31 results about "Manual segmentation" patented technology

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 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 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 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.

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.

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.

Embodiments of the present application provide an ultrasound image segmentation model training method and device, electronic equipment and storage medium, applied to the field of medical imaging technology. By taking N as the number of nodes in the image segmentation network, K as the number of connections between each node and other nodes in the image segmentation network, and disconnecting or reconnecting the connection relationship between each node with a probability P, an image segmentation network is generated; the image segmentation network is connected with the tolerance generation network to obtain an ultrasound image segmentation model and is trained. By applying the method of the embodiments of the present application, the connection between each node can be disconnected or reconnected by the probability P, the image segmentation network is generated, the image segmentation network is connected with the tolerance generation network, the ultrasound image segmentation model is generated, so that the ultrasound image segmentation model can be trained and applied to ultrasound image segmentation, and then the automatic segmentation detection of ultrasound images is realized, without manual segmentation, reducing the cost of ultrasound image segmentation.

The invention provides a device and a method for acquiring a three-dimensional model of an abdominal organ. The device comprises a coil supporting assembly, a nuclear magnetic resonance spectrometer and image processing equipment, the coil supporting assembly is clamped and fixed to the side portion of the examination bed and used for supporting the abdomen coil to reduce compression on the abdomen of the user to be examined. The nuclear magnetic resonance spectrometer is arranged above the abdomen of a to-be-tested user, the axial line of the examination positioning lamp is aligned with the axial midpoint of the coil supporting assembly, and a magnetic resonance scanning sequence is obtained through scanning of the nuclear magnetic resonance spectrometer; and the image processing equipment is in communication connection with the nuclear magnetic resonance spectrometer, obtains a magnetic resonance scanning sequence and obtains an intra-abdominal organ three-dimensional model according to the magnetic resonance scanning sequence. According to the method, full-level accurate extraction of the abdominal organs is realized through a multi-region seed point strategy, adaptive threshold segmentation and other technologies, the low efficiency and subjective limitation of traditional manual segmentation are broken through, the reliability of the result is ensured through an automatic algorithm, and support is provided for image analysis of the abdominal organs.

The present invention discloses a cancer prognosis prediction system and method that combines medical large model assistance and multimodal image fusion. The system uses the medical large model MedSAM for assisted segmentation. Compared with existing manual segmentation, it reduces the workload of doctors in determining the lesion area and optimizes the availability of tumor slice data. On this basis, resampling, minimum envelope rectangle selection, and rectangular expansion are sequentially performed to obtain PET tumor slices and CT tumor slices, and the PET tumor slices and CT tumor slices are fused to obtain fused slices. The slices obtained in this way can ensure the accuracy and reliability of subsequent analysis. The present invention uses a cancer prognosis prediction model based on multimodal image fusion to effectively integrate multimodal slices and then perform cancer prognosis prediction, thereby improving the accuracy of cancer prognosis prediction results. At the same time, the cancer prognosis prediction results can be used to guide doctors in selecting cancer treatment plans.

This invention relates to a deep learning-based image segmentation method and system for auricular cartilage and its substructures. The image segmentation method includes: acquiring a magnetic resonance imaging (MRI) image of the external ear contour using a UTE sequence; preprocessing the acquired MRI image of the external ear contour using the UTE sequence; and training a deep learning-based segmentation model for auricular cartilage and its substructures using manually segmented auricular cartilage images and manually segmented auricular cartilage substructure images as label images, thereby segmenting the auricular cartilage and its substructures respectively. The deep learning-based image segmentation method and system for auricular cartilage and its substructures can automatically and efficiently acquire auricular cartilage models suitable for 3D bioprinting.

Improving accuracy of a predicted segmentation mask, comprising: extracting a ground truth red green blue (RGB) image buffer and a binary contour image buffer from a ground truth RGB image container used for segmentation training; generating the predicted segmentation mask from the ground truth RGB image buffer; generating a second binary contour from the predicted segmentation mask using a specific algorithm; calculating a segmentation loss between a manually segmented mask of the ground truth RGB image buffer and the predicted segmentation mask; calculating a contour accuracy loss between a contour of the binary contour image buffer and a binary contour of the predicted segmentation mask; calculating a total loss as a weighted average of the segmentation loss and the contour accuracy loss; and generating an improved binary contour by compensating the contour of the binary contour image buffer with the calculated total loss, wherein the improved binary contour is used to improve accuracy of the predicted segmentation mask.

