43 results about "Manual segmentation" patented technology

The invention discloses a thick cloud-thin cloud integrated detection method and system based on spectral features and a multi-scale depth model, and belongs to the technical field of remote sensing image processing, and the method comprises the steps: 1, training a plurality of scene-level models of different scales based on a cloud detection data set, generating a plurality of scene-level cloud probability graphs and scene-level binary cloud masks of different scales; step 2, based on a plurality of scene-level cloud probability graphs of different scales and dark pixel features of the image, pixel-level cloud probability graphs facing thick cloud and thin cloud are generated respectively; and step 3, combining the scene-level binary cloud mask, fusing the pixel-level cloud probability graphs facing the thick cloud and the thin cloud by adopting dark pixel gradient, and then combining an adaptive threshold and distance weighting to generate a pixel-level binary cloud mask so as to realize thick cloud-thin cloud integrated detection. According to the method, manual segmentation threshold setting is avoided, and fusion of the thick cloud probability graph and the thin cloud probability graph and full-automatic accurate detection of the thick cloud, the thin cloud and the cloud edge are realized.

The invention belongs to the technical field of building construction, and particularly relates to a three-dimensional point cloud virtual assembling method based on image recognition. A three-dimensional point cloud virtual splicing method based on image recognition comprises the steps that point cloud data of splicing plates and side cross beam splicing plates at the joints of side main beams, side box beams and steel truss web plates are collected through a base station type scanner, standard size frame lines are constructed by conducting manual segmentation, voxel filtering and Gaussian filtering on the point cloud data, an image is intercepted, and the three-dimensional point cloud virtual splicing method based on image recognition is obtained. Gray processing, threshold segmentation, perspective transformation and size identification are carried out on the image, two-dimensional coordinates of bolt hole groups and frame lines are obtained, a two-dimensional to three-dimensional coordinate system is constructed, three-dimensional coordinates in the bolt hole groups are obtained through conversion, and virtual splicing is carried out according to the corresponding relation between the bolt hole groups of different components. According to the method, the efficiency and the precision of virtual assembly are remarkably improved.

The invention provides a target boundary determination method, a three-dimensional mask generation method and electronic equipment. According to the method, the initial boundary curves sketched only on the two orthogonal anatomical planes by the user are received, the geometric constraint effect of the orthogonal cutting planes on the space boundary control points is utilized, and the image features of the medical image are combined to carry out real boundary calculation, so that the heavy operation burden of manual segmentation layer by layer is avoided, and the real boundary calculation efficiency is improved. The dependence of a deep learning model on specific training data is broken through, the tumor boundary recognition precision is ensured, the segmentation efficiency is remarkably improved, and finally the technical effect of efficiently obtaining a high-quality three-dimensional tumor segmentation result is achieved.

The invention provides methods and systems for identifying shape-based atypical segmentations. In one example, a method includes receiving a segmentation of a region of interest (ROI) of a medical image, the segmentation output by a segmentation model; computing a confidence measure for the segmentation, the confidence measure indicating how well a shape of the segmentation can be encoded by one or more dominant shape variation patterns of a set of predetermined segmentations of the ROI; and in response to the confidence measure satisfying a predetermined condition with respect to a threshold, displaying the segmentation, storing the segmentation, and / or using the segmentation for one or more downstream processes; otherwise, prompting a user to perform a manual segmentation.

The invention relates to the field of high-speed railway measurement, and relates to a three-dimensional laser point cloud power line and electric tower segmentation method and system, and the method comprises the steps: obtaining first information which comprises the point cloud data of the periphery of a to-be-segmented electric tower; performing gridding processing on the first information to obtain a first grid unit; positioning the electric tower in the first grid unit to obtain the position information of the electric tower; processing the position information of the electric tower to obtain electric tower point cloud data after coarse segmentation; processing the electric tower point cloud data after coarse segmentation to obtain electric tower point cloud data after fine segmentation; and processing the point cloud data of the power tower after fine segmentation to obtain the point cloud data of the power line after segmentation, so that the automatic segmentation technology of the power line and the power tower is realized, manual segmentation processing can be replaced to a great extent, and the labor cost is greatly reduced.

