device, processing program, processing method, and processing system

Abstract

To control treatment beams more efficiently.SOLUTION: A first learned determination model generated by learning on the basis of a plurality of first learning medical images in which at least part of a human site is included as a subject and subject label information showing the position of a disease in each first learning medical image. and a second learned determination model are acquired by performing additional learning on the basis of one or a plurality of second learning medical images acquired by imaging a treatment object person and subject label information showing the position of the disease in each second learning medical image. Control information for treatment beams with which the subject is irradiated in the treatment is generated on the basis of the position of the disease determined by inputting a determination medical image acquired by imaging the subject to the second learned determination model.SELECTED DRAWING: Figure 1B

Description

[Technical Field]

[0001] The present disclosure relates to a processing device, a processing program, a processing method, and a processing system configured to perform processing related to controlling a treatment beam irradiated to a subject of treatment. [Background technology]

[0002] 2. Description of the Related Art Conventionally, there have been known processing devices for detecting the location of a disease such as a tumor and irradiating the disease location with a treatment beam to treat the disease such as a tumor. For example, Patent Document 1 describes a moving-body tracking irradiation device comprising: a linac that irradiates a tumor with a treatment beam; a tumor marker implanted near the tumor; a first X-ray fluoroscopy device that images the tumor marker from a first direction; a second X-ray fluoroscopy device that images the tumor marker from a second direction simultaneously with the first X-ray fluoroscopy device; first and second image input units that digitize the first and second fluoroscopic images output from the first and second X-ray fluoroscopy devices; first and second recognition processing units that perform template matching using a gray-normalized cross-correlation method in which a pre-registered tumor marker template image is applied to the image information digitized by the first and second image input units in real time at a predetermined frame rate to determine the first and second two-dimensional coordinates of the tumor marker; a central processing unit that calculates the first and second two-dimensional coordinates of the tumor marker from the first and second two-dimensional coordinates calculated by the first and second recognition processing units; and an irradiation control unit that controls the treatment beam irradiation of the linac based on the determined three-dimensional coordinates of the tumor marker. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2000-167072 Summary of the Invention [Problem to be solved by the invention]

[0004] Therefore, in light of the above-described technology, the present disclosure aims to provide a processing device, a processing program, a processing method, and a processing system that can control treatment beams more efficiently through various embodiments. [Means for solving the problem]

[0005] According to one aspect of the present disclosure, there is provided a processing device having at least one processor, wherein the at least one processor is configured to: acquire a first learned judgment model by learning based on a plurality of first training medical images that include at least a portion of a human body part as a subject and subject label information indicating the location of a disease in each of the first training medical images; acquire a second learned judgment model by additionally learning the first learned judgment model based on one or more second training medical images obtained by photographing a subject of treatment and subject label information indicating the location of the disease in the second training medical images; and execute processing to generate control information for a treatment beam to be irradiated to the subject during the treatment based on the location of the disease determined by inputting the judgment medical images obtained by photographing the subject into the second learned judgment model.

[0006] According to one aspect of the present disclosure, there is provided a processing program that, when executed by at least one processor, causes the at least one processor to function to generate control information for a treatment beam to be irradiated to the subject during the treatment based on the location of the disease determined by inputting judgment medical images obtained by photographing the subject into the second trained judgment model, using a first trained judgment model generated by learning based on a plurality of first training medical images that include at least some parts of a human as subjects and subject label information indicating the location of the disease in each of the first training medical images.

[0007] According to one aspect of the present disclosure, there is provided a processing method executed by at least one processor, the processing method including: a step of acquiring a second trained judgment model by additionally learning a first trained judgment model generated by learning based on a plurality of first training medical images including at least some parts of a human as subjects and subject label information indicating the location of a disease in each of the first training medical images, based on one or more second training medical images obtained by photographing a subject of treatment and subject label information indicating the location of the disease in the second training medical images; and a step of generating control information for a treatment beam to be irradiated to the subject during the treatment based on the location of the disease determined by inputting the judgment medical images obtained by photographing the subject into the second trained judgment model.

[0008] According to one aspect of the present disclosure, there is provided a processing system including at least one processor, wherein the at least one processor is configured to: acquire a second trained judgment model by additionally learning a first trained judgment model generated by learning based on a plurality of first training medical images that include at least a portion of a human body part as a subject and subject label information indicating the location of a disease in each of the first training medical images, based on one or more second training medical images obtained by photographing a subject of treatment and subject label information indicating the location of the disease in the second training medical images; and execute processing to generate control information for a treatment beam to be irradiated to the subject during the treatment based on the location of the disease determined by inputting the judgment medical images obtained by photographing the subject into the second trained judgment model.

[0009] According to one aspect of the present disclosure, there is provided a processing method executed by at least one processor for generating a second trained judgment model used to generate control information for a treatment beam to be irradiated to a subject in treating the subject from a judgment image obtained by photographing the subject, the processing method including: a step of inputting one or more second training medical images obtained by photographing the subject of the treatment and subject label information indicating the location of the disease in the second training medical images to the first trained judgment model generated by the at least one processor learning based on a plurality of first training medical images that include at least some parts of a human as subjects and subject label information indicating the location of the disease in each of the first training medical images; and a step of generating a second trained judgment model by additionally training the first trained judgment model. [Effects of the Invention]

[0010] According to the present disclosure, it is possible to provide a processing device, a processing program, a processing method, and a processing system that are capable of controlling a treatment beam more efficiently.

[0011] It should be noted that the above effects are merely illustrative for the sake of convenience and are not limiting. In addition to or instead of the above effects, any effect described in this disclosure or an effect obvious to a person skilled in the art may be achieved. [Brief explanation of the drawings]

[0012] [Figure 1A] FIG. 1A is a block diagram showing the configuration of a processing system 1 according to an embodiment of the present disclosure. [Figure 1B] FIG. 1B is a diagram conceptually illustrating the configuration of a medical device 200 according to an embodiment of the present disclosure. [Figure 2] FIG. 2 is a block diagram showing a configuration of the processing device 100 according to an embodiment of the present disclosure. [Figure 3A] FIG. 3A is a diagram conceptually illustrating a learning object management table stored in the processing device 100 according to an embodiment of the present disclosure. [Figure 3B] FIG. 3B is a diagram conceptually illustrating an example of a CT image and a simulation image according to an embodiment of the present disclosure. [Figure 3C] FIG. 3C is a diagram conceptually illustrating an example of a subject management table stored in the processing device 100 according to an embodiment of the present disclosure. [Figure 4A] FIG. 4A is a diagram showing a processing flow executed in the processing device 100 according to an embodiment of the present disclosure. [Figure 4B] FIG. 4B is a diagram showing a processing flow executed in the processing device 100 according to an embodiment of the present disclosure. [Figure 5] FIG. 5 is a diagram showing a processing flow executed in the processing device 100 according to an embodiment of the present disclosure. [Figure 6] FIG. 6 is a diagram showing a processing flow executed in the processing device 100 according to an embodiment of the present disclosure. [Figure 7] FIG. 7 is a diagram conceptually illustrating the timing of generating control information for a treatment beam according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0013] 1. Configuration of Processing System 1 A processing system 1 according to the present disclosure is a system used to generate control information for a treatment beam irradiated to a treatment subject. In particular, the processing system 1 acquires a second trained determination model by additionally training a previously generated first trained determination model based on second trained medical images acquired by imaging the treatment subject and subject label information indicating the location of a disease, and controls the treatment beam based on the location of a disease determined by inputting the determination-use medical images acquired by imaging the treatment subject into the second trained model.

