76 results about "Lesion mapping" patented technology

The invention discloses a focus segmentation method fusing PET and CT bimodal images, and relates to the technical field of computers. The method comprises the following steps: acquiring focus image data, separating the focus image data to obtain CT data and PET data, and inputting the CT data and the PET data into a bimodal medical image segmentation model to obtain a preliminary segmentation result; a difference region is generated based on the preliminary segmentation result, difference region detection is carried out on the difference region, and click signal data is generated; the difference region is a region which is not marked as a focus in the preliminary segmentation result; training the bimodal medical image segmentation model for a preset number of times through the click signal data, the CT data and the PET data to obtain a segmentation result; and determining a segmentation result generated by the last training as a lesion image segmentation result. The method can improve the medical image lesion segmentation precision.

The invention relates to the technical field of image segmentation, in particular to a tumor prediction method and system based on image segmentation processing, and the method comprises the following steps: dividing a lesion image into sub-regions according to space, extracting probability construction mapping, generating a prediction map, analyzing gradient change, judging a difference region, generating a density mutation map, extracting a gray scale trend, and generating a time consistency mapping table. And fusing the inter-frame probability reconstruction distribution to generate a high-sensitivity image, and aggregating a high-value region to extract a boundary index and mark the boundary index as a suspicious lesion position list. According to the method, the image sequence is divided into the sub-regions, and the prediction map is constructed in combination with the pixel probability and position relationship, so that the region recognition accuracy is improved, the probability gradient in the region is extracted to recognize the abnormal density mutation region, the time gray change direction is tracked, and dynamic consistency classification is realized; adjacent image pixel prediction results are fused to reconstruct local probability distribution, prediction stability is enhanced, abnormal areas are aggregated in the high-sensitivity image, boundaries are marked, and recognition sensitivity and reliability are improved.

The invention relates to the technical field of medical image processing, and particularly discloses a lesion image generation method for ultrasonic diagnosis, which comprises the following steps of: 1, acquiring initial state data of a lesion; 2, obtaining intervention factor data; 3, performing feature coding on the initial state data and the intervention factor data to obtain an initial feature vector and an intervention feature vector; and 4, inputting the initial feature vector and the intervention feature vector into a pre-trained time sequence generation model, and generating a continuous image sequence of the focus changing along with time. By constructing the time sequence generation model fusing the multi-modal feature coding and the space-time attention mechanism, the continuous image sequence of the focus changing along with time can be accurately generated, the disease progress or treatment response process can be accurately simulated, and a powerful reference basis is provided for clinical prognosis evaluation.

The invention discloses an ulcerative colitis image classification method based on a multi-scale attention fusion network, belongs to the technical field of image information mining and classification, and combines a staged feature extraction backbone network, a multi-scale attention fusion module (SCHL) and a classification module. The ulcerative colitis lesion image classification method has the advantages that significant advantages are shown in ulcerative colitis lesion image automatic classification tasks, an OfficientNet backbone network has staged feature extraction capacity, multi-level information from shallow texture to deep semantics in lesion images can be effectively captured, an SCHL module performs modeling by fusing channel attention, space attention and level high-frequency information, and the ulcerative colitis lesion image classification efficiency is improved. The model is guided to focus on a key lesion area, background interference is inhibited, feature discrimination is enhanced, the recognition ability of the model to a fine-grained lesion form and the generalization ability of the model to a complex clinical image are remarkably improved, and higher accuracy and robustness are achieved in refined classification of ulcerative colitis.

The invention relates to the field of image processing, in particular to a fundus disease image segmentation method, system and device. The method comprises the following steps: transforming the shape, position and color of a focus area of a real fundus disease image, and fusing with a focus-free image to obtain a preliminary synthesized focus image and a segmentation label; respectively inputting the features into a high-frequency detail optimization model consisting of a dual-path feature extractor and a decoder, acquiring global context features by the model by using a mask auto-encoder, extracting local detail features through a convolutional neural network, and judging whether to fuse along a channel according to a spatial pixel quantity relationship between local and global features of different scales; the target features of different scales are transmitted to a decoder through jump connection, and an optimized composite image is obtained through transposition convolution up-sampling; and taking the optimized multi-category synthetic image and the tag as an expansion data set, training a focus segmentation network, and segmenting an actual disease image by using a trained model, thereby effectively solving the problem of scarcity of fundus disease samples.

