114 results about "Thyroid nodular" patented technology

The invention discloses a thyroid nodule diagnosis method based on a deep learning network, and belongs to the field of image processing and artificial intelligence aided disease diagnosis. The methodcomprises the following steps: searching an ultrasonic original image and a pathological report of a thyroid nodule of a thyroid patient, and constructing a thyroid nodule database; preprocessing theultrasonic image; performing semantic segmentation on the ultrasonic image preprocessed in the step 2 through a Deeplab v3+ method based on Xception-JFT, and forming a semantic segmentation result graph; judging benign and malignant thyroid nodules based on a deep learning network; and forming a thyroid nodule diagnosis information report. According to the method, the Deeplab v3+ algorithm basedon Xeption-JFT is adopted to establish the thyroid ultrasound image segmentation network model, the optimal segmentation effect is achieved by continuously improving the backbone network Xception, nodule information can be automatically and rapidly recognized under high accuracy and high robustness, image features are automatically extracted for accurate segmentation to obtain a better diagnosis result, and an objective reference is provided for clinical diagnosis.

The invention provides a method for evaluating thyroid nodules by combining protein mass spectrometry with deep learning. According to the scheme, a genetic algorithm is adopted to obtain a protein combination capable of reflecting benign and malignant probabilities of thyroid nodules; based on a deep learning model, thyroid nodules are accurately classified by utilizing the selected protein combination; the possibility of malignant nodules is assessed, the surgical treatment cost and the treatment cost generated by subsequent medication can be greatly reduced, and meanwhile, the medical resource waste caused by excessive treatment can be reduced while the life quality of a patient is greatly improved.

The invention discloses a thyroid nodule ultrasonic image processing method based on cross-layer sparse cavity convolution. A novel cross-layer cavity convolution network structure, a sparse constraint network, a separation sparse cavity convolution layer, and a loss function with adaptive weight adjustment and sparse constraint are established, the method overcomes the defects that an existing method is poor in thyroid nodule ultrasonic image nodule region semantic resolution capability, and semantic feature extraction of the nodule region is susceptible to interference of similar backgrounds, and solves the problem of poor extraction effect of a thyroid nodule ultrasonic image semantic probability heat map caused by limited receptive field expansion ability of a deep learning network ina forward propagation step.

The invention discloses a thyroid nodule Tirads grading automatic identification model construction method and device. The method comprises the following steps: S1, carrying out data preprocessing on a thyroid ultrasound image; s2, performing ellipse fitting conversion on the preprocessed thyroid ultrasound image to generate a segmentation mask as a training label; and S3, building a multi-task model based on a convolutional neural network, inputting the training label into the multi-task model, and performing training by adopting a preset loss function to obtain a trained thyroid nodule Tirads grading automatic identification model. According to the invention, in thyroid nodule TI-RADS grading automatic identification, the latest convolutional neural network framework is used for carrying out automatic identification tasks on thyroid ultrasound images in an end-to-end mode, TI-RADS grading is completed, detection work can be completed in the early stage of thyroid cancer, and therefore expensive work such as puncture does not need to be used; and a doctor is assisted to complete thyroid cancer screening.

The invention relates to a biomarker for preoperative identification of benign and malignant thyroid nodules, a kit and application thereof. The level of 17 metabolites in peripheral blood plasma of a thyroid nodule patient is detected through metabonomics analysis, and omics analysis and a machine learning method are combined to construct a diagnosis model and evaluate the thyroid cancer diagnosis efficiency by using a subject working curve (ROC). The area of the metabolic marker combination diagnosis model composed of the metabolites under the ROC curve in thyroid nodule diagnosis can reach 95.05%, the sensitivity and specificity are both higher than 88%, and the metabolite marker combination diagnosis model can be applied to identification of thyroid benign and malignant nodules.