[0014] Such treatment beams are primarily radiation beams such as X-rays, proton beams, and heavy particle beams. The irradiation position is preset so that the beam is appropriately irradiated to the disease location identified through CT images or the like during the treatment planning stage. However, during the actual treatment stage, the pre-identified disease location may move due to movements such as breathing of the treatment subject. Therefore, the treatment beam must be irradiated when the disease location overlaps with the pre-set irradiation position by imaging the subject at arbitrary intervals during the treatment stage. The processing system 1 can more efficiently control the treatment beam.

[0015] 1A is a block diagram showing the configuration of a processing system 1 according to an embodiment of the present disclosure. According to FIG. 1A, the processing system 1 includes at least a medical device 200 including an imaging device 200-1 for imaging a subject of treatment and an irradiation device 200-2 for irradiating the subject with a treatment beam, and a processing device 100 for executing a process for generating control information for the treatment beam. The processing device 100 and the medical device 200 are communicatively connected to each other via at least one of a wireless communication network and a wired communication network.

[0016] FIG. 1B is a conceptual diagram illustrating a configuration of a medical device 200 according to an embodiment of the present disclosure. Specifically, FIG. 1B is a conceptual diagram illustrating a radiation irradiation device, which is an example of the medical device 200 illustrated in FIG. 1A. According to FIG. 1B, the medical device 200 is used to irradiate a treatment beam to a subject lying on a treatment table 210, also called an examination table, couch, operating table, etc., installed relative to the medical device 200. Therefore, the medical device 200 includes a head 200-2a of an irradiation device 200-2 that emits the treatment beam. The medical device 200 can move a gantry 200-2b, on which the head 200-2a is installed, two-dimensionally or three-dimensionally relative to a base 200-2c so as to align the treatment beam with a preset irradiation direction.

[0017] Furthermore, as described above, since the location of the disease constantly moves due to the subject's movements such as breathing, it is necessary to image the subject to determine whether the location of the disease overlaps with the irradiation position of the predetermined treatment beam. Therefore, the medical device 200 includes, as the imaging device 200-1, an X-ray tube 200-1b that irradiates X-rays to image the subject, and a flat panel detector (FPD) 200-1a that images the X-rays irradiated from the X-ray tube 200-1b and transmitted through the subject. This allows the imaging of a medical image for determination used to determine the location of the disease in the subject.

[0018] 1B shows an example of an X-ray image using the X-ray tube 200-1b and its flat panel detector 200-1a, but the medical image for determination is not limited to an X-ray image. The medical image for determination may be any image that can be used to determine the location of a disease, and in addition to an X-ray image, for example, an MR image, a CT image, a PET image, or a combination of these can also be suitably used.

[0019] 1B illustrates an example in which the imaging device 200-1 includes a pair of X-ray tube 200-1b and its flat panel detector 200-1a, but two pairs of X-ray tubes and flat panel detectors with different X-ray irradiation directions may be used to acquire medical images for determination. Alternatively, three or more pairs of X-ray tubes and flat panel detectors with different X-ray irradiation directions may be installed, and the medical images for determination may be acquired by selecting the optimal pair or two pairs of X-ray tubes and flat panel detectors depending on the orientation of the subject, the location of the disease, the orientation of the gantry 200-2b, etc.

[0020] Also, although FIG. 1B shows the use of a flat panel detector as the detector, other detectors such as a scintillator can also be used.

[0021] Returning to FIG. 1A again, in the processing system 1, typically, when a medical image for determination captured by the imaging device 200-1 is transmitted to the processing device 100 via a communication network, the processing device 100 determines the disease location by inputting the received medical image for determination into a second trained determination model. Then, the processing device 100 generates control information based on the determined location and transmits the control information to the medical device 200. The medical device 200 controls the treatment beam irradiated from the irradiation device 200-2 based on the received control information. As a result, the treatment beam is irradiated onto the subject placed on the treatment table 210, and the disease is treated.

[0022] In the present disclosure, the processing system 1 is exemplified as being configured only with the processing device 100 and the medical device 200. However, in addition to these devices, the processing system 1 can also be configured by combining various devices, such as an electronic medical record device, a database device, an interview device, various diagnostic devices, a model generation device, and a treatment planning device, depending on the application and purpose. Specifically, as will be described in detail below, the processing system 1 performs each process in multiple stages, such as a stage for generating first and second trained determination models, a treatment planning stage, and a treatment stage. For example, the process for generating simulation images (first training medical images) used to generate the first trained determination model and the learning process using the images are executed by a server device or a model generation device. Furthermore, the process related to the generation of a treatment plan in the treatment planning stage is executed by a treatment planning device. Furthermore, the process related to positioning in the treatment stage and the process related to the generation of control information for controlling the treatment beam are executed by the processing device 100. By distributing the processes among multiple devices in this way, it becomes possible to perform treatment planning and learning for another subject in parallel, for example, even while an actual treatment is being performed by irradiating a treatment beam.

[0023] Furthermore, in the present disclosure, a "subject" may be any person who can be the target of treatment, and is not limited to only those with specific attributes. Therefore, such a subject may include any person, such as a patient, a test subject, a diagnosed person, or a healthy person. Note that, although the term "person" is used simply in the present disclosure, this may refer to the subject, or may also refer to other people in addition to the subject.

[0024] In the present disclosure, the term "disease" refers to any disease that can be treated using a treatment beam, and is not limited to a specific disease. Examples of such diseases include brain tumors, head and neck cancer, esophageal cancer, lung cancer, breast cancer, hepato-biliary-pancreatic cancer, rectal cancer, cervical cancer, prostate cancer, skin cancer, malignant lymphoma, or a combination thereof.

[0025] Furthermore, although the present disclosure includes descriptions such as "first training medical image," "second training medical image," "first trained judgment model," and "second trained judgment model," these are merely names given to distinguish training medical images and trained judgment models from one another and are not intended to limit the number or order to a specific number. Furthermore, unless otherwise specifically mentioned, the names with "first" or "second" attached may have different meanings or may have the same meaning.

[0026] In the present disclosure, "control information" may be any information used to control a treatment beam. Examples of such control information include, but are not limited to, the following: Information for instructing at least one of turning on and off irradiation of the treatment beam Information for instructing at least one of the irradiation direction and intensity of the treatment beam - Information indicating the location of the disease determined based on the medical image for assessment Combination of the above information In the following, unless otherwise specified, a case will be described in which the control information is information for instructing at least one of turning on and off irradiation of a treatment beam.