The application discloses a liver lesion image description method based on a semantic segmentation network, comprising the following steps: building a Unet semantic segmentation network based on a lightweight network GhostNet, and constructing a liver lesion segmentation model; acquiring a historical liver lesion ultrasound image dataset, inputting the liver lesion segmentation model for training, and obtaining an optimal segmentation model; and describing the features of the lesion image based on the segmentation result. The application segments the liver lesion by constructing a lesion segmentation model, and describes based on the segmentation result, thereby improving the accuracy and reliability of liver disease diagnosis, reducing misjudgment and subjective bias; by combining the advantages of deep learning and traditional image processing, the characteristics of both are fully utilized, the performance and stability of the algorithm are improved; automatic processing and analysis of the ultrasound image are realized, the lesion description result provides accurate disease positioning and type judgment, and a better treatment scheme is provided for doctors, thereby improving the treatment effect and treatment experience of patients.

The present application relates to the technical field of pest and disease monitoring, and specifically discloses a method and system for monitoring and early warning of agricultural pests and diseases based on image processing, which continuously collects under-exposed, normally exposed, and over-exposed images of farmland crops, and aligns and combines the three into a high dynamic range image that fully retains the details of highlights and shadows. Subsequently, target detection is performed on the high dynamic range image to generate a binary mask map of the suspicious lesion area, and the binary mask map is further used to extract the lesion area images from the three original images with different exposures. By performing deep pest and disease feature extraction and multi-source feature dynamic compensation aggregation on the three groups of multi-source suspicious lesion images containing complementary information, a comprehensive and integrated representation of the pest and disease characteristics is formed, and precise identification of the pest and disease types is achieved. This method can improve the accuracy and robustness of agricultural pest and disease monitoring under complex natural lighting conditions through the comprehensive utilization of multi-exposure image data.

A method for mapping one or more cutaneous conditions of a cutaneous surface may include: identifying a body map; identifying one or more body regions corresponding to the body map; identifying at least one representation corresponding to the one or more body regions; identifying one or more icons corresponding to the at least one representation; and defining the one or more icons on the at least one representation.

According to the ultrasonic imaging equipment and the lesion distribution presentation method provided by the invention, the volume data is firstly obtained, then the target tissue and the target lesion are identified from the volume data to obtain the three-dimensional contours of the target tissue and the target lesion, and then the three-dimensional schematic diagram is generated based on the two three-dimensional contours. The three-dimensional schematic diagram is used for presenting the form of the target tissue and the form and the position of the target focus in the target tissue, so that a user can intuitively know the distribution condition of the focus in space, the distribution of the focus is easier to understand and master compared with an ultrasonic image, and the working efficiency of a doctor is improved. And generating a two-dimensional diagram corresponding to at least one target section according to the three-dimensional contour of the target tissue and the focus, wherein the two-dimensional diagram corresponding to the target section comprises a target tissue graph used for presenting the form of the target tissue and a target focus graph used for presenting the form and the position of the target focus. Therefore, the user can know the distribution of the focus through the two-dimensional schematic diagram.

The present application relates to the technical field of image processing, and more particularly to a lesion segmentation method of adaptive dynamic text prompt, comprising: acquiring a lesion image, and generating an adaptive dynamic text prompt based on the lesion image; constructing a multi-modal enhanced fusion text prompt adapter based on a FiLM global channel recalibration module, a spatial cross-attention interaction module and a zero initialization gate nonlinear integration module cascade; the global channel recalibration module of FiLM utilizes a feature linear modulation mechanism to perform channel-level weighting on a visual feature map; before the feature enters spatial interaction, according to the text semantic enhancement band response, the activation of the background noise channel is inhibited. The present application overcomes the problems of insufficient utilization of multi-modal prompts, dependence on artificial static labeling of text prompts, serious dependence on prior information in the reasoning stage and difficulty in multi-modal feature fusion in a low-contrast environment in the existing medical image segmentation technology.

