44 results about "Lesion mapping" patented technology

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 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 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 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 application discloses a mobile terminal nasopharyngeal carcinoma identification method and belongs to the technical field of intelligent identification systems. The system can identify the nature of nasopharyngeal position lesions after training image information of confirmed nasopharyngeal carcinoma patient lesion sites, and has the characteristics of fast identification speed and high accuracy. The system comprises an image preprocessing module, an identification module, and a training identification module. The image preprocessing module is used for preprocessing images of nasopharyngeal sites. The identification module is used for storing a trained identification model. The training identification module is used for training an identification model through lesion images of known nasopharyngeal carcinoma patients. The image preprocessing module is connected with the identification module and the training identification module. The training identification module comprises a case library, an identification model unit, and a training unit. The case library is used for storing identification preprocessing data. The identification model unit is used for storing a trained identification model. The training unit is used for training a neural network identification model in the identification model unit through the identification preprocessing data in the case library.

The present invention relates to the technical field of stomatology. Disclosed is an oral disease detection method based on a large artificial intelligence model. The method comprises: acquiring a dental lesion image and a corresponding intraoral state description; using a feature extraction module BERT and a multi-scale feature extraction module (MFEM) to respectively extract text feature information of an intraoral state and image feature information of the dental lesion image; identifying and excluding false negative samples therein; introducing a total loss function and a dynamic label assignment strategy into a YOLOv8 network to obtain an improved YOLOv8 network; dividing a data set into a training set and a test set, and sequentially inputting the training set and the test set into the improved YOLOv8 network for validation, so as to obtain a validated YOLOv8 network; and inputting the dental lesion image and the corresponding text information into the validated YOLOv8 network, so as to obtain lesion region type detection and a text description. The present invention can effectively improve the detection and classification of various intraoral diseases, and can give corresponding lesion region descriptions and suggestions.

[Problem] To provide an endoscopic image diagnosis assistance processor 2 for stably identifying a to-be-observed organ which is being observed by means of an endoscope 9. [Solution] This endoscopic image diagnosis assistance processor 2 comprises: a site inference unit 13 that uses a first model for an endoscopic image to infer a certainty factor of each of a plurality of site images; a site identification unit 14 that identifies a to-be-observed site of the endoscopic image, on the basis of a predetermined condition using the certainty factor; an organ identification unit 14 that identifies a first to-be-observed organ corresponding to the to-be-observed site; a lesion detection unit 20 that uses a plurality of second models, which are trained on a plurality of lesion images, as teacher data, for each of a plurality of organs, to infer a lesion in the aforementioned image; an organ specification unit 17 that enables a user to specify a second model of a second to-be observed organ among the plurality of second models; and a warning unit 18 that issues a warning on the basis of a predetermined condition when the first to-be-observed organ and the second to-be-observed organ are different.

The application belongs to the technical field of deep learning and medical image processing, and relates to a skin lesion segmentation method based on cross-domain feature correction, comprising: I, a scanning process, given a to-be-segmented lesion image, spatial domain and frequency domain features of the to-be-segmented image are extracted respectively, and a double-domain segmentation confidence is obtained; II, a fixation process, forward propagation is performed again, and under the guidance of the confidence, complementary information of double-domain same-resolution features is fused; III, the modified double-domain density probability distribution of the last layer is averaged and fused, and a fused segmentation image is obtained; IV, end-to-end training is performed, and the model is optimized end to end by calculating the cross-entropy loss of the model output segmentation image and the real label segmentation image; the application can provide accurate and reliable lesion segmentation results and quantitative calculation basis for clinical skin disease diagnosis, and has good practical application value.