Embodiments of the present invention disclose a remote sensing image segmentation method, device, and electronic device based on semi-supervised learning. The method includes: obtaining an annotation set based on a preset remote sensing image, the annotation set including a training set and a validation set; training a target segmentation model using the training set; inputting the validation set into the target segmentation model to obtain a predicted segmentation result for the validation set; if the predicted segmentation result of the validation set does not meet the preset validation requirements, updating the training set with the validation images belonging to validation samples whose segmentation results in the validation set meet the preset prediction requirements and the predicted category labels corresponding to the validation samples; returning to the step of training at least one target segmentation model using the training set; and inputting a remote sensing image to be tested into at least one target segmentation model to obtain a predicted segmentation result for the remote sensing image to be tested. This solution can quickly and efficiently segment the remote sensing image to be tested, saving time for manual segmentation and annotation.

The invention relates to a glioma grading and molecular feature prediction deep learning method based on a multi-modal MRI image, and the method comprises the following steps: S1, carrying out the preprocessing and image processing of three MRI images T2, FLAIR and CET1, S2, constructing three independent neural networks, inputting the three MRI images into the three neural networks respectively, outputting 512-dimensional feature vectors by the neural networks, and carrying out the recognition of the feature vectors. S3, endowing slices corresponding to different 512-dimensional feature vectors with different weights based on an attention algorithm of a gating mechanism, and carrying out weighted summation on all slice features based on attention scores to obtain patient-level feature vectors, and S4, carrying out series fusion on the three patient-level feature vectors, carrying out multi-task joint classification through a full connection layer, and carrying out multi-task joint classification on the three patient-level feature vectors. Outputting prediction probability values of the three key tasks; the method has the advantages that manual segmentation is needed, the method is based on the multi-mode MRI sequence, and the multi-task prediction capability is achieved.

The present invention discloses a method for segmenting brain tumors in MRI images based on an improved 3D-UNet, which specifically includes: 1) preprocessing the brain MRI images in the dataset to obtain a training set; 2) constructing an improved 3D-UNet framework by combining dilated convolution, channel attention mechanism, and residual convolution; 3) training the improved 3D-UNet, the brain tumor segmentation network, with the training set data; 4) importing the test data of the brain MRI image to be segmented into the brain tumor segmentation network to obtain the segmented result. The present invention provides an automatic, accurate, and repeatable tumor segmentation algorithm, which can effectively solve the problems that the traditional manual segmentation method is time-consuming and overly dependent on the subjective experience of experts, and has a higher segmentation accuracy than the method for segmenting brain MRI images based on UNet. At the same time, the method proposed by the present invention can be further applied to other medical MRI image segmentation tasks.

The invention discloses a visible light region-of-interest and 3D point cloud mapping method, device, equipment and medium, and relates to the technical field of three-dimensional vision and biological recognition crossing. According to the method, a face ROI can be quickly recognized through a visible light image deep learning model (such as YOLO / UNet), manual segmentation is replaced, and the processing speed is increased by 80% or above; a pre-calibrated projection matrix is used to realize sub-pixel-level two-dimensional-three-dimensional mapping, and error accumulation caused by a complex registration process is effectively avoided.

The present invention discloses a sentinel lymph node ultrasound image semantic segmentation method based on deep learning, which relates to the technical field of image semantic segmentation. First, the present invention obtains a sentinel lymph node ultrasound image data set and preprocesses the data set to obtain a training set; then constructs a RA-U-Net++ network for image semantic segmentation, and tunes the network parameters based on the training set to obtain a medical image semantic segmentation model for target lesions; the network adopts a multi-layer U-shaped structure, and an encoding node using a residual atrous pyramid module is set at each level. The output feature map of the encoding node at the current level is downsampled and then input into the encoding node at the next level; decoding nodes are set from the first layer to the penultimate layer, and the number of decoding nodes decreases layer by layer. The decoding node at each level is located behind the encoding node, and the input of each decoding node includes the output feature maps of all previous nodes and the output feature map of the previous adjacent node in the next level; the final segmentation result is obtained based on the output feature map of the last decoding node in the first layer. The present invention realizes the automatic segmentation of sentinel lymph node ultrasound images, getting rid of the cumbersome, time-consuming and laborious manual segmentation; and has a good segmentation effect, and can obtain a high intersection over union IoU.

