WO2023286336A1 - システム、プログラム及び判定方法 - Google Patents

システム、プログラム及び判定方法 Download PDF

Info

Publication number
WO2023286336A1
WO2023286336A1 PCT/JP2022/009693 JP2022009693W WO2023286336A1 WO 2023286336 A1 WO2023286336 A1 WO 2023286336A1 JP 2022009693 W JP2022009693 W JP 2022009693W WO 2023286336 A1 WO2023286336 A1 WO 2023286336A1
Authority
WO
WIPO (PCT)
Prior art keywords
energy
tissue
image
learning
output
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/JP2022/009693
Other languages
English (en)
French (fr)
Japanese (ja)
Inventor
貫太郎 西川
雅敏 飯田
新二 安永
吉隆 本田
豪 新井
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Olympus Corp
Original Assignee
Olympus Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Olympus Corp filed Critical Olympus Corp
Priority to JP2023535111A priority Critical patent/JP7602645B2/ja
Publication of WO2023286336A1 publication Critical patent/WO2023286336A1/ja
Priority to US18/368,100 priority patent/US20230419486A1/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/60—Static or dynamic means for assisting the user to position a body part for biometric acquisition
    • G06V40/67—Static or dynamic means for assisting the user to position a body part for biometric acquisition by interactive indications to the user
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0093—Detecting, measuring or recording by applying one single type of energy and measuring its conversion into another type of energy
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B17/00—Surgical instruments, devices or methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B18/00—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body
    • A61B18/04—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body by heating
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B18/00—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body
    • A61B18/04—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body by heating
    • A61B18/12—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body by heating by passing a current through the tissue to be heated, e.g. high-frequency current
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B18/00—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body
    • A61B18/04—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body by heating
    • A61B18/12—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body by heating by passing a current through the tissue to be heated, e.g. high-frequency current
    • A61B18/14—Probes or electrodes therefor
    • A61B18/1442—Probes having pivoting end effectors, e.g. forceps
    • A61B18/1445—Probes having pivoting end effectors, e.g. forceps at the distal end of a shaft, e.g. forceps or scissors at the end of a rigid rod
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/20—Surgical navigation systems; Devices for tracking or guiding surgical instruments, e.g. for frameless stereotaxis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N7/00—Ultrasound therapy
    • A61N7/02—Localised ultrasound hyperthermia
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/40—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B17/00—Surgical instruments, devices or methods
    • A61B2017/0042—Surgical instruments, devices or methods with special provisions for gripping
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B18/00—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body
    • A61B2018/00571—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body for achieving a particular surgical effect
    • A61B2018/00577—Ablation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B18/00—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body
    • A61B2018/00636—Sensing and controlling the application of energy
    • A61B2018/00696—Controlled or regulated parameters
    • A61B2018/00702—Power or energy
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B18/00—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body
    • A61B2018/00636—Sensing and controlling the application of energy
    • A61B2018/00773—Sensed parameters
    • A61B2018/00791—Temperature
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B18/00—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body
    • A61B2018/00636—Sensing and controlling the application of energy
    • A61B2018/00898—Alarms or notifications created in response to an abnormal condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A61B5/0538—Measuring electrical impedance or conductance of a portion of the body invasively, e.g. using a catheter
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4848—Monitoring or testing the effects of treatment, e.g. of medication
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6846—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive
    • A61B5/6847—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive mounted on an invasive device
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10068—Endoscopic image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30024—Cell structures in vitro; Tissue sections in vitro
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/03—Recognition of patterns in medical or anatomical images
    • G06V2201/031—Recognition of patterns in medical or anatomical images of internal organs