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

This invention relates to the field of image processing technology, and particularly to a method and system for three-dimensional reconstruction of gynecological tumor MRI images based on deep learning. The method includes: processing coarsely labeled tumor regions using a target detection model based on the original image to generate bounding boxes for small lesions; segmenting the images using these bounding boxes to obtain images of small and non-small lesions; proportionally reducing the size of the small lesions to obtain lesion images; selecting samples based on the generated boundary regions, calculating the mean grayscale value and difference coefficient, and weighted merging to obtain normal tissue images; compressing the tumor core region and boundary region to obtain the overall tumor image; stitching the lesion images, normal tissue images, and tumor images together to form a low-resolution image to train the model, and then segmenting and extracting features to construct a three-dimensional tumor model. This invention preserves the features of small lesions and boundaries, improves segmentation accuracy, constructs a high-precision three-dimensional model, and adapts to clinical diagnostic needs.

The invention relates to an image segmentation method, an image segmentation model training method and computer equipment. The method comprises the following steps: acquiring a three-dimensional focus image corresponding to a target focus, and preprocessing the three-dimensional focus image to obtain a preprocessed three-dimensional focus image; performing space clipping on the preprocessed three-dimensional focus image to obtain a candidate mask image; determining an interfering organ category corresponding to the three-dimensional focus image, and generating an organ mask graph corresponding to the interfering organ category; identifying a redundant structure region corresponding to the three-dimensional lesion image, and generating a redundant structure mask graph corresponding to a redundant structure; removing an area corresponding to the organ mask image and the redundant structure mask image from the candidate mask image to obtain a residual area mask image; and carrying out image segmentation by using the image segmentation model according to the residual region mask graph to obtain an image segmentation result. By adopting the method provided by the invention, the computing power required when the image segmentation model performs image segmentation on the medical image can be reduced.

Techniques are described for augmenting chest X-ray (CXR) images with synthetic lesions using a lesion-aware machine learning framework in association with optimizing thoracic disease detection models. In an example, a system can comprise a lesion augmentation component that generates synthetic lesion images comprising synthetic lesion image data objects integrated on or within medical images using lesion generator, trained to generate the synthetic lesion image data objects, and tailor the synthetic lesion image data objects to account for different types of lesions and different anatomical locations of the lesions. The system can further comprise a training component that trains a lesion detector using the synthetic lesion images. The training component can further perform an alternate training strategy to integrate the training of the lesion generator and the detector for mutual boosting.

The application discloses a surgical path determination method and device, electronic equipment and storage medium. A lesion image and an angiogram image of a lesion site are acquired; a plurality of candidate surgical starting points are determined on the edge contour line of the lesion image, and a plurality of candidate surgical paths are determined based on the plurality of candidate surgical starting points and a corresponding surgical endpoint of the lesion site; first path feature information corresponding to each candidate surgical path is determined based on the lesion image, and second path feature information corresponding to each candidate surgical path is determined based on the angiogram image; and a target neural network model obtained through pre-training, the first path feature information and the second path feature information corresponding to the candidate surgical path are used to determine a target surgical path corresponding to the lesion site from the plurality of candidate surgical paths, thereby solving the problems of low efficiency and low accuracy of surgical path determination and improving the efficiency and accuracy of surgical path determination.

The application relates to a lesion region segmentation method and device based on normal tissue image information contrast, and belongs to the technical field of image segmentation. The method and device are characterized in that: a to-be-segmented lesion image is input into a pre-constructed VEA network to obtain a single-mode normal image; the to-be-segmented lesion image and the single-mode normal image are input into an encoding-decoding network to obtain first image features extracted from the to-be-segmented lesion image and second image features extracted from the single-mode normal image; the first image features and the second image features are subjected to feature alignment and feature comparison to obtain an attention map; and the attention map is used to enhance lesion region features in the to-be-segmented lesion image to obtain a lesion region segmentation result of the to-be-segmented lesion image. Therefore, by comparing the lesion image with the normal image, the differences between the lesion image and the normal image are fully considered, the lesion is accurately segmented, and the precision of lesion region segmentation is improved.

