1200 results about "Accurate segmentation" patented technology

The invention discloses a remote sensing image semantic segmentation method based on a double-branch multi-scale fusion network, and relates to the technical field of remote sensing image semantic segmentation. According to the method, a double-encoder and single-decoder structure, a multi-scale feature fusion channel and spatial path information are adopted; an adaptive efficient parameter fine tuning SAM2 large model knowledge is utilized to accelerate new task learning, global feature and local feature information of the SAM2 large model knowledge is effectively captured through a global local window multi-head self-attention module, a mixed Logit adjustment loss function based on mutual information is adopted to adjust category weight aiming at the problem of sample category imbalance of a traditional remote sensing data set, and the new task learning is accelerated. The remote sensing image semantic segmentation effect is improved; according to the method, pixel classification and ground feature correspondence are realized, the segmentation problems of complex background, unbalanced sample category and large scale change are effectively solved, the accuracy and stability of image segmentation are improved, and rapid and accurate segmentation of the remote sensing image is realized.

The invention provides an ultrahigh-resolution remote sensing image segmentation method based on frequency domain information fusion. The ultrahigh-resolution remote sensing image segmentation method based on frequency domain information fusion comprises the following steps: step 1, acquiring a Vaihingen and Potsdam remote sensing data set; step 2, building an ultrahigh resolution remote sensing image segmentation network fused with multivariate frequency domain information; step 3, inputting the Vaihinge training set into the ultra-high resolution remote sensing image segmentation network for training to obtain an optimal parameter model; 4, inputting the Vaihinge test set into the optimal parameter model, and outputting an accurate segmentation image of the remote sensing image; and 5, replacing the data set with a Potsdam training set, a Potsdam verification set and a Potsdam test set, and repeating the operations from the step 3 to the step 4. According to the model, the LKResNet-18 network is introduced, so that the receptive field and the feature representation capability of local branches are improved.

The invention discloses a medical image segmentation method fusing multi-modal graphic and text information, and the method comprises the steps: carrying out the preprocessing and coding of a metadata text of a patient through a pre-trained BERT model, extracting the feature representation, and carrying out the attention-guided data enhancement operation of a pathological image. And finally, constructing a medical image segmentation network composed of an image encoder, a text encoder and a decoder, carrying out pathological image segmentation based on the feature representation extracted in the step 1 and the pathological image after data enhancement, and visualizing a segmentation result. In the segmentation network, a multi-modal feature fusion module is designed to fuse information of two modals, the module adopts a Transform architecture, a cross-modal attention mechanism and a self-attention mechanism are introduced, and the effectiveness of information fusion is ensured. According to the method, a more accurate segmentation result is obtained, the generalization ability on new data is improved, and the common problem of data imbalance in the medical image is effectively solved.

The invention discloses a brain tumor MRI image segmentation model and method based on an improved Swin UNETR network, and belongs to the technical field of image segmentation. The brain tumor MRI image segmentation model is obtained by improving a Swin UNETR as a baseline model and comprises an encoder, a jump connection and a decoder, the decoder comprises a plurality of convolution fusion modules and an attention mechanism module, feature information of different levels is fused in the decoder by using the plurality of convolution fusion modules, and the attention mechanism module is used for identifying the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the convolution fusion modules. And the attention mechanism module is used to focus the feature information obtained by the plurality of convolutions. By adopting the scheme of the invention, the problem that the existing medical image segmentation technology based on deep learning is insufficient in brain tumor MRI image segmentation precision and is difficult to realize accurate segmentation especially in the face of different types of tumors and variability can be effectively solved, the calculation amount is small, and the image segmentation efficiency can be effectively improved.

The invention relates to the technical field of medical treatment, in particular to an AI-based medical image automatic segmentation and labeling system, which comprises an image data acquisition module, a data preprocessing module, a feature extraction module, an automatic segmentation module and an intelligent labeling module, the data preprocessing module improves image quality through noise suppression, artifact removal and modal alignment, the feature extraction module extracts a feature map of a potential lesion area, and the automatic segmentation module combines a full convolutional network, a bidirectional long-short-term memory network and an attention mechanism to realize accurate segmentation of an organ area and the lesion area. The intelligent labeling module is used for labeling anatomical names, lesion types and pathological features of the segmentation results by using a medical knowledge graph and a semantic embedding technology; the method has the advantages of being high in segmentation precision, comprehensive in marking and high in expansibility, and the automation and intelligence level of medical image analysis can be improved.