[0027] 2. Configuration of the Processing Device 100 FIG. 2 is a block diagram showing the configuration of a processing device 100 according to an embodiment of the present disclosure. According to FIG. 2, the processing device 100 includes a processor 111, a memory 112, and a communication interface 113. These components are electrically connected to one another via control lines and data lines. The processing device 100 does not need to include all of the components shown in FIG. 2; some components may be omitted, or other components may be added. For example, an external memory, a database device, a server device, a terminal device, or the like connected in a communicable manner as a memory may be used together with the processing device 100. It is also possible to distribute some of the processing to other devices. In other words, the processing device 100 is not limited to a single device, but may be distributed across multiple devices depending on the information handling and processing load.

[0028] Such a processing device 100 can be used in various forms, such as a server device installed on the cloud, an on-premise server device installed in a specific facility or equipment, a terminal device used by medical personnel at a medical institution that operates the medical device 200, or a control device connected to the medical device 200 via a communication network.

[0029] The processor 111 functions as a control unit that controls other components of the processing system 1 based on a processing program stored in the memory 112. Specifically, the processor 111 executes processing related to generating control information for a treatment beam based on the processing program. Specifically, the processor 111 executes, based on the processing program stored in the memory 112, the following processes, among others: "a process of generating a first trained judgment model by learning based on a plurality of first training medical images including at least a portion of a human body as a subject and subject label information indicating the location of a disease in each of the first training medical images," "a process of acquiring a second trained judgment model by additionally training the first trained judgment model based on one or more second training medical images acquired by imaging a subject of treatment and subject label information indicating the location of a disease in the second training medical images," and "a process of generating control information for a treatment beam to be irradiated to a subject in treatment based on the location of a disease determined by inputting a judgment medical image acquired by imaging the subject into the second trained judgment model." The processor 111 is mainly composed of one or more CPUs, but may also be appropriately combined with a GPU, FPGA, or the like.

[0030] Note that the process for generating the first trained determination model does not necessarily have to be executed by the processing device 100, and may be executed by a device other than the processing device 100.

[0031] The memory 112 is composed of RAM, ROM, nonvolatile memory, HDD, etc., and functions as a storage unit. The memory 112 stores instructions and commands for various control operations of the processing system 1 according to this embodiment as programs. Specifically, the memory 112 stores programs executed by the processor 111, such as "a process of generating a first trained judgment model by learning based on a plurality of first training medical images including at least a portion of a human body part as a subject and subject label information indicating the location of a disease in each of the first training medical images," "a process of acquiring a second trained judgment model by additionally training the first trained judgment model based on one or more second training medical images acquired by photographing a subject of treatment and subject label information indicating the location of a disease in the second training medical images," and "a process of generating control information for a treatment beam to be irradiated to a subject during treatment based on the location of a disease determined by inputting a judgment medical image acquired by photographing the subject into the second trained judgment model." In addition to the programs, the memory 112 also stores information from a subject management table and a subject management table. Furthermore, the memory 112 stores the first trained determination model and the second trained determination model as programs.

[0032] The memory 112 may also include a database device for storing information, particularly information on the subject management table and the subject management table, via a communication network. The program for the first trained determination model does not necessarily need to be stored in the memory 112, but may be stored in a memory of a device other than the processing device 100.

[0033] The communication interface 113 functions as a communication unit for transmitting and receiving determination-use medical images and control information to and from the medical device 200 or other devices via a wired or wireless network. Examples of the communication interface 113 include a connector for wired communication such as USB, SCSI, or wired LAN, a transmitting and receiving device for wireless communication such as wireless LAN, Bluetooth (registered trademark), or wideband wireless communication such as LTE, or infrared, and various connection terminals for printed circuit boards or flexible circuit boards.

[0034] 3. Other equipment Although the specific configuration of the medical device 200 is not specifically illustrated, it includes a processor that functions as a control unit, a memory that functions as a storage unit, a communication interface that functions as a communication unit, etc. These elements are electrically connected to each other via control lines and data lines. Note that the medical device 200 does not need to include all of these components; it may be configured with some of them omitted, or other components may be added.

[0035] 4. Various information used in processing by processing system 1 3A is a diagram conceptually illustrating a learning object management table stored in the processing device 100 according to an embodiment of the present disclosure. According to FIG. 3A, the object management table stores various information used to generate a first trained determination model. Specifically, the object management table stores CT images, simulation images, and object label information in association with object ID information.

[0036] The "subject ID information" is information for identifying a simulation image, which is a first training medical image used for training, and is information unique to each first training medical image. The subject ID information is newly generated each time a CT image used to generate the first training medical image is received.

[0037] A "CT image" refers to each of multiple CT images, or all of multiple CT images, in which cross sections of a human subject are taken along the body axis over a predetermined range (e.g., one or more body parts or the entire body). That is, the CT image includes at least a portion of the human body as the subject. Such a CT image may be image data, which is the image itself taken by a CT device, or processed image data obtained by subjecting the image data to image processing. Furthermore, a CT image may be either a still image or a video, and may be in any image format, such as a black-and-white image or a color image. It may be a two-dimensional image, or may be a three-dimensional or four-dimensional image.

[0038] Here, the first trained judgment model is typically not a personalized trained model trained only on simulation images generated from CT images of a specific treatment subject, but a general-purpose trained model trained on multiple simulation images each containing multiple different subjects. Therefore, it is preferable that CT images are also taken of multiple people, rather than storing images of only a specific subject.

[0039] A "simulation image" is an image that simulates a two-dimensional X-ray image by integrating the CT values ​​of multiple CT images taken consecutively along the body axis direction along a projection line connecting the X-ray tube 200-1b and the flat panel detector 200-1a. Typically, a DRR (Digital Reconstructed Radiograph) image is used as this simulation image. The simulation image is used as the first training medical image for training the first trained decision model.

[0040] As described above, the simulation image is obtained by integrating the CT values ​​of multiple CT images along the projection line connecting the CT tube 200-1b and the flat panel detector 200-1a. At this time, it is preferable to randomly shift the projection direction and position within a predetermined range. This makes it possible to obtain multiple simulation images with slightly different imaging directions and / or positions, thereby enabling more efficient collection of first training medical images. Furthermore, by learning from such multiple first training medical images, it is possible to compensate for differences in imaging direction and position and differences in tumor position between the simulation image and the actual medical image for evaluation (e.g., X-ray image) captured for treatment beam control information.

[0041] Here, the first trained judgment model is a general-purpose trained model as described above, and therefore, it is preferable that the generated simulation image is generated based on multiple CT images acquired with multiple people as subjects, rather than based on CT images acquired from only a specific subject.

