5459 results about "Endoscopy" patented technology

The invention discloses a robust polyp segmentation method based on improved SAM-Med2D, and the method comprises the following steps: S1, generating a record file for a given data set; s2, constructing an SAM-Med2D model, and keeping the aspect ratio of a non-square image; s3, extracting multi-scale features of the image through a double-branch encoder; s4, in a mask decoder, carrying out fusion processing on the multi-scale features; s5, generating a preliminary segmentation result based on the fusion feature and the prompt code; s6, constructing a lightweight reverse attention refinement module; s7, carrying out loss calculation and parameter optimization on the SAM-Med2D model; and S8, polyp segmentation of the current image is ended, the next image is entered, and the steps from S1 to S7 are repeatedly executed. According to the method, the ViT-CNN double-branch structure and the reverse attention mechanism are fused, so that high-precision and low-calculation-overhead robust segmentation of the polyp area in a complex endoscope scene is realized.

The invention discloses a Transform-CNN medical image segmentation method and system based on multi-scale fusion semantic enhancement, and relates to the technical field of image segmentation, and the method comprises the steps: collecting medical image data, and constructing a medical image data set; constructing a medical image segmentation network model; training the medical image segmentation network model through the medical image data set; and inputting real-time acquired data into the trained medical image segmentation network model to obtain a medical image segmentation result. According to the invention, double encoders, LGDA modules and the like are adopted to capture multi-scale global features of kidney tumors and enhance representation, and the LGDA module is used for adapting to size and form changes of the kidney tumors; the MLCF module is used for supplementing information of the main encoder; the PSE module captures multi-scale global semantic information and integrates local context information, local features are fused through the SAM module, and kidney tumor positioning in an endoscope image is achieved.

To provide an endoscope system for calculating a region of a subject with a high degree of recognition precision while performing endoscopic observation, and to provide an operation method thereof.SOLUTION: An endoscope system 10 includes an endoscope 11, an air supply device 16, and a processor. The processor controls emission of illumination light for illuminating a subject S and measurement light for measuring a distance between a plurality of places of the subject S and the endoscope 11. The measurement light is emitted with a first air supply amount, the subject S is imaged by emitting or turning off the illumination light with a light amount suppressed with respect to the measurement light, and a region of the subject S is calculated from acquired first distance information. The measurement light is emitted with a second air supply amount, the subject S is imaged by emitting or turning off the illumination light with a light amount suppressed with respect to the measurement light, and a region of the subject S is calculated from acquired second distance information.SELECTED DRAWING: Figure 7

This specification describes systems and methods for performing endoscopy, obtaining medical images for inflammatory bowel disease (IBD) and scoring severity of IBD in patients. The methods and systems are configured for using machine learning to determine measurements of various characteristics related to IBD. The methods and systems may also obtain and incorporate electronic health data of patients along with endoscopic data to use for scoring purposes.

The invention discloses an endoscope with a built-in grabbing mechanism, and relates to the technical field of medical instruments. According to the technical scheme, the device comprises a clamp assembly, a driving outer pipe and a driving inner wire; the grabbing mechanism further comprises a grabbing mechanism shell, a first moving seat and a second moving seat, wherein the first moving seat and the second moving seat are arranged in the grabbing mechanism shell. The near end of the driving outer tube is connected with the first moving seat, and the near end of the driving inner wire is connected with the second moving seat; when the second moving seat is fixed relative to the grabbing mechanism shell, the first moving seat moves towards the far end direction relative to the grabbing mechanism shell so as to control the clamp assembly to execute the clamping action. According to the invention, the clamping action is carried out while forward extension is carried out, so that high-precision operation is facilitated, and tissues can be accurately clamped; and moreover, the grabbing mechanism is integrated in the endoscope handle, so that accurate single-hand control is facilitated, meanwhile, the clamping action is controlled in a button mode, the human engineering is met, and operation is convenient.

The invention relates to the technical field of oral medical treatment, in particular to an oral disease analysis method and system based on AI intelligent recognition. Comprising the following steps that oral cavity three-dimensional surface data are obtained through an oral scanning module integrated with a dental chair, and the oral scanning module adopts a structured light coding and multi-view fusion algorithm; an oral soft tissue high-definition image is collected through an endoscope module, and an AI analysis unit is arranged in an endoscope to recognize mucous membrane lesion features in real time; calling CBCT image data of the patient, and aligning a three-dimensional image with oral scanning data through a spatial registration algorithm; the mouth scanning data, the endoscope image and the CBCT data are subjected to multi-modal fusion by adopting a dynamic weight fusion mechanism. According to the scheme, the problem of accurate registration of cross-modal data, the lack of a dynamic quality evaluation mechanism of heterogeneous data and the real-time collaboration bottleneck of dispersed equipment can be solved.