Definitions

  • the present invention relates to systems, programs, determination methods, and the like.
  • Patent Literature 1 discloses a surgical system that determines the type of tissue grasped by an energy device based on energy device energy output data and tissue position, patient status, or optical tissue sensor information. For example, vascular or non-vascular, presence or absence of nerves, etc. are recognized as tissue types. This surgical system stops energy output and warns the user when the treatment content for the recognized type is inappropriate.
  • Patent Document 1 does not consider the degree of heat diffusion to or around the tissue to be treated or the prevention of heat diffusion, the energy output is appropriately adjusted according to the state of the tissue or device during treatment by the energy device.
  • I have a problem that I can't. Specifically, if heat diffusion occurs widely or rapidly due to the treatment tissue or the like, the heat may propagate to the important tissue before thermally denaturing the treatment target tissue, causing thermal damage to the important tissue.
  • providing various sensors such as an optical tissue sensor is not preferable from the viewpoint of sterilization.
  • One aspect of the present disclosure is a learning device tissue image that is an image obtained by capturing at least one energy device and at least one biological tissue during energy output that receives energy supply and outputs energy, or the at least one biological tissue estimating a thermal diffusion region from the energy device to the living tissue and a specific tissue region in the living tissue, generated by energy output from the energy device, from a learning tissue image that is an image of the tissue captured;
  • a captured image that is an image during energy output in which at least one of the energy devices and at least one of the living tissues is imaged, including a storage unit that stores a trained model that has been learned, and a control unit.
  • the thermal diffusion region and the specific tissue region are estimated from the captured image by processing based on the learned model stored in the storage unit, and from the estimated thermal diffusion region and the specific tissue region, It relates to a system for determining the risk of thermal damage to the specific tissue due to the energy output of the energy device.
  • another aspect of the present disclosure is to acquire in real time a captured image that is an image of at least one energy device outputting energy that receives energy supply and outputs energy and at least one living tissue; From the learning device tissue image, which is an image obtained by imaging the at least one energy device during energy output and the at least one biological tissue, or the learning tissue image, which is an image obtained by imaging the at least one biological tissue, Learning to output image recognition information, which is at least one of information related to a thermal diffusion area from the energy device to the living tissue and a specific tissue area in the living tissue generated by the energy output of the energy device. estimating the image recognition information from the captured image, and determining the risk of thermal damage to the specific tissue due to the energy output of the energy device from the estimated image recognition information, by processing based on the completed model. related to the program you want to run.
  • another aspect of the present disclosure is to acquire in real time a captured image that is an image of at least one energy device outputting energy that receives energy supply and outputs energy and at least one living tissue; From the learning device tissue image, which is an image obtained by imaging the at least one energy device during energy output and the at least one biological tissue, or the learning tissue image, which is an image obtained by imaging the at least one biological tissue, Learning to output image recognition information, which is at least one of information related to a thermal diffusion area from the energy device to the living tissue and a specific tissue area in the living tissue generated by the energy output of the energy device.
  • the thermal damage risk is determined by estimating the image recognition information from the captured image and determining the thermal damage risk of the specific tissue due to the energy output of the energy device from the estimated image recognition information by processing based on the completed model. It is related to the method of making the determination of
  • FIG. 4 is an explanatory diagram when the control unit detects a specific tissue;
  • FIG. 4 is an explanatory diagram when a control unit detects a thermal diffusion area;
  • Explanatory drawing when a control part determines a heat damage risk.
  • An example of output adjustment when the control unit adjusts the energy output.
  • a configuration example of a learning device Explanatory diagram of the learning stage for estimating important tissues. Examples of images used in the learning phase for estimating the thermal diffusion region. Explanatory drawing of 2nd Embodiment.
  • FIG. 4 is an explanatory diagram of input information used in a processing example of the embodiment; FIG. 4 is a diagram showing the dependence of the distance between the specific tissue region and the heat diffusion region on the energy output time.
  • Explanatory drawing of the input information of 3rd Embodiment Explanatory drawing of the learning stage for estimating the thermal diffusion region.
  • Explanatory drawing of 4th Embodiment Explanatory drawing of the learning stage for estimating the thermal diffusion region.
  • a processing example of the fifth embodiment. A processing example of the sixth embodiment.
  • FIG. 1 is a configuration example of a system 10 in this embodiment.
  • FIG. 1 shows an example of a system configuration for photographing an operative field with an endoscope.
  • System 10 shown in FIG. 1 includes controller 100 , endoscope system 200 , generator 300 and energy device 310 .
  • System 10 is a surgical system for performing surgery using at least one energy device under an endoscope. Although an example in which the system 10 includes one energy device 310 is shown here, the system 10 may include multiple energy devices.
  • the endoscope system 200 is a system that performs imaging with an endoscope, image processing of endoscopic images, and monitor display of endoscopic images.
  • Endoscope system 200 includes endoscope 210 , main unit 220 and display 230 .
  • a rigid endoscope for surgical operation will be described as an example.
  • the endoscope 210 includes an insertion section to be inserted into a body cavity, an operation section connected to the proximal end of the insertion section, a universal cord connected to the proximal end of the operation section, and connected to the proximal end of the universal cord. and a connector portion.
  • the insertion section includes a rigid tube, an objective optical system, an imaging device, an illumination optical system, a transmission cable, and a light guide.
  • An objective optical system and an imaging element for photographing the inside of the body cavity, and an illumination optical system for illuminating the inside of the body cavity are provided at the distal end of an elongated cylindrical rigid tube.
  • the distal end of the rigid tube may be configured to be bendable.
  • a transmission cable that transmits an image signal acquired by the imaging element and a light guide that guides illumination light to the illumination optical system are provided inside the rigid tube.
  • the operation unit is held by a user and receives operations from the user.
  • the operation unit is provided with buttons to which various functions are assigned.
  • the operation section is provided with an angle operation lever.
  • the connector section includes a video connector that detachably connects a transmission cable to main device 220 and a light guide connector that detachably connects a light guide to main device 220 .
  • the main device 220 includes a processing device that performs endoscope control, image processing of an endoscopic image, and display processing of an endoscopic image, and a light source device that generates and controls illumination light.
  • the main unit 220 is also called a video system center.
  • the processing device is composed of a processor such as a CPU, processes an image signal transmitted from the endoscope 210 to generate an endoscopic image, and outputs the endoscopic image to the display 230 and the controller 100 .
  • the illumination light emitted by the light source device is guided by the light guide to the illumination optical system and emitted from the illumination optical system into the body cavity.
  • the energy device 310 outputs energy in the form of high-frequency power, ultrasonic waves, or the like from its tip, thereby performing treatments such as coagulation, sealing, hemostasis, incision, dissection, or ablation on the tissue with which the tip is in contact. It is a device that performs The energy device 310 is also called an energy treatment instrument.
  • the energy device 310 includes a monopolar device that applies high-frequency power between the electrode at the tip of the device and an electrode outside the body, a bipolar device that applies high-frequency power between two jaws, a probe and jaws, and ultrasonic waves from the probe.
  • An ultrasound device that emits ultrasonic waves, or a combination device that applies high-frequency power between a probe and a jaw and emits ultrasonic waves from the probe.
  • the generator 300 supplies energy to the energy device 310 , controls the energy supply, and acquires electrical information from the energy device 310 .
  • the generator 300 supplies RF power and the energy device 310 outputs that RF power from the electrodes or jaws.
  • the generator 300 supplies power, and the probe of the energy device 310 converts that power into ultrasound and outputs it.
  • the electrical information is the electrical information of the tissue with which the electrodes, probes, or jaws of the energy device 310 are in contact. Specifically, it is the information obtained as a response to the energy device 310 outputting high-frequency power to the tissue. Electrical information is, for example, impedance information of tissue treated by energy device 310 . However, the electrical information is not limited to impedance information as will be described later.
  • the generator 300 performs control to temporally change the energy output from the energy device 310 according to the output sequence.
  • Generator 300 may vary its energy output in response to changes in impedance information over time.
  • the output sequence may define how the energy output varies with changes in the impedance information.
  • the generator 300 may automatically turn off the energy output according to the temporal change of the impedance information. For example, the generator 300 may determine that the treatment is completed and turn off the energy output when the impedance rises above a certain level.
  • the controller 100 recognizes the living tissue and the energy device 310 from the endoscopic image by image recognition processing using machine learning or the like, and outputs an energy output adjustment instruction to the generator 300 based on the recognized information.
  • Information about the living tissue recognized from the endoscopic image, information about the energy device 310, or both of these are called image recognition information. These pieces of information are information on matters affecting heat diffusion by the energy device 310 .
  • the energy device 310 is an energy device such as a monopolar device 320, a bipolar device 330, an ultrasonic device 340, etc., as described in FIGS. 4 to 6 below, but may be other than these.
  • the generator 300 adjusts the energy output of the energy device 310 according to the energy output adjustment instruction. That is, the system 10 of this embodiment is a system that automatically adjusts the energy output from the energy device 310 based on the endoscopic image.
