40 results about "Nodular thyroid" patented technology

The invention discloses a thyroid nodule image classification method. The thyroid nodule image classification method comprises the steps of obtaining an image of an existing thyroid part and performing preprocessing to construct a training data set; constructing a thyroid nodule image classification initial model based on the convolution module, the attention mechanism and the full connection layer, and training to obtain a thyroid nodule image classification model; and classifying actual thyroid nodule images by using the obtained thyroid nodule image classification model. The invention also discloses an imaging method comprising the thyroid nodule image classification method. The thyroid nodule image classification model is constructed and trained based on the convolution module, the attention mechanism and the full connection layer, classification and imaging of the thyroid nodule images are achieved, and the thyroid nodule image classification method is higher in reliability, better in accuracy and better in applicability.

The invention discloses a thyroid nodule home intervention system and method based on AR navigation and DRL, and relates to the technical field of thyroid nodule home management and intervention.The thyroid nodule home intervention system based on AR navigation and DRL comprises a client module arranged on a user mobile terminal and used for carrying out thyroid nodule home intervention on a thyroid nodule; the guiding module is used for guiding a user to perform self-operation in a home environment, collecting data and receiving feedback; the cloud service module is in communication connection with the client module through the Internet and is used for remote data processing, model operation and personalized intervention scheme management; according to the thyroid nodule home management system, an innovative system of thyroid nodule home management is constructed through cooperation of multiple modules, that is, the maximum acupuncture point positioning error is 3 mm through an AR navigation unit of a client by means of facial feature point fitting and a neck statistical shape model, and the problem that acupuncture points are not found accurately in traditional home operation is solved.

The utility model relates to the field of medical instruments, in particular to an endoscope drag hook which comprises an endoscope drag hook body and a bending head, the bending head is installed at the left end of the endoscope drag hook body, a connecting rotating shaft is installed at the connecting position of the bending head and the endoscope drag hook body, a connecting ring is installed on the surface of the bending head, and the connecting rotating shaft is connected with the connecting ring. A transmission steel wire is arranged in the connecting ring in a penetrating mode, one end of the transmission steel wire is connected with an adjusting sliding block, a limiting groove is formed in the connecting position of the adjusting sliding block and the endoscope drag hook body, a fixing piece is fixed to the position, close to the right side, of the upper portion of the endoscope drag hook body, and a threaded piston rod is installed in the endoscope drag hook body. The angle between the bending head and the endoscope drag hook body can be changed according to needs, and when a thyroid nodule does not need to be hooked, the bending head and the endoscope drag hook body can be kept to be of a linear structure, so that the bending head of a right-angle structure is prevented from scratching tissue in the body of a patient, and the possibility of trauma is reduced.

The invention provides a thyroid ultrasound image segmentation method and device and a readable storage medium of the thyroid ultrasound image segmentation method and device, and provides a C2F-TransUNet network architecture. According to the network, on the basis of a traditional TransUNet, a cooperative multipath feature enhancement module (CMFEM) and a cross-layer feature interaction fusion module (CFIFM) are introduced. The CMFEM optimizes the channel discrimination ability of bottleneck layer features and spatial detail recovery through two-dimensional attention coupling, grouping feature interaction and a dynamic fusion gate; the CFIFM strengthens complementary association of high-layer and low-layer features of the decoder through multi-domain feature perception and channel and space dynamic weight fusion. Experiments show that according to the method, Dice coefficients on DDTI and a self-built data set reach 84.62% and 80.03% respectively, the 95% Hausdorff distance is remarkably reduced, the precision, noise immunity and stability of thyroid nodule segmentation are effectively improved, and reliable technical support is provided for clinical diagnosis.

The invention provides a thyroid nodule detection system, and the system comprises a receiving module which is configured to receive a thyroid ultrasound video; the multi-target instance segmentation module is configured to perform frame-by-frame detection on target objects in each video frame through a trained target instance segmentation model to obtain detection data of each target object in each video frame, the detection data comprises a detection frame and confidence, and each target object comprises a carotid artery, a thyroid gland and a thyroid nodule; the instance tracking module is configured to track each target object in the thyroid ultrasound video by adopting a multi-target tracking algorithm based on target detection based on each detection data to obtain each tracking trajectory of each target object; and the determination module is configured to determine a target thyroid nodule detection result corresponding to the thyroid ultrasound video based on each tracking trajectory of each target object. According to the invention, the video can be processed for an end-to-end screening scene, and the detection accuracy is improved.