The invention discloses a data compression transmission method and system applied to a ferry inspection image, and the method comprises the steps: collecting and obtaining the ferry inspection image in real time, recognizing a key inspection target region in the image, and carrying out the segmentation and partitioning of the image; compressing the key inspection target area based on lossless compression coding; the quantization step size is dynamically adjusted by comparing statistical variances of background pixels between continuous frames, and lossy compression coding is carried out on a background area; based on the boundary distance between the key inspection target area and the background area, adaptive compression coding is carried out on the transition area; constructing a hierarchical data packet; and constructing a data transmission optimization model, dynamically allocating data transmission links, and obtaining a transmission scheme with the highest total transmission. The method has the advantages that efficient data compression transmission is realized by accurately segmenting the image area and adopting a lossless, lossy and adaptive compression technology, the overall transmission efficiency is improved through intelligent transmission optimization, and the definition and real-time performance of the inspection image are ensured.

The invention relates to the field of image processing, in particular to a pin shaft forging forming quality detection method and system, and the method comprises the steps: carrying out the image collection of a to-be-detected pin shaft forge piece; then determining a total energy function of the active contour model and determining a neighborhood window, obtaining a structure tensor based on a gradient magnitude in the neighborhood window, and calculating to obtain local gradient direction dispersion; then, a defect edge structure enhancement index is calculated; then calculating a self-adaptive external energy scaling adjustment factor, and fusing the self-adaptive external energy scaling adjustment factor into an energy function of the active contour model to form an improved active contour model; and finally, accurately segmenting and extracting the surface defects of the pin shaft, and carrying out quality detection by combining the extracted defect characteristics. According to the method, by constructing local gradient direction dispersion and defect edge structure enhancement, a self-adaptive external energy scaling adjustment factor is calculated, and an active contour model is improved so as to accurately detect the surface defects of the pin shaft forge piece.

The invention discloses a passive unsupervised domain adaptive medical image segmentation method and device based on pseudo labels, and relates to the technical field of computers, and the method comprises a supervised source domain model training stage, an unsupervised adaptive target domain model training stage and a segmentation stage. The method specifically comprises the following steps of: performing pixel-level self-marking on a source domain medical image by applying pseudo-tag pixel-level uncertainty estimation to generate a reliable pseudo-tag; and on this basis, a strong and weak enhanced teacher-student target domain model is used, and a high-quality pseudo tag is fully utilized to train a target domain medical image, so that the network adapts to representation specific to the target domain medical image. According to the method, unsupervised segmentation is carried out on the target domain data graph on the premise that the source domain model and the target domain unmarked data are known, source domain data do not need to be accessed, the features of the source domain model can be reasonably utilized, and the problem existing in SFUDA can be well relieved; and the accurate segmentation capability on the target domain is still maintained under the condition that less data is available compared with the UDA method.

Implementations relate to generation of segmentation masks for images in a zero-shot, unsupervised manner. Implementations also relate to generation of labels for the segmentation layers of the segmentation mask. Implementations use self-attention maps from a pass of the image through a generative image model to determine the segmentation mask and may use cross-attention maps generated when a prompt describing the image is provided with the image to the generative image model. Implementations aggregate maps from different resolutions to determine the mask and labels. The disclosed techniques enable accurate segmentation for any image without apriori training, facilitating applications in image processing, computer vision, extended reality applications, and robotics.

The invention belongs to the technical field of image recognition, and discloses a few-sample defect detection method based on a prototype prompt fine-tuning visual basic model SAM. Prototype prior information about an image to be segmented is extracted from a small number of example image-mask pairs, so that prior guidance of human beings is replaced; for defect data, a representative foreground prompt point, a high-confidence background prompt point (sparse prompt) and a foreground Mask (dense prompt) are extracted from prototype prior to serve as prototype prompt, and a visual basic model SAM is guided to achieve more accurate segmentation. A visual basic model SAM is finely adjusted by using prototype prompt and a small amount of defect data, a prompt encoder and an image encoder of the SAM are frozen, an LoRA bypass is injected into the image encoder, and the SAM is enabled to better adapt to defect data distribution by updating parameters of the LoRA bypass and a lightweight mask decoder.

