WO2019132169A1

WO2019132169A1 - Method, apparatus, and program for surgical image playback control

Info

Publication number: WO2019132169A1
Application number: PCT/KR2018/010334
Authority: WO
Inventors: 이종혁; 허성환
Original assignee: (주)휴톰
Priority date: 2017-12-28
Filing date: 2018-09-05
Publication date: 2019-07-04
Also published as: KR101880246B1; KR20190080703A; KR102298412B1; KR20190080702A; KR20190088375A

Abstract

Disclosed is a method for surgical image playback control, comprising the steps of: acquiring, by a computer, a surgical image; obtaining information in which the surgical image is divided into one or more regions; obtaining information on surgical stages corresponding to the divided one or more regions, respectively; determining a level of importance for each of the surgical stages; and controlling playback of the surgical image on the basis of the determined importance level.

Description

Surgical image reproduction control method, apparatus and program

The present invention relates to a surgical video image reproduction control method, apparatus and program.

In the surgical procedure, development of techniques capable of providing information for assisting the surgeon of the doctor is required. In order to provide information for assisting the operation, it is necessary to be able to recognize the operation.

Therefore, it is required to develop a technique for a computer to recognize a surgical operation from a surgical image, reproduce an operation image, and provide auxiliary information corresponding to an operation image.

In recent years, deep learning has been widely used in the analysis of medical images. Deep learning is defined as a set of machine learning algorithms that try to achieve high levels of abstraction (a task that summarizes key content or functions in large amounts of data or complex data) through a combination of several nonlinear transformation techniques. Deep learning can be viewed as a field of machine learning that teaches computers how people think in a big way.

SUMMARY OF THE INVENTION [0006] The present invention provides a method, an apparatus, and a program for controlling a surgical image reproduction.

The problems to be solved by the present invention are not limited to the above-mentioned problems, and other problems which are not mentioned can be clearly understood by those skilled in the art from the following description.

According to an aspect of the present invention, there is provided a method of controlling an operation of regenerating a surgical image, the method comprising: obtaining a surgical image by a computer; acquiring information obtained by dividing the surgical image into one or more regions; Obtaining information on a surgical stage corresponding to each of the areas, determining importance of each of the surgical steps, and controlling reproduction of the surgical image based on the determined importance.

The step of acquiring the information on the operation step may include the step of determining the type of the operation, and the step of determining the importance may include determining the importance of each of the operation steps based on the type of the operation Step < / RTI >

The step of controlling the regeneration of the surgical image may include determining whether to reproduce the surgical image corresponding to each of the surgical steps and the summary level based on the importance of each of the surgical steps.

The step of controlling the regeneration of the surgical image may further include the steps of: determining a division level of each of the surgical steps based on the importance of each of the surgical steps; dividing each of the surgical steps according to the determined division level; And regenerating the surgical image corresponding to each of the divided surgical steps and determining whether to reproduce the surgical image corresponding to each of the divided surgical steps and the summary level based on the divided level of each of the divided surgical steps .

The step of acquiring the divided information may include acquiring information obtained by hierarchically dividing the surgical image into one or more classification units, and the step of acquiring information on the surgical step may include: Searching for information that is divided into hierarchically divided information from an upper layer, and acquiring information about a surgical stage corresponding to each of the hierarchically divided information.

The step of determining the importance may include determining a degree of importance of a surgical stage corresponding to each of the hierarchically divided information, and the step of controlling the reproduction of the surgical image may include: Determining whether or not hierarchical division of each of the operation steps is performed based on the importance levels, and determining whether to reproduce operation images corresponding to each of the hierarchically divided operation steps based on importance of each of the hierarchically divided operation steps, And determining a summary level.

The method may further include recognizing at least one event included in the surgical image and reproducing the surgical image corresponding to the event.

In addition, the step of reproducing the surgical image corresponding to the event may include the step of determining the importance of the recognized event and the step of controlling the reproduction of the surgical image corresponding to the event based on the importance of the event have.