The invention provides a construction scheme determination method, system and device for a steel structure building and a storage medium, and relates to the technical field of building engineering.The method comprises the steps that analysis is conducted according to building basic information and preset design information of a to-be-designed building, and design index information of the to-be-designed building is obtained; performing simulation calculation according to the design index information to obtain component information of the to-be-designed building; target preset design information meeting design and construction requirements is determined according to the component information; and determining a construction scheme of the to-be-designed building according to the target preset design information. Structural design, simulation verification and construction output are integrated into a closed-loop process, manual segmentation operation is replaced with a parameterized model and automatic calculation, and the construction efficiency and the construction quality are effectively improved.

The present invention discloses a multi-task self-distillation facial expression recognition method and system based on coarse-grained labels, which involves artificial intelligence and proposes this solution to solve the problems in the prior art. The following steps are performed during expression recognition: S1. Feature pre-extraction; S2. Fine-grained feature extraction; S3. Coarse-grained feature extraction; S4. Self-distillation loss; S5. Feature alignment; S6. Overall training loss. The advantages are: (1) Multi-task learning is used to guide the learning of fine-grained facial expression features through relatively simple coarse-grained classification tasks, thereby reducing the learned non-expression-related redundant features. In addition, self-distillation loss is introduced to further realize the knowledge transfer from coarse-grained to fine-grained branches. (2) Coarse-grained labels are based on facial expression priors and are obtained through manual segmentation, avoiding the need for additional expert labeling. (3) Coarse-grained facial expression features are mapped to fine-grained feature space, further improving the effect of knowledge distillation.

The application discloses a three-dimensional body construction method and device based on CT images, a storage medium and a terminal. A CT sequence image of a target object is acquired. In response to a user's manual contour segmentation operation on a first target CT image in the CT sequence image, the first target CT image is subjected to first contour segmentation, and a first contour image of the target object is obtained. Based on each first contour image, a second target CT image in the CT sequence image, except for the first target CT image, is subjected to second contour segmentation, and a second contour image of the target object is obtained. A three-dimensional body of the target object is constructed according to each first contour image and each second contour image. Since the second contour segmentation is active processing and calculation on the manually segmented first contour image, when the second contour image is obtained, manual operation on a large number of images can be avoided, human-computer interaction is reduced, the three-dimensional body construction efficiency is improved, and the accuracy of the three-dimensional body of the target object can be ensured by the manually segmented first contour image.

The present application relates to ear cartilage and its substructure image segmentation method and system based on matching and segmentation cascade deep learning network, which comprises: acquiring the magnetic resonance image of UTE sequence of the contour of the external ear; the acquired magnetic resonance image of UTE sequence of the contour of the external ear is pretreated; the pretreated UTE sequence image is used as input, a registration network is trained, a deformation field is obtained, the deformation field is applied to the manual label image of the reference image or the manual label image of the template image to obtain a coarse label image, and the obtained ear cartilage coarse label image and ear cartilage substructure coarse label image are used to train a segmentation network respectively, so that the ear cartilage and ear cartilage substructure are segmented respectively. Based on ear cartilage UTE sequence imaging and very small amount of manual segmentation results, the present application obtains the model of high-quality ear cartilage and substructure morphology of automatic segmentation through cascading deep learning network.

The application discloses an acute ischemic stroke prognosis prediction method based on CTA images, relates to the technical field of medical image processing, and fuses a three-dimensional visual transformer and a meta-learning strategy to train a target prognosis prediction model based on the three-dimensional visual transformer and matched with a target data acquisition condition.The target prognosis prediction model can effectively model a spatial dependence relationship in a three-dimensional CTA image across regions and layers, significantly improves the recognition ability of complex cerebral vascular structures and ischemic regions compared with a traditional convolution network which only depends on a local receptive field, and further more, the introduction of the meta-learning strategy can improve the model generalization ability under small samples and multiple data acquisition conditions, so that the target prognosis prediction model directly takes the CTA image to be predicted as input to realize automatic prognosis prediction, does not need manual segmentation or feature extraction, avoids the dependence on manual experience in the traditional method, and improves the standardization degree and repeatability of the model application.