The invention relates to the technical field of infrared image recognition, and discloses a hot target extraction method and system based on infrared image recognition, and the system comprises a multi-sensor collection module, a data dimension reduction module, a data verification module, a segmentation optimization module, and a dynamic feedback module. Multi-modal data dimension reduction processing is achieved by collecting infrared images, visible light images and environment dynamic parameters and combining Laplacian feature mapping, and a target area is extracted through geometric verification and topological analysis. An energy optimization model and a distribution alignment method are adopted to complete accurate segmentation of a target area, and meanwhile, segmentation model parameters are adjusted in real time based on environment dynamic parameters, so that the system stability is ensured. According to the method, high-precision and robust extraction of the thermal target is realized in a complex dynamic environment, the problems of fuzzy target boundary, noise interference and insufficient dynamic environment adaptability in the prior art are effectively solved, and the method is suitable for the fields of military reconnaissance, fireproof monitoring, electric power inspection and the like.

The invention relates to the field of image processing, in particular to a paint defect detection method and system based on machine vision, paint surface images are collected through an industrial camera, and brightness correction is carried out in combination with an illumination correction factor; fusing color fluctuation and texture features to construct a fluctuation factor, and optimizing a distance measurement formula of super-pixel segmentation to realize accurate segmentation of a complex surface area; and finally, carrying out classification detection on the super-pixel region features by utilizing a convolutional neural network, and identifying the type of the paint surface defect. According to the invention, through illumination correction, fluctuation factor construction, improved superpixel segmentation algorithm and convolutional neural network classification, automatic and accurate detection and classification of paint surface defects are realized.

The invention discloses an intelligent inspection system and method for a photovoltaic module of a photovoltaic power generation system, and relates to the technical field of intelligent inspection. The method comprises the steps that a high-definition camera and an infrared imager carried by an unmanned aerial vehicle automatically inspect a photovoltaic panel, real-time operation data are acquired and transmitted to a preprocessing module, the preprocessing module optimizes an image through denoising, graying, contrast enhancement and geometric correction, a Unet algorithm is utilized to accurately segment strings and assemblies in the image, and the photovoltaic panel is obtained. And a defect identification module analyzes the processed image, calculates the average temperature, compares the average temperature with a threshold value, identifies hot spot defects, and finally outputs specific information, fault types and maintenance suggestions of a defective component by the system, and generates an inspection report. According to the technical scheme, a fault source can be quickly positioned, and cloud storage and an AI analysis tool are used for managing data and predicting potential maintenance requirements.

The invention discloses a rice plant segmentation method and system based on view angle transformation and dense point clouds, and the method comprises the steps: scanning a rice field through employing an unmanned plane laser radar system, and obtaining the dense point clouds of a rice canopy; performing angle transformation processing on the dense point cloud by adopting an overlooking angle to obtain a two-dimensional image of the dense point cloud at the overlooking angle; calculating a homography matrix from the dense point cloud to the two-dimensional image, and establishing an accurate mapping relation between the three-dimensional point cloud and the two-dimensional image; for the two-dimensional image, carrying out segmentation marking on the rice plant by adopting a region marking method so as to realize accurate segmentation and individual marking of the rice plant on a two-dimensional plane; and endowing each point in the point cloud with a corresponding region mark number by using a mapping relationship between the three-dimensional point cloud and the two-dimensional image, and further obtaining a rice plant segmentation result of the whole dense point cloud. According to the method, high-precision rice plant segmentation is realized, and more reliable and efficient technical support is provided for precision agricultural management and phenotype analysis of rice.

The invention belongs to the field of disaster early warning, and provides a disaster early warning method and system based on key target detection and image segmentation, and the method comprises the steps: obtaining the real-time image data of a site, and carrying out the preprocessing of an image; processing the preprocessed image through a YOLOv5 target detection algorithm, detecting a key target in a disaster site, and outputting a category label and a bounding box of the target; performing pixel-by-pixel segmentation processing on a bounding box region output by target detection by using a U-Net image segmentation algorithm to generate an accurate segmentation mask of the target; and inputting the image segmentation mask, historical disaster data, meteorological data and geographic information system data into an LSTM prediction model, carrying out multi-source data fusion and time sequence analysis, and outputting dynamic prediction information and early warning levels of disasters. According to the invention, real-time monitoring and early warning of disasters are realized, the response speed of the early warning system is obviously improved, more comprehensive disaster early warning information is provided, high precision of disaster prediction is ensured, and the phenomena of false alarm and missing alarm are reduced.