According to an aspect of the present invention, there is provided a learning data management apparatus including a memory for storing one or more instructions and a processor for executing the one or more instructions stored in the memory, The method comprising the steps of: obtaining a surgical image by a computer; obtaining information obtained by dividing the surgical image into one or more regions; obtaining information about a surgical stage corresponding to each of the divided regions; Determining the importance of each of the surgical steps, and controlling the reproduction of the surgical image based on the determined importance.

According to an aspect of the present invention, there is provided a computer program stored in a recording medium readable by a computer, the computer program being capable of performing a surgical video playback control method in combination with a hardware computer.

Other specific details of the invention are included in the detailed description and drawings.

According to the disclosed embodiment, it is possible to easily review the surgical image by easily skipping or summarizing the surgical image according to the importance.

The effects of the present invention are not limited to the above-mentioned effects, and other effects not mentioned can be clearly understood by those skilled in the art from the following description.

1 is a simplified schematic diagram of a system capable of performing robotic surgery in accordance with the disclosed embodiments.

2 is a flowchart illustrating a method of controlling reproduction of a surgical image according to an embodiment.

FIG. 3 is a diagram showing an example of a method of hierarchically dividing and recognizing a surgical operation.

FIG. 4 is a view for explaining a method of determining a division level of each surgical stage and reproducing a surgical image according to an embodiment.

5 is a view for explaining a method of controlling reproduction of a surgical image including an event according to an embodiment.

6 is a configuration diagram of an apparatus according to an embodiment.

BRIEF DESCRIPTION OF THE DRAWINGS The advantages and features of the present invention and the manner of achieving them will become apparent with reference to the embodiments described in detail below with reference to the accompanying drawings. It should be understood, however, that the invention is not limited to the disclosed embodiments, but may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, Is provided to fully convey the scope of the present invention to a technician, and the present invention is only defined by the scope of the claims.

The terminology used herein is for the purpose of illustrating embodiments and is not intended to be limiting of the present invention. In the present specification, the singular form includes plural forms unless otherwise specified in the specification. The terms " comprises "and / or" comprising "used in the specification do not exclude the presence or addition of one or more other elements in addition to the stated element. Like reference numerals refer to like elements throughout the specification and "and / or" include each and every combination of one or more of the elements mentioned. Although "first "," second "and the like are used to describe various components, it is needless to say that these components are not limited by these terms. These terms are used only to distinguish one component from another. Therefore, it goes without saying that the first component mentioned below may be the second component within the technical scope of the present invention.

Unless defined otherwise, all terms (including technical and scientific terms) used herein may be used in a sense that is commonly understood by one of ordinary skill in the art to which this invention belongs. In addition, commonly used predefined terms are not ideally or excessively interpreted unless explicitly defined otherwise.

As used herein, the term "part" or "module" refers to a hardware component, such as a software, FPGA, or ASIC, and a "component" or "module" performs certain roles. However, "part" or " module " is not meant to be limited to software or hardware. A "module " or " module " may be configured to reside on an addressable storage medium and configured to play back one or more processors. Thus, by way of example, "a" or " module " is intended to encompass all types of elements, such as software components, object oriented software components, class components and task components, Microcode, circuitry, data, databases, data structures, tables, arrays, and variables, as used herein. Or " modules " may be combined with a smaller number of components and "parts " or " modules " Can be further separated.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1, the robotic surgery system includes a medical imaging apparatus 10, a server 20, a control unit 30 provided in an operating room, a display 32, and a surgical robot 34. Depending on the embodiment, the medical imaging equipment 10 may be omitted from the robotic surgery system according to the disclosed embodiment.

In one embodiment, the surgical robot 34 includes a photographing device 36 and a surgical tool 38.