[0042] Here, the simulation images used are images generated from multiple CT images of multiple cross sections. However, other images may also be used, such as images generated using a generative trained model, images drawn by operator input, simulated two-dimensional X-ray images, and pseudo X-ray images generated from CT images using Monte Carlo simulation. Furthermore, image processing such as contrast adjustment, noise addition, and edge enhancement may be randomly performed within a predetermined range on the generated simulation images to create multiple simulation images with different contrast, noise, and edges. This allows multiple simulation images to be obtained from CT images taken of a single subject, thereby enabling more efficient collection of first training medical images. Furthermore, using the multiple simulation images acquired as described above as the first training medical images can compensate for differences in image quality between the simulation images and the actual medical images (e.g., X-ray images) used for evaluation of treatment beam control information.

[0043] "Subject label information" is information indicating the location of a specific disease identified by a medical professional such as a doctor. The subject label information is typically generated by using information indicating the location of the disease for each CT image acquired as described above (e.g., contour information such as a CTV (clinical target volume), a PTV (planned specimen volume), a VOI (volume of interest), or a ROI (region of interest)). Such contour information can be generated by projecting the contour information onto the CT image in the same direction as the projection line used to generate the simulation image. Then, a body model including information indicating the disease location is reconstructed from each CT image into which information indicating the disease location has been input, and a simulation image is generated from the body model. This eliminates the need to assign label information to each simulation image, enabling efficient generation of subject label information.

[0044] Such object label information can be various information such as a specific location where a disease is observed, an area including the location, a region where a disease is observed, or a combination of these. Note that the object label information is not limited to information obtained by a medical professional. It can also be obtained by preparing an image in which the location of a disease is specified in advance, calculating the degree of agreement between the image and a simulation image through image analysis processing, and based on this degree of agreement. The object label information is used to generate the first trained judgment model.

[0045] 3B is a diagram conceptually illustrating an example of a CT image and a simulation image according to an embodiment of the present disclosure. Specifically, FIG. 3B is a diagram illustrating the relationship between the CT image stored in the object management table of FIG. 3A and the simulation image generated from the CT image.

[0046] CT images are typically acquired by capturing cross sections in the thickness direction of a human body over a predetermined range (one or more regions or the entire body) at a spatially constant interval (e.g., slice interval = 5 mm) from the head to the legs. In Fig. 3B, multiple CT images including at least CT images Bm-7 to Bm and CT images Bn-7 to Bn are captured over one or more regions at a spatially constant interval (e.g., slice interval = 5 mm).

[0047] As described above, the multiple CT images acquired in this manner are taken at regular intervals in the direction from the head to the legs. Therefore, by sequentially overlapping the CT images in this direction and integrating the CT images at the same coordinate positions, it is possible to virtually form a three-dimensional body model. The simulation images are pseudo X-ray images obtained by virtually transmitting the pseudo body model from a predetermined direction. That is, according to FIG. 3B, simulation image Bm is generated as a pseudo X-ray image of the body model reconstructed from CT images Bm-7 to Bm, and simulation image Bn is generated as a pseudo X-ray image of the body model reconstructed from CT images Bn-7 to Bn.

[0048] The CT image and the simulation image thus obtained are typically used as the CT image and the simulation image shown in FIG. 3A.

[0049] 3C is a diagram conceptually illustrating a subject management table stored in the processing device 100 according to an embodiment of the present disclosure. According to FIG. 3C, the subject management table stores treatment plan information, medical images for learning, subject label information, medical images for assessment, and disease locations in association with subject ID information.

[0050] "Subject ID information" is information for identifying each subject who is the target of treatment and is unique to each subject. Subject ID information is generated anew, for example, each time a subject registers for treatment at a medical institution or is registered as a subject for radiation therapy.

[0051] "Treatment plan information" refers to information indicating a radiation therapy plan generated prior to treatment, for example, based on CT images acquired by imaging a subject with a CT device. Such treatment plan information includes various information, such as the intensity (e.g., energy amount) of the treatment beam, the irradiation direction, the irradiation field, the dose fractionation, and the irradiation dose. Among these pieces of information, the irradiation field indicates the irradiation position of the treatment beam, and specifies a position (including both a specific point and an area including that point) in three-dimensional coordinate space in a pseudo-body model reproduced from the subject's CT images. The irradiation position is identified by a medical professional, such as a physician, identifying the location of the disease in each CT image.

[0052] The "training medical image" is a medical image acquired by photographing a subject using the imaging device 200-1 of the medical device 200 immediately before treatment or on the day of treatment, and is used as the second training medical image. That is, the training medical image is an image in which the subject of treatment is the subject, and differs in this respect from the simulation image, which is the first training medical image that also includes subjects other than the subject of treatment. A typical training medical image is an X-ray image captured by the imaging device 200-1. However, other images such as MR images, CT images, PET images, or any combination thereof can also be suitably used. The training medical image is used to generate a second trained determination model, which is a personalized trained determination model, by additionally learning the first trained determination model generated as a general-purpose trained determination model.

[0053] The "subject label information" is information indicating the location of a specific disease in the training medical image of Figure 3C, which is the second training medical image. Such subject label information can be a variety of information, such as a specific point where the disease is present, an area including that point, a region where the disease is present, or a combination of these. Here, the disease assigned as label information is typically the same disease as the disease assigned to the first training medical image. Examples of such diseases include tumors, as exemplified above. That is, if "tumor" is used as the subject label information for the first training medical image, "tumor" will also be used as the subject label information for the second training medical image. Note that "same" here can include cases where the tumors are the same type, such as pharyngeal cancer, as well as cases where the tumors are different types.

[0054] The subject label information is typically acquired by inputting a second training medical image into a first trained judgment model that has been generated in advance. However, this method is not limited to this. The subject label information may be acquired by a medical professional such as a doctor making corrections to the information acquired from the first trained judgment model, by the medical professional identifying the location of the disease, or based on the degree of match with an image in which the location of the disease has been identified in advance. The subject label information is used to generate the second trained judgment model.

[0055] The "medical image for determination" is a medical image obtained by imaging the subject using the imaging device 200-1 of the medical device 200 after treatment for the subject has begun, and is information used to generate control information for irradiating the treatment beam. The medical image for determination is input into the second trained determination model and used to obtain information on the location of the disease. A typical example of the medical image for determination is an X-ray image taken by the imaging device 200-1, but in addition to these images, any of MR images, CT images, PET images, or a combination of these can also be suitably used.

[0056] Here, the irradiation position of the treatment beam set as the treatment plan information is set in a three-dimensional coordinate space in which a pseudo-body model reproduced from CT images is placed. Therefore, if there is a misalignment between the coordinate space in which the medical image for assessment is formed and the coordinate space in which the irradiation position of the treatment beam is set, the treatment beam cannot be irradiated at the irradiation position set in the treatment plan in the first place. Therefore, the coordinate space in which the medical image for assessment is formed and the coordinate space in which the irradiation position of the treatment beam is set are formed by positioning the treatment couch 210 in advance with the subject on it so that they correspond to each other.