  • the generator 300 supplies energy to the energy device 310 at the energy supply amount instructed by the energy output adjustment instruction, and the energy device 310 receives the energy supply and outputs energy, so that the energy output is increased according to the energy output adjustment instruction. adjusted.
  • the energy output adjustment instruction is an instruction to increase or decrease the output of the entire waveform of the output sequence, or an instruction to set one of a plurality of selectable output sequences.
  • the energy output adjustment instructions are instructions indicating the stepwise scale factor for the energy output.
  • the generator 300 increases or decreases the high frequency output or the ultrasonic output according to the indicated magnification. Magnification may be continuously adjustable.
  • the energy output adjustment instruction is an instruction indicating any one of the plurality of output sequences.
  • Generator 300 outputs energy from energy device 310 according to the instructed output sequence.
  • the energy output adjustment instruction may include both an instruction to increase or decrease the energy output and an instruction to change the output sequence.
  • Controller FIG. 2 is a configuration example of the controller 100 .
  • Controller 100 includes control unit 110 , storage unit 120 , I/O device 180 and I/O device 190 .
  • 1 and 2 show an example in which the controller 100 is configured as a separate device from the generator 300.
  • the controller 100 is configured by an information processing device such as a PC or a server device.
  • the controller 100 may be implemented in a cloud or the like in which one or a plurality of information processing devices connected via a network execute processing.
  • the I/O device 180 receives image data of endoscopic images from the main unit 220 of the endoscope system 200 .
  • the I/O device 180 is a connector to which a cable for image transmission is connected, or an interface circuit connected to the connector to communicate with the main device 220 .
  • the control unit 110 estimates the specific tissue and the heat diffusion region from the endoscopic image by image recognition processing using the trained model 121, and outputs an energy output adjustment instruction based on the estimation result.
  • Control unit 110 includes one or more processors as hardware.
  • the processor is a general-purpose processor such as a CPU (Central Processing Unit), GPU (Graphical Processing Unit) or DSP (Digital Signal Processor).
  • the processor may be a dedicated processor such as an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array).
  • the storage unit 120 stores a trained model 121 used for image recognition processing.
  • storage unit 120 stores a program describing an inference algorithm and parameters used in the inference algorithm as trained model 121 .
  • Trained models 121 include a first trained model 122 and a second trained model 123 .
  • the first trained model 122 is a trained model 121 for estimating a specific tissue region, which will be described later with reference to FIG. 12, and the second trained model 123 is a trained model 121 for estimating a thermal diffusion region.
  • the storage unit 120 stores parameters used in the inference algorithm as a trained model 121 .
  • the storage unit 120 is a storage device such as a semiconductor memory, hard disk drive, or optical disk drive.
  • the semiconductor memory may be RAM, ROM, non-volatile memory, or the like.
  • a neural network for example, can be used as an inference algorithm for image recognition processing. Weighting factor and bias of node-to-node connection in neural network are parameters.
  • a neural network consists of an input layer that receives image data, an intermediate layer that performs arithmetic processing on the data input through the input layer, and an output layer that outputs recognition results based on the operation results output from the intermediate layer. ,including.
  • a neural network used for image recognition processing for example, a CNN (Convolutional Neural Network) can be employed.
  • the control unit 110 also includes a thermal diffusion detection unit 111 , an important tissue detection unit 112 , a heat damage risk determination unit 114 and an output setting unit 113 .
  • the storage unit 120 stores a program in which functions of the thermal diffusion detection unit 111, the important tissue detection unit 112, the thermal damage risk determination unit 114, and the output setting unit 113 are described.
  • One or more processors of the control unit 110 read out the program from the storage unit 120 and execute the program, so that the thermal diffusion detection unit 111, the important tissue detection unit 112, the heat damage risk determination unit 114, and the output setting unit 113 Realize the function of each part of
  • a program describing the function of each unit can be stored in a non-temporary information storage medium that is a computer-readable medium.
  • the information storage medium can be implemented by, for example, an optical disc, memory card, HDD, semiconductor memory, or the like.
  • the semiconductor memory is for example ROM or non-volatile memory.
  • the I/O device 190 transmits an energy output adjustment instruction signal to the generator 300 .
  • the I/O device 190 is a connector to which a signal transmission cable is connected, or an interface circuit connected to the connector to communicate with the generator 300 .
  • FIG. 3 is a flow chart explaining the processing performed by the controller 100 and the system 10.
  • the control unit 110 acquires an endoscope image from the main unit 220 of the endoscope system 200 via the I/O device 180 .
  • the control unit 110 executes steps S2A and S2B.
  • step S2A the important tissue detection unit 112 performs image recognition processing using the first trained model 122 on the endoscopic image, thereby estimating and detecting the important tissue from the image.
  • the thermal diffusion detection unit 111 performs image recognition processing using the second trained model 123 on the endoscopic image, thereby estimating the thermal diffusion area around the jaw of the energy device 310. To detect.
  • a thermal diffusion area is an area where heat is diffused by the energy output of the energy device 310 .
  • the thermal damage risk determination unit 114 determines the risk of heat diffusing into the important tissue and causing thermal damage to the important tissue based on the estimation results in steps S2A and S2B. Send a signal to 113.
  • Thermal damage includes, for example, denaturation of proteins, deactivation of intracellular enzymes, and the like. Below, the said risk is described as a heat damage risk.
  • the output setting section 113 adjusts the energy output of the energy device 310 based on the signal.
  • FIG. 4 is a configuration example of the monopolar device 320.
  • the monopolar device 320 includes an elongated cylindrical insertion portion 322, an electrode 321 provided at the distal end of the insertion portion 322, an operation portion 323 connected to the proximal end of the insertion portion 322, an operation portion 323 and a connector (not shown). and a cable 325 connecting the .
  • the connector is detachably connected to generator 300 .
  • the high-frequency power output by the generator 300 is transmitted through the cable 325 and output from the electrode 321 .
  • a counter electrode is provided outside the patient's body, and current is passed between the electrode 321 and the counter electrode. Thereby, high-frequency energy is applied to the tissue with which the electrode 321 is in contact, and Joule heat is generated in the tissue.
  • Various shapes of electrodes are employed for the electrodes 321 depending on the content of the treatment.
  • the monopolar device 320 can adjust the degree of coagulation and incision by changing the energization pattern. In general, the treatment target of the monopolar device 320 is the tissue with which the electrode 321 is in contact, but the surrounding tissue may be affected by the heat diffused around the tissue with which the electrode 321 is in contact.
  • FIG. 5 is a configuration example of the bipolar device 330.
  • the bipolar device 330 includes an elongated cylindrical insertion portion 332, two jaws 337 and 338 provided at a distal end portion 331 of the insertion portion 332, an operation portion 333 connected to the proximal end of the insertion portion 332, and an operation portion 333. and a cable 335 connecting the connector (not shown).
  • the connector may be removably connected to generator 300 .
  • Jaws 337 and 338 are grasping portions for grasping tissue and applying energy to the grasped tissue, and are configured to be openable and closable about an axis provided at proximal end 336 .
  • the operation portion 333 is provided with a grip portion for opening and closing the jaws 337 and 338 . As the doctor squeezes the grasper, the jaws 337, 338 close and grasp the tissue.
  • the high-frequency power output by the generator 300 is transmitted by the cable 335 and conducts electricity between the two jaws 337, 338 when the jaws 337, 338 grip the tissue.
  • high-frequency energy is applied to the tissue sandwiched between the two jaws 337 and 338, generating Joule heat in the tissue and coagulating the tissue.
  • the generator 300 may measure impedance information of the tissue grasped by the jaws 337, 338, detect treatment completion based on the impedance information, and automatically stop energy output.
  • the generator 300 may also automatically adjust the energy applied to the tissue based on the impedance information.
  • the device temperature of the bipolar device 330 only rises, for example, to about 100 degrees Celsius, there is a possibility that a stray current will occur around the portion gripped by the jaws 337 and 338, and that stray current will cause heat diffusion.
  • a vessel sealing device is a device in which a cutter is provided on the jaw of a bipolar device. After coagulating tissue by energization, the tissue is cut by running the cutter.
  • FIG. 6 is a configuration example of the ultrasonic device 340.
  • the ultrasonic device 340 includes an elongated cylindrical insertion portion 342, a jaw 347 and a probe 348 provided at a distal end portion 341 of the insertion portion 342, an operation portion 343 connected to the proximal end of the insertion portion 342, and an operation portion 343. and a cable 345 connecting the connector (not shown).
  • the connector is detachably connected to generator 300 .
  • the jaw 347 is movable around an axis provided at the proximal end 346 and configured to be openable and closable with respect to the immovable probe 348 .
  • the operation portion 343 is provided with a grip portion for opening and closing the jaws 347 .
  • the operation unit 343 is provided with an operation button 344a to which the first output mode is assigned and an operation button 344b to which the second output mode is assigned.
  • the output mode is selected according to the treatment content, and when the operation button for each output mode is pressed, ultrasonic energy is output in the output sequence of that mode.
  • the number of operation buttons provided on the operation unit 343 is not limited to the configuration in FIG.
  • the power output by the generator 300 is transmitted by the cable 335, and when the operation button 344a or 344b is pressed, the probe 348 converts the power into ultrasonic waves and outputs them. As a result, frictional heat is generated in the tissue sandwiched between jaw 347 and probe 348, and the tissue is coagulated or dissected.
  • a combination device that uses both high-frequency power and ultrasonic waves has the same configuration as the ultrasonic device in FIG. 6, for example.
  • the combination device can generate Joule heat in the tissue gripped by the jaw and the probe by applying high-frequency power between the jaw and the probe, and coagulate the tissue. Further, the combination device can incise the tissue gripped by the jaws and the probe by outputting ultrasonic waves from the probe, like the ultrasonic device.
  • a high frequency mode is assigned to one of the two operation buttons provided on the operation unit, and a seal & cut mode is assigned to the other.