The invention discloses an MRI medical image segmentation method and system based on deep learning, and the method comprises the steps: obtaining a mammary gland MRI image, carrying out the standardization processing of the mammary gland MRI image, and removing a non-region of interest to obtain a target MRI image; performing detail enhancement on the suspected lesion area to reconstruct an image to be preprocessed; performing noise reduction processing, contrast enhancement and edge enhancement preprocessing on the to-be-preprocessed image to generate three versions of input images, and inputting the three versions of input images into a pre-trained deep learning model as three channels to complete breast tumor identification; and carrying out image segmentation on the breast tumor region, and classifying segmented breast tumors. According to the MRI medical image segmentation method and system based on deep learning provided by the invention, accurate and efficient processing of the mammary gland MRI image is realized, efficient identification, accurate segmentation and accurate classification of the mammary gland tumor are realized, the accuracy and robustness of mammary gland tumor detection can be improved, and efficient and reliable auxiliary diagnosis is provided for clinic.

The invention discloses an immunohistochemical digital pathological image processing method, and relates to the technical field of image processing, and the method comprises the steps: obtaining an immunohistochemical staining pathological image, and carrying out the preprocessing; carrying out region division on the preprocessed pathological image, carrying out feature extraction and fusion on each sub-region, and generating a multi-dimensional feature vector; based on the multi-dimensional feature vector, optimizing the pathological image by using a region growing and merging algorithm; carrying out dyeing intensity detection on the optimized pathological image, and classifying dyeing areas through a K-means clustering algorithm; and based on a classification result, performing quantitative analysis and generating an immunohistochemical image analysis report. According to the method, through preprocessing, feature extraction and fusion, and in combination with a region growing and merging algorithm, precise segmentation and classification analysis of the immunohistochemical staining region are realized, and the automation and recognition precision of image processing are improved.

The invention relates to the technical field of image processing, and provides a vertebral body three-dimensional reconstruction method based on image segmentation, and a bone detection method and system.The method comprises the steps that firstly, an SAD-UNetR image segmentation model capable of achieving accurate segmentation of an image target is constructed through improvement of a Swinin-UNetR network, three-dimensional modeling is conducted on the basis of an accurate segmentation image, and a three-dimensional reconstruction model is obtained; an accurate three-dimensional model of a vertebra structure can be obtained; based on an accurate segmentation result, a bone mineral density prediction regression model is provided at the same time, bone substances are identified based on multi-instance learning and an attention mechanism, a bone substance classification result and bone mineral density data can be provided, intelligent assistance is provided for diagnosis of doctors, radiation risks, economic consumption and time consumption caused by special bone mineral density measurement are avoided, and the accuracy of bone mineral density measurement is improved. Measurement means of bone detection are expanded, and the method has a wide application prospect.

The invention relates to a broiler instance segmentation method based on thermal imaging and RGB fusion, and belongs to the field of image processing, and the method comprises the steps: collecting image data, and carrying out the preprocessing of the image data; inputting the labeled training data after data preprocessing into the constructed network model as a training set for training to obtain a trained and optimized network model; and inputting the acquired image data into the trained and optimized network model again, extracting feature information of two modes of RGB and thermal imaging in the image, fusing the feature information of the two modes through a cross-modal attention mechanism, and optimizing cross-modal information interaction in combination with a deformable attention mechanism and multi-source position coding, so as to obtain the multi-source position code. And finally, generating a final broiler instance segmentation mask through a decoder. According to the method, accurate segmentation and tracking of broiler individuals are realized, and the robustness and the recognition capability of the model in a complex agricultural environment are further improved through temperature information of a thermal imaging image.

The invention relates to the technical field of image defect detection, in particular to an intelligent drug particle detection system and method based on image processing. According to the method, a high-resolution industrial camera and a polarized light source are used for dynamically shooting assembly line medicine particles so as to obtain multi-angle high-quality original images; noise suppression and microstructure enhancement are realized through multi-scale residual information enhancement and structure maintenance adaptive filtering, and the detail resolution is improved by using local contrast normalization; a multi-scale structure gradient of direction perception and region shape prior are fused, and a hierarchical contour evolution and dynamic threshold strategy is adopted to accurately segment a particle main contour and a microcrack; micro defects are identified based on a multi-scale texture sensing algorithm guided by a boundary relative potential graph, and multi-class defect classification is realized through a spatial and semantic dependency relationship between graph neural network modeling regions; and finally, a traceable quality detection report is generated. The intelligent level, the accuracy level and the automation level of medicine particle detection are improved.