The invention discloses a medical image encryption method based on a trap door network and a diffusion model, and relates to the technical field of image encryption. The method comprises the following steps: encrypting a focus image scanned by medical equipment of a patient by using a session key through key negotiation between the patient and a doctor, and sending the encrypted image to an image filing and communication server; and when the doctor needs to look up the lesion image, decrypting the ciphertext image through the previously established session key, and recovering the corresponding original lesion image. The image encryption / decryption network of the invention is composed of a Diffusion model and a trap door network. The trap door network enhances the security of the medical image encryption method, and the forward generation and sampling process based on the Diffusion model ensures the security of the encrypted image and the high-quality reconstruction effect of the decrypted image.

The application belongs to the technical field of image recognition, and discloses a monkeypox virus classification method and system based on image comparison and a storage medium, which comprises an individual reference modeling module, collects normal skin and initial lesion images of a healthy side of a patient, generates a healthy texture template and an initial lesion reference library, performs gray scale normalization on the healthy texture template and the initial lesion reference library through adaptive histogram stretching, and outputs a comparison reference image group; a symptom density quantification module, acquires a current monitoring image of the patient, segments a lesion area in combination with the comparison reference image group, extracts layered features of the lesion area to generate a three-dimensional symptom density vector, extracts texture roughness of a surface of the lesion area, and constitutes a lesion quantification data set; the accuracy of disease monitoring is improved, and a clinical nursing process is optimized.

The present invention discloses an endoscope-based digital image enhancement method and system, which aims to solve the imaging limitations of traditional endoscopes in multi-lesion positioning and targeted intervention. The endoscope-based digital image enhancement method can perform endoscopic image acquisition, wireless transmission, abnormal anchor point identification and controllable terminal targeted intervention operations. The enhancement system for implementing the endoscope-based digital image enhancement method identifies suspected lesions through image analysis, the control unit issues a release instruction, causes the endoscope to release the controllable terminal and establish communication, the scheduling system navigates the controllable terminal to the abnormal anchor point according to the position information, acquires and transmits the digitally enhanced lesion image, and the judgment unit analyzes whether there is a target lesion with intervention value. This method can enhance the digital image in the human body cavity and improve the accuracy of lesion identification and intervention efficiency.

The invention discloses a focus image recognition method and system based on a deep learning model, and relates to the technical field of medical equipment. Comprising the following steps: acquiring medical images of various lesion types, and preprocessing the medical images to generate sample images; marking a focus area, extracting image feature parameters, and constructing a sample training data set; and based on the data set, training a segmentation identification model, preprocessing a medical image to be identified, identifying and segmenting a focus area by using the trained segmentation identification model, inputting a feature extraction model to obtain an image feature parameter prediction value, and calculating a biochemical discrimination influence index in combination with biochemical index data of a patient. And generating a comprehensive lesion identification coefficient according to the lesion texture complexity index, the edge influence index and the biochemical discrimination influence index, and comparing the comprehensive lesion identification coefficient with a set threshold to complete lesion image identification. And the accuracy and reliability of focus identification are improved.

The invention relates to the technical field of magnetic resonance imaging, in particular to an intra-operative low-field magnetic resonance intelligent navigation method based on preoperative high-field data guidance, which comprises the following steps: acquiring a high-field MRI image of a patient before an operation and extracting a three-dimensional segmentation mask and clinical features of a focus; adjusting according to the clinical features to obtain an intraoperative scanning protocol; and performing low-field magnetic resonance examination during the operation to generate an intra-operation navigation image, and marking focus information. Aiming at the problem that the accuracy of a focus image provided by an ultra-low field system in the operation process in the prior art is not enough, a high-field MRI image collected before the operation is introduced as a reference, scanning sequence parameters of the ultra-low field system used in the operation are dynamically adjusted based on clinical features embodied in the high-field MRI image, a better imaging effect is achieved, and meanwhile, the accuracy of the focus image provided by the ultra-low field system in the operation process is improved. Focus information is marked on the intraoperative navigation image based on the three-dimensional segmentation mask as prior information, so that more refined segmentation of focuses on the intraoperative navigation image is realized, and the surgical requirements are met.