[0057] "Disease location" is information indicating the location of a disease contained in a medical image for assessment. Such a disease location is obtained by inputting the medical image for assessment into the second trained assessment model. The disease location can be indicated as various information, such as a specific point where the disease is observed, an area including that point, a region where the disease is observed, or a combination of these. The disease location is used to generate treatment beam control information based on a preset treatment beam irradiation position.

[0058] Although not specifically shown in FIG. 3C, it is also possible to add various information such as medical interview information, electronic medical record information, and subject attribute information to the subject management table in association with the subject ID information.

[0059] 5. Processing flow executed by the processing device 100 (1) Processing flow from the treatment planning stage to the treatment stage Therapies using therapeutic beams (e.g., radiation therapy) have become an important treatment alongside surgery and chemotherapy for tumors such as brain tumors, head and neck cancer, esophageal cancer, lung cancer, breast cancer, hepato-biliary-pancreatic cancer, rectal cancer, cervical cancer, prostate cancer, skin cancer, malignant lymphoma, or a combination thereof. However, these therapies are required to completely cure tumors or alleviate symptoms by minimizing the dose irradiated to normal tissue surrounding the tumor. Therefore, appropriate and individualized treatment plans must be created for each case, including the intensity, irradiation direction, irradiation field, dose fractionation, irradiation dose, and concomitant chemotherapy. Therefore, these treatments are generally divided into a treatment planning phase, which is performed in advance of the treatment day; a positioning phase, which corrects for differences between the subject's position and posture during CT imaging in the treatment planning phase and the subject's position and posture during X-ray imaging in the treatment phase; and a treatment phase, in which the treatment is actually performed.

[0060] Fig. 4A is a diagram showing a processing flow executed in the processing device 100 according to an embodiment of the present disclosure. Specifically, Fig. 4A is a diagram showing a processing flow executed by the processor 111 of the processing device 100 from the treatment planning stage to the treatment stage. The processing flow is mainly executed by the processor 111 of the processing device 100 reading and executing a program stored in the memory 112.

[0061] 4A, the steps of the process are shown in chronological order, but the steps do not have to be performed consecutively. For example, after the steps of the treatment planning stage are completed, the steps of the positioning stage and treatment stage may be performed after an arbitrary period of time.

[0062] 4A, in the treatment planning stage, processor 111 acquires CT images by receiving CT images of a subject captured by a CT device from a CT device or a PACS (Personal Image Control System) via communication interface 113 (S101). Then, processor 111 generates a simulation image from a body model reconstructed based on the received CT image, as shown in FIG. 3B. Then, processor 111 stores treatment plan information set by a medical professional such as a doctor based on the generated simulation image in memory 112 (S102). As described in FIG. 3C, the treatment plan information includes various information such as the intensity (e.g., energy amount) of the treatment beam, the irradiation direction, the irradiation field indicating the irradiation position of the treatment beam, the dose fractionation, and the irradiation dose. Although not specifically shown, processor 111 may store the acquired CT image and the body model (image data) in a three-dimensional coordinate space reconstructed based on the CT image in association with subject ID information.

[0063] Each process in this treatment planning stage is carried out in advance rather than on the day of treatment, since various considerations must be made, such as creating fixtures to fix the subject to the treatment table 210 and planning the treatment.

[0064] Systems using markers (e.g., tumor markers), such as the technology described in Japanese Patent Application Laid-Open No. 2000-167072 (Patent Document 1), have been known for some time. Therefore, the processing system 1 can also use such marker-based technology. This allows the processing system 1, which does not use markers, to switch to marker-based processing when it is difficult to detect the location of a disease due to some inconvenience. However, in the case of marker-based technology, marker images are included in the CT images taken in advance to place markers inside the subject's body and in the simulation images reproduced from the CT images. If such images are used as first training medical images, the presence or absence of a disease and its location would be determined based on the presence or absence of a marker, which is not desirable as training images. Therefore, in such cases, image processing may be performed to remove the marker images from the CT images or simulation images.

[0065] Next, a positioning step is performed immediately before treatment or on the day of treatment. The positioning step begins with placing the subject on the treatment couch 210 installed in the medical device 200 while restricting the subject's movement using a fixture or the like. After that, when an X-ray image is captured by the imaging device 200-1 of the medical device 200, the processor 111 of the processing device 100 receives the X-ray image from the medical device 200 via the communication interface 113 (S103). The processor 111 compares the received X-ray image with a body model reconstructed from CT images captured in the treatment planning stage and calculates the positional deviation of the subject (S104). Then, the processor 111 generates control information for moving the position of the treatment couch 210 based on the calculated positional deviation (S105) and transmits the control information to the medical device 200 via the communication interface 113. The medical device 200 moves the position of the treatment table 210 based on the received control information to precisely set the positional relationship between the patient and the irradiation device 200-2, thereby ensuring that the coordinate space in which the X-ray image is formed and the coordinate space in which the irradiation position of the treatment beam is set correspond accurately to each other.

[0066] Such a positioning step is performed to adjust according to the subject's condition when the treatment is actually performed, and is therefore preferably performed immediately before the treatment or on the day the treatment is performed.

[0067] Next, in the treatment stage, the processor 111 executes a control information generation process for generating control information for the treatment beam (S106), and transmits the generated control information to the medical device 200 via the communication interface 113 (S107). Note that the processes of S106 and S107 are repeated every time a determination medical image is received from the medical device 200.

[0068] Every time the medical device 200 receives the control information, it operates the irradiation device 200-2 based on the control information and executes a process of irradiating the subject with a treatment beam. In this way, in the treatment stage, S106 to S107 in the processing device 100 and the irradiation process in the medical device 200 are repeatedly executed according to the treatment plan information.

[0069] (B) Processing flow for control information generation processing Here, Fig. 4B is a diagram showing a processing flow executed in the processing device 100 according to an embodiment of the present disclosure. Specifically, Fig. 4B is a diagram showing a processing flow of the control information generation processing of S116 in Fig. 4A. This processing flow is mainly performed by the processor 111 of the processing device 100 reading and executing a program stored in the memory 112.

[0070] 4B, the processor 111 reads out from the memory 112 a first trained determination model that has been generated in advance based on a simulation image, which is a first training medical image, and object label information (S111). Note that the process for generating the first training determination model will be described later with reference to FIG.

[0071] Next, the processor 111 acquires, via the communication interface 113, training medical images (second training medical images), which are X-ray images of the subject captured by the imaging device 200-1 of the medical device 200 (S112), and acquires subject label information assigned to the acquired training medical images (S113). Here, as described above, irradiation of the treatment beam is affected by movements such as breathing of the subject. Therefore, the training medical images are acquired over a period of at least one respiratory cycle, i.e., at least one respiratory phase, by having the subject take at least one deep breath. That is, the processor 111 acquires multiple training medical images over at least one respiratory cycle and acquires subject label information for each of them. At this time, the processor 111 inputs each of the multiple training medical images into a first trained determination model generated in advance, thereby acquiring subject label information for each training medical image as an output from the first trained determination model. The processor 111 stores the acquired learning medical image (second learning medical image) and subject label information in the subject management table in association with the subject ID information of the photographed subject.