  • the high-frequency mode is a mode in which treatment such as coagulation is performed only by high-frequency energy output.
  • the seal & cut mode is a mode that uses both high-frequency energy and ultrasonic energy, and is a mode that coagulates and cuts tissue by outputting high-frequency energy.
  • thermal diffusion of combination devices for example, thermal diffusion similar to bipolar devices, thermal diffusion similar to ultrasonic devices, or both may occur.
  • the bipolar device 330 is mainly used as the energy device 310 will be described as an example. However, this embodiment is applicable when using various energy devices described above in which thermal diffusion may occur.
  • FIG. 7 shows a processing example of the first embodiment.
  • an endoscopic image is input to the control unit 110 as shown in S21. Specifically, each frame image of the moving image captured by the endoscope is sequentially input to the control unit 110 .
  • An endoscopic image input to the control unit 110 shows one or more energy devices that are outputting energy and one or more living tissues.
  • the control unit 110 detects important tissues from the endoscopic image by executing an estimation program adjusted by machine learning. Specifically, the control unit 110 detects the important tissue by inputting the endoscopic image during surgery to a network having an estimation program learned by annotating the important tissue to the endoscopic image.
  • important tissues include, but are not limited to, large blood vessels, pancreas, duodenum, and the like.
  • FIG. 8 is a diagram explaining the detection of the important tissue shown in S22A of FIG. As shown in FIG. 8, the control unit 110 estimates the region of the important tissue from the subject captured in the endoscopic image based on the estimation program, and colors the region of the important tissue. do the labeling. Then, the control unit 110 outputs the labeled endoscopic image to the heat damage risk determination unit 114 .
  • the control unit 110 detects the heat diffusion area from the endoscopic image by executing the estimation program described above.
  • the control unit 110 inputs the endoscopic image during surgery to a network having an estimation program learned by annotating the temperature range above the threshold to the endoscopic image, thereby detecting the heat diffusion region. do.
  • FIG. 9 is a diagram explaining the detection of the thermal diffusion area in S22B of FIG.
  • the control unit 110 colors an area of the internal organ shown in the endoscopic image that is estimated to have a temperature equal to or higher than a predetermined temperature. Label the extent of diffusion. Then, the control unit 110 outputs the labeled endoscopic image to the heat damage risk determination unit 114 .
  • the above-described labeling of the important tissue and the heat diffusion region may be performed by means other than color.
  • the thermal damage risk determination unit 114 of the control unit 110 receives the information of the estimation result of the important tissue and the thermal diffusion area, and calculates the thermal damage risk of the important tissue based on the estimation result. judgment is made.
  • FIG. 10 is a diagram for explaining a technique for determining the heat damage risk in S23 of FIG. Based on the endoscopic images output from the important tissue detection unit 112 and the heat diffusion detection unit 111, the heat damage risk determination unit 114 of the control unit 110 labels important tissues and heat diffusion regions as shown in FIG. generates an image in which is superimposed and displayed. Then, the distance between the region estimated to be the important tissue and the region estimated to have a temperature equal to or higher than a predetermined temperature is calculated.
  • FIG. 10 is a diagram for explaining a technique for determining the heat damage risk in S23 of FIG.
  • the distance is the actual distance between the heat diffusion area shown in gray around the jaws and the closest portion of the stomach, which is the critical tissue, for example, if the energy device 310 is a bipolar device.
  • the distance can be obtained, for example, by analyzing an image displayed on the display 230 .
  • the control unit 110 uses the distance as a margin M to determine the thermal damage risk.
  • the margin M is data or the like that can be used by the control unit 110 as information for determining the risk of thermal damage. For example, the distance described above is the margin M.
  • Thermal damage risk determination unit 114 outputs a signal regarding the determination result as to the presence or absence of thermal damage risk described above to output setting unit 113 .
  • the output setting unit 113 issues an output change instruction based on the signal output by the thermal damage risk determination unit 114.
  • storage unit 120 stores table data about energy output adjustment instructions corresponding to the presence or absence of thermal damage risk
  • control unit 110 refers to the table data to determine the presence or absence of thermal damage risk.
  • a corresponding energy output adjustment instruction is output to generator 300 via I/O device 190 .
  • Generator 300 then adjusts the output sequence of the bipolar device in accordance with the energy output adjustment instruction output by control section 110 .
  • the algorithm for outputting the energy output adjustment instruction according to the presence or absence of heat damage risk is not limited to the above. FIG.
  • FIG. 11 shows an example of output adjustment when the output setting unit 113 of the control unit 110 issues an energy output adjustment instruction based on the result of the heat damage risk determination.
  • the output setting unit 113 determines that there is a risk of thermal damage to important tissues, it adjusts the output so as to weaken the energy output. Specifically, the output setting unit 113 suppresses or stops voltage, power, or the like. Also, if it is determined that there is no risk of thermal damage to important tissues, the energy output is adjusted so as to maintain or increase the energy output.
  • the output setting unit 113 outputs signals regarding these output adjustment instructions to the generator 300 .
  • control unit 110 recognizes the important tissue and the heat diffusion area due to the energy output from the endoscopic image, and when it is determined that there is a risk of thermal damage to the important tissue, automatic adjustment of the energy output of the generator 300 is performed. done.
  • the threshold value of the margin M for determining the presence or absence of the thermal damage risk may differ according to the tissue type of the specific tissue detected in S22A.
  • the threshold may be variable. By doing so, it is possible to avoid the occurrence of thermal damage to the organ for which the occurrence of thermal damage is particularly problematic. For example, in the case of living tissue such as the pancreas and duodenum, the margin M is increased, and in the case of living tissue such as the stomach and liver, the margin M is decreased. The occurrence of thermal damage to vital tissues with a greater degree of severity can be avoided.
  • the energy output adjustment instruction is an instruction to increase, decrease, or maintain the energy output based on the reference energy output.
  • the generator 300 has an operation unit that accepts an energy output setting operation, and the energy output can be set to one of five levels of intensity 1 to 5, for example, by the operation unit. An intensity of 1 indicates the lowest energy output and an intensity of 5 indicates the highest energy output.
  • the reference energy output is, for example, a predetermined energy output such as "intensity 3".
  • the instruction to increase the reference energy output is an instruction to set "intensity 4" or "intensity 5"
  • the instruction to decrease the reference energy output is to set "intensity 2" or "intensity 1".
  • the reference energy output may be the current energy output set by the operator of generator 300 .
  • an instruction to increase the energy output above the reference energy output is an instruction to set the energy output higher than the currently set energy output
  • an instruction to decrease the energy output below the reference energy output is an instruction to increase the energy output below the currently set energy output.
  • the reference energy output may be a range of intensities 1-5 that can be set on the generator 300 .
  • the instruction to increase the reference energy output is an instruction to set the energy output higher than "intensity 5"
  • the instruction to decrease the reference energy output is to set the energy output lower than "intensity 1". This is an instruction to
  • the system 10 acquires captured images in which the energy device 310 and living tissue are captured, estimates the thermal diffusion region and the specific tissue region by processing based on the learned model, Determine the risk of thermal injury to specific tissues by Based on this thermal damage risk, the system 10 can adjust the appropriate amount of energy applied to the tissue to be treated, thereby reducing the risk of causing thermal damage to critical tissue without noticing thermal diffusion. In this way, the system 10 adjusts the output energy of the energy device, which has been conventionally performed by a doctor, thereby reducing the burden on the doctor. In addition, since the system 10 autonomously adjusts the output, even a non-expert can perform stable treatment. As described above, the stability of the operation can be improved, or the uniformity of the procedure can be achieved without depending on the experience of the doctor.
  • FIG. 12 is a configuration example of a learning device 500 that performs machine learning for estimation processing of important tissues and heat diffusion regions.
  • the learning device 500 is implemented by an information processing device such as a PC or a server device, and includes a processing unit 510 and a storage unit 520 .
  • the processing unit 510 is a processor such as a CPU
  • the storage unit 520 is a storage device such as a semiconductor memory or hard disk drive.
  • Storage unit 520 stores learning model 522 and teacher data 521 .
  • the teacher data 521 includes first teacher data 521A and second teacher data 521B.
  • Learning models 522 also include a first learning model 522A and a second learning model 522B.
  • the processing unit 510 causes the learning model 522 to learn using the teacher data 521 to generate the trained model 121 .
  • the teacher data 521 includes image data of a plurality of learning images and correct data attached to each learning image.
  • the first teacher data 521A is data about the important tissue
  • the second teacher data 521B is data about the thermal diffusion area.
  • the first learning model 522A is a learning model for important tissues
  • the second learning model 522B is a learning model for thermal diffusion regions.
  • the plurality of training images includes endoscopic images showing one or more body tissues and one or more energy devices 310 . This endoscopic image is also called a learning device tissue image. Also, the plurality of learning images may include endoscopic images in which one or a plurality of living tissues are shown and the energy device 310 is not shown. This endoscopic image is also called a learning tissue image. Correct data are annotations in segmentation (area detection), annotations in detection (position detection), correct labels in classification, or correct labels in regression (regression analysis).
  • the processing unit 510 inputs a learning image to the inference processing by the learning model 522, feeds back to the learning model 522 based on the error between the result of the inference processing and the correct data, and repeats it with a large number of teacher data. , to generate a trained model 121 .
  • the generated learned model 121 is transferred to the storage unit 120 of the controller 100 .
  • FIG. 13 is a diagram explaining the learning stage for estimating important organizations.