In one embodiment, robotic surgery is performed by the user controlling the surgical robot 34 using the control unit 30. [ In one embodiment, robot surgery may be performed automatically by the control unit 30 without user control.

The server 20 is a computing device including at least one processor and a communication unit.

The control unit 30 includes a computing device including at least one processor and a communication unit. In one embodiment, the control unit 30 includes hardware and software interfaces for controlling the surgical robot 34.

The photographing apparatus 36 includes at least one image sensor. That is, the photographing device 36 includes at least one camera device and is used to photograph a target object, that is, a surgical site. In one embodiment, the imaging device 36 includes at least one camera coupled with a surgical arm of the surgical robot 34.

In one embodiment, the image photographed at the photographing device 36 is displayed on the display 340. [

In one embodiment, the surgical robot 34 includes one or more surgical tools 38 that can perform cutting, clipping, anchoring, grabbing, etc., of the surgical site. The surgical tool 38 is used in combination with the surgical arm of the surgical robot 34.

The control unit 30 receives information necessary for surgery from the server 20, or generates information necessary for surgery and provides the information to the user. For example, the control unit 30 displays on the display 32 information necessary for surgery, which is generated or received.

For example, the user operates the control unit 30 while viewing the display 32 to perform the robot surgery by controlling the movement of the surgical robot 34.

The server 20 generates information necessary for the robot surgery using the medical image data of the object photographed previously from the medical imaging apparatus 10 and provides the generated information to the control unit 30. [

The control unit 30 provides the information received from the server 20 to the user by displaying the information on the display 32 or controls the surgical robot 34 using the information received from the server 20. [

In one embodiment, the means that can be used in the medical imaging equipment 10 is not limited, and various other medical imaging acquiring means such as CT, X-Ray, PET, MRI and the like may be used.

In the disclosed embodiment, the surgical image obtained in the photographing device 36 is transmitted to the control section 30. [

In one embodiment, the control unit 30 may segment the surgical image obtained during the operation in real time.

In one embodiment, the control unit 30 transmits a surgical image to the server 20 during or after surgery.

The server 20 can divide and analyze the surgical image.

The server 20 learns and stores at least one model for dividing and analyzing the surgical image. In addition, the server 20 learns and stores at least one model for generating an optimized surgical process.

In addition, the server 20 or the client (including the control unit 30, for example) can display the obtained surgical image. However, since the surgical image is generally very long, it is practically difficult for the researcher or the doctor to review the surgical image.

Hereinafter, a method of displaying a surgical image by the server 20 or a client, omitting each part according to importance, or displaying in summary, will be described in detail.

Referring to FIG. 2, steps that may be performed on the server 20 or client shown in FIG. 1 are shown in a time-series manner. Hereinafter, it is assumed that the computer performs the steps shown in FIG. 2 for convenience of explanation. However, all or some of the steps shown in FIG. 2 may be performed in the server 20 or the client, respectively.

In step S110, the computer acquires a surgical image.

In the present specification, the surgical image may be a surgical image actually performed by the surgical robot 34, or may be a simulation image performed based on the image obtained from the medical imaging apparatus 10. [ In addition, the surgical image may be an image according to the optimized surgical method, which is generated based on the image obtained from the medical imaging apparatus 10.

In addition, the surgical image in the present specification may mean an image in which a surgical procedure is actually photographed, or a 3D modeling image generated based on a medical image obtained from the medical image photographing apparatus 10. Likewise, the type of surgical image referred to in the present specification is not limited, and may be understood as meaning including all types of images including at least a part of the surgical procedure.

In one embodiment, the computer obtains information obtained by segmenting the surgical image obtained in step S110 into one or more areas. In one embodiment, the surgical image is automatically segmented by the computer.

In one embodiment, the surgical image may be segmented by various criteria. As an example, a surgical image may be segmented based on the type of object included in the image. The division method based on the kind of object requires a step in which the computer recognizes each object.