The invention belongs to the technical field of medical image processing, and particularly relates to a semi-supervised cardiac image segmentation method based on a Mamb-Transform double structure and contrast learning. A segmentation model used in the method comprises an MTSeg encoder and a VNet decoder, an input image enters the MTSeg encoder after being subjected to linear layer and position embedding operation, the MTSeg encoder comprises a plurality of MTSeg encoding modules, the VNet decoder comprises a plurality of decoding modules, and output features of the MTSeg encoding modules are in jump connection with the corresponding decoding modules after being subjected to projection operation; performing normalization operation on the output feature vector of the VNet decoder to obtain a segmentation result; the MTSeg coding module comprises an improved Mama branch and a Transform branch, and output feature vectors of the two branches are fused through a cross attention layer to obtain an output feature vector of the MTSeg coding module; the improved Mama branch comprises an MS module and a DRFB module which are connected in series, the MS module is used for enhancing the feature extraction and calculation efficiency of the model, and the DRFB module is used for enhancing the expression ability and training stability of the model. The method achieves the effective integration of a long-range dependency relationship, global information, multi-scale information and local information, improves the feature extraction capability of the model, and obtains a finer and more accurate segmentation result.

The invention relates to a segmentation model based on a CNN and Transform parallel double-branch structure, and belongs to the technical field of medical data prediction. The method comprises the following steps: acquiring a CT image, and preprocessing the CT image by executing windowing processing and contrast limited adaptive histogram equalization; extracting features of lung lesions in the preprocessed CT image through a parallel double-branch structure; inputting the extracted features into a cross enhancement fusion module, and performing complementary fusion on the features through dynamic weight distribution to obtain fused features; the fusion features are input into a multi-scale context information extraction module, and lesion boundary sensitivity is enhanced through cavity convolution of different expansion rates; the encoder features and the decoder features are fused through jump connection, and a segmentation result is output after resolution is recovered based on up-sampling; and optimizing model training by adopting a weighted loss function. Accurate segmentation of the lung lesion in the pulmonary tuberculosis CT image is realized, and clearer and more accurate lesion area information can be provided.

The invention relates to the technical field of image processing, and provides an enteromorpha remote sensing image detection method and system based on deep learning, and the method comprises the following steps: carrying out the edge gradient extraction and small target enhancement processing of an obtained to-be-detected remote sensing image, and obtaining a first feature map after the edge enhancement and small target feature enhancement; transmitting the first feature map to a U-shaped backbone network formed by cascading multiple stages of Ep-VSS block modules, performing multi-stage feature extraction, and obtaining a detection result of the enteromorpha remote sensing image based on the feature map output by the last stage of Ep-VSS block module; according to the method, through edge enhancement, small target sensitive detection, multi-scale texture extraction and spatial context modeling, precise boundary segmentation and long-range dependence modeling are realized, and the accuracy, real-time performance and reliability of enteromorpha remote sensing monitoring are improved.

The invention discloses a remote sensing image ground object segmentation method based on hierarchical feature extraction and decoding, which relates to the technical field of remote sensing image processing, and comprises the following steps: step 1, collecting and preprocessing a remote sensing image, and constructing a training sample set; 2, constructing an image segmentation network, wherein the image segmentation network comprises a hierarchical encoder, a receptive field module unit and a network decoder which are connected in sequence; 3, training and optimizing the image segmentation network by adopting the training sample set to obtain a remote sensing image ground object segmentation model; and 4, collecting a remote sensing image to be segmented, and inputting the remote sensing image to the remote sensing image ground object segmentation model to obtain a ground object segmentation result. According to the method, the advantages of the advanced visual basic model and the U-shaped network architecture are combined, so that rapid and accurate segmentation of the ground target is realized.