  • a learning device 500 (not shown) feeds back a learning device tissue image or a learning tissue image as first teacher data 521A to a first learning model 522A.
  • a learning device tissue image is an image obtained by capturing at least one energy device 310 that is receiving energy and outputting energy, and at least one living tissue.
  • a tissue image for learning is an image obtained by capturing at least one living tissue. Then, the learning device tissue image or the learning tissue image is labeled with the region of the important tissue on the image. The labeling is applied, for example, by a doctor.
  • the control unit 110 can estimate the important tissue. The accuracy is improved.
  • FIG. 14 is an example of an endoscope image obtained by coloring the thermal diffusion range of various living tissues using temperature information captured by a thermo camera.
  • the thermal diffusion state is displayed in three temperature ranges of 59 degrees Celsius or below, 60 degrees Celsius to 79 degrees Celsius, and above the threshold temperature.
  • the threshold temperature is a temperature at which there is a risk of thermal damage when the temperature exceeds the threshold temperature, and in the case of FIG. 14, it is 80 degrees Celsius. In this case, the image shown in FIG.
  • risk assessment based on a simple temperature range risk assessment based on the history of the product of temperature and time, risk assessment based on the amount of heating, or risk assessment based on activation energy by the Arrhenius equation is known.
  • simple temperature ranges for example, 60 degrees Celsius or 50 degrees Celsius is the basis for risk assessment.
  • 0.1 seconds at 60 degrees Celsius or 0.1 seconds at 50 degrees Celsius is the criterion for risk evaluation.
  • Risk assessment based on the amount of heat is used for cauterization of the liver and the like, and risk assessment based on activation energy by the Arrhenius equation is mainly used for burns.
  • FIG. 15 is a diagram explaining the learning stage for estimating the thermal diffusion area.
  • a learning device 500 (not shown)
  • a history image captured using, for example, the above-described thermo camera is fed back to a second learning model 522B as second teacher data 521B.
  • the history image is, for example, an endoscopic image in which the heat diffusion area is indicated by color or the like based on the temperature information obtained by the above-described thermo camera. In this way, the most recent history image can be used as the second teacher data 521B for learning the network used in the thermal diffusion detection function.
  • the output energy history information of the energy device 310 can be used as the second teacher data 521B.
  • the output energy history information of the energy device 310 can be used as the second teacher data 521B.
  • the above history image and output energy history information includes a large number of data such as scene 1, scene 2, .
  • This second teacher data 521B is fed back to the second learning model 522B, and the second trained model 123 is generated.
  • output energy history information can also be fed back to the second learning model 522B, and the control unit 110 can estimate the thermal diffusion region with higher accuracy. .
  • FIG. 16 shows an example of input information used in S21 of the processing example shown in FIG.
  • the control unit 110 can use a plurality of endoscopic images captured at different timings as input information in S21.
  • the endoscopic images with different imaging timings are, for example, each frame image of a moving image.
  • the control unit 110 can also use the information on the energy output history output from the energy device 310 as input information together with the plurality of endoscopic images. Then, the detection of the thermal diffusion area in step S22B of FIG. 7 is performed based on a plurality of endoscopic images and energy output history information.
  • the control unit 110 can estimate the heat diffusion area with high accuracy.
  • FIG. 17 is a diagram showing the dependence of the distance between the important tissue region and the heat diffusion region, ie, the margin M, on the energy output time T described above.
  • t on the horizontal axis shown in FIG. 17 is the current time
  • t0 is the estimated time when thermal diffusion reaches the important tissue. Therefore, time t0-n, which is, for example, a specified time n before time t0, is the timing at which it is determined that there is a risk of thermal damage to important tissues.
  • the behavior of the time margin M from time 0 to time t in the graph of FIG. 17 is, as described with reference to FIG. can be estimated. As shown in FIG. 17, the margin M decreases with the passage of time from time 0 to time t.
  • the estimated time t0 at which the thermal diffusion reaches the important tissue can be predicted.
  • the control unit 110 extrapolates the dependence of the margin M on the energy output time T from the behavior of the margin M from time 0 to time t, and obtains the intersection with the horizontal axis indicating the energy output time T. t0 can be predicted.
  • the time t0-n which is a specified time n before t0 obtained in this way, is the timing at which it is determined that there is a risk of thermal damage to important tissues.
  • the control unit 110 can output the time required for thermal diffusion to reach the specific tissue region as a prediction result, determine the thermal damage risk, and output the determination result. By doing so, the risk of heat damage to important tissues can be reduced, and surgery can be safely performed.
  • the input information of S21 in the first embodiment shown in FIG. 7 may include history information of multi-wavelength images.
  • the input information can include a real-time image and a history image with special light in addition to the energy output history and the image with normal light of the endoscope.
  • An image captured by special light is, for example, a multi-wavelength image by illumination light using narrow-band light narrower than the wavelength band of visible light, or a plurality of narrow-band lights. Examples of such multi-wavelength images include AFI (Auto Fluorescence Imaging) and NBI (Narrow Band Imaging).
  • the normal light is also called white light.
  • FIG. 19 is an explanatory diagram in the case where the history information of multi-wavelength images is included in the second teacher data 521B.
  • the second teacher data 521B includes real-time multi-wavelength images and their history images.
  • the second trained model 123 is generated by feeding back the second teacher data 521B including the multi-wavelength image to the second learned model 522B.
  • FIG. 20 is an image example of the result of observation of an excised blood vessel by AFI.
  • the control unit 110 can clearly distinguish the blood vessel and the connective tissue and observe the removed blood vessel.
  • tissue that tends to thermally contract can be observed separately, and the accuracy of image recognition can be improved.
  • an image including white burn due to thermal denaturation can be used as the captured image acquired by the control unit 110 .
  • a white burn is a state in which heat damage is caused to a living tissue, and the body tissue becomes whiter than other non-thermally damaged portions, and looks different from other regions. In this way, it is possible to estimate a region at risk of thermal damage from the whitened area.
  • FIG. 21 is a diagram illustrating a case where an image including white burn due to thermal denaturation is used as the captured image acquired by the control unit 110 .
  • the control unit 110 inputs the intraoperative endoscopic image to the network of this function, and performs image processing to estimate the whitened range.
  • control unit 110 defines a range obtained by adding a specified range to the extracted white-spotted range as a thermal diffusion region, and uses it as input information for thermal damage risk determination.
  • the whitened range can be obtained from the endoscopic image, so there is no need to use a device such as a thermo camera when creating the teaching data 521. Also, the number of networks used by the control unit 110 can be reduced, and the processing speed of the control unit 110 can be increased.
  • FIG. 22 is an explanatory diagram of the learning stage when the input information includes an image labeled with a whitened area.
  • a learning device 500 (not shown) feeds back a learning device tissue image or a learning tissue image in which a living tissue labeled with a whitened range is used as second teacher data 521B to a second learning model 522B. 2 Create a trained model 123 .
  • the fifth embodiment shown in FIG. 23 is different from the first embodiment shown in FIG. 7 in the estimation processing of S22A for detecting the important tissue and S22B for detecting the thermal diffusion region. Specifically, in the estimation process of the fifth embodiment, S42C for recognizing the heat capacity and heat resistance of the electric heating path is added. For example, even if the distance between the important tissue and the heat diffusion area is estimated, if the tissue heat transfer characteristics of the living tissue between the important tissue and the heat diffusion area are different, the time until heat diffuses to the important tissue will change. come. Therefore, in the first embodiment shown in FIG. 7, the influence of the electrothermal characteristics is not considered, and the accuracy of the thermal diffusion prediction results remains at a certain level.
  • the trained model 121 learns to estimate tissue heat transfer characteristics from the learning energy output information of the energy device 310, the learning device tissue image, or the learning tissue image.
  • the control unit 110 performs processing based on the learned model 121 stored in the storage unit 120 to estimate tissue heat transfer characteristics from the energy output information of the energy device 310 or the captured image. Therefore, the control unit 110 can predict the distance between the heat diffusion region and the important tissue, that is, the reduction of the margin M from the characteristics of the heat transfer path. Therefore, the risk of heat damage to important tissues can be reduced, and surgery can be performed safely. It should be noted that changes in the rate of decrease of the margin M can be predicted by using machine learning or the like, for example.
  • FIG. 24 differs from the first embodiment shown in FIG. 7 in the processing after S23 for determining the thermal damage risk.
  • the control unit 110 determines the risk of thermal damage, it is determined in S54 whether or not it is necessary to present an alert. Specifically, the control unit 110 determines that it is necessary to present an alert when it is determined that there is a risk of heat damage in the determination of S53, and does not need to present an alert when it is determined that there is no risk of heat damage. We judge that it is.
  • the control unit 110 presents an alert in S55, and if it is determined that there is no risk of heat damage, the control unit 110 does not present an alert in S56.
  • the alert presentation is performed, for example, by presenting a recommendation to reduce the energy output from the current energy output or a recommendation to stop the energy output due to the risk of heat damage on the monitor screen viewed by the doctor performing the surgery. In this way, the energy output can be reduced or stopped at the doctor's discretion after being notified of the risk of thermal injury.
  • Seventh Embodiment A seventh embodiment shown in FIG. 25 is different from the first embodiment shown in FIG. 7 in S22A regarding important tissue detection.
  • detection of important tissues is estimated by 3D matching with preoperative images such as endoscopic images instead of endoscopic images.
  • Important tissues are detected by 3D matching with CT (Computed Tomography) or MRI (Magnetic Resonance Imaging). In this way, it becomes possible to detect important tissue that cannot be seen with an endoscope.