The object recognized in the surgical image includes the human body, the object introduced from the outside, and the object generated by itself. The human body includes a body part taken by medical imaging (e.g., CT) followed by surgery and a body part not taken.

For example, a body part photographed by a medical imaging includes an organ, a blood vessel, a bone, a tendon, etc., and such a body part can be recognized based on a 3D modeling image generated based on a medical image.

Specifically, the position, size, and shape of each body part are recognized in advance by a 3D analysis method based on a medical image. The computer defines an algorithm that can grasp the position of a body part corresponding to a surgical image in real time, and based on the information, information on the position, size, and shape of each body part included in the surgical image Can be obtained. Also, body parts not taken by medical imaging include omentum, which is not captured by medical images, so it is necessary to recognize them in real time during surgery. For example, the computer can determine the location and size of the omentum using image recognition methods, and predict the location of the vessel in the presence of blood vessels within the omentum.

Objects introduced from the outside include, for example, surgical tools, gauzes, clips, and the like. Since it has predetermined morphological characteristics, it can recognize the computer in real time through image analysis during surgery.

Internally generated objects include, for example, bleeding from the body part. This allows the computer to recognize images in real time during image analysis during surgery.

The movements of organs and organs included in body parts and the causes of objects are all caused by the movement of objects from outside.

Thus, in addition to recognizing each object, a surgical image can be segmented based on the motion of each object. In one embodiment, the surgical image may be segmented based on the motion, i.e., action, of the externally introduced object.

The computer judges the type of each object recognized in the surgical image. The computer determines a motion of each object, that is, a motion of each object based on a predetermined operation defined in advance, a series of operations, The action can be recognized.

The computer recognizes the type of each action, and also recognizes the cause of each action. The computer can divide the surgical image based on the recognized action, and it can recognize from each detailed operation to the type of the whole operation through the stepwise division.

Furthermore, the computer determines the type of predefined operation corresponding to the surgical image from the judgment of the action. When determining the type of surgery, information about the entire surgical procedure can be obtained. If there are multiple surgical processes for the same kind of surgery, one surgical process may be selected based on the doctor's choice, or based on actions recognized up to a certain point in time.

The computer can recognize and predict the surgical stage based on the acquired surgical procedure. For example, if a particular step in a series of surgical procedures is recognized, then the steps following it can be predicted or candidates for possible steps can be culled. Therefore, it is possible to greatly reduce the error rate of the surgical image recognition caused by the occlusion or the like. Further, when the surgical image deviates greatly from the predictable surgical stage by more than a predetermined error range, it may be recognized that a surgical error situation has occurred.

In addition, the computer can make a judgment on each action based on the recognition of each action. For example, a computer can recognize the necessity and effectiveness of each action.

Specifically, the computer can make a judgment as to whether each action was necessary or unnecessary. In addition, the computer can determine whether each action was performed efficiently, even if each action was required. This is used to provide an operative report, eliminate unnecessary operations in the surgical procedure, and streamline inefficient operations.

As described above, the surgical image is largely divided into components including body parts (organ and omentum), objects introduced from the outside, objects generated inside, actions, types of surgery, necessity and efficiency of each action . That is, instead of recognizing the surgical image as a whole, the computer divides the surgical image into a component unit that minimizes mutual overlapping, including all elements of the surgical image as much as possible. Based on the divided component units, By recognizing, the surgical image can be recognized more specifically and more easily.

In one embodiment, the computer may divide the surgical image hierarchically (or stepwise).

In one embodiment, the computer divides the surgical image hierarchically (or stepwise) into one or more classification units, and recognizes the operations corresponding to each of the divided classification units hierarchically (or stepwise).

For example, the computer may sequentially recognize the operations of the first classification unit, the second classification unit, the third classification unit, and the fourth classification unit included in the surgical image.

For example, the first classification unit is a component unit, the second classification unit is a subsegment unit, the third classification unit is a segmentation unit, the fourth classification unit is a subsegmentation unit, but it is not limited thereto.