The invention discloses a non-alcoholic fatty liver image grading method based on a diffusion neural network, and belongs to the field of medical image processing. The method comprises the following steps: collecting an abdominal ultrasound image file of a patient, screening 10 layers of complete liver and spleen continuous slices / frames, preprocessing, manually segmenting and marking, and constructing a special training data set; constructing a diffusion neural network model containing diffusion feature extraction, attention fusion and segmentation output modules, dividing a data set, and carrying out 20 rounds of iterative training until convergence; and finally, carrying out 16 * 16 gridding cutting on the segmented liver and spleen mask pattern, randomly selecting 8 liver and 6 spleen sampling areas, carrying out weighted fusion on a gray mean and an ultrasonic texture feature value to obtain a liver and spleen ultrasonic feature value, and realizing grading by calculating the ratio of the two. According to the method, the ultrasonic image characteristics are optimally designed, the segmentation precision is high, the sample adaptability is high, the grading result fits the clinical pathological characteristics, and reliable technical support is provided for non-invasive and accurate diagnosis of the non-alcoholic fatty liver disease.

The application relates to a grinding area planning method and device, electronic equipment and a storage medium, wherein the method comprises the following steps: obtaining medical image data of a hip joint part of a target object; inputting the medical image data into a pre-trained deep learning model to obtain bone tissue data and position information of a hip socket center point output by the deep learning model; generating a pelvis model to be ground based on the bone tissue data; determining a grinding area of the pelvis model according to the pelvis model, the position information and a preset acetabular cup model, and visually displaying the pelvis model and the grinding area. Through the application, the problem that manual segmentation based on pelvis tissue depends on the experience and proficiency of operators in the related art, resulting in low segmentation efficiency and low segmentation accuracy, is solved, automatic segmentation of medical image data based on a pre-trained deep learning model is realized, the segmentation accuracy and efficiency can be improved, and the pelvis model and the grinding area can be visually displayed.

The invention relates to the technical field of semiconductor detection, in particular to an automatic segmentation and labeling method for a dark field wafer image, which comprises the following steps of: acquiring an original dark field wafer image and preprocessing the original dark field wafer image to obtain a binary image; obtaining the coordinates of the bright spots in the image, carrying out the iterative fitting of the coordinates through an RANSAC algorithm, obtaining a candidate optimal model, and calculating the interior points of the candidate optimal model; performing secondary fitting on the inner points to obtain an optimal model; segmenting the original dark field wafer image, and marking segmented sub-images according to the optimal model to obtain segmented marked images of the dark field wafer image; according to the method, the optimal model is obtained through multi-round iteration to represent the distribution of the defect bright spots, the defect distribution curve can be quickly and effectively separated, the problems of low efficiency, poor reliability, high labor cost, high burden and the like of an existing manual segmentation and labeling scheme are remarkably improved, meanwhile, the problem of inconsistent judgment can be avoided, and the accuracy of judgment is improved. And the consistency of the sample data is maintained.

The invention provides a photo selecting and processing method for training a heart disease recognition model, which comprises the following steps of: establishing a section screening rule based on DICOM metadata and anatomical features by combining a clinical guide and an image analysis technology, matching a standard section based on a field, quantifying a motion artifact degree, and filtering an artifact image by combining a heart rate and a respiration signal, so that the heart disease recognition model is trained. OpenCV histogram kurtosis analysis and frequency domain high-frequency energy detection are adopted, low-resolution and high-noise images are eliminated, a heart structure is manually segmented, a boundary is repaired through morphological closed operation, pathological labeling is achieved based on a LabelImg plug-in, local sharpening and equipment simulation enhancement are conducted, sensitive information is processed based on OCR and Gaussian blur, and standardized naming is conducted. The method comprises the following steps: establishing a training set, evaluating annotation consistency, performing stratified sampling to construct the training set, and ensuring the accuracy, integrity and compliance of model training data through multi-modal quality control, semi-automatic annotation and privacy protection strategies.

The application provides a page processing method and device, wherein the page processing method comprises the following steps: determining an element set corresponding to a target page according to a layout sketch of the target page; drawing an element block corresponding to each element in the element set according to attribute information of each element, and generating an element block distribution diagram according to the element block corresponding to each element; performing iterative segmentation processing on the element block distribution diagram to determine a target segmentation region generated by a segmentation processing result of each iterative segmentation period; and constructing a page layout tree structure of the target page according to a generation order of each target segmentation region. The element block distribution diagram corresponding to the target page is drawn according to the layout sketch of the target page, and then the element block distribution diagram is subjected to iterative segmentation processing to obtain the target segmentation region corresponding to each iterative segmentation period, so that the page layout tree structure of the target page is constructed according to the generation order of each target segmentation region, without relying on manual segmentation of the target page, thereby improving the segmentation efficiency.