The invention relates to the technical field of medical image processing, in particular to a thyroid nodule ultrasonic detection device based on masks and dense prompts, which comprises a MaskSAM module and a training strategy, the invention firstly proposes that a prediction result of an auxiliary module is converted into sparse prompts and dense prompts by freezing the weight of the auxiliary module, and the sparse prompts and the dense prompts are combined to obtain a sparse prompt and a dense prompt. The method provides more prompt information for the model, improves the segmentation performance of the model, and achieves the mutual cooperation of a backbone network, a cascaded pixel encoder, a Transfomer encoder based on mask attention, a prompt encoder, a mask decoder and an auxiliary module. A mask attention mechanism is utilized to focus on a thyroid nodule area. On the multiple performance indexes, the image segmentation effect of thyroid nodule ultrasound is greatly improved, the model can adapt to thyroid nodule ultrasound images with different resolutions and complexities, and robustness is enhanced.

This invention relates to the field of ultrasound image analysis technology, specifically to a method for classifying and rating thyroid images. The method includes: acquiring thyroid images from a patient; preprocessing the thyroid images to obtain preprocessed images; inputting the preprocessed images into a feature extraction network to obtain nodule extraction features; and feeding the nodule extraction features into corresponding classifiers to obtain corresponding evaluation results. Addressing the problem that existing thyroid nodule identification models are black-box and lack interpretability in assisted diagnosis, this invention introduces a specific feature extraction network to extract nodule features and echogenicity features across multiple dimensions. Each type of feature is labeled to facilitate subsequent diagnosis by doctors, and the output is based on relatively sufficient evidence and has strong interpretability.

The invention discloses an ultrasonic thyroid nodule detection method and device based on deep learning feature fusion. The method comprises the following steps: firstly, acquiring an ultrasonic video; extracting an image sequence of the ultrasonic video; then training a BDA-YOLO thyroid nodule prediction model, and predicting a target frame and a focus area of the thyroid nodule; and finally, the thyroid nodules, the shapes, the sizes, the positions and the area contours of the nodules are visualized, a doctor is helped to diagnose thyroid diseases more easily through a visual visualization result, and support is provided for follow-up disease treatment of a patient. According to the method, the thyroid nodule positioning and segmentation precision of the model can be improved.

The application discloses a thyroid nodule home intervention system and method based on AR navigation and DRL, relates to the technical field of thyroid nodule home management and intervention, and discloses a thyroid nodule home intervention system based on AR navigation and DRL, which comprises a client module, a cloud service module and the like. The client module is arranged on a user mobile terminal and is used for guiding the user to perform self-operation, collect data and receive feedback in a home environment. The cloud service module is connected with the client module through internet communication and is used for remote data processing, model operation and personalized intervention scheme management. Through the cooperation of multiple modules, an innovative system for thyroid nodule home management is constructed, that is, the AR navigation unit of the client is used to fit the facial feature points and the statistical shape model of the neck, so that the maximum error of acupoint positioning is 3mm, and the problem that acupoints cannot be accurately found in traditional home operation is solved.

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

The invention relates to the technical field of thyroid nodule detection, in particular to a thyroid nodule detection model construction method, which comprises the following steps of: performing multi-modal imaging on a thyroid nodule by using an image enhancement technology and a photoacoustic imaging technology; building a thyroid nodule detection model based on the built multi-modal image data; and optimizing the thyroid nodule detection model by using the loss function, according to the method, various modal data such as ultrasonic images, photoacoustic imaging and gene data are fused, the advantages of each type of data are fully utilized, the possible limitation of single modal data is overcome, and the comprehensiveness and accuracy of detection are improved; a graph neural network is used for gradually extracting high-order relation features between nodule samples, global embedding features are generated, and the complex nonlinear relation of the nodule samples can be captured; through the attention mechanism, the important relationship between nodule samples can be automatically identified and weighted, the feature redundancy is reduced, and the robustness and accuracy of the model are improved.

The invention relates to the technical field of thyroid nodule puncture training, and discloses a real-time feedback type ultrasonic-guided thyroid nodule puncture training device which comprises a training model, the training model comprises a base, a support is clamped in the base, a sleeve is fixedly connected to the inner wall of the support, and a simulation trachea is inserted into the outer surface of the sleeve; a simulated laryngeal joint bone is inserted into one end of the simulated trachea, a thyroid simulation body is clamped on the outer surface of the simulated laryngeal joint bone, a plurality of simulated nodules are fixedly connected on the outer surface of the thyroid simulation body, and the outer surface of the stent is coated with simulated skin. The system has the functions of real-time feedback and data calibration, a microcontroller of the sampling assembly receives puncture force data collected by a micro pressure sensor and puncture motion parameters collected by an acceleration sensor in real time and compares the puncture force data and the puncture motion parameters with preset threshold values, and if the puncture force is too large or the speed is too high, operation errors are judged, and an operator is reminded through an alarm.