The invention relates to the technical field of image processing, in particular to a gynecological tumor MRI image three-dimensional reconstruction method and system based on deep learning, and the method comprises the steps: processing a coarse label tumor region through a target detection model based on an original image, and generating a tiny focus positioning frame; using a positioning frame for segmentation to obtain a tiny focus image and a non-tiny focus image, and scaling down the tiny focus according to a proportion to obtain a focus image; according to the generated boundary region, selecting a sample to calculate a gray average value and a difference coefficient, and performing weighted merging to obtain a normal tissue image; compressing the tumor core area and the boundary area to obtain an overall tumor image; the focus image, the normal tissue image and the tumor image are spliced together to form a low-resolution image training model, and then features are segmented and extracted to construct a tumor three-dimensional model. According to the method, tiny lesions and boundary features are reserved, the segmentation precision is improved, a high-precision three-dimensional model is constructed, and clinical diagnosis requirements are met.

A method for mapping one or more cutaneous conditions of a cutaneous surface may include: identifying a body map; identifying one or more body regions corresponding to the body map; identifying at least one representation corresponding to the one or more body regions; identifying one or more icons corresponding to the at least one representation;and defining the one or more icons on the at least one representation.

The invention discloses a skin disease auxiliary system based on computer vision, and particularly relates to the technical field of skin disease assistance, which comprises a multi-modal data acquisition module, an image preprocessing module, a deep learning diagnosis module and a clinical decision support module, according to the method, integrated hardware for synchronously collecting visible light and polarized light images and temperature and humidity parameters is adopted, a focus area is automatically segmented through self-adaptive filtering denoising and an improved U-Net network, and a Dice loss function optimization model is adopted; the method comprises the following steps: constructing a double-branch CNN model, extracting lesion image features by a first branch, normalizing physiological parameters by a second branch, obtaining a disease classification result through a feature fusion layer, storing a skin disease clinical knowledge base, matching similar cases and treatment schemes according to diagnosis results, and generating a structured diagnosis report; if the diagnosis confidence is high, matching according to disease categories; otherwise, expanding the range to assist identification.

The invention relates to the technical field of image processing, particularly provides a lesion image determination method and device, electronic equipment and a storage medium, and aims to solve the problems of inaccurate lesion image determination and poor compatibility data. The method comprises the following steps: step 1, acquiring an apparent dispersion coefficient image based on a dispersion weighted image of the head of a target object; 2, based on the diffusion weighted image and the apparent diffusion coefficient image, obtaining a lesion-free image and a first candidate lesion image; 3, obtaining a second candidate focus image from the first candidate focus image based on the apparent dispersion coefficient value and the focus prediction probability value of each pixel point of the non-focus image and the first candidate focus image; 4, if the change rate of the second candidate focus image and a third candidate focus image obtained through last iterative calculation exceeds a preset change rate, executing the step 5; otherwise, determining the second candidate lesion image as a lesion image; and 5, expanding the first candidate focus image to a preset number of times of the current first candidate focus image, and executing the step 3.

This invention discloses a medical image segmentation method and system based on a diffusion model and multi-level feature fusion, belonging to the field of medical image processing technology. The method includes: acquiring the original medical skin lesion image and preprocessing it, including resizing and normalization; adding Gaussian noise to the input image using a forward diffusion process to obtain the noise-added image; constructing a denoising network encoder, inputting the preprocessed original input image and the noise image into the denoising network encoder; and constructing a denoising network decoder, feeding the encoder's output features into the decoder through skip connections and a multi-level feature fusion module, and outputting the segmentation result image. This invention improves segmentation accuracy and enhances the model's robustness to noise through a boundary-aware loss function and a multi-level feature fusion mechanism.