Referring to FIG. 3, an example of a method of hierarchically dividing and recognizing a surgical operation is shown.

Referring to FIG. 3, the surgical operation is divided into a first classification unit 210 and a second classification unit 220, a third classification unit 230, and a fourth classification unit 240, And a method of recognizing the divided data is schematically shown.

In one embodiment, each code shown in FIG. 3 may refer to a pre-established code that can identify actions included in each classification unit.

By way of example, and not by way of limitation, the operation of the first classification unit may include capturing, cutting, moving, etc., and the operation of the second classification unit may include vascularization, fat removal, Long term resection, long term resection, suture and the like, and the operation of the fourth classification unit may include gastric cancer surgery.

In other words, when gastric cancer surgery is taken as an example, each operation of gastric cancer surgery can largely include laparotomy, gastrectomy, long-term connection and suture, and each operation is more concretely a vasectomy, And the connection of some parts of other organs and the like, and each operation can be more specifically embodied by cutting the blood vessels, removing obstacles such as fat, etc., and this can be accomplished by more simple operations such as movement, Can be further specified.

According to the disclosed embodiment, the hierarchy can be used in reverse, and the operation can be divided into the minimum detail unit, and the computer can be learned to recognize the upper operation step by step using the divided result.

It is relatively difficult to accurately recognize the surgical operation corresponding to the upper stage by image processing the surgical image without such a stepwise approach. The surgical site is different for each patient, each disease differs in shape, and the operation patterns are different depending on the type of operation.

According to the disclosed embodiment, it is possible to start from a recognition of a detailed operation (for example, cutting, catching, etc.) less affected by the physical condition of the patient or the type of operation, It is possible to provide a learning model capable of recognizing the upper operation operation which is meant by a larger unit of operation and further recognizing the type of surgery.

Using the learning model according to the disclosed embodiment, it is possible to provide a surgical motion recognition model that can be applied to the patient regardless of the physical condition of the patient or the type of surgery, and, if necessary, Or may provide a surgical motion recognition model that is tailored to the type of condition or surgery.

In one embodiment, the computer can recognize an event that occurs in a surgical image. For example, the event includes a surgical error situation, such as bleeding. When an event occurs, the computer can recognize this through image processing of the surgical image.

When the event is recognized, the computer may divide the surgical image into one or more event groups including recognized events. The divided event groups may be managed separately, included in a classification unit according to the disclosed embodiment, or may be utilized as an independent classification unit for analysis of a surgical operation.

In one embodiment, the computer can determine the cause of the event based on the recognized event and the surgical operation before and after the event was recognized.

For example, when an event occurs, the computer may generate learning data for analyzing the cause of the event by storing the operations of the predetermined classification unit before and after the occurrence of the event together with information on the event.

The computer can perform learning using the generated learning data, and learn the correlation between the operation and the events of each classification unit.

Based on the learning result, the computer can determine the cause of the event occurrence and provide feedback to the user.

In one embodiment, the computer may perform learning for optimization of surgical operations based on operation of a given classification unit.

For example, the computer can learn an optimized sequence and method for performing the operation of each classification unit according to the physical condition of the patient and the type of surgery.

In one embodiment, the computer may perform learning for optimization of the surgical operation based on the operation of the first classification unit. For example, the computer may obtain one or more reference surgery information. The computer can perform learning based on the order of the operation operations included in the one or more reference operation information and determine the order of the optimized operation operations for each operation according to the learning results.

Since the operation of the first classification unit is a minimum unit operation commonly applied in any operation, when learning is performed based on the first classification unit, the order of the optimized operation operations regardless of the type of operation and the body condition of the patient Can be obtained. Likewise, it is also possible to obtain an optimized learning model according to the type of surgery and the patient's physical condition through fine adjustment to the learned model.

In step S130, the computer obtains information on the surgical steps corresponding to each of the one or more areas divided in step S120.