Disclosed herein is a system for linking images of a lesion taken over different periods of time comprising an imaging device that is operative to image one or more lesions present in a living being. The imaging device takes a first image at a first point in time T1 and a second image at a second point in time T2. A microprocessor is operative to receive the first image and the second image and to perform an adaptive search on the respective images. The adaptive search comprises selecting a first voxel in a first lesion in the first image and radially searching for one or more second lesions in the second image that share one or more overlapping first voxels with the first lesion in the first image. A probability is assigned if there is an overlap between the first lesion and one or more second lesions. Each voxel in the first lesion based on the probability.

The application relates to the technical field of medical image processing, and provides a fundus lesion image segmentation method and system, which solves the problem of insufficient spatial continuity and anatomical rationality of three-dimensional segmentation results in the prior art. The method comprises the following steps: acquiring optical coherence data and structural light data of a retina, and registering and fusing the data into enhanced point clouds; extracting deep features of each point of the enhanced point clouds, and determining global dependency relationships; inputting a segmentation model to perform three-dimensional instance segmentation, and obtaining an initial segmentation result; introducing a geometric constraint function corresponding to a prior model of a normal anatomical structure of the retina, adjusting the initial result so that a lesion boundary conforms to the anatomical structure, and obtaining a target segmentation result; identifying key points of the lesion in the enhanced point clouds according to the target segmentation result; outputting a three-dimensional mask of the key points, and calculating the volume, surface area and spatial position of the lesion relative to the retinal layer according to the three-dimensional mask. The application can realize high-precision three-dimensional segmentation and quantitative analysis of fundus lesions.

The invention relates to the technical field of medical image processing, provides a fundus focus image segmentation method and system, and solves the problems of insufficient spatial coherence and dissection rationality of a three-dimensional segmentation result in the prior art. The method comprises the following steps: acquiring optical coherence body data and structured light data of a retina, and registering and fusing into an enhanced point cloud; deep features of each point of the enhanced point cloud are extracted, and a global dependency relationship is determined; inputting a segmentation model to carry out three-dimensional instance segmentation to obtain an initial segmentation result; introducing a geometric constraint function corresponding to the prior model of the normal anatomical structure of the retina, adjusting the initial result to enable the lesion boundary to accord with the anatomical structure, and obtaining a target segmentation result; identifying focus key points in the enhanced point cloud according to a target segmentation result; and outputting the three-dimensional masks of the key points, and calculating the volume and the surface area of the focus and the spatial position of the focus relative to the retina layer according to the three-dimensional masks. According to the invention, high-precision three-dimensional segmentation and quantitative analysis of the fundus focus can be realized.

In an image processing device, the image acquisition means acquires a lesion candidate image including a lesion candidate detected from an endoscopic image. The resize means performs resize processing for resizing a size of the lesion candidate image according to a target size while maintaining an aspect ratio of the lesion candidate image. The padding means performs padding processing for matching a size of a resized image obtained by the resize processing to the target size.

The invention discloses a dermatoscope lesion image segmentation method, system and device and a storage medium, and relates to the technical field of medical image processing, and the method comprises the steps: carrying out the layer-by-layer down-sampling of a lesion image into feature maps of different scales; semantic information decoupling is carried out on the deepest feature map, and feature maps of a focus area, a normal area and a joint area are explicitly generated; and for other feature maps with different scales except the deepest layer feature map, carrying out joint decomposition of a spatial domain and a frequency domain, and carrying out weighted jump connection on the spatial domain and the frequency domain to enhance semantic expression of the feature maps, thereby accurately separating and enhancing key frequency components and spatial position features related to the lesion, and improving the lesion accuracy. Irrelevant background noise and disordered hair artifacts are inhibited; and finally, progressive decoding is carried out based on the mahalanobis distance between the decoupled feature map and the feature map after double-domain weighting, so that the accuracy of space details is kept while the accuracy of advanced semantic information is ensured, and accurate segmentation of the dermatoscope lesion image is realized.