The invention relates to the technical field of endoscopes, and discloses an endoscope image display method, device and equipment and a computer readable storage medium. The method comprises the following steps: acquiring a target image acquired by an endoscope, and displaying the target image in a first display area of a display corresponding to the endoscope; based on the target image, determining a target focus map in a preset focus map database; and displaying the target lesion map in a second display area of the display, so that a doctor completes lesion diagnosis on the target image in combination with the target lesion map. The target image acquired by the endoscope and the target lesion map corresponding to the target image are respectively displayed in the display corresponding to the endoscope, so that doctors can quickly and accurately judge the property of the lesion displayed by the target image, the diagnosis difference between different doctors is reduced, and the homogenization of the overall medical quality is improved.

The application discloses an automatic prognosis system for nasopharyngeal carcinoma primary focus and lymph node nuclear magnetic resonance image, which comprises a data preparation module, a segmentation module, a feature sharing module, a data processing module and a prognosis module; the data preparation module puts all nuclear magnetic resonance images of a patient into a folder in a patient unit; the segmentation module is used for processing data obtained by the data preparation module; the feature sharing module uses an expansion convolution group to reduce dimensions of each layer multi-scale deep features obtained by the segmentation module but maintains the scale unchanged; the data processing module uses a prediction map generated by the segmentation module and the nuclear magnetic resonance image obtained by the data preparation module to perform a mask strategy; the prognosis module is used for processing a lesion map obtained by the data processing module and the multi-scale deep features processed by the feature sharing module to obtain a prediction value, finally uses the prediction value to obtain a survival probability of the patient, and realizes risk stratification of the patient and gives a suggestion on whether to accept chemotherapy; the application can automatically realize prognosis prediction of the nasopharyngeal carcinoma patient, realizes automation in the whole process and avoids manual participation.

The invention belongs to the technical field of path planning, and discloses a gynecological tumor multidisciplinary combined operation path planning system and method. The method comprises the following steps: acquiring first focus image data from a gynecological tumor operation site, evaluating a first physiological function parameter of a patient state, and processing a first operation state parameter of an operation doctor to obtain a first combined operation path planning feature; constructing a multidisciplinary combined operation path planning model by using the first combined operation path planning feature and a corresponding first tumor operation planning method, inputting a second combined operation path planning feature of a target to be operated into the model to obtain a second tumor operation planning method, and performing reference by combining physiological parameters such as images and electroencephalogram. The method is advantaged in that a tumor operation path planning suggestion is acquired, a long-time operation doctor is assisted to carry out path selection, doctor fatigue and operation comfort are reduced, operation path management is accurately carried out, and an operation completion rate is improved.

The invention relates to the technical field of data processing, in particular to a traditional Chinese and western medicine rehabilitation factor analysis method and system for osteoporotic pain, and the method comprises the steps: obtaining reference patient data, obtaining a diseased region based on a reference focus image, obtaining a region monitoring stress set based on a monitoring region and the diseased region, obtaining a scale value by using the influence disease name set and the reference patient data, obtaining an initial historical data set, retrieving a target historical data set from the initial historical data set by using the reference patient data, the regional monitoring stress set and the scale value, obtaining a component index set, and obtaining a component index set; screening the target historical data set by using the component index set to obtain a screened historical data set, obtaining an evaluation value set based on the screened historical data set, obtaining an evaluation data sequence according to the evaluation value set, and realizing analysis of the traditional Chinese and western medicine rehabilitation factors based on the evaluation data sequence. According to the invention, the intelligent degree and accuracy of processing the historical data can be improved.