The application provides a thyroid nodule image segmentation method, system, computer device and medium, and belongs to the field of medical image processing, and comprises the following steps: acquiring a three-modal tensor of a thyroid nodule ultrasound image to be segmented; performing feature extraction of different resolutions on the modal tensor in sequence to obtain a feature extraction result; performing feature extraction on each resolution feature extraction result in parallel through different receptive fields to obtain a plurality of context features of different scales, fusing and optimizing based on a channel attention mechanism to obtain an optimized feature; optimizing based on a spatial attention mechanism to obtain a feature optimized based on spatial attention; fusing to obtain a feature extraction result optimized at the scale; fusing and performing scale reconstruction on the feature extraction result and scale reconstruction features of a lower scale to obtain scale reconstruction features at the scale; and determining a nodule probability map from the scale reconstruction features of the highest scale. The method can identify nodules with irregular edges and different sizes, and improves the segmentation accuracy.

The invention relates to the technical field of image segmentation, in particular to a self-adaptive segmentation method of an ultrasonic image. The method comprises the following steps: processing a neck ultrasonic image of a thyroid nodule patient by using a plurality of high-pass filters with different scales to obtain a filtered image with each scale; screening the target direction of each region according to the change degree of the high-frequency information content in different directions in each region in the filtered image of each scale; determining the high-frequency information change direction of the filtered image by combining the proportion condition of the high-frequency information content in the target direction of all the regions; according to the difference between the target direction of the single region and the high-frequency information change direction, obtaining a direction change consistency factor of each region; and obtaining a target image by synthesizing the direction change consistency factor of the same region in the filtered images of the adjacent scales, the target direction and the difference of the high-frequency information content, and segmenting the target image. The accuracy of the neck ultrasound image segmentation result of the thyroid nodule patient is improved.

The invention provides a thyroid nodule detection model construction method and application based on intermediate frequency feature interaction, and the method comprises the steps: obtaining a plurality of training images which are thyroid ultrasound images marked with thyroid nodules; and constructing a thyroid nodule detection architecture formed by sequentially connecting a backbone network, a feature fusion network and a classification network, and training the thyroid nodule detection architecture by using a plurality of training images to obtain a constructed thyroid nodule detection model. According to the scheme, feature processing is carried out through depth separable convolution with different expansion rates by using the expansion convolution unit, the mid-distance context can be aggregated while tiny nodule details are kept, and then efficient coupling of local details and mid-distance semantics is realized through the intermediate frequency feature interaction unit, so that the thyroid nodule detection model has a better classification effect.

The invention provides an automatic identification method and system for a thyroid nodule ultrasonic image, and the method comprises the steps: obtaining a to-be-identified thyroid nodule ultrasonic image, and converting the to-be-identified thyroid nodule ultrasonic image into a nodule gray image; segmenting the nodule grayscale image into a plurality of sub-image blocks, determining a plurality of calcification regions of thyroid nodules in the sub-image blocks, and determining local calcification features of the sub-image blocks based on convolution kernels of the sub-image blocks in different scale channels and all the calcification regions; determining the recognition depth when thyroid nodule recognition is carried out on the sub-image blocks; determining a significance coefficient based on the recognition depth and the aspect ratio of the thyroid nodules in the sub-image blocks; and performing feature cascading on all the local calcification features based on the significance coefficient to obtain global calcification features of the thyroid nodule ultrasound image to be identified. By adopting the scheme of the application, the calcification part can be subjected to multi-dimensional identification under the interference of various factors, so that accurate description of thyroid calcification features is realized.

The invention discloses medical detection equipment for the ultrasonic department, and relates to the technical field of medical instruments. The device comprises a probe, and an angle adjusting mechanism is arranged on one side of the probe. The thyroid nodule of a patient is subjected to ultrasonic detection through the probe, so that a doctor knows the nodule depth, a driven plate is rotated according to the nodule depth, a first rotating block rotates along with the driven plate, the rotating angle is observed through scales, and after the driven plate rotates to a specified angle, a first limiting block and a second limiting block are placed on the surface of a puncture needle; the anti-skid pad in the anti-skid pad is attached to the surface of the puncture needle, and the first limiting block and the second limiting block are inserted into the first rotating block, so that the problem that the angle of the puncture needle cannot be adjusted when a nodule is detected is effectively solved, the effect of adjusting the puncture needle according to the depth of the nodule can be achieved, and meanwhile, the anti-skid pad in the anti-skid pad is attached to the surface of the puncture needle. The stability during puncture is improved, the corresponding adjustment time is shortened, and the universality of the detection device is also improved.