The invention provides a group of thyroid cancer markers and an application thereof. The markers comprise thyroid cancer molecular typing related genes, targeted medication related genes, chemotherapymedication related genes, operation prompt related genes, prognosis related genes and thyroid cancer heredity related genes. According to the invention, aiming at all exon regions of 44 genes relatedto thyroid molecular typing, targeted medication, chemotherapy medication, operation prompt, prognosis and heredity, high-depth sequencing is carried out; meanwhile, various variation types like SNV / Indel and gene fusion of genes are analyzed; and molecular level information of thyroid nodules and thyroid cancer is deeply analyzed. Aiming at a capture probe designed for the 44 genes, 558 targeting target regions are covered; the probe comprises 3633 sequences with a total size of 254.096 Kbp; and a prepared kit is wide in coverage, high in cost performance and high in effectiveness, can provide a reference basis for further molecular typing, medication prompt, genetic risk assessment and the like of a thyroid patient, and is applicable to clinical popularization and application.

The invention belongs to the technical field of image processing, and particularly relates to a thyroid nodule invasiveness prediction method based on target detection. The method comprises the following steps: S1, preprocessing a thyroid ultrasound image obtained clinically to obtain an original data set; S2, constructing a positioning network model of a network structure based on a traditional Faster RCNN, and pre-training the positioning network model; S3, extracting nodule form information in the ultrasonic image by using a positioning network, and obtaining aspect ratio information of the nodules and context information of gland tissues; S4, constructing a classification network model; S5, establishing a multi-model fusion thyroid nodule invasiveness prediction network, and predicting the thyroid nodule invasiveness in the ultrasonic image; and S6, training and updating the classification network model in the fused network model, and storing the model with the highest accuracy in the verification set. According to the method, end-to-end full-automatic auxiliary diagnosis can be achieved, and the defects that a traditional method is insufficient in accuracy and low in detection rate are overcome.

The invention discloses a method and a device for automatically diagnosing benign and malignant thyroid nodules. The method comprises the following steps: step 1, carrying out noise reduction preprocessing on an original thyroid ultrasound image; 2, performing data enhancement on the preprocessed thyroid ultrasound image; 3, the thyroid ultrasound image after data enhancement being respectively put into three convolutional neural network models which are constructed through ResNet, DenseNet or ResNext in advance for training; and 4, carrying out model integration on images trained by different models in the step 3. According to the method, the problem that the automatic classification effect of benign and malignant thyroid nodules is poor is solved, the deep learning image automatic recognition technology is applied, the latest framework is used for extracting image features, the automatic classification task of benign and malignant thyroid nodules is completed in an end-to-end mode, and meanwhile high accuracy is guaranteed.

The invention relates to a thyroid nodule positioning and ultrasonic report error correction method based on a target detection network. The method comprises the following steps: establishing a deep convolutional neural network, wherein the deep convolutional neural network comprises a target detection network part and a classifier part, and the target detection network part detects a to-be-classified image to obtain a trachea and carotid artery detection result; the classifier part classifies the images according to the recognized relative positions of the trachea and the carotid artery in the images; and based on an input box event monitoring mechanism, comparing the classification result with keywords in the ultrasonic report, and if the classification result is inconsistent with the keywords in the ultrasonic report, giving a prompt. According to the invention, the phenomenon that left and right focuses are reversed due to manual errors is effectively avoided, so that the consistency and accuracy of text reports and pictures are ensured, and adverse medical accidents are reduced.

The invention discloses a thyroid nodule automatic identification model construction method, system and device based on a convolutional neural network, and the thyroid nodule automatic identificationmodel construction method comprises the steps: carrying out the data noise reduction of a thyroid ultrasonic image, and obtaining a training data set and a test data set; inputting the training data set into ResNet, DenseNet, and / or ResNext to carry out thyroid nodule malignant probability training to obtain a thyroid nodule automatic identification model; and testing the thyroid nodule automaticidentification model by adopting the test data set.

The invention relates to a thyroid nodule benign and malignant related marker and application thereof. The marker is the following target markers: a TTC34 gene or a TTC34 sequence of a genome, an SLC16A3 gene or an SLC16A3 sequence of the genome, an RARG gene or an RARG sequence of the genome, and any one or all of two selected from an RCOR2 sequence of an RCOR2 gene or a genome and an ITPKA sequence of an ITPKA gene or a genome. The invention relates to application of primer molecules and / or probe molecules hybridized with the target marker in preparation of a detection reagent or a diagnostic kit for diagnosing benign and malignant thyroid nodules, and application of a device for determining the methylation level of the target marker in preparation of the diagnostic kit for diagnosing benign and malignant thyroid nodules.

The invention relates to a system, a model and a kit for evaluating the malignancy degree or probability of thyroid nodules. The method comprises the following steps: processing a fine needle puncture tissue biopsy sample by adopting a pressure circulation technology, detecting proteomics data of target protein or polypeptide in the obtained sample by adopting a high performance liquid chromatography method and a mass spectrometry method, processing peptide fragment peak area information of the mass spectrometry data, and carrying out AI modeling, the method obtains a final evaluation result (malignant probability), can provide a reference for the thyroid nodule malignant degree for clinic, and also can provide a second evaluation result (malignant probability) for doctors for thyroid nodules which cannot be clinically identified in the prior art. The invention also provides a corresponding kit, a system, an evaluation model and related uses.

The external traditional Chinese medicine preparation comprises the following raw materials in parts by weight: 15-25 parts of dragon bone, 18-25 parts of oyster, 18-22 parts of rhizoma corydalis, 15-25 parts of asarum, 15-25 parts of eupolyphaga, 18-25 parts of semen momordicae, 3-8 parts of borneol, 16-23 parts of honeysuckle, 16-22 parts of dandelion, 14-22 parts of selfheal, 15-22 parts of turtle shell, 15-22 parts of thunberg fritillary bulb, 9-15 parts of ligusticum wallichii, 8-10 parts of angelica sinensis and 4-12 parts of rhizoma cyperi. The traditional Chinese medicine composition has remarkable effects of regulating qi-flowing for removing phlegm, eliminating goiter and resolving masses, nourishing yin to lessen fire and calming heart and liver, can be used for fundamentally treating thyroid nodules, has the total effective rate of 95% and has no adverse reaction.

The invention provides a malignant nodule edge detection image processing method based on a benign thyroid template. The method comprises the steps: learning an obtained thyroid three-dimensional image data set through a neural network, and forming a parameterized benign thyroid template; matching with the benign thyroid template according to the labeling information and the image information of the thyroid nodule ultrasonic image to be recognized, and performing coarse positioning on the nodule by combining the three-dimensional image and the feature information of the benign thyroid templateto obtain an initial contour curve of the nodule; adopting a local binary fitting model to iterate and evolve the initial contour curve to obtain an evolved contour curve; and performing effective contour selection on the evolved contour curve to obtain a final peripheral contour curve and a segmentation result, and performing fine positioning on the position of the nodule. The method is higher in autonomous recognition capability, better in adaptability to input of different thyroid images, better in segmentation effect, more accurate in numerical value implementation and higher in segmentation adaptability to images with uneven gray levels.

The invention is applicable to the computer field, and provides an analysis method for of thyroid nodules. The analysis method includes the steps: receiving a thyroid nodule analysis parameter input by a user, wherein the thyroid nodule analysis parameter includes basic information of the user and basic information of a thyroid; and determining a malignant risk for thyroid nodules of the user according to the thyroid nodule analysis parameter and a thyroid nodule analysis model generated through training, and outputting the result, wherein the thyroid nodule analysis model is generated throughtraining based on a random forest algorithm or a neural network algorithm. The analysis method for thyroid nodules directly outputs the diagnosis result according to the relevant information input bythe user, and the diagnosis process is convenient and quick, and the thyroid nodule analysis model which is generated through training based on the random forest algorithm or the neural network algorithm, utilized in the diagnosis process can effectively guarantee the accuracy of the diagnosis results.

The invention discloses a method for dynamically identifying benign and malignant nodules based on thyroid ultrasonic video streams. The method comprises the steps of obtaining a transverse scanning section video and a longitudinal scanning section video, the transverse scanning section video and the longitudinal scanning section video are sent to an ultrasonic doctor for labeling; preprocessing the transverse scanning section video and the longitudinal scanning section video labeled by the sonographer; respectively obtaining a preprocessed transverse scanning section video and a preprocessedlongitudinal scanning section video; and respectively inputting the transverse scanning section video and the longitudinal scanning section video into the two Retina positioning networks to respectively obtain nodule related information of each frame of image in the transverse scanning section video and the longitudinal scanning section video, and performing denoising processing on the transversescanning section video and the longitudinal scanning section video. The method can overcome the technical problems that an existing thyroid nodule benign and malignant identification method has greatmisjudgment possibility, misdiagnosis and missed diagnosis are easy to occur, and correct treatment of a patient is delayed.

The invention discloses an ultrasonic thyroid nodule segmentation method based on an asymmetric network, and belongs to the field of deep learning image segmentation. The coding layer is a U-Net network, more context semantic feature information can be obtained, thyroid nodules with variable scales, shapes, positions and the like can be accurately detected, and the decoding layer uses the combination of hole convolution, residual connection and convolution kernel decomposition, so that detail textures and edge information can be gradually restored from high-dimensional global context semanticfeature information. According to the model constructed by the method, the segmentation precision similar to that of a high-precision model is achieved on a thyroid data set, the IOU is about 2% higher than that of a U-Net model, the time for averagely segmenting one picture is about 1.9 s, and the efficiency is greatly improved compared with the manual segmentation efficiency of doctors. The method solves the problems of excessive interference factors and serious loss of image edge information in a traditional method, and has good accuracy and generalization ability.

The invention discloses Rhizoma pinelliae, herba taraxaci and bock greenbrier rhizome nodule rehabilitation oral liquid and a preparation method thereof. The preparation method comprises the following steps of: S1, extracting five types of poria cocos, pericarpium citri reticulatae viride, bighead atractylodes rhizome, radix astragali seu hedysari and ganoderma lucidum pieces for two times according to a conventional water extraction method; fermenting an extracting solution with edible yeast for 24 h; then inactivating bacteria at high temperature to obtain a fermentation solution; S2, washing 11 types of rhizoma chuanxiong, rhizoma pinelliae, radix bupleuri, radix curcumae, herba taraxaci, spica prunellae, rhizoma sparganii, rhizoma curcumae, herba patriniae, bock greenbrier rhizome and herba euphorbiae hirtae with water; extracting for two times according to a water extraction technology to obtain an extracting solution; S3, mixing the fermentation solution and the extracting solution and uniformly stirring in a reaction kettle for 35 min; and S4, filling a 50ML glass bottle with a liquid material through a special production line to obtain the Rhizoma pinelliae, herba taraxaci and bock greenbrier rhizome nodule rehabilitation oral liquid. According to the Rhizoma pinelliae, herba taraxaci and bock greenbrier rhizome nodule rehabilitation oral liquid disclosed by the invention, all the medicinal materials are reasonably matched and have the effects of detoxifying and eliminating swelling, removing stasis and reducing phlegm, tonifying Qi and nourishing Yin, removing dampness and discharging turbidity, strengthening body resistance and eliminating evil; and thyroid nodules are reduced and the serum thyroglobulin level is reduced, so that cancer recurrence and metastasis are prevented and the living quality of patients is improved.

The invention provides a traditional Chinese medicine composition for treating a thyroid nodule and a preparation method. The traditional Chinese medicine composition comprises the preparation raw materials: radix bupleuri, fructus aurantii, rhizoma cyperi, radix ranunculi ternati, ossa draconis, oyster shells, Chuanxiong rhizomes, radix paeoniae rubra, dried tangerine peels, rhizoma pinelliae and radix glycyrrhizae. The traditional Chinese medicine composition is used for treating preliminary-stage thyroid nodule by taking clinical symptoms, nodule size, hormone level and constitution regulation as multiple targets and taking the idea of promoting Qi circulation and dispelling melancholy and regulating Qi and resolving phlegm. The traditional Chinese medicine composition provided by the application can be used for effectively improving the clinical symptoms of a thyroid nodule sufferer, regulating and controlling the level of thyroid hormones, controlling and delaying the disease development of the thyroid nodule, improving the size of the nodule, regulating the global function of the sufferer and changing problems of constitutions prone to the thyroid nodule, thereby performing intervention fundamentally. Furthermore, drug dependence and thyroid function changes caused by long-term oral administration of hormone drugs and risks such as lifetime thyroid hormone drug dependence and nodule recurrence caused after thyroid nodule excision can be avoided to a relatively large extent.

The invention relates to an ultrasonic image-based thyroid nodule automatic segmentation method. The method comprises the following steps of 1, establishing a training set and a test set by using a public thyroid nodule data set; processing an image and an XML file, performing cutting to obtain the image and a label corresponding to the image, and dividing the image into a training set, a test set and a cross validation set in proportion; 2, establishing a thyroid nodule segmentation network, wherein network structures such as cavity convolution, dense connection and repair-module (correction module) are used; 3, training the thyroid nodule segmentation network by using the training set to obtain a trained segmentation model; and 4, verifying the segmentation model in a test set, and segmenting the thyroid nodule to obtain nodule lesion information. The thyroid nodule segmentation network is trained by using the training set, the trained segmentation model is obtained, and doctors are assisted in improving the diagnosis efficiency.

The invention discloses a gene fusion kit for papillary carcinoma of a thyroid nodule patient, and relates to the technical field of medical detection, in particular to the gene fusion kit for the papillary carcinoma of the thyroid nodule patient. The kit comprises a base, a kit body and a storage rack, first driving air cylinders are mounted at the left side and the right side of the base, first telescopic rods are connected to the upper portions of the first driving air cylinders, the kit body is installed above the base, a first water suction pump is installed above a refrigerator part, a cold water pipeline is connected to the upper portion of the first water suction pump, a heat preservation layer is arranged in the kit body, and a cold water layer is arranged at the inner side of the heat preservation layer; and therefore, a reagent can be kept in a low-temperature state, repeated freezing and thawing of a reagent library are avoided, and degradation, polymerization, denaturation and inactivation of a reagent sample are prevented.

The invention discloses a thyroid nodule real-time segmentation method based on a full convolution dense hole network. The method comprises the following steps: 1, obtaining thyroid data and carryingout preprocessing; 2, labeling the obtained data to serve as a data set for training a full convolution dense hole network model; 3, constructing a full convolution dense hole network model based on dense connection, and performing parameter training; 4, replacing a convolution kernel in the convolution layer with hole convolution, and decomposing the convolution kernel by convolution kernel decomposition; 5, performing data standardization and nonlinear activation processing on the input of the convolution layer; and 6, analyzing and comparing the segmentation effect and efficiency of the full convolution dense hole network model.

The invention discloses a thyroid nodule identification and segmentation method, and the method comprises the steps: obtaining a preprocessing sample, and inputting the preprocessing sample into a skeleton network for sample feature extraction; respectively inputting the extracted feature samples into a first branch and a second branch for training; respectively calculating the classification loss of the trained first branch and the segmentation loss of the trained second branch; obtaining a total loss training model through linear weighting of classification loss and segmentation loss, wherein the total loss training model can judge benign and malignant categories and lesion areas of thyroid nodules of the input image. According to the invention, dependence on human resources such as doctors and the like can be reduced, the possibility of human errors is reduced, and intelligent detection is realized. Meanwhile, the iconography characteristics of the thyroid nodules are fully considered, so that the interference problem caused by inconsistency of image categories and pathological categories of the deep learning model is relieved, the classifier can better learn proper iconography characteristics, and accurate segmentation of lesion areas on the classifier is achieved.

The invention discloses an ultrasonic image thyroid nodule classification method based on feature decoupling, which is applied to the technical field of image processing and aims at solving the problem that the prior art is limited to change the input of a neural network to obtain features under different views. According to the method, a new local / global feature extraction method is established, a tissue-anatomy decoupling module is designed by using a self-attention mechanism to connect two paths of What and Where, and the task of thyroid nodule benign and malignant classification is completed in a multi-task learning form; according to the method, local / global feature decoupling can be completed in a feature space in a self-adaptive mode, compared with an existing method, a larger view field range is achieved, and more effective and stable features can be obtained. The benign and malignant thyroid nodule classification under the ultrasonic image has better diagnosis performance than doctors.

The invention provides intelligent auxiliary identification equipment and method for autonomously evaluating benign and malignant thyroid nodules. The intelligent auxiliary identification equipment comprises a simulation ultrasonic probe, a simulation skin platform, a control module, a field execution mechanism, a DICOM workstation, a benign and malignant nodule classification module, a nodule region calculation module, a visual operation module and report output equipment. An ultrasonic scanning method comprises the following steps: acquiring the pose of the simulation ultrasonic probe, the positive pressure between the simulation ultrasonic probe and the simulation skin platform and two-dimensional coordinates of the simulation ultrasonic probe on the surface of the simulation skin platform according to the movement of the simulation ultrasonic probe on the simulation skin platform so as to control a field manipulator to perform ultrasonic scanning. A benign and malignant thyroid nodule classification method comprises the step of completing the classification task of thyroid nodule ultrasonic images by a machine learning method. The invention provides an auxiliary workstation which is convenient to operate. The intelligent auxiliary equipment increases the telepresence of an operator greatly, can obtain identification results in time, and provides reference for further diagnosis by doctors.

The invention discloses a thyroid nodule detection method based on Transform, and relates to the technical field of image processing, and the method comprises the steps: obtaining a to-be-detected ultrasonic image of a thyroid region, carrying out the image preprocessing of the obtained to-be-detected ultrasonic image, inputting the to-be-detected ultrasonic image into a nodule detection model which is obtained through the training based on a Transform network in advance, and obtaining a nodule detection model; according to the output of the nodule detection model, determining the position and type of the nodule in the to-be-detected ultrasonic image, and completing the detection of the nodule in the to-be-detected ultrasonic image, the nodule type being used for indicating that the nodule is a benign nodule or a malignant nodule; the method can automatically complete positioning and classification of nodules, is high in automation degree and good in objectivity, does not need to construct a dense Anchor Box, does not need to use NMS complex post-processing operation, is easy to implement, and has low requirements for computing resources.

The invention discloses a traditional Chinese medicine composition for treating thyroid nodules, and relates to the technical field of traditional Chinese medicines. The traditional Chinese medicine composition is prepared from the following raw materials: astragalus membranaceus, radix paeoniae rubra, angelica sinensis, rhizoma atractylodis macrocephalae, poria cocos, rhizoma pinellinae praeparata, thunberg fritillary bulb, raw oyster shell, radix curcumae, rhizoma cyperi, turtle shell, selfheal, ligusticum wallichii, kelp and liquorice. The medicines are matched with a prescription according to a proportion; the preparation method comprises the following steps: putting the compatible medicines into a medicine pot, adding water for soaking, putting on fire, boiling with big fire, then decocting with small fire for 30-40 minutes, filtering, and collecting the decoction. The traditional Chinese medicine has the effects of soothing liver and strengthening spleen, activating blood and reducing phlegm, and softening hardness to dissipate stagnation, and has a remarkable effect on treating thyroid nodules.

The invention relates to the technical field of traditional Chinese medicines, in particular to a traditional Chinese medicine formula preparation for treating thyroid nodules and a preparation method of the traditional Chinese medicine formula preparation. The traditional Chinese medicine formula preparation comprises the following raw materials: spica prunellae, oldenlandia diffusa, edible tulip, angelica sinensis, thunberg fritillary bulb, dandelion, spina gleditsiae, radix curcumae, semen cuscutae, curcuma zedoary, campsis grandiflora, platycodon grandiflorum, radix scrophulariae and liquorice. Through reasonable compatibility of the traditional Chinese medicine raw materials and optimization of the preparation process, the traditional Chinese medicine formula preparation can effectively improve nodule symptoms of patients and has high clinical application value. All the medicines are used together to achieve the effects of eliminating stagnation, eliminating goiter, soothing liver-qi stagnation and promoting blood circulation to remove blood stasis, the thyroid nodules of the patients can be remarkably inhibited and reduced, the thyroid nodule level of the patients is not affected, safety is high, and toxic and side effects are avoided.