The invention discloses a multi-agent inquiry method and system fusing fuzzy semantics and focus image features. The method comprises the following steps: preprocessing a patient subjective description text and an original medical image to obtain a cleaned text and an enhanced image; analyzing the quantized text fuzzy semantics to generate a fuzzy semantic vector, guiding a visual model through the vector to extract focus features of an enhanced image, and generating a fuzzy visual prompt and attention thermodynamic diagram; then forming a consensus diagnosis result by multi-agent collaborative inquiry and debate reasoning based on the information; and finally, combining a diagnosis result, a thermodynamic diagram and a debate process to generate a structured report containing a diagnosis conclusion, visual evidence and a reasoning path. According to the method, accurate fusion of subjective fuzzy semantics and objective image features is realized, the medical illusion rate is reduced, the interactivity, interpretability and accuracy of diagnosis are improved, and the method is suitable for various intelligent medical auxiliary diagnosis scenes.

This invention discloses a method for surgical lesion image segmentation based on a multi-scale dynamic segmentation kernel, belonging to the field of image data processing technology. It includes: S1, acquiring raw frames of medical images, performing preprocessing, dividing the image into scales and segmentation units, and acquiring the local feature parameter set of each segmentation unit; S2, quantifying the lesion-sensitive features of each segmentation unit, screening out sub-centimeter-level lesion risk areas, and constructing a medical image segmentation model; S3, for sub-centimeter-level lesion risk areas, performing structural feature evaluation and orientation consistency quantification, generating dynamic segmentation kernel output results, achieving adaptive segmentation of the lesion area, and screening out abnormal areas; S4, for abnormal areas, performing multi-scale inference and boundary accuracy evaluation, achieving smoothing and blur correction of sub-centimeter-level lesion edges. This invention solves the problem that sub-centimeter-level lesion signals are weak, have blurred boundaries, and are easily missed, making it difficult to perform highly sensitive multi-scale fine segmentation of lesion images.

A tool to create virtual lesions inside a 3D lung tumor image. The image can be an MRI or CT image of a patient's lung to be treated. This virtual pre procedural ablation allows the operator to identify optimal ablation sites and select the optimal ablation parameters for the given anatomy. Also, the actual procedure will be performed with the lung parenchyma surrounding the tumor preventing ultrasound to penetrate beyond the tumor walls. A diagnostic mode will guide the operator to optimize ablation parameters. This virtual pre ablation, the parenchymal ultrasound barrier and the diagnostic catheter operation should enable the operator to conduct the therapeutic procedure safe, fast and effective.

The invention relates to a method for tracking a focus position in real time by a monocular endoscope and dynamically displaying a focus image in a three-dimensional image reconstruction form based on deep learning. The method comprises the following steps: 1, constructing a binocular endoscope camera system; 2, constructing a convolutional neural network based on a Mnas network; 3, using a ScanNet data set and other data sets to carry out a first round of training on the convolutional neural network based on the Mnas network; 4, using the collected monocular video and the corresponding calculation depth map as a training data set, and performing a second round of training on the convolutional neural network based on the Mnas network; and 5, inputting an image acquired by a monocular endoscope software camera system into the convolutional neural network based on the Mnas network in a video streaming media mode, outputting a three-dimensional image, and tracking the track of the monocular endoscope on the three-dimensional image in real time. The invention not only provides a three-dimensional reconstruction method which is simple and convenient to operate, but also is convenient for doctors to find and more specifically observe lesions, and the operation efficiency of the doctors is improved.

The invention provides ultrasonic imaging equipment and a lesion distribution presentation method. The method comprises the following steps: acquiring a target tissue diagram of a target section of target tissue; taking the target tissue diagram as a background of a focus distribution schematic diagram and displaying the target tissue diagram on a display interface to serve as a reference for a user to edit a focus diagram in the focus distribution schematic diagram; determining morphological parameters of the lesion map based on the editing operation of the user, wherein the morphological parameters comprise at least one of the appearance, the size, the direction and the relative position of the target tissue map; and displaying the lesion map in the corresponding area of the target tissue map according to the morphological parameters of the lesion map so as to obtain the lesion distribution diagram of the target section, so that a user can obtain the lesion distribution diagram by editing the lesion map to present the lesion distribution condition of a patient, and the lesion distribution is more easily understood and mastered than an ultrasonic image; and the working efficiency of doctors is improved.