The invention relates to the technical field of machine learning, in particular to a knee osteoarthritis assessment method based on machine learning, which comprises the following steps: acquiring knee joint T1 and T2 weighted images of a patient with knee osteoarthritis; manually segmenting the knee joint cartilage in the T1 weighted image and measuring the volume of the knee joint cartilage; cutting the T2 weighted image to obtain a meniscus image, manually segmenting the meniscus image to obtain annotation data, and constructing an image data set; training a meniscus automatic segmentation model, and segmenting the meniscus in the meniscus image; carrying out three-classification on meniscus pixels of the middle five layers of the meniscus image, and calculating a meniscus space specificity signal index; the performance of the meniscus space specific signal index in diagnosis of knee osteoarthritis is systematically evaluated. According to the knee joint meniscus damage diagnosis method, the knee joint T1 and T2 weighted images of a patient with knee osteoarthritis are collected, the meniscus automatic segmentation model and a meniscus space specificity signal index calculation method are applied, the damage condition of the knee joint meniscus is accurately evaluated, and knee osteoarthritis diagnosis is achieved. The process can provide a reliable diagnosis basis for orthopedists, so that clinical diagnosis of knee osteoarthritis is effectively assisted.

This invention relates to a thyroid gland and nodule segmentation method based on an improved U-Net network. Ultrasound is the preferred method for thyroid nodule examination; however, ultrasound images are noisy, and thyroid nodules vary in shape, posing a significant challenge to doctors' diagnosis. Current automatic thyroid nodule segmentation methods perform poorly on irregularly edged and small nodules. To overcome the shortcomings of inaccurate segmentation of thyroid gland edges and small nodules due to insufficient feature extraction from high-resolution data, unsatisfactory segmentation results for large and irregular targets, and the tedious and time-consuming nature of manual segmentation, this invention proposes a thyroid gland and nodule segmentation method based on an improved U-Net network. By introducing ResNeSt modules, Atrous Spatial Pyramid Pooling (ASPP), and Deformable Convolution (DC) v3 into the encoder and decoder of the U-Net network for feature extraction, a Deformable-Pyramid Split Attention Residual U-Net (DSRU-Net) is established. Experimental and methodological analyses show that using the DSRU-Net network can significantly improve the accuracy of thyroid gland and nodule segmentation.

This invention relates to the field of medical image processing technology, specifically to a method for constructing an intelligent segmentation model for magnetic resonance imaging (MRI) images. The method includes: acquiring LGE-CMR images; constructing a cardiac region localization network, which is used to locate the cardiac region in the LGE-CMR image; and constructing a region-of-interest (ROI) intelligent enhancement module, which enhances the RIO based on the output of the cardiac region localization network. This invention achieves automatic cardiac region localization by constructing a cardiac region localization network, and combines this with the RIO intelligent enhancement module to selectively adjust contrast and sharpness to highlight lesion areas. Furthermore, it incorporates a feature fusion module from a multi-scale, multi-class segmentation network to enhance the feature expression of small regions. This allows for automated multi-class segmentation of myocardial scars and microvascular occlusions, solving the problems of time-consuming manual segmentation and inter-observer errors. It also avoids the difficulty of existing automatic segmentation models in simultaneously and accurately identifying three types of regions and the possibility of missed or false detections of small regions.

This invention provides a device and method for acquiring a three-dimensional model of abdominal organs, comprising: a coil support assembly, an MRI scanner, and an image processing device; the coil support assembly is clamped and fixed to the side of the examination bed to support the abdominal coil and reduce pressure on the abdomen of the user; the MRI scanner is positioned above the abdomen of the user, with the axis of the examination positioning light aligned with the midpoint of the axis of the coil support assembly, and a magnetic resonance imaging (MRI) scan sequence is acquired by scanning with the MRI scanner; the image processing device is communicatively connected to the MRI scanner, acquires the MRI scan sequence, and obtains a three-dimensional model of the abdominal organs based on the MRI scan sequence. This invention achieves precise extraction of abdominal organs at all levels through multi-region seed point strategies and adaptive threshold segmentation, overcoming the inefficiency and subjective limitations of traditional manual segmentation, and ensuring the reliability of the results through automated algorithms, thus providing support for the image analysis of abdominal organs.

The application is suitable for the technical field of medical image analysis and artificial intelligence assisted diagnosis, and provides a breast cancer neoadjuvant chemotherapy efficacy prediction method based on DCE-4DNeRF, first, a DCE-4DNeRF model is used, non-uniformly sampled original DCE-MRI sequences are reconstructed into uniformly sampled sequences which are continuous in time and space through spherical harmonic functions and projection mechanism; second, a tumor perception prediction network is constructed, the network introduces biological position coding based on anatomical prior, and combines a differentiable sampling mechanism, so that the network can automatically focus on the key tumor area without manual segmentation; finally, the dynamic spatio-temporal features of the two stages before and after chemotherapy are fused to predict the efficacy. The application effectively overcomes the data time inconsistency, realizes the end-to-end and label-free accurate prediction, significantly improves the prediction performance, and provides a reliable basis for clinical individualized treatment decision.