  • the system of this embodiment can also be implemented as a program. That is, the program of the present embodiment acquires in real time a captured image, which is an image of at least one energy device outputting energy that receives energy supply and outputs energy, and at least one living tissue; From the learning device tissue image, which is an image obtained by imaging the energy device and at least one biological tissue during output of at least one energy, or the learning tissue image, which is an image obtained by imaging at least one biological tissue, the energy device By processing based on a trained model that has learned to output image recognition information, which is at least one of information related to a thermal diffusion area from the energy device to the living tissue and a specific tissue area in the living tissue generated by the energy output, A computer can be caused to estimate image recognition information from the captured image and determine the risk of thermal damage to a specific tissue due to the energy output of the energy device from the estimated image recognition information.
  • the computer is assumed to be a network terminal such as a personal computer.
  • the computer may be a wearable terminal such as a smart phone, a tablet
  • the system 10 of the present embodiment described above includes a storage unit 120 that stores a trained model 121 and a control unit 110.
  • the trained model 121 is designed to estimate a thermal diffusion region from the energy device 310 to the living tissue and a specific tissue region in the living tissue generated by the energy output of the energy device 310 from the learning device tissue image or the learning tissue image.
  • a learning device tissue image is an image obtained by capturing at least one energy device 310 that receives energy supply and outputs energy, and at least one living tissue.
  • a learning tissue image is an image obtained by capturing at least one living tissue.
  • the control unit 110 acquires a captured image, which is an image during energy output in which at least one energy device 310 and at least one living tissue are imaged.
  • the control unit 110 estimates the thermal diffusion region and the specific tissue region from the captured image by processing based on the learned model 121 stored in the storage unit 120 .
  • the control unit 110 determines the risk of thermal damage to the specific tissue due to the energy output of the energy device 310 from the estimated thermal diffusion area and specific tissue area.
  • the system 10 acquires captured images in which the energy device 310 and the living tissue are captured, estimates the thermal diffusion region and the specific tissue region by processing based on the learned model, and calculates the specific tissue by the energy device. determine the risk of heat damage to Based on this thermal damage risk, the system 10 can adjust the appropriate amount of energy applied to the tissue to be treated, thereby reducing the risk of causing thermal damage to critical tissue without noticing thermal diffusion. In this way, the system 10 adjusts the output energy of the energy device, which has been conventionally performed by a doctor, thereby reducing the burden on the doctor. In addition, since the system 10 autonomously adjusts the output, even a non-expert can perform stable treatment. As described above, the stability of the operation can be improved, or the uniformity of the procedure can be achieved without depending on the experience of the doctor.
  • the learning device tissue image, the learning tissue image, the specific organization, and the important organization are described in "1. System".
  • control unit 110 may determine that there is a risk of thermal damage when the distance between the specific tissue region and the heat diffusion region is equal to or less than a preset threshold.
  • the distance between the specific tissue region and the heat diffusion region can be evaluated based on the result estimated from the image acquired by the controller 100, and the presence or absence of the risk of heat damage is determined based on the distance. can be used as a reference for A method for determining the heat damage risk using the threshold is described in "4. First Embodiment".
  • the threshold may differ according to the tissue type of the specific tissue.
  • the captured images may be a plurality of endoscopic images with different imaging timings for each endoscopic image.
  • a thermal diffusion area is created. Therefore, the thermal diffusion area can be estimated in consideration of dynamic information, and the estimation accuracy of the thermal diffusion area can be improved.
  • a plurality of endoscopic images captured at different timings are described in FIG. 16 of "5. Second Embodiment".
  • the captured images are a plurality of endoscopic images captured at different timings for each endoscopic image
  • the control unit 110 estimates the thermal diffusion region and the specific tissue region from each image. Based on the plurality of heat diffusion regions and the specific tissue region estimated from the image, the time required for heat diffusion to reach the specific tissue region may be output as a prediction result.
  • the controller 100 can acquire the decrease in the distance between the thermal diffusion area and the specific tissue in chronological order, and predict the time when the thermal diffusion reaches the specific tissue area. This allows the operator to adjust the output of the energy device in advance based on the predicted results. Therefore, it is possible to reduce the risk of heat damage to the specific tissue region and safely perform surgery. Prediction of the time at which thermal diffusion reaches the specific tissue region is described in FIG. 17 of "5. Second Embodiment.”
  • control unit 110 may determine the thermal damage risk before the time that is the prediction result, and output the determination result.
  • the determination result regarding the thermal damage risk is output before thermal diffusion reaches the specific tissue region. Therefore, it is possible to adjust the energy output a predetermined time before thermal damage to the specific tissue occurs, reduce the risk of thermal damage to the specific tissue region, and safely perform surgery. Prediction of the time at which thermal diffusion reaches the specific tissue region is described in FIG. 17 of "5. Second Embodiment.”
  • the control unit 110 sends an energy output adjustment instruction, which is an instruction to reduce the energy output from the current energy output or an instruction to stop the energy output, to the energy device 310 based on the determination result. You may output with respect to the generator 300 which controls a supply amount based on an energy output adjustment instruction
  • the system 10 can autonomously adjust the energy output according to the risk of thermal damage occurring in a specific tissue region. Therefore, even non-experts can avoid the risk of heat damage occurring in specific tissue regions. Note that the energy output adjustment instruction is described in FIG. 11 of "4. First Embodiment".
  • control unit 110 may present a recommendation to lower the energy output from the current energy output or a recommendation to stop the energy output based on the determination result.
  • the trained model 121 is derived from the learning energy output information of the energy device 310 and the learning device tissue image, or from the learning tissue image, the thermal diffusion region and the specific tissue region from the energy device 310 to the tissue.
  • the control unit 110 performs processing based on the learned model 121 stored in the storage unit 120 to determine the heat diffusion region and the specific heat diffusion region from the energy output information of the energy device 310 and the captured image A tissue area may be estimated.
  • the control unit 110 can estimate the heat diffusion area with higher accuracy. Note that the learning energy output information is described in FIG. 15 of "5. Second Embodiment".
  • the energy device 310 is a device that has two jaws 337 and 338 capable of gripping tissue, and is a device that receives energy supply from the generator 300 and outputs energy from the two jaws 337 and 338.
  • the energy device 310 may be a bipolar device 330.
  • Bipolar devices are described, for example, in FIG. 5 of “3. Energy Devices”.
  • the energy device 310 may be an ultrasound device 340 .
  • the ultrasonic device 340 may be a combined device of the ultrasonic device 340 and the bipolar device 330.
  • the ultrasound device 340 and the combined device are described, for example, in FIG. 6 in “3. Energy Devices”.
  • the captured image may include white burn due to thermal denaturation.
  • information on the whitened range can be obtained from the endoscopic image, so there is no need to use other equipment when creating the teacher data 521. Also, the number of networks used by the control unit 110 can be reduced, and the processing speed of the control unit 110 can be increased.
  • the white burn is described in "7. Fourth Embodiment".
  • the captured image may include an endoscopic image captured with special light different from normal light.
  • multi-wavelength images are also used as time-series data when learning the network used in the thermal diffusion detection function and when estimating the thermal diffusion area, so that a specific tissue such as a connective tissue around a blood vessel can be focused on. Additional information can be input. In this way, dynamic information such as cohesion of connective tissue can be easily obtained, and the accuracy of estimation of the specific tissue region, thermal diffusion region, etc. can be improved by utilizing the information. Note that normal light and special light are described in "6. Third Embodiment.”
  • the trained model 121 is a trained model trained to estimate tissue heat transfer characteristics from the learning energy output information of the energy device 310, the learning device tissue image, or the learning tissue image.
  • the control unit 110 may estimate the tissue heat transfer characteristics from the energy output information of the energy device 310 or the captured image by processing based on the learned model 121 stored in the storage unit 120 .
  • control unit 110 can predict the decrease in the distance between the heat diffusion area and the specific tissue area from the characteristics of the heat transfer path. Therefore, it is possible to reduce the risk of heat damage to the specific tissue region and safely perform surgery.
  • the tissue heat transfer characteristics are described in "8. Fifth Embodiment”.
  • the above processing may be described as a program. That is, the program of the present embodiment acquires a captured image, and obtains information about a thermal diffusion region from the energy device to the living tissue and a specific tissue region in the living tissue from the captured image by processing based on the learned model 121. estimating at least one piece of image recognition information; and determining a risk of thermal damage to a specific tissue due to the energy output of the energy device 310 from the estimated image recognition information.
  • the above processing may also be described as a method. That is, the method of the present embodiment acquires in real time a captured image, which is a captured image of at least one energy device outputting energy that receives energy and outputs energy, and at least one biological tissue; From the learning device tissue image, which is an image obtained by imaging the energy device and at least one biological tissue during output of at least one energy, or the learning tissue image, which is an image obtained by imaging at least one biological tissue, the energy device By processing based on a trained model that has learned to output image recognition information, which is at least one of information related to a thermal diffusion area from the energy device to the living tissue and a specific tissue area in the living tissue generated by the energy output, The thermal damage risk is determined by estimating image recognition information from the captured image and determining the thermal damage risk of the specific tissue due to the energy output of the energy device from the estimated image recognition information.

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Biomedical Technology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Surgery (AREA)
  • Physics & Mathematics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Animal Behavior & Ethology (AREA)
  • Veterinary Medicine (AREA)
  • Epidemiology (AREA)
  • Primary Health Care (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Molecular Biology (AREA)
  • Radiology & Medical Imaging (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Otolaryngology (AREA)
  • Plasma & Fusion (AREA)
  • Pathology (AREA)
  • Multimedia (AREA)
  • General Business, Economics & Management (AREA)
  • Business, Economics & Management (AREA)
  • Quality & Reliability (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Biophysics (AREA)
  • Human Computer Interaction (AREA)
  • Urology & Nephrology (AREA)
  • Robotics (AREA)
  • Surgical Instruments (AREA)
  • Endoscopes (AREA)
PCT/JP2022/009693 2021-07-13 2022-03-07 システム、プログラム及び判定方法 Ceased WO2023286336A1 (ja)

Priority Applications (2)

Application Number Priority Date Filing Date Title
JP2023535111A JP7602645B2 (ja) 2021-07-13 2022-03-07 システム
US18/368,100 US20230419486A1 (en) 2021-07-13 2023-09-14 System

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US202163221128P 2021-07-13 2021-07-13
US63/221,128 2021-07-13
US202163222252P 2021-07-15 2021-07-15
US63/222,252 2021-07-15

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US18/368,100 Continuation US20230419486A1 (en) 2021-07-13 2023-09-14 System

Publications (1)

Publication Number Publication Date
WO2023286336A1 true WO2023286336A1 (ja) 2023-01-19

Family

ID=84919163

Family Applications (4)

Application Number Title Priority Date Filing Date
PCT/JP2022/009693 Ceased WO2023286336A1 (ja) 2021-07-13 2022-03-07 システム、プログラム及び判定方法
PCT/JP2022/009691 Ceased WO2023286334A1 (ja) 2021-07-13 2022-03-07 システム、プログラム及びエネルギー出力調整方法
PCT/JP2022/009694 Ceased WO2023286337A1 (ja) 2021-07-13 2022-03-07 システム、プログラム及び情報処理方法
PCT/JP2022/009692 Ceased WO2023286335A1 (ja) 2021-07-13 2022-03-07 システム、プログラム及び情報処理方法

Family Applications After (3)

Application Number Title Priority Date Filing Date
PCT/JP2022/009691 Ceased WO2023286334A1 (ja) 2021-07-13 2022-03-07 システム、プログラム及びエネルギー出力調整方法
PCT/JP2022/009694 Ceased WO2023286337A1 (ja) 2021-07-13 2022-03-07 システム、プログラム及び情報処理方法
PCT/JP2022/009692 Ceased WO2023286335A1 (ja) 2021-07-13 2022-03-07 システム、プログラム及び情報処理方法

Country Status (3)

Country Link
US (5) US12484788B2 (https=)
JP (4) JP7602645B2 (https=)
WO (4) WO2023286336A1 (https=)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023286336A1 (ja) 2021-07-13 2023-01-19 オリンパス株式会社 システム、プログラム及び判定方法
JP2025058623A (ja) * 2023-09-28 2025-04-09 オムロン株式会社 モデル生成方法及び推論プログラム
WO2025115599A1 (ja) 2023-11-28 2025-06-05 オリンパスメディカルシステムズ株式会社 支援装置、支援方法および記録媒体
EP4574007A1 (en) * 2023-12-22 2025-06-25 Olympus Winter & Ibe GmbH Medical camera system and method for capturing images and processing said images
CN118680657B (zh) * 2024-06-05 2025-06-13 河北壹雪制冷科技有限公司 一种基于传感反馈的冷冻笔控制装置

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140296842A1 (en) * 2013-04-02 2014-10-02 Siemens Corporation Patient Specific Planning and Simulation of Ablative Procedures
WO2020104308A1 (en) * 2018-11-20 2020-05-28 Koninklijke Philips N.V. Temporal mapping of thermal ablation areas
US20210038314A1 (en) * 2019-08-11 2021-02-11 phenoMapper, LLC Method and System for Personalized Treatment Planning in Ablation of Cancerous Tissue
JP2021029979A (ja) * 2019-08-29 2021-03-01 国立研究開発法人国立がん研究センター 教師データ生成装置、教師データ生成プログラム及び教師データ生成方法
JP2021029258A (ja) * 2019-08-13 2021-03-01 ソニー株式会社 手術支援システム、手術支援方法、情報処理装置、及び情報処理プログラム
JP2021049341A (ja) * 2019-09-22 2021-04-01 バイオセンス・ウエブスター・（イスラエル）・リミテッドＢｉｏｓｅｎｓｅ Ｗｅｂｓｔｅｒ （Ｉｓｒａｅｌ）， Ｌｔｄ． 機械学習（ｍｌ）を使用する心臓アブレーションの案内
JP2021083970A (ja) * 2019-11-29 2021-06-03 キヤノンメディカルシステムズ株式会社 医用画像処理装置、医用画像処理方法及び医用画像処理プログラム
JP2021514724A (ja) * 2018-02-23 2021-06-17 バイオコンパティブルズ ユーケー リミテッド ボリュームの画像処理

Family Cites Families (37)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH11197159A (ja) 1998-01-13 1999-07-27 Hitachi Ltd 手術支援システム
JPH11267133A (ja) * 1998-03-25 1999-10-05 Olympus Optical Co Ltd 治療装置
US8398541B2 (en) 2006-06-06 2013-03-19 Intuitive Surgical Operations, Inc. Interactive user interfaces for robotic minimally invasive surgical systems
US8048069B2 (en) 2006-09-29 2011-11-01 Medtronic, Inc. User interface for ablation therapy
KR20130010952A (ko) * 2011-07-20 2013-01-30 삼성전자주식회사 이중 에너지 엑스선 영상 장치 및 그 제어방법
US20150272653A1 (en) 2014-04-01 2015-10-01 Siemens Medical Solutions Usa, Inc. Thermal therapy ablation detection with ultrasound
CN106456242B (zh) 2015-02-18 2019-09-03 奥林巴斯株式会社 关节用手术系统
KR102419454B1 (ko) 2015-04-02 2022-07-12 하트플로우, 인크. 생체열 전달의 개인화된 추정치들을 제공하기 위한 시스템들 및 방법들
US9839483B2 (en) 2015-04-21 2017-12-12 Heartflow, Inc. Systems and methods for risk assessment and treatment planning of arterio-venous malformation
JP2017006337A (ja) 2015-06-19 2017-01-12 ソニー株式会社 医療支援装置および方法、並びに、医療支援システム
WO2017090165A1 (ja) 2015-11-26 2017-06-01 オリンパス株式会社 処置システム、及び処置具
US10786298B2 (en) * 2016-03-01 2020-09-29 Covidien Lp Surgical instruments and systems incorporating machine learning based tissue identification and methods thereof
US10537394B2 (en) 2016-12-19 2020-01-21 Ethicon Llc Hot device indication of video display
US11908584B2 (en) 2017-05-15 2024-02-20 Ne Scientific, Llc Methods and systems for modeling a necrotized tissue volume in an ablation procedure
WO2019046152A1 (en) 2017-08-29 2019-03-07 Intuitive Surgical Operations, Inc. VISUAL DETECTION OF ELECTROCAUTERIZATION ARCS
US11051876B2 (en) 2017-12-28 2021-07-06 Cilag Gmbh International Surgical evacuation flow paths
US11424027B2 (en) 2017-12-28 2022-08-23 Cilag Gmbh International Method for operating surgical instrument systems
US11147607B2 (en) 2017-12-28 2021-10-19 Cilag Gmbh International Bipolar combination device that automatically adjusts pressure based on energy modality
WO2019142252A1 (ja) 2018-01-17 2019-07-25 オリンパス株式会社 制御装置及び制御装置の作動方法
JP7043317B2 (ja) * 2018-03-29 2022-03-29 富士フイルムヘルスケア株式会社 医療システム
US11523839B2 (en) 2018-04-03 2022-12-13 Intuitive Surgical Operations, Inc. Systems and methods for grasp adjustment based on grasp properties
WO2020012530A1 (ja) 2018-07-09 2020-01-16 日本電気株式会社 施術支援装置、施術支援方法、及びコンピュータ読み取り可能な記録媒体
US10307209B1 (en) 2018-08-07 2019-06-04 Sony Corporation Boundary localization of an internal organ of a subject for providing assistance during surgery
US11896279B2 (en) * 2018-09-07 2024-02-13 Cilag Gmbh International Surgical modular energy system with footer module
WO2020095389A1 (ja) * 2018-11-07 2020-05-14 オリンパス株式会社 医療装置、残熱判定方法、及び残熱判定プログラム
US11033344B2 (en) 2018-12-13 2021-06-15 Cilag Gmbh International Improving surgical tool performance via measurement and display of tissue tension
EP4382021A3 (en) 2019-02-08 2024-08-28 The Board Of Trustees Of The University Of Illinois Image-guided surgery system
US12333423B2 (en) 2019-02-14 2025-06-17 Covidien Lp Systems and methods for estimating tissue parameters using surgical devices
JP6801897B2 (ja) 2019-03-15 2020-12-16 リバーフィールド株式会社 力覚表示装置および力覚表示方法
US10758309B1 (en) 2019-07-15 2020-09-01 Digital Surgery Limited Methods and systems for using computer-vision to enhance surgical tool control during surgeries
US12303187B2 (en) 2019-11-29 2025-05-20 Canon Medical Systems Corporation Medical image processing apparatus
JP2021083969A (ja) 2019-11-29 2021-06-03 キヤノンメディカルシステムズ株式会社 医用画像処理装置、医用画像処理方法及び医用画像処理プログラム
US11284963B2 (en) 2019-12-30 2022-03-29 Cilag Gmbh International Method of using imaging devices in surgery
EP3886039A1 (en) * 2020-03-26 2021-09-29 Koninklijke Philips N.V. Identification of advisory regions in breast magnetic resonance imaging
JP7482690B2 (ja) * 2020-06-04 2024-05-14 リンナイ株式会社 加熱調理システム、加熱調理方法、学習装置および学習方法
WO2022168905A1 (ja) 2021-02-04 2022-08-11 オリンパス株式会社 内視鏡システム、手技支援方法および手技支援プログラム
WO2023286336A1 (ja) 2021-07-13 2023-01-19 オリンパス株式会社 システム、プログラム及び判定方法

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140296842A1 (en) * 2013-04-02 2014-10-02 Siemens Corporation Patient Specific Planning and Simulation of Ablative Procedures
JP2021514724A (ja) * 2018-02-23 2021-06-17 バイオコンパティブルズ ユーケー リミテッド ボリュームの画像処理
WO2020104308A1 (en) * 2018-11-20 2020-05-28 Koninklijke Philips N.V. Temporal mapping of thermal ablation areas
US20210038314A1 (en) * 2019-08-11 2021-02-11 phenoMapper, LLC Method and System for Personalized Treatment Planning in Ablation of Cancerous Tissue
JP2021029258A (ja) * 2019-08-13 2021-03-01 ソニー株式会社 手術支援システム、手術支援方法、情報処理装置、及び情報処理プログラム
JP2021029979A (ja) * 2019-08-29 2021-03-01 国立研究開発法人国立がん研究センター 教師データ生成装置、教師データ生成プログラム及び教師データ生成方法
JP2021049341A (ja) * 2019-09-22 2021-04-01 バイオセンス・ウエブスター・（イスラエル）・リミテッドＢｉｏｓｅｎｓｅ Ｗｅｂｓｔｅｒ （Ｉｓｒａｅｌ）， Ｌｔｄ． 機械学習（ｍｌ）を使用する心臓アブレーションの案内
JP2021083970A (ja) * 2019-11-29 2021-06-03 キヤノンメディカルシステムズ株式会社 医用画像処理装置、医用画像処理方法及び医用画像処理プログラム

Also Published As

Publication number Publication date
US20230380695A1 (en) 2023-11-30
JP7465413B2 (ja) 2024-04-10
US12484788B2 (en) 2025-12-02
JPWO2023286337A1 (https=) 2023-01-19
WO2023286335A1 (ja) 2023-01-19
US12593984B2 (en) 2026-04-07
JP7602645B2 (ja) 2024-12-18
WO2023286334A1 (ja) 2023-01-19
US20230404652A1 (en) 2023-12-21
JPWO2023286336A1 (https=) 2023-01-19
US20230410298A1 (en) 2023-12-21
WO2023286337A1 (ja) 2023-01-19
JPWO2023286334A1 (https=) 2023-01-19
US20230419486A1 (en) 2023-12-28
JP7493684B2 (ja) 2024-05-31
JP7602646B2 (ja) 2024-12-18
JPWO2023286335A1 (https=) 2023-01-19
US20260053370A1 (en) 2026-02-26

Similar Documents

Publication Publication Date Title
JP7602645B2 (ja) システム
JP5789387B2 (ja) 重要な構造に対する近接を決定するためのシステムおよび方法
JP5814569B2 (ja) 重要な構造に対する近接を決定するためのシステムおよび方法
US20240233126A9 (en) Surgery details evaluation system, surgery details evaluation method, and computer program
EP3001941A1 (en) Endoscopic device and endoscopic device operation method
US20100076431A1 (en) Apparatus, System and Method for Performing an Electrosurgical Procedure
US12324616B2 (en) Tissue therapy energy delivery using optical imaging
US12609195B2 (en) Medical imaging device, medical system, method for operating a medical imaging device, and method for medical imaging
JPWO2020174666A1 (ja) 医療用システム、エネルギー制御方法、およびプロセッサ
US20240260811A1 (en) Medical system and notification method
US20230071343A1 (en) Energy-based surgical systems and methods based on an artificial-intelligence learning system
US20230042032A1 (en) Energy-based surgical systems and methods based on an artificial-intelligence learning system
CN117460474A (zh) 使用光学成像进行组织治疗能量递送
CN116887771A (zh) 系统、程序以及能量输出调整方法
US20250204763A1 (en) Medical camera system and method for capturing images and processing said images
JP6184649B1 (ja) 処置システム及び制御装置
US20250169876A1 (en) Assistance device, assistance method, and recording medium
WO2025136834A1 (en) Tissue therapy energy feedback systems and methods

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22841686

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 2023535111

Country of ref document: JP

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 22841686

Country of ref document: EP

Kind code of ref document: A1