In one embodiment, the computer determines the importance of each surgical step (step S140), and controls the reproduction of the surgical image based on the determined importance (step S150).

In one embodiment, the computer determines the type of operation and determines the importance of each surgical stage based on the type of operation that was determined. For example, certain surgical steps may be important for certain operations, but may be less important for other operations. Accordingly, the computer can determine the type of operation from the surgical image according to the above-described method, and determine the importance of each surgical step in which the surgical image is divided based on the determined type of operation.

Further, the computer determines whether or not to reproduce the surgical image corresponding to each surgical stage and the summarization level, based on the importance of each of the surgical stages. For example, a surgical stage with a relatively low importance may not be reproduced or may be reproduced in large numbers. Likewise, relatively more important surgical steps may not be summarized, or may be reproduced with less summary.

In one embodiment, the computer determines the segmentation level of each surgical step based on the determined importance for each surgical step. The computer divides each of the surgical steps according to the determined division level and regenerates the surgical image corresponding to each of the divided surgical steps so that the surgical image corresponding to each of the divided surgical steps, And the level of summarization.

Referring to FIG. 4, a tree 300 corresponding to an example of hierarchically dividing a surgical image is shown. Although the tree 300 shown in FIG. 4 is shown as a binary tree, a data structure for hierarchically dividing an operation image is not limited to a binary tree, and may have a higher-order tree structure, .

In FIG. 4, for convenience of explanation, it is assumed that information on the surgical images divided hierarchically as shown in FIG. 3 is stored in a tree form as shown in FIG.

In one embodiment, the root node 310 of the tree 300 may correspond to a fourth classification unit, i.e., the type of surgery.

In addition, the

child nodes

320 and 330 of the root node 310 correspond to the third classification unit, the child nodes thereof correspond to the second classification unit, and the child nodes correspond to the first classification unit. But is not limited to.

In one embodiment, the computer can determine the type of surgery at the root node 310 and determine the importance of the surgical stage corresponding to each of the

child nodes

320 and 330 below. For example, node 320 may correspond to a fat removal operation, and node 330 may correspond to a gastrostomy operation.

In one embodiment, the computer may play a summarized surgical image, or skip playback, without further segmentation of the surgical stage corresponding to the node 320 corresponding to a relatively less important fat removal operation.

In addition, the computer may further divide the surgical stage corresponding to node 330 and determine the importance of the surgical stage corresponding to each of the

segmented nodes

340 and 350.

In one embodiment, the node 350 may stop further partitioning in response to the relatively insignificant surgical stage, and the surgical operation corresponding to the node 350 may be summarized and regenerated. The summary level for node 350 may be lower than the summary level for node 320. [ That is, the surgical image corresponding to the node 320 may be displayed more than the surgical image corresponding to the node 350.

Further, the node 340 may be further divided in correspondence to a relatively important surgical stage, and the importance of the surgical stage corresponding to each of the

nodes

360 and 370 may be determined.

If the node 360 corresponds to a more important surgical stage than the node 370, then the computer displays the surgical image corresponding to the node 360 longer (or less summarized) than the surgical image corresponding to the node 370 can do.

In one embodiment, an event may occur in the surgical image regardless of the importance of each surgical step. For example, an event may include bleeding or a surgical error situation.

For example, the node 410 included in the tree 400 corresponds to the fat removal operation of which the relative importance is low, but in the operation stage corresponding to the node 420, which is one of the child nodes of the node 410, (E. G., Bleeding) may occur.

In this case, the computer may summarize and display the surgical image corresponding to the node 410, but may not summarize the surgical image corresponding to the node 420 corresponding to the surgical stage in which the event occurred, have. For example, a surgical image corresponding to node 420 may be reproduced to occupy a substantial portion of the summarized surgical image corresponding to node 410. [

In addition, the computer can determine the importance of each recognized event. The computer can control the reproduction of the surgical image corresponding to the event based on the determined degree of importance. For example, the computer may determine whether to reproduce the surgical image corresponding to the event and the summary level, and may reproduce the surgical image corresponding to the event according to the determined replayability and summary level.