This application provides a thyroid nodule identification device, method, storage medium, and electronic device. The thyroid nodule identification device includes: an ultrasound video acquisition module for acquiring thyroid ultrasound video; a bounding box acquisition module for acquiring nodule bounding boxes of multiple frames in the thyroid ultrasound video, the nodule bounding boxes being used to identify the location and extent of the thyroid nodules; and an identification result acquisition module for acquiring keyframes and / or multiple nodules in the thyroid ultrasound video based on the nodule bounding boxes of the multiple frames. The thyroid nodule identification device can automatically identify keyframes and / or multiple nodules in a thyroid ultrasound video.

The embodiment of the invention discloses a medical image processing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a target thyroid ultrasound image; the target thyroid ultrasound image is input into a thyroid image processing model which is trained in advance, a thyroid contour area of the thyroid ultrasound image and at least one reference thyroid nodule marking box are determined, and the thyroid image processing model which is trained in advance is obtained based on improved yo11 model training; and determining a target thyroid nodule labeling frame in the at least one reference thyroid nodule labeling frame according to a position relationship between the thyroid contour region and the at least one reference thyroid nodule labeling frame, and displaying the target thyroid nodule labeling frame in the target thyroid ultrasound image. According to the technical scheme of the embodiment of the invention, the thyroid nodule in the thyroid ultrasound image can be quickly and accurately identified, and objective reference can be provided for clinical diagnosis.

The present invention relates to the technical field of image segmentation, and particularly to a method for segmenting thyroid nodules based on an improved Unet network, including: performing operations of unifying image size, randomly flipping, rotating, and enhancing contrast on a public thyroid ultrasound image dataset; constructing an improved Unet network. First, adding self-attention residual connection blocks to the first 3 downsampling convolutions of the Unet network; secondly, adding a high-low frequency self-attention adaptive fusion module after the 4th downsampling; finally, connecting a semantic enhancement module after the high-low frequency self-attention adaptive fusion module; using argmax to determine whether the pixel value belongs to a nodule or the background. The present invention helps doctors to accurately segment thyroid nodules, precisely locate the nodule area, while enhancing global context and local information, and reducing the error of thyroid nodule segmentation.

The invention discloses a thyroid nodule result analysis method based on a large model and a series of ultrasonic reports, and relates to the technical field of medical image analysis. The thyroid nodule result analysis method based on the large model and the series of ultrasonic reports mainly comprises the steps of preprocessing thyroid ultrasonic report data to obtain preprocessed thyroid ultrasonic report data, and performing fine tuning on a large language model by utilizing a specific data set and a medical knowledge base to obtain a fine-tuned large language model, and designing a professional prompt template, performing nodule information extraction and unit unified processing on a to-be-analyzed ultrasonic report by using the fine-tuned large language model to obtain standardized structured data, and performing size comparison and analysis on the extracted nodule information by using the fine-tuned large language model to obtain a structured report. By implementing the thyroid nodule result analysis method based on the large model and the series of ultrasonic reports, the efficiency and accuracy of thyroid nodule diagnosis and follow-up visit management can be improved.

The invention discloses a thyroid nodule ultrasound image segmentation method, system and device, a medium and a product, and relates to the technical field of medical image processing.The method comprises the steps that after a thyroid nodule data set is obtained, the thyroid nodule data set is preprocessed, and the preprocessed data set comprises a training set, a verification set and a test set; respectively training and verifying the U-net network by using the training set and the verification set to obtain a thyroid nodule image segmentation model; testing the thyroid nodule image segmentation model by using the test set to obtain a thyroid nodule image segmentation result; the problems that the omission ratio of small nodules is high and nodule boundaries are difficult to accurately segment can be solved, and the thyroid nodule detection precision is improved.

1. The name of the design product: thyroid nodule treatment instrument (wearing type). 2. The use of the design product: for auxiliary treatment of thyroid nodules. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

This application proposes a method and application for constructing a thyroid nodule detection model based on mid-frequency feature interaction. The method includes: acquiring multiple training images, wherein the training images are thyroid ultrasound images annotated with thyroid nodules; constructing a thyroid nodule detection architecture consisting of a backbone network, a feature fusion network, and a classification network connected sequentially; and training the thyroid nodule detection architecture using multiple training images to obtain the constructed thyroid nodule detection model. This scheme uses dilated convolutional units with depthwise separable convolutions at different dilation rates for feature processing, which can aggregate mid-range context while preserving details of small nodules. Furthermore, the mid-frequency feature interaction unit achieves efficient coupling between local details and mid-range semantics, thereby enabling the thyroid nodule detection model to achieve better classification performance.

The invention discloses a thyroid nodule intelligent segmentation and automatic rating method based on an ultrasonic image, and relates to an image intelligent segmentation and automatic rating method. The invention aims to solve the problem that the existing thyroid nodule ultrasonic image cannot realize intelligent segmentation and automatic rating. The method comprises the following steps: segmenting an ultrasonic image by using a provided thyroid nodule segmentation method based on a GADRU-Net network; performing preprocessing operation on the segmented nodule image; 6 diagnostic features are extracted from the nodule according to a rating index specified by C-TIRADS; and classifying by using a support vector machine or a convolutional network, and rating according to indexes. The invention belongs to the technical field of ultrasonic medical image processing.

The present application relates to a kind of thyroid ultrasound abnormal region capture method, system and electronic equipment, the method includes: obtaining the detection frame of the same abnormal region in the thyroid ultrasound image of current frame and the previous N frame of current frame, constitute N+1 detection frame;N+1 detection frame is subjected to spatial coordinate attention mechanism processing, obtain the detection frame to be measured;The detection frame to be measured is input into long short-term memory network model, obtains the parameter information of the detection frame to be measured;According to the parameter information of the detection frame to be measured, the parameter information of the detection frame in the thyroid ultrasound image of current frame and the previous 1 frame of current frame, obtain the optimized detection frame of abnormal region in the thyroid ultrasound image of current frame, as abnormal region detection result.The present application is combined with the mobility of thyroid nodule, based on spatial coordinate attention mechanism and long short-term memory network model, the detection frame of abnormal region of continuous multiple frames is remembered and analyzed, realizes the capture of thyroid ultrasound abnormal region.

The application discloses a thyroid nodule detection method based on dynamic blur adaptive fusion, relates to the technical field of medical images, and takes into account the problems of real-time performance and image quality blur; the method does not suppress noise by regarding blurred images as noise interference, but learns and understands the blur structure, and the process is realized by outputting clear features and blur features through a double-branch perception network; a clear-blur switching unit is designed according to the change of the scanning speed and the average Laplacian variance of the image, the strategy can be adaptively adjusted according to the blur degree of the input image, and the clear features and the blur features are dynamically mixed. Moreover, the application is a plug-and-play component, and can be organically embedded in mainstream target detection frameworks, such as the YoLO series and the DETR series.

The application relates to the field of intelligent medical treatment, in particular to a thyroid nodule positioning method, device and program product. The method comprises the following steps: acquiring a thyroid image; inputting the thyroid image into a nodule positioning model to obtain the width and height of a predicted bounding box, a predicted center point coordinate and a predicted heat map; calculating a heat map centroid coordinate through the predicted heat map; performing weighted fusion on the predicted center point coordinate and the heat map centroid coordinate to obtain a nodule center coordinate; and generating a predicted bounding box through the width and height of the predicted bounding box and the nodule center coordinate. The application has good clinical value.

The invention discloses a semantic guidance and geometric constraint combined driven thyroid nodule segmentation method, which belongs to the technical field of medical image analysis, and comprises the following steps: constructing a thyroid nodule segmentation data set, and dividing a training set, a verification set and a test set; the method comprises the following steps: embedding an attention gating module at a jump joint of a U-Net encoder and a decoder, calculating a loss value by adopting a composite loss function weighted by Dice loss, Focal loss and boundary loss, and constructing an improved U-Net network model; introducing a deep learning probability guide item to construct a level set energy function; fusing the improved U-Net network model with a level set energy function; training the fusion model by adopting the training set, the verification set and the test set; and after the training is completed, inputting a to-be-detected image, and outputting to obtain a thyroid nodule detection segmentation result. According to the method, a high-quality initial segmentation contour is provided through the improved U-Net, the boundary is further optimized through the customized level set energy function, and the smoothness and accuracy of nodule boundary recognition are improved.

The application discloses a thyroid nodule classification method and system, an intelligent terminal and a storage medium. The thyroid ultrasound image is preprocessed by a preprocessing module to obtain a target image. A multi-branch hollow convolution module is used to extract features of the target image from multiple scales and multiple directions. A feature interaction module is used for feature interaction and dense connection is adopted to fully utilize global features and local features. A multi-branch non-local self-attention fusion module is used for self-attention operation and fusion operation to enhance the features. Finally, a classification module is used for classification to obtain the category of the thyroid nodule. Compared with the prior art, the features can be fully extracted, and the thyroid nodule can be accurately classified.