[0072] The processor 111 reads out the training medical image (second training medical image) and the subject label information associated with the subject's ID information from the subject management table, and performs additional training on the first trained judgment model based on this information. The processor 111 then acquires a second trained judgment model personalized for the subject through the additional training and stores the second trained judgment model in the memory 112 (S114). As described above, the first trained judgment model is a general-purpose trained model trained based on training medical images (first training medical images) including subjects other than the subject of treatment. In FIG. 4B, a personalized second trained judgment model is acquired by additionally training the general-purpose first trained judgment model using the subject's training medical images, etc., in S114. Therefore, it is possible to quickly generate a second trained judgment model that enables more accurate judgment for each subject with a short training time. The process for generating the second trained judgment model will be described later with reference to FIG. 6.

[0073] Next, the processor 111 acquires a medical image for determination, which is an X-ray image of the subject captured by the imaging device 200-1 of the medical device 200, via the communication interface 113 (S115), and stores the acquired medical image for determination in the subject management table in association with the subject's subject ID information. The processor 111 reads the medical image for determination of the subject from the subject management table and inputs the image for determination to the generated second trained determination model (S116). The processor 111 then acquires information indicating the disease location of the disease contained in the medical image for determination as output from the second trained determination model (S117), and stores the acquired disease location in the subject management table.

[0074] When the processor 111 reads out the disease location from the subject management table, it executes a process of generating control information (S118). As an example of such control information, as described above, Information for instructing at least one of turning on and off irradiation of the treatment beam Information for instructing at least one of the irradiation direction and intensity of the treatment beam - Information indicating the location of the disease determined based on the medical image for assessment Combination of the above information Examples include:

[0075] FIG. 7 is a diagram conceptually illustrating the timing of generating treatment beam control information according to an embodiment of the present disclosure. Specifically, FIG. 7 is a diagram for explaining the generation of control information in relation to the irradiation position of the treatment beam and the disease location acquired in S117. In FIG. 7, the irradiation position of the treatment beam, which was previously set as the irradiation field in the treatment planning stage, is shown as irradiation position 31 in a two-dimensional coordinate space. In contrast, FIG. 7 shows the disease locations acquired by performing the processes of S115 to S117 in FIG. 4B each time a medical image for assessment is acquired as disease locations 32a to 32e. In other words, it shows that disease locations 32a to 32e move each time a medical image for assessment is acquired due to movement such as breathing of the subject.

[0076] When the processor 111 acquires a disease location 32a that does not overlap the preset irradiation location 31 at all, the processor 111 skips the process of S118 in FIG. 4B without generating control information. Next, when the processor 111 acquires a disease location 32b that partially overlaps the preset irradiation location 31, the processor 111 skips the process of S118 in FIG. 4B without generating control information. Next, when the processor 111 acquires a disease location 32c that completely overlaps the preset irradiation location 31, the processor 111 generates control information for instructing to turn on irradiation of the treatment beam. Next, when the processor 111 acquires a disease location 32d that partially overlaps the preset irradiation location 31, the processor 111 generates control information for instructing to turn off irradiation of the treatment beam. Then, when the processor 111 acquires a disease location 32e that does not overlap the preset irradiation location 31 at all, the processor 111 skips the process of S118 in FIG. 4B without generating control information.

[0077] That is, processor 111 generates control information to instruct to turn on irradiation of the treatment beam while all of the acquired disease locations overlap with the preset irradiation location 31, and generates control information to instruct to turn off irradiation of the treatment beam when all of the disease locations no longer overlap. In this way, by generating control information based on the positional relationship between irradiation location 31 and the disease location, it is possible to reduce irradiation of the treatment beam to normal cells surrounding tumor cells.

[0078] 7 illustrates a case in which processor 111 generates control information based on whether the entire acquired disease location overlaps with irradiation location 31. However, instead of this, processor 111 may generate control information to instruct turning on irradiation of the treatment beam while at least a portion of the disease location overlaps with irradiation location 31 (disease locations 32b to 32d in FIG. 7), and to instruct turning off irradiation of the treatment beam when at least a portion of the disease location no longer overlaps (disease locations 32a and 32e in FIG. 7). Processor 111 may also generate control information to turn on or off the treatment beam based on whether the overlapping area between irradiation location 31 and the disease location exceeds a predetermined threshold.

[0079] 7, the case where the irradiation position 31 and each disease position have a range has been described. However, instead, each disease position may indicate a specific point, and the processor 111 may generate control information for turning the treatment beam on or off based on whether the point is included in the irradiation position 31.

[0080] For convenience of explanation, FIG. 7 illustrates the irradiation position 31 and each disease location in a two-dimensional coordinate space defined by the x-axis and y-axis. However, instead, the irradiation position 31 and each disease location may be identified in a three-dimensional coordinate space including the z-axis, and the processor 111 may generate control information for turning the treatment beam on or off based on the degree of overlap between the two locations in the three-dimensional coordinate space. In such a case, although one pair of X-ray tube and flat panel detector is used in FIG. 1B, it is possible to obtain the disease location in the three-dimensional coordinate space by using at least two pairs of X-ray tube and flat panel detector to identify the disease location from different directions. Note that, in this case, the disease locations identified in each X-ray image obtained using the two pairs of X-ray tube and flat panel detector should normally be identified at positions that coincide with each other in the three-dimensional coordinate space. Therefore, the processor 111 may generate control information for turning off the treatment beam if the disease locations identified in each X-ray image differ by more than a predetermined threshold.

[0081] 7 illustrates a case in which the processor 111 of the processing device 100 determines whether to turn the treatment beam irradiation on or off and generates control information for instructing at least one of on and off. However, the processor 111 may generate information about each disease position as control information, and the processor of the medical device 200 may generate information for instructing at least one of on and off based on the control information by the method shown in FIG. 7. Furthermore, the processor 111 may generate information specifying the positional relationship between the irradiation position 31 and each disease position (e.g., information indicating whether the irradiation position 31 overlaps with the disease position) as control information, and the processor of the medical device 200 may generate information for instructing at least one of on and off based on the control information.

[0082] FIG. 7 illustrates a case in which information for instructing at least one of turning the treatment beam on and off is generated as control information. However, instead of or in addition to this, the control information may include information for instructing the intensity and method of the treatment beam. For example, processor 111 generates control information for increasing the intensity as the overlap area between irradiation position 31 and the disease location increases and decreasing the intensity as the overlap area decreases. This enables a treatment beam with a higher intensity to be irradiated closer to the center of a disease such as a tumor. Processor 111 also generates control information for changing the irradiation direction to track the disease location. This means that if the irradiation direction is fixed, the treatment beam needs to be turned on / off depending on the overlap with the decoy disease location, which requires additional treatment time. However, by tracking the irradiation direction to the treatment location, the off time can be minimized, enabling more efficient treatment beam irradiation.