The embodiment of the invention provides a multi-modal large model image segmentation method and device based on hierarchical lexical representation, a mask image is coded into a lexical sequence by designing a mask marker, and progressive generation from a shape prototype to local details is realized through a causal attention mechanism. A three-stage training strategy is adopted, firstly, a marker is trained through a mask reconstruction task, then mask lexical elements are integrated into a large-scale multi-modal model and combined training is carried out, and finally fine tuning is carried out by utilizing high-resolution data. And performing multi-level supervision based on a hierarchical mask loss function to realize accurate segmentation of a natural language description target. According to the method, the defects of the traditional technology in the aspects of complex scene understanding, model training, mask generation and the like are effectively overcome, and the performance of multi-modal image segmentation is remarkably improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a segmentation and matching method, device, equipment and medium for an image background region. Multi-scale features are obtained through cavity convolution branch, fusion features are generated through weighted fusion, the fusion features are input into a decoder network to obtain a background segmentation mask, a background area image is obtained by combining the preprocessed image, and a visual converter model is input to extract background feature vectors; and performing similarity matching with a target feature vector in a preset feature database to retrieve a target image. According to the method, the segmentation accuracy and robustness are improved through combination of multi-scale feature fusion and the visual converter, meanwhile, the retrieval discrimination and reliability are enhanced through feature matching, the problems that an existing method is insufficient in segmentation precision and limited in retrieval capacity are solved, and precise segmentation and efficient retrieval of the background in a complex scene are achieved.

The invention belongs to the technical field of intelligent ocean and remote sensing image processing, and discloses an ocean remote sensing coastline segmentation method based on a text-guided semi-supervised pseudo tag, which comprises the following steps: collecting ocean remote sensing coastline image data, dividing into tag data and non-tag data, and constructing a text prompt; constructing a segmentation model comprising a shared image encoder, a text encoder, two decoders and a pseudo label calibration module, cooperatively extracting image features and text features by all the parts, generating a pseudo label and a prediction mask, and optimizing the pseudo label through uncertainty calibration; training the model in a supervised stage and an unsupervised stage, and adding loss function values of the two stages to a back propagation optimization model; and finally, based on the trained model, precise segmentation of the ocean remote sensing coastline image is realized. According to the method, the efficiency and the accuracy of cross-regional ocean remote sensing coastline segmentation are effectively improved by utilizing text guidance and semi-supervised pseudo labels, and the dependence on a large amount of labeled data is reduced.

The invention discloses an MRI (Magnetic Resonance Imaging) image segmentation method based on WKA-Unet. The MRI image segmentation method comprises the following steps: 1) data preprocessing; 2) constructing a WKA-Unet network; and 3) testing. According to the technical scheme, on the basis of a U-Net network structure, an MRI image segmentation model is improved; a traditional Unet network is combined with a self-designed WKA module, multi-scale features are fully captured by using wavelet transform convolution and KAN convolution, and the loss of traditional Unet global information is relieved; an attention mechanism is introduced to pay attention to key parts, and redundant information is removed; according to the technical scheme, the network is trained more flexibly and accurately, a more accurate segmentation result is obtained, the segmentation precision is high, and the model structure is efficient and stable.

The invention discloses a high-resolution remote sensing image double-branch segmentation method, which comprises the following steps: acquiring a high-resolution remote sensing image data set, preprocessing the high-resolution remote sensing image data set, and dividing the high-resolution remote sensing image data set into a training set, a verification set and a test set; constructing a semantic segmentation network model of the high-resolution remote sensing image, wherein the semantic segmentation network model comprises a detail branch, a semantic branch and an up-sampling part; inputting the training set into a high-resolution remote sensing image semantic segmentation network model for training, calculating a loss function, performing back propagation, updating network model parameters, obtaining an optimal parameter network model, and verifying the optimal parameter network model by using the verification set; and inputting the test set into the trained optimal parameter network model, and outputting an accurate segmentation image of the high-resolution remote sensing image. According to the method, feature extraction and fusion are carried out in a multi-level and multi-scale manner by capturing target information of different types and scales in the high-resolution remote sensing image, so that the segmentation precision of the high-resolution remote sensing image is improved.

The invention discloses an overlapped cervical cytoplasm region segmentation method based on deep learning and a conditional diffusion model, and relates to the technical field of artificial intelligence analysis of medical images. According to the method, accurate segmentation of the overlapped cytoplasm region in the cervical cell image is realized through a morphological prior guided conditional diffusion process. The method comprises the following steps: constructing a multi-scale cervical cytoplasm mask pair image; designing a cytoplasm specific data enhancement and preprocessing process; building a multi-branch cervical cell morphology perception condition diffusion network; using a self-adaptive multi-scale combination loss function to optimize training; and a hierarchical classifier is adopted to freely guide sampling for reasoning. According to the method, the frequency domain and space domain features are fused, a cellular morphology and statistics priori knowledge base is established, and a strategy of generating complete cytoplasm by adopting non-overlapped parts is adopted, so that the problem that the traditional method is difficult to segment in complex backgrounds and overlapped regions is successfully solved, and reliable technical support is provided for early screening of cervical cancer.