The invention discloses a surgical instrument segmentation method based on a double prior guidance network, and the method specifically comprises the steps: constructing a hybrid encoder composed of a visual basic model and a state space model, and carrying out the coding processing of an input surgical endoscope image; the visual basic model is an SAM2-Hira model; the state space model is a MambaVision model; an adapter layer composed of a standard adapter and a Mama enhancement adapter is arranged between the coding layer and the decoding layer, and coding features are processed; setting a multi-context guide decoder, and performing multi-scale feature recovery and mask segmentation; a hybrid encoder, a Mama enhancement adapter and a multi-context guidance decoder form a double-priori guidance network DPG-Net, and high-precision surgical instrument segmentation is realized by using the DPG-Net. Accurate visual support is provided for the surgical robot, and the accuracy and safety of surgical operation are greatly improved.

The invention discloses an endoscope multi-region semantic perception time domain fusion adaptive white balance algorithm. The algorithm comprises the following steps: acquiring an endoscope image; performing tissue region division on the endoscope image through a lightweight semantic segmentation network to obtain a plurality of region images; converting each regional image from an RGB space to a YCbCr color space, separating a brightness channel Y from chrominance channels Cb and Cr, and calculating a channel mean value of the chrominance channels Cb and Cr; calculating the average offset of the chrominance channels Cb and Cr according to the calculated channel average of the chrominance channels Cb and Cr; performing white point detection on each regional image to obtain an RGB three-channel mean value; combining the calculated mean offset of the chromaticity channels Cb and Cr, the RGB three-channel mean obtained by white point detection of each regional image and a statistical index to form a multi-dimensional regional color feature vector; and fusing historical frame features with the regional color feature vector to obtain a feature vector based on space-time fusion.

Provided are a self-locking apparatus, an endoscope handle, and an endoscope. The self-locking apparatus comprises a driving lever (310), a rotating shaft (320), a traction rope driving member (330), an elastic member (340), and a first limiting assembly. The traction rope driving member (330) is synchronously and rotationally connected to the rotating shaft (320). A sliding stroke is provided between the traction rope driving member (330) and the rotating shaft (320) in an axial direction of the rotating shaft (320). The first limiting assembly comprises a resistance member (351). An axial movement stroke of the rotating shaft (320) comprises a first position and a second position. The elastic member (340) comprises a mounting portion (342) and an arc-shaped elastic portion (341). The self-locking apparatus can effectively avoid the problem in the prior art that teeth and tooth grooves on a driven gear and a driving gear are damaged due to the action of the elastic member on the driven gear. Meanwhile, the resistance corresponding to a unit length deformation generated by the arc-shaped elastic portion (341) of the elastic member (340) is reduced, the movement space for elastic deformation of the arc-shaped elastic portion (341) is increased, and the resistance encountered by the arc-shaped elastic portion (341) during a relatively large elastic deformation is reduced, so that a medical worker can easily and quickly complete state switching control, thereby improving the use convenience of the endoscope.

Laser lithotripsy systems and methods with a controlled retropulsion setting to facilitate manipulation, migration, and ablation of a calculi structure are disclosed. An endoscopic lithotripsy system comprises a laser system to provide laser pulses to a calculi structure in an anatomical environment, and a controller circuit to localize the calculi structure. If it is determined that the calculi structure is not at a target location, the laser system emits first laser pulses according to a first laser output setting, which produces a high retropulsion effect to mobilize and reposition the calculi structure to the target location. For calculi structure located at or migrating to the target location, the laser system emits second laser pulses according to a second laser output setting, which produces a lower retropulsion effect than the first laser pulses, but a higher efficiency to ablate or fragment the calculi structure.

The invention discloses a defogging method based on an endoscope image and an endoscope system.The method is applied to a processor of the endoscope system.The method comprises the steps that the endoscope image is obtained; extracting a texture feature, at least one color feature and a global contrast feature of the endoscope image; determining that fog exists in the endoscope image according to the texture feature, the at least one color feature and the global contrast feature; determining the fog concentration of the endoscope image; and according to the fog concentration, carrying out defogging processing on the endoscope image so as to obtain a defogged image. The accuracy of fog detection and fog removal can be improved, automatic fog monitoring and removal are achieved, and the automation degree of the endoscope is improved.