[0083] (C) Processing flow for generating the first trained decision model Fig. 5 is a diagram showing a processing flow executed in the processing device 100 according to an embodiment of the present disclosure. Specifically, Fig. 5 is a diagram showing a processing flow related to generation of the first trained determination model read out in S111 of Fig. 4B, i.e., production of the first trained determination model. This processing flow is mainly performed by the processor 111 of the processing device 100 reading and executing a program stored in the memory 112.

[0084] According to Fig. 5, the processor 111 generates a simulation image as a first training medical image by the method described in Fig. 3B etc. based on CT images acquired by using a CT device to photograph a person other than the subject of treatment (S211). Such a simulation image is typically a DRR image, which reproduces a two-dimensional X-ray image by integrating the CT values ​​of multiple CT images taken consecutively along the body axis direction along a projection line connecting the X-ray tube 200-1b and the flat panel detector 200-1a. However, in addition to this image, it is also possible to use an image generated using a generative trained model, an image drawn by operational input from an operator, or a simulated two-dimensional X-ray image.

[0085] Next, the processor 111 executes a process of storing object label information indicating the disease location where a predetermined disease exists in association with each generated simulation image (S212). The object label information may be associated by any method, such as by receiving an input from a medical professional such as a doctor, or by being associated based on the degree of agreement with an image in which the disease location has been specified in advance.

[0086] Once multiple simulation images (first training medical images) and object label information associated with each simulation image are obtained, the processor 111 performs machine learning of a disease location determination pattern (S213). For example, the machine learning is performed by providing a set of the simulation images (first training medical images) and the object label information to a learning device including a neural network configured by combining neurons, and repeating learning while adjusting the parameters of each neuron so that the output of the neural network is the same as the label information. The processor 111 then acquires a first trained determination model (e.g., a neural network and its parameters) (S214). This causes the processor to terminate generation of the first trained determination model.

[0087] In the above, the first trained decision model is generated using a neural network. However, instead of or in combination with a neural network, it is also possible to generate the model using a neural network such as a convolutional neural network, multi-layer Herceptron (MLP), LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), GNN (Graph Neural Network), or Transformer; a gradient boosting decision tree (GBDT) such as LightGBM (Light Gradient Boosting Machine), XGBoost, or CatBoost; or machine learning such as ridge regression, logistic regression, support vector regression (SVR), nearest neighbor algorithm, decision tree, regression tree, or random forest.

[0088] In addition, in Figure 5, a case has been described in which the first learned judgment model is generated by processing by the processor 111 of the processing device 100, but the first learned judgment model may also be generated in a similar manner by processing by a server device or model generation device other than the processing device 100.

[0089] (D) Processing flow for generating the second trained decision model Fig. 6 is a diagram showing a processing flow executed in the processing device 100 according to an embodiment of the present disclosure. Specifically, Fig. 6 is a diagram showing a processing flow related to the generation of the second trained determination model in S114 of Fig. 4B, i.e., the production of the second trained determination model. This processing flow is mainly performed by the processor 111 of the processing device 100 reading and executing a program stored in the memory 112.

[0090] 6, the processor 111 acquires a training medical image (e.g., an X-ray image) acquired by imaging the subject to be treated using the imaging device 200-1 of the medical device 200 as a second training medical image by receiving it from the medical device (S311). Then, the processor 111 associates the acquired training medical image with subject label information indicating the location of a predetermined disease and stores it (S312). The subject label information is acquired as an output from a first trained determination model by the processor 111 inputting the second training medical image into a first trained determination model generated in advance. By using such a method, the subject label information can be acquired more efficiently. However, the method is not limited to this. Information acquired from the first trained determination model may be acquired by a medical professional, such as a doctor, making corrections to the information, or by the medical professional identifying the location of the disease, or based on the degree of match with an image in which the location of the disease has been identified in advance.

[0091] When a training medical image (second training medical image) including the subject as a subject and subject label information associated with the training medical image are obtained, the processor 111 performs additional learning of a disease location determination pattern (S313). As an example, this additional learning involves providing a pair of the training medical image (second training medical image) and the subject label information to the first trained determination model generated as shown in FIG. 5, and adjusting parameters set in the first trained determination model so that the same results as those of the subject label information are output from the training medical image. In other words, by correcting the parameters of the first trained determination model generated as a general-purpose trained model based on the training medical image with the subject as a subject and the subject label information, it is possible to obtain a trained model personalized to the subject.

[0092] Then, processor 111 adjusts the parameters of the first trained determination model as described above to obtain a second trained determination model (S314).

[0093] Note that the generation of the second trained judgment model by the above-described additional learning is merely an example, and it is naturally possible to obtain the second trained judgment model by other methods. For example, another neural network (second trained judgment model) that uses the output from the first trained judgment model as input is connected to the first trained judgment model, and only the part of the other neural network (second trained judgment model) is additionally trained using training medical images (second training medical images) and subject label information. This allows the connected other neural network (second trained judgment model) to correct the output of the first trained judgment model and output a more accurate disease location. Although the second trained judgment model to be connected is composed of a neural network, other machine learning methods can be used instead of a neural network, as exemplified in the first trained judgment model.

[0094] Furthermore, in Figure 6 etc., it is stated that the second trained judgment model is generated from the first trained judgment model, but this includes both cases where the second trained judgment model is essentially the first trained judgment model itself, such as when simply modifying the parameters of the first trained judgment model, and cases where the model configuration is the same as the first trained judgment model.

[0095] Typically, generating a personalized trained judgment model requires a large amount of training medical images and label information of the subject himself / herself, and the training itself takes time. However, in Figure 6, the processor 111 of the processing device 100 acquires a personalized second trained judgment model by additionally training a general-purpose first trained judgment model using the subject's own training medical images, etc. Therefore, it is possible to generate a second trained judgment model personalized to the subject himself / herself more quickly, and it can be processed sufficiently even within a limited time, such as immediately before treatment.

[0096] As described above, in this embodiment, it is possible to provide a processing device, a processing program, a processing method, and a processing system that are capable of controlling a treatment beam more efficiently.

[0097] 6.Other In FIG. 4B, a case has been described in which additional learning is performed on the first trained determination model using a training medical image captured by the imaging device 200-1. However, additional learning may also be performed on the first trained determination model using a simulation image generated from a CT image of the subject himself / herself captured in the treatment planning stage. In this case, preferably, -Generating the first trained decision model - Additional learning using simulated images generated from the subject's own CT images Additional training using the subject's own second training medical images (e.g., X-ray images) The second trained judgment model is obtained by processing in this order. In this way, by performing additional training using simulation images generated from the subject's own CT images, it is possible to more accurately determine the location of the disease.

