232 results about "Imaging diagnosis" patented technology

The invention discloses a multi-modal X-ray image diagnosis report generation method based on reinforcement learning optimization. The method comprises the following steps: acquiring a multi-source public data set and a private data set in a specific range; performing format standardization processing on the X-ray image, performing normalization processing on text information, and constructing a structured public training data pair and a private enhanced data pair; training an end-to-end multi-modal neural network by using the public training data pair in a supervised fine tuning mode; carrying out reinforcement learning on the first-stage end-to-end multi-mode neural network by adopting a strategy gradient algorithm optimization mode of GRPO and using candidate reports and weighted rewards to obtain a second-stage end-to-end multi-mode neural network; and generating a diagnosis report by using the two-stage end-to-end multi-modal neural network. The method is suitable for personalized application scenes of X-ray image diagnosis, can quickly adapt to specific clinical requirements on the basis of limited professional data, and has good practical value and popularization prospect.

The invention discloses a brain tumor image diagnosis large model pre-training method, diagnosis method and system, and the method comprises the steps: obtaining the image data of a brain tumor patient and a corresponding diagnosis text, and the image data comprises a plurality of sequences; constructing a visual unified model, carrying out complete sequence standard training and missing sequence distillation training on the visual unified model by utilizing the image data, learning unified visual representation of the image data of any sequence combination, and taking characteristics of the complete sequence image data as teacher characteristics in the missing sequence distillation training; the features of the missing sequence image data serve as student features, and distribution alignment of the student features and the teacher features in the feature space is restrained; constructing a visual language model, taking unified visual representation output by the trained visual unified model as input based on a multi-task target, and training the visual language model in combination with the diagnosis text; the trained model can effectively process the sequence missing condition, and the accuracy and robustness of diagnosis are improved.

The invention relates to the technical field of medical image processing, in particular to a neurosurgery image diagnosis method and system based on image processing, and the method comprises the following steps: obtaining gray matter edge nodes of a triaxial section, constructing a symmetric path unit, collecting an edge direction vector, and generating a direction trajectory diagram; and extracting continuous slices, constructing a rotation track sequence, identifying an abnormal region, filling gaps, combining path voxels, and dividing spatial levels to generate an image structure chart. According to the method, the grey matter edge nodes in the three-axis tangent plane are obtained, and the node paths with the symmetrical characteristics are screened out according to the space projection trend, so that the continuous region of the structure can be accurately recognized, the direction vectors in the continuous slices are extracted, the direction mutation region in the track is recognized, and the jump and fracture performance of the structure can be timely captured; and the abnormal region is accurately labeled, so that higher-dimensional expression and finer-grained recognition of the neural structure are realized, and spatial modeling and visual analysis of complex neuropathy are effectively supported.

The invention discloses a thyroid ultrasound image diagnosis method based on deep learning, and the method comprises the following steps: collecting a thyroid ultrasound original image set, and carrying out the preprocessing; performing focus segmentation on the standardized thyroid ultrasound image set; performing morphological constraint and boundary refinement; calculating the blood flow direction, blood flow velocity and blood flow power of each thyroid focus area and neighborhood; generating a preliminary fusion feature map based on a feature adaptive deep learning network, and fusing the preliminary fusion feature map with the thyroid focus blood flow feature vector set; obtaining a thyroid focus detection list through thyroid focus benign and malignant discrimination branches; and generating thyroid focus structured diagnosis data records based on the thyroid focus detection list, and writing the thyroid focus structured diagnosis data records into a computer-aided diagnosis system. According to the method, deep learning and multi-modal blood flow features are fused, intelligent diagnosis of the thyroid focus is achieved, and the method has the advantages of being high in precision, high in interference resistance and structured in result.

The invention discloses a medical analysis method and system based on a multi-modal large language model and chain reasoning, and belongs to the crossing field of artificial intelligence and medical image analysis. The method comprises the following steps: firstly, carrying out preprocessing and modal alignment on medical multi-modal data to obtain image visual features and text embedding vectors of the same vector spatial dimension; capturing visual evidence in a structured pathological feature form through a self-excitation mechanism; on the premise of visual evidence, a thinking chain text containing causal logic is generated through evidence anchoring chain type reasoning; and finally, performing vision and text dual consistency verification on the thinking chain text, and outputting an analysis conclusion according to a result, or triggering a negative feedback correction mechanism to regenerate the thinking chain text. According to the method, medical reasoning full-process logic visualization is realized, medical illusion is eliminated, the diagnosis accuracy of complex cases is improved, and the method is suitable for medical scenes such as medical visual questions and answers and image diagnosis report generation.

Systems and methods are provided herein for developing and deploying active-prompt-tuned, domain-specific image diagnosis and categorization applications. Processes of the present disclosure may control and provide for human-confirmable diagnostics from images, based on controlled instructions provided to vision-language models. The controlled instructions may be developed by systems provided herein, which generate system prompts and example