The invention provides a geological core image crack and pore identification method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting core image data in the same drilling well, carrying out the initialization processing, and inputting the processed images into a model A and a model B; after the model A receives the image, performing preliminary identification on core cracks in the image, outputting a mask for each core crack, segmenting each core crack in the image by using the model B, and enhancing the mask generated by the model A; and adjusting the size of the segmented image to be adaptive to the format and size of the model A, inputting the segmented image into the model A and the model B again for iterative training until the core fracture recognition result in the trained image tends to be stable, and outputting core fracture recognition result data after training. According to the method, the model A and the model B are combined, rapid and accurate identification of the crack holes in the rock core image is achieved, and time and energy in the manual segmentation and calculation process are saved.

According to the heart magnetic resonance image automatic segmentation method based on deep learning provided by the invention, the automatic segmentation process can be completed within less than 1 minute, and the traditional manual segmentation method needs more than 30 minutes on average, so that the processing time is shortened by more than 30 times, and the processing time is greatly shortened. By adopting the technical scheme provided by the invention, in actual clinical application, a doctor can obtain a diagnosis result more quickly, and the diagnosis and treatment efficiency is improved. Through an automatic deep learning model, interference of human factors is reduced, and objectivity and consistency of segmentation results are improved. According to the method, the CMR image sequences of different pathological groups are included in the training process, so that the adaptability of the model to different pathological states is improved. According to the invention, good adaptability and stability can be shown for three pathological types of HCM, DCM and VA. By optimizing the network structure and the training strategy, the demand on high-performance computing equipment is reduced.

A method for generating a transducer layout for delivering a tumor therapy electric field to a subject includes presenting a user-selectable icon to display slices in a medical image, which may be an MRI medical image and a CT medical image registered together. The slices include corresponding slices in the MRI medical image and the CT medical image superimposed on each other. The medical image includes voxels. The method further includes presenting a user-selectable icon to manually segment the slice in the medical image, thereby obtaining a manually segmented slice in the medical image. The method further includes presenting a user-selectable icon to automatically clean the manually segmented slice, thereby obtaining a cleaned manually segmented slice in the medical image. The method further includes presenting a user-selectable icon to generate a transducer layout for applying a tumor therapy electric field to the subject based on the cleaned manually segmented slices in the medical image.

The invention provides a three-dimensional segmentation method and device for a DME focus in a human fundus OCT image, and relates to the technical field of OCT medical image segmentation, and the method comprises the steps: obtaining an initial fundus optical coherence tomography image outputted by an optical coherence tomography imaging system; pre-training a three-dimensional deep learning neural network model which is used for segmenting a diabetic macular edema focus area in the fundus OCT image and is based on a transformer architecture; inputting the fundus OCT image into a trained three-dimensional deep learning neural network model to carry out real-time full-automatic three-dimensional segmentation of a DME focus area, and obtaining a stereoscopic three-dimensional focus image with good continuity in a space area; according to the method, the three-dimensional lesion image with better continuity in the space area can be obtained, a doctor is better helped to obtain three-dimensional information in the lesion, the manual segmentation burden of the doctor is relieved, meanwhile, the doctor is helped to analyze the morphology and change of the lesion more visually and clearly, and convenience is provided for clinical diagnosis of diabetic macular edema.

The application belongs to the technical field of medical image processing and computer-aided diagnosis, and discloses an orbital lymphoma and inflammatory pseudotumor classification method, system, device and terminal, in the obtained orbital DCE-MRI image, the tumor region and the eye cone triangular region in the original image are manually segmented and pretreated; the neural network is used for extracting the features of the tumor region, and the features are subjected to clustering statistical analysis as the tumor region features; the eye cone triangular region is subjected to feature extraction as a similarity judgment standard, and the self-classification and self-recovery network is used for extracting the eye cone region features; the original image is used as the input network and combined with the features of the tumor region and the eye cone triangular region to train the network model; the multi-modal orbital data is input into the classification model for processing, so that the orbital lymphoma and inflammatory pseudotumor are classified. The application comprehensively considers the texture features of the tumor and the depth features of the eye cone region, effectively improves the prediction accuracy, and has the characteristics of high accuracy.