[0098] Furthermore, a plurality of simulation images containing at least some parts of a person other than the treatment subject himself / herself as subjects were used as the first training medical images for the first trained judgment model. However, instead of this, a simulation image generated from a CT image of the subject himself / herself may be used as the first training medical image to generate the first trained judgment model, and the first trained judgment model thus generated may be additionally trained using a second training medical image (e.g., an X-ray image) of the subject himself / herself to generate the second trained judgment model.

[0099] 1A to 7, the information on the disease location determined by the second trained determination model is used to generate control information for the treatment beam, but it may also be used for other purposes. For example, the information on the disease location can be accumulated as a medical history and used as information on the treatment progress.

[0100] Furthermore, various images such as CT images, simulation images, and X-ray images may not be image data itself, but may be image data that has undergone various pre-processing such as high definition, area extraction, noise removal, edge enhancement, image correction, and image conversion, using filter processing such as band pass filters including high pass filters and low pass filters, averaging filters, Gaussian filters, Gabor filters, Canny filters, Sobel filters, Laplacian filters, median filters, and bilateral filters, blood vessel extraction processing using Hessian matrices, segmentation processing of specific areas (e.g., disease areas) using machine learning, trimming processing of the segmented areas, haze removal processing, super-resolution processing, and combinations thereof.

[0101] 1A to 7, the imaging device 200-1 is used to determine the location of the disease, but it is also possible to use data captured or detected by other devices in addition to the imaging device 200-1.

[0102] The embodiments and modifications of the present disclosure are presented as examples and are not intended to limit the scope of the present disclosure. The embodiments and modifications can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the present disclosure. These embodiments and modifications are included within the scope and spirit of the invention, and are also included in the inventions and their equivalents as set forth in the claims.

[0103] The processes and procedures described in this disclosure can be realized not only by those explicitly described in the embodiments, but also by software, hardware, or a combination thereof. Specifically, the processes and procedures described in this disclosure can be realized by implementing logic corresponding to the processes in media such as integrated circuits, volatile memory, non-volatile memory, magnetic disks, and optical storage. Furthermore, the processes and procedures described in this disclosure can be implemented as computer programs and executed by various computers, including processing devices and server devices.

[0104] Although processes and procedures described in this disclosure are described as being performed by a single device, software, component, or module, such processes or procedures may be performed by multiple devices, multiple software, multiple components, and / or multiple modules. Furthermore, although various information described in this disclosure is described as being stored in a single memory or storage unit, such information may be stored in multiple memories within a single device or multiple memories distributed across multiple devices. Furthermore, software and hardware elements described in this disclosure may be realized by integrating them into fewer components or by decomposing them into more components. [Explanation of symbols]

[0105] 1 Processing System 100 Processing equipment 200 Medical equipment 200-1 Imaging device 200-2 Irradiation device

Claims

1. A processing device comprising at least one processor, the at least one processor: a first trained judgment model generated by training based on a plurality of first training medical images including at least a portion of a human body part as a subject and subject label information indicating the location of a disease in each of the first training medical images, and a second trained judgment model is acquired by additionally training the first trained judgment model based on one or more second training medical images acquired by photographing a subject to be treated and subject label information indicating the location of the disease in the second training medical images; generating control information for a treatment beam to be irradiated to the subject during the treatment based on the location of the disease determined by inputting a medical image for determination obtained by photographing the subject into the second trained determination model; a processing unit configured to perform processing for:

2. The processing device according to claim 1 , wherein the first training medical image is generated based on a CT image taken by a CT device.

3. The processing device according to claim 1 , wherein the treatment is to treat the disease present at the location of the disease by irradiating the subject with a treatment beam.

4. The processing device according to claim 1 , wherein the second training medical image is taken immediately before the treatment or on the day the treatment is performed.

5. The processing device of claim 1 , wherein the second trained decision model is obtained immediately before the treatment or on the day the treatment is administered.

6. The processing device according to claim 1 , wherein the control information is information for instructing at least one of turning on and off irradiation of the treatment beam.

7. The processing device according to claim 1 , wherein the control information is information for instructing at least one of an irradiation direction and intensity of the treatment beam.

8. The processing device according to claim 7 , wherein at least one of the irradiation direction and intensity is preset when planning the treatment and then controlled based on the location of the disease.

9. The processing device according to claim 1 , wherein the control information is generated by overlapping at least a portion of the position of the disease determined in the second trained determination model with a predetermined irradiation position of the treatment beam.

10. The processing device according to claim 1 , wherein the treatment beam is irradiated when at least a portion of the position of the disease determined in the second trained determination model overlaps with a predetermined irradiation position of the treatment beam.

11. The processing device according to claim 1 , wherein the disease in the first training medical image and the disease in the second training medical image are the same disease.

12. The processing device according to claim 1 , wherein the subject label information is acquired by inputting the second training medical image into the first trained determination model.

13. When executed by at least one processor, a first trained judgment model generated by training based on a plurality of first training medical images including at least a portion of a human body part as a subject and subject label information indicating the location of a disease in each of the first training medical images, and a second trained judgment model is acquired by additionally training the first trained judgment model based on one or more second training medical images acquired by photographing a subject to be treated and subject label information indicating the location of the disease in the second training medical images; generating control information for a treatment beam to be irradiated to the subject during the treatment based on the location of the disease determined by inputting a medical image for determination obtained by photographing the subject into the second trained determination model; A processing program that causes the at least one processor to function in such a manner.

14. A processing method executed by at least one processor, comprising: a step of acquiring a second trained judgment model by additionally training a first trained judgment model generated by training based on a plurality of first training medical images including at least a portion of a human body part as a subject and subject label information indicating the location of a disease in each of the first training medical images, based on one or more second training medical images acquired by photographing a subject to be treated and subject label information indicating the location of the disease in the second training medical images; generating control information for a treatment beam to be irradiated to the subject during the treatment based on the location of the disease determined by inputting a medical image for determination obtained by photographing the subject into the second trained determination model; A processing method comprising:

15. 1. A processing system comprising at least one processor, the at least one processor: a first trained judgment model generated by training based on a plurality of first training medical images including at least a portion of a human body part as a subject and subject label information indicating the location of a disease in each of the first training medical images, and a second trained judgment model is acquired by additionally training the first trained judgment model based on one or more second training medical images acquired by photographing a subject to be treated and subject label information indicating the location of the disease in the second training medical images; generating control information for a treatment beam to be irradiated to the subject during the treatment based on the location of the disease determined by inputting a medical image for determination obtained by photographing the subject into the second trained determination model; a processing system configured to perform processing for

16. A processing method executed by at least one processor for generating a second learned judgment model used to generate control information for a treatment beam irradiated to a subject in treatment of the subject from a judgment image acquired by photographing the subject, the method comprising: a step of additionally learning a first trained judgment model generated by the at least one processor through training based on a plurality of first training medical images including at least a portion of a human body part as a subject and subject label information indicating the location of a disease in each of the first training medical images, by inputting one or more second training medical images acquired by photographing the subject of treatment and subject label information indicating the location of the disease in the second training medical images; generating a second trained decision model by additionally training the first trained decision model; A processing method comprising:

Images