In addition, the surgical image regeneration control method according to the disclosed embodiment can be applied to a situation in which a surgical image is divided into one or more stages, and each stage is grouped into one or more groups.

The computer determines the importance of each of the grouped groups, and the surgical images corresponding to the group of relatively low importance may be omitted or many summaries can be summarized. Similarly, surgical images corresponding to relatively high importance groups may not be summarized or may be summarized less.

In one embodiment, the computer may obtain information about the time to reproduce the surgical image and determine the level of abstraction for each surgical image such that all of the surgical images may be reproduced within the acquired time. For example, if a surgical image needs to be reproduced in a relatively short time, it may be omitted and further summarized depending on the importance.

In one embodiment, the computer may obtain information about a surgical step desired to be viewed from a user. In this case, the computer does not summarize the surgical steps corresponding to the acquired information, or can reproduce with less summary.

In one embodiment, the computer can search the data structure in real time, determine the importance for each step, and determine whether to play and summary levels. The computer may determine that it is less important for a particular surgical step and may decide to omit or summarize it a lot. At this time, when the selection input is received from the user, the computer may further divide the surgical stage to display the surgical stage in more detail.

6 is a configuration diagram of an apparatus 100 according to an embodiment.

The processor 102 may include one or more cores (not shown) and a connection path (e.g., a bus, etc.) to transmit and receive signals to and / or from a graphics processing unit (not shown) .

The processor 102 in accordance with one embodiment performs one or more instructions stored in the memory 104 to perform the training data management method described with reference to Figures 1-8.

For example, the processor 102 may obtain an operation image by executing one or more instructions stored in a memory, obtain information obtained by dividing the operation image into one or more regions, Acquiring information about the step, determining importance for each of the operation steps, and controlling reproduction of the operation image based on the determined importance.

The processor 102 may include a random access memory (RAM) (not shown) and a read-only memory (ROM) for temporarily and / or permanently storing signals (or data) , Not shown). In addition, the processor 102 may be implemented as a system-on-chip (SoC) including at least one of a graphics processing unit, a RAM, and a ROM.

The memory 104 may store programs (one or more instructions) for processing and control of the processor 102. Programs stored in the memory 104 may be divided into a plurality of modules according to functions.

As described above, the learning data management method according to an embodiment of the present invention can be implemented as a program (or an application) to be executed in combination with a computer, which is hardware, and can be stored in a medium.

The above-described program may be stored in a computer-readable medium such as C, C ++, JAVA, machine language, or the like that can be read by the processor (CPU) of the computer through the device interface of the computer, And may include a code encoded in a computer language of the computer. Such code may include a functional code related to a function or the like that defines necessary functions for executing the above methods, and includes a control code related to an execution procedure necessary for the processor of the computer to execute the functions in a predetermined procedure can do. Further, such code may further include memory reference related code as to whether the additional information or media needed to cause the processor of the computer to execute the functions should be referred to at any location (address) of the internal or external memory of the computer have. Also, when the processor of the computer needs to communicate with any other computer or server that is remote to execute the functions, the code may be communicated to any other computer or server remotely using the communication module of the computer A communication-related code for determining whether to communicate, what information or media should be transmitted or received during communication, and the like.

The medium to be stored is not a medium for storing data for a short time such as a register, a cache, a memory, etc., but means a medium that semi-permanently stores data and is capable of being read by a device. Specifically, examples of the medium to be stored include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage, and the like, but are not limited thereto. That is, the program may be stored in various recording media on various servers to which the computer can access, or on various recording media on the user's computer. In addition, the medium may be distributed to a network-connected computer system so that computer-readable codes may be stored in a distributed manner.