The application discloses a kind of humanoid robot teleoperation teaching data automatic segmentation marking and training sample generation method.The method comprises: obtaining the continuous teaching data corresponding to a teleoperation teaching task, the continuous teaching data includes robot state data, end effector state data, environment image data, end image data and timestamp data;According to the continuous teaching data, generate teaching process feature sequence;According to the teaching process feature sequence, identify candidate segmentation point;Based on candidate segmentation point, the continuous teaching data is divided into multiple teaching data segments;According to the robot motion state in each teaching data segment, end effector state change and visual data change generate stage annotation information;Teaching data segment is detected for abnormal segment, and training sample is generated according to stage annotation information and corresponding teaching data segment;Training sample, stage annotation information, timestamp range and quality identification are written into training sample dataset.The application can reduce the workload of manual segmentation and marking of teleoperation teaching data, improve the consistency, availability and reviewability of training sample.

The present application discloses a method, apparatus, and computer device for segmenting medical images, including: obtaining a click position of a target medical image, taking the click position as the center, obtaining multiple equiangular rays and the distance measurement values of equidistant voxels on each ray outward, based on the distance measurement values, taking the click position as the center, performing multiple segmentations, obtaining the union region of the target connection region, the target segmentation region, and the spherical convex hull region, and further determining the final segmentation result of the tissue organ corresponding to the click position. It solves the problem that due to the lack of existing technologies, there is an urgent need for a new method for segmenting medical images to solve the problems of time-consuming and laborious manual segmentation and low efficiency. On the basis of manual clicks, an unsupervised and untrained intelligent method is used to maximize the automatic recognition and segmentation of the edges of the clicked area, significantly improving the efficiency of manual segmentation and retaining the high compatibility and high accuracy of manual segmentation.

A medical imaging method is disclosed herein. The method includes receiving an image processing neural network (122) configured to output a segmentation. The method further includes receiving a training three-dimensional medical image (123). The method comprises repeatedly: drawing a cross-sectional view (124) of the training three-dimensional medical image; receiving split edit data (129) from a split entry tool (128); constructing a manual partition in the cross-sectional view using the partition edit data (132); and collecting per voxel tool usage metadata that describes the use of the split entry tool. The method further includes determining, at least in part, a per-voxel confidence score representing a quality of annotation of a region suitable for training using the metadata using a per-voxel tool (138); constructing training data (140); and training the image processing neural network using the training data. The training of the image processing neural network is adjusted using the per voxel confidence score, thereby modifying the training of the image processing network in response to the annotation quality.

The invention provides a trigeminal neuralgia segmentation and classification system based on a Merkel cavity, and relates to the technical field of disease classification. Comprising a trigeminal nerve magnetic resonance imaging segmentation subsystem which is used for automatically segmenting a medical image which is obtained based on magnetic resonance imaging and contains a trigeminal nerve Merkel cavity so as to obtain a segmentation result of the Merkel cavity, and determining a region of interest for classification and use according to the segmentation result; and the trigeminal neuralgia identification and classification subsystem is used for performing trigeminal neuralgia and normal identification and classification on the tested object based on the quantitative characteristics of the segmentation result, and outputting a classification result and probability information. The problems that in the prior art, in trigeminal neuralgia iconography application, manual segmentation is mainly adopted, and segmentation precision and small sample generalization are insufficient are solved.

The invention relates to the technical field of part machining and manufacturing, in particular to a model segmentation setting method and system based on a bottle body contour and a computer program product, and the method comprises the steps: if a to-be-machined bottle body model does not have a segmentation template file, constructing a to-be-machined bottle body segmentation model in a visual interface in a manual segmentation mode; and if the to-be-processed bottle body model has the segmented template file, all segments of the segmented template file are mapped to the to-be-processed bottle body model in an equal proportion, and a to-be-processed bottle body segmented model is obtained. Visual segmentation is directly carried out on the CAD model of the bottle body to be processed, so that an operator does not need to master a complex programming technology, a template segmentation method is further introduced on the basis of manual segmentation, the segmented bottle body model is stored as a template, equal-proportion mapping is carried out on the bottle body models to be processed with similar shapes, and therefore the processing efficiency is improved. Standardized node positioning is realized, the production efficiency is effectively improved, and the production cost is reduced.