Systems, devices, and methods for planning an endoscopic tissue acquisition procedure for acquiring tissue from an anatomical target are disclosed. An endoscopic system comprises a steerable elongate instrument and a processor. The steerable elongate instrument can be positioned and navigated in a patient anatomy and acquire tissue from an anatomical target via a biopsy tool associated with the steerable elongate instrument. The processor can receive an image of the anatomical target, apply the received image to a trained machine-learning (ML) model to determine a tissue acquisition plan that includes a recommended biopsy tool and operational parameters for navigating the steerable elongate instrument or maneuvering the recommended biopsy tool. The tissue acquisition plan can be presented to a user, or used to facilitate a robot-assisted tissue acquisition procedure.

The invention discloses a tumor image segmentation method and system of a multi-scale feature fusion network based on boundary enhancement, and relates to the technical field of image segmentation, and the method comprises the steps: obtaining a to-be-segmented tumor image; constructing a tumor image segmentation model based on a pyramid visual converter PVTv2 backbone network; training the tumor image segmentation model through the to-be-segmented tumor image and the known tumor image to obtain an optimal tumor image segmentation model; and acquiring a real-time to-be-segmented tumor image and inputting the real-time to-be-segmented tumor image into the optimal tumor image segmentation model to obtain a tumor image segmentation result. Aiming at the endoscope image segmentation of the kidney tumor, the kidney tumor in the endoscope image is efficiently, stably and automatically segmented through the boundary-enhanced multi-scale feature fusion network, clinical doctors are helped to provide accurate tumor area positioning in endoscopy and surgical operations, and compared with the most advanced method, the method has the advantages that the accuracy is high, and the efficiency is high. According to the method, better segmentation capability and stronger generalization capability are obtained.

The invention relates to the technical field of image focus detection, in particular to a fiber endoscope image focus detection method and system, and the method comprises the following steps: providing a data quality guide interface; capturing narrow-band light images of the endoscope away from a first preset position and a second preset position of the intestinal wall in the axial movement process, calculating brightness gain values of a blue light channel and a green light channel, calculating an asymmetric scattering correction coefficient, and updating indication of color information calibration integrity; tracking the pixel moving speed of the interferent in the view in the propulsion operation process, calculating the relative depth of the interferent, and updating the indication of the validity of the depth data of the interference layer; identifying the image area which is not shielded by the interferent, and updating the indication of the information coverage degree of the background area; triggering an image synthesis operation; performing color compensation on the image information of the image area which is stored in the background canvas and is not shielded by the interferent; reconstructing a clear image; and performing focus detection on the clear image. The method improves the accuracy of focus detection.

The invention relates to the technical field of data analysis, in particular to an operating room monitoring system based on intraoperative operation data real-time analysis, which comprises a real-time operation stage identification module, a dynamic weight configuration module, a multi-modal fusion analysis module and an intelligent early warning decision module, wherein the real-time operation stage recognition module generates a stage judgment signal by fusing an instrument movement track feature and an endoscope video stream feature; the dynamic weight configuration module activates a corresponding weight distribution strategy after receiving the stage judgment signal, and establishes a dynamic weight mapping relationship among the heterogeneous data; the multi-modal fusion analysis module performs time sequence alignment and weighted fusion on the heterogeneous data based on the dynamic weight mapping relationship, and outputs a multi-dimensional risk assessment vector; and the intelligent early warning decision module analyzes the multi-dimensional risk assessment vector and associates the context of the operation process, and triggers a differentiated alarm mode according to the risk grade gradient.

The invention provides a splicing method and a splicing device for a magnetic control capsule endoscope image of a digestive tract. The method comprises the following steps: screening out a candidate lesion area image sequence from an original alimentary canal image sequence collected by a magnetic control capsule endoscope; preprocessing the candidate lesion area image sequence to obtain a preprocessed candidate lesion area image sequence; performing feature extraction on the preprocessed candidate lesion area image sequence based on an OfficientLoFTR improved model, further performing feature matching on the preprocessed candidate lesion area image sequence to obtain matched feature point pairs, and performing image registration processing on each image of the preprocessed candidate lesion area image sequence according to the matched feature point pairs to obtain a pre-processed candidate lesion area image sequence; the improved model can improve the accuracy of feature detection and matching, a good foundation is laid for subsequent image registration and image fusion, image registration can be carried out by combining global homography transformation and local thin plate spline transformation, and the problem of non-rigid deformation is solved.

The invention discloses an endoscope image processing method and system for improving image quality, and relates to the technical field of image processing.The method comprises the steps that a highlight reflection area of an endoscope image is extracted by setting a detection and extraction mechanism of the highlight reflection area; the method comprises the following steps: dividing an image of which a highlight reflection region is extracted into a base layer and a detail layer, performing global illumination adjustment on the base layer, performing detail enhancement on the detail layer, and fusing the enhanced base layer and detail layer to obtain a pre-enhanced image; if the pre-enhanced image extracts an over-high-brightness area, repairing a high-brightness reflection area and outputting the high-brightness reflection area; according to the method, global enhancement of the endoscope image is realized through highlight reflection area detection, image hierarchical processing and highlight reflection area repair, and the visibility and availability of the endoscope image are improved.

The invention discloses a flow field peeping type PIV measuring system and method in a backflow combustion chamber, and belongs to the technical field of flow field measurement in the backflow combustion chamber of an aero-engine, the flow field peeping type PIV measuring system in the backflow combustion chamber comprises a cooling purging device and a laser endoscope and / or a camera endoscope arranged in the cooling purging device; a cooling liquid channel, a purging gas channel and an endoscope channel which are not communicated with one another are arranged in the cooling purging device; the cooling liquid channel comprises a cooling liquid inlet channel from the connecting end to the detection end and a cooling liquid outlet channel from the detection end to the connecting end; the purging gas channel comprises a gas inlet located at the connecting end and a gas outlet located at the detecting end. The detection end is provided with the airflow guide structure, so that the purging gas flows to the end face of the endoscope channel along the end face of the detection end in a wall-attached mode, a stable wall-attached airflow barrier is formed, high-temperature fuel gas and soot are prevented from making direct contact with the mirror face of the endoscope, and mirror face pollution is effectively prevented.

The invention discloses an oral cavity data monitoring and early warning method and system based on deep learning, and the method comprises the steps: solving a problem that the focus recognition is inaccurate because the image collection process of an oral cavity endoscope of a patient is affected by various environment and equipment parameters; according to the method, synchronous binding of collected images and equipment parameters and intelligent image preprocessing are proposed, historical time sequence images and variation trend features are combined, lesion space and time features are extracted through combination of a lightweight convolutional neural network and a long-short term memory network, feature weighted reasoning is realized by using a context awareness attention fusion network, and the focus space and time features are extracted. According to the method, the model parameters and the risk threshold value can be dynamically adjusted, the focus recognition accuracy and the early warning individuation are improved, and clinical grading treatment and intelligent health management are effectively assisted.

The invention provides a capsule endoscope intestinal panoramic image splicing method and device, a computer readable storage medium and computer equipment. The method comprises the following steps: performing feature matching on an original intestinal tract image set acquired by a capsule endoscope to obtain a feature point set of each image of the original intestinal tract image set; performing polar coordinate expansion on each original intestinal image to obtain a rectangular intestinal image and a new feature point set corresponding to the feature point set in the rectangular intestinal image; calculating the displacement difference of each new feature point according to the coordinates of each new feature point of the new feature point sets of the two adjacent frames of rectangular intestinal tract images; cutting according to the displacement difference to obtain a stepping region image between two adjacent frames of original intestinal tract images; performing polar coordinate expansion on the stepping area image to obtain a newly added image segment; and splicing the newly added image segments in sequence to obtain an intestinal panoramic image. According to the method, a more accurate real stepping distance is obtained through calculation, and the smoothness and continuity of the image splicing position can be improved.

The invention discloses a vocal cord problem recognition and feedback system for the ophthalmology and otorhinolaryngology department. The vocal cord problem recognition and feedback system comprises a sound collection module, an image collection module, a biological feedback module, a data processing module, an AI diagnosis module and a rehabilitation guidance module. The sound acquisition module comprises a microphone array and a self-adaptive noise reduction unit; the image acquisition module is provided with an endoscope camera and an image enhancement processor; the biological feedback module integrates a laryngeal myoelectricity sensor and a three-dimensional motion simulator; the data processing module executes multi-modal feature extraction and fusion; through mutual cooperation of the sound acquisition module, the image acquisition module, the biological feedback module and the data processing module, data can be accurately acquired, the early canceration detection rate is improved and the misdiagnosis rate is reduced through multi-modal fusion, the data acquired by the multi-modal structure is analyzed through the data processing module, the model is combined with a weekly updated disease map, and the early canceration detection rate is improved. And the recurrence prediction accuracy is improved.

The invention discloses an insertion tube and a manufacturing method thereof, an insertion part and an endoscope, and relates to the technical field of medical instruments, the insertion tube comprises an insertion body and an elastic tube, a first reinforcing layer and a second reinforcing layer are arranged in the insertion body, the second reinforcing layer is arranged outside the first reinforcing layer in a surrounding mode, and the first reinforcing layer is provided with a concave part. The first reinforcing layer and the second reinforcing layer can form double limiting and wrapping supporting on the elastic pipe, the concave shape of the concave part of the first reinforcing layer is utilized to be attached to the outer wall outline of the elastic pipe, and the outer side of the second reinforcing layer is matched for enclosing and restraining, so that the installation stability of the elastic pipe in the first channel can be greatly improved; bending moment generated by self-gravity, external force and the like of the elastic tube is effectively counteracted, the elastic tube is prevented from being bent under the action of self-gravity, external force and the like, the whole insertion tube is prevented from deforming along with bending of the elastic tube, and the form stability of the insertion tube in the cavity in the propelling process is guaranteed. And the straightness of a traction rope transmission path and the accurate conduction of a control instruction are ensured.

The invention discloses an endoscope monocular dynamic scene reconstruction method based on dynamic Gaussian splashing and motion tracking, and the method comprises the steps: firstly obtaining a dynamic scene image, obtaining an initial three-dimensional track through introducing auxiliary prior information, and then obtaining three-dimensional Gaussian; based on the auxiliary prior information, generating a group of compact Sim3 motion bases, and obtaining each three-dimensional Gaussian transformation function in the dynamic scene image by using the weighted combination of the Sim3 motion bases, the parameters of the three-dimensional Gaussian transformation function including the transformation of the position and the shape along with the time; and performing three-dimensional Gaussian transformation to each frame of image of the dynamic scene according to the three-dimensional Gaussian transformation function, and performing image rendering on each frame to obtain a rendered image. According to the method, monocular depth and dense displacement field prior are combined, and the problem of monocular scale drift is solved by utilizing depth relative sequence stability. According to the method, real-time rendering is ensured, and meanwhile, the dynamic scene reconstruction precision is remarkably improved.

The invention provides a steering lock control device of an endoscope, a handle and the endoscope, and relates to the technical field of endoscopes, the steering lock control device comprises the handle and an insertion part installed at the front end of the handle, the steering lock control device comprises an operation piece, a clutch piece, a connecting piece and a steering piece, the input end of the operating part is arranged on the outer side of the handle; the clutch piece is arranged in the handle and located at the position close to the traction wheel, and the clutch piece can be switched among different positions under the operation of the operating piece so as to be selectively connected with the traction wheel and / or the input end of the connecting piece; the output end of the connecting piece is connected with the input end of the steering piece, and the output end of the steering piece is connected with the insertion part of the endoscope so as to drive the active bending section of the insertion part to bend and / or drive the insertion part to rotate in the axial direction. Compared with the prior art, the endoscope insertion part has the advantages that the operation flexibility of the endoscope insertion part is improved, and an operator can conveniently and accurately control rotation of the insertion part.

The invention provides an ear-nose-throat department intelligent diagnosis and monitoring system based on multi-modal data fusion, and relates to the technical field of multi-modal data fusion. Multi-modal monitoring data is acquired, and an endoscope monitoring image of a patient is extracted; performing edge monitoring on the endoscope monitoring image to obtain an endoscope monitoring edge image, generating a secretion disturbance mask according to the endoscope monitoring edge image, and determining an endoscope monitoring restoration image according to the secretion disturbance mask and the endoscope monitoring image; determining a pixel decision window of each pixel point in the endoscope monitoring restored image, and performing information decision based on the corresponding pixel decision window to obtain an endoscope monitoring decision image; and extracting monitoring text data, and performing feature fusion on the monitoring text data and the endoscope monitoring decision image to generate a fusion association map. According to the method, the image quality of the endoscope monitoring image can be improved, and the correlation map of the patient is generated in combination with the monitoring text, so that knowledge fusion in ear-nose-throat monitoring is realized.

A multispectral endoscope image intelligent fusion method relates to the technical field of medical image processing, and mainly comprises the following steps: directionally enhancing images in different channels; determining a target of image fusion network design; on the basis of a U-Net network architecture, a multi-expert encoder, a medical priori enhanced cross-channel attention mechanism, a focus adaptive spectral attention decoder and a physically driven reflectivity-illumination decoupling module are introduced, and a deep learning fusion network architecture with pathological interpretation is constructed; jointly optimizing image quality and medical priori knowledge to construct a loss function; a plurality of groups of four-channel endoscope images are collected, and staged progressive training and optimization are carried out. The method has multispectral feature fusion capability and physical interpretability, cross-scale fusion of deep blood vessels and mucous membrane textures can be achieved through a cross attention mechanism, and the visual identification degree and diagnosis specificity of early lesions are remarkably improved.

The invention provides a feature point matching method for digestive tract capsule endoscope images. The method comprises the following steps: after preprocessing, converting each image into a grayscale image, sequentially rotating each grayscale image from an initial angle according to different angles, carrying out feature point identification on the image after each rotation, and taking a subsequent feature point set with the maximum number of feature points as a feature point set; feature point matching is carried out on the feature point sets of the two adjacent frames of grayscale images, and part of matched feature points in the matched feature point sets are removed according to feature point position threshold conditions, feature point density threshold conditions and feature point brightness threshold conditions; and according to a geometric consistency condition and a confidence threshold condition, removing a part of matched feature points in the preliminary matched feature point set to obtain a final matched feature point set. According to the method, more feature points can be obtained, the distribution uniformity of the feature points is improved, and the quality of the feature points is improved.

The invention discloses a digital image enhancement method and system based on an endoscope, and aims to solve the problem of imaging limitation of a traditional endoscope in multi-focus positioning and targeted intervention. According to the digital image enhancement method based on the endoscope, endoscope image acquisition, wireless transmission, abnormal anchor point identification and controllable terminal targeted intervention operation can be carried out. The enhancement system for realizing the digital image enhancement method based on the endoscope identifies a suspected lesion point through image analysis, the control unit sends a release instruction to enable the endoscope to release the controllable terminal and establish communication, the scheduling system navigates the controllable terminal to an abnormal anchor point according to position information, and the abnormal anchor point is sent to the control unit. A focus image after digital image enhancement is collected and transmitted, and a judgment unit analyzes whether a target focus with an intervention value exists or not. According to the method, the digital image in the human body cavity can be enhanced, and the focus recognition accuracy and intervention efficiency are improved.

The invention discloses a constipation patient defaecation device and mobile terminal integrated system based on endoscope visualization and air bag assistance, and relates to the technical field of medical instruments, the constipation patient defaecation device comprises a display terminal, a connecting line, an operating handle and a comprehensive pipe, and the two ends of the operating handle are connected with the connecting line and the comprehensive pipe respectively. According to the non-invasive, high-safety and portable constipation patient defecation device, through the integrated design of endoscope real-time visual positioning, air bag auxiliary mechanical defecation and mobile terminal data interaction, the invasive operation pain is reduced, the defecation time is shortened, the complication risk is reduced, and the constipation patient defecation efficiency is improved. The pressure sensor monitors the internal pressure of the air bag in real time and feeds back the internal pressure to the dynamic pressure upper limit module and the mobile terminal in real time, and double protection of a hardware pressure release valve and software pressure limiting is triggered when the pressure exceeds the limit. The problems of high intestinal injury risk, low treatment efficiency, limited application scene and the like caused by operation blindness, function singleness or high invasiveness in the prior art are solved.

The present application relates to a kind of snare cutting knives for endoscope, including proximal handle, tube body assembly, cutting assembly and limiting component;Proximal handle is equipped with limiting sliding slot;The proximal end of tube body assembly is connected with proximal handle, and the distal end of tube body assembly is equipped with insulating protection tube;Cutting assembly includes cutting ring and cutting head, and cutting head is set to the distal end of cutting ring;Limiting component includes sliding part and limiting control wire, sliding part is slidably arranged in limiting sliding slot, and the proximal end of limiting control wire is fixedly connected with sliding part, and sliding part is used to drive limiting control wire axial movement;When limiting control wire moves to the distal end of insulating protection tube, cutting head and insulating protection tube abut each other and are fixed;When limiting control wire exits insulating protection tube, cutting head can continue to move relative to insulating protection tube.