The steps of a method or algorithm described in connection with the embodiments of the present invention may be embodied directly in hardware, in software modules executed in hardware, or in a combination of both. The software module may be a random access memory (RAM), a read only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a hard disk, a removable disk, a CD- May reside in any form of computer readable recording medium known in the art to which the invention pertains.

While the present invention has been described in connection with what is presently considered to be practical exemplary embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, You will understand. Therefore, it should be understood that the above-described embodiments are illustrative in all aspects and not restrictive.

[Description of Symbols]

100: Device

102: Processor

104: Memory

Claims

Obtaining a surgical image by a computer;

Obtaining information obtained by dividing the surgical image into one or more regions;

Obtaining information on a surgical stage corresponding to each of the divided one or more regions;

Determining a degree of importance for each of the surgical steps; And

Controlling reproduction of the surgical image based on the determined importance; And a control unit operable to control the operation of the surgical image.
The method according to claim 1,

Wherein the step of acquiring information on the surgical step comprises:

Determining a type of the operation; Lt; / RTI >

Wherein the step of determining the importance comprises:

Determining the importance of each of the surgical steps based on the type of surgery; And a control unit operable to control the operation of the surgical image.
The method according to claim 1,

Wherein the step of controlling the reproduction of the surgical image comprises:

Determining whether to reproduce an operation image corresponding to each of the operation steps and a summary level based on the importance of each of the operation steps; And a control unit operable to control the operation of the surgical image.
The method of claim 3,

Wherein the step of controlling the reproduction of the surgical image comprises:

Determining a division level of each of the surgical steps based on the importance of each of the surgical steps;

Dividing each of the surgical steps according to the determined division level; And

Determining whether to reproduce the surgical image corresponding to each of the divided surgical steps and the summary level based on the divided level of each of the divided surgical steps, while reproducing the surgical image corresponding to each of the divided surgical steps; And a control unit operable to control the operation of the surgical image.
The method according to claim 1,

Wherein the step of acquiring the divided information comprises:

And obtaining information obtained by hierarchically dividing the surgical image into one or more classification units,

Wherein the step of acquiring information on the surgical step comprises:

Searching for hierarchically divided information, searching for hierarchically divided information from an upper layer; And

Obtaining information on a surgical stage corresponding to each of the hierarchically divided information; And a control unit operable to control the operation of the surgical image.
6. The method of claim 5,

Wherein the step of determining the importance comprises:

Determining importance of a surgical stage corresponding to each of the hierarchically divided information; Lt; / RTI >

Wherein the step of controlling the reproduction of the surgical image comprises:

Determining whether each of the operation steps is hierarchically divided based on the importance of each of the operation steps; And

Determining whether to reproduce the surgical image corresponding to each of the hierarchically divided surgical steps and the summary level based on the importance of each of the hierarchically divided surgical steps; And a control unit operable to control the operation of the surgical image.
The method according to claim 1,

Recognizing at least one event included in the surgical image; And

Reproducing an operation image corresponding to the event; Further comprising the steps of:
8. The method of claim 7,

Wherein the step of reproducing the surgical image corresponding to the event comprises:

Determining the importance of the recognized event; And

Controlling the reproduction of the surgical image corresponding to the event based on the importance of the event; And a control unit operable to control the operation of the surgical image.
A memory for storing one or more instructions; And

And a processor executing the one or more instructions stored in the memory,

The processor executing the one or more instructions,

Obtaining a surgical image by a computer;

Obtaining information obtained by dividing the surgical image into one or more regions;

Obtaining information on a surgical stage corresponding to each of the divided one or more regions;

Determining a degree of importance for each of the surgical steps; And

Controlling reproduction of the surgical image based on the determined importance; And a control unit for controlling the operation of the surgical video regeneration controller.
A computer program stored in a computer readable recording medium in combination with a computer which is hardware and which is capable of performing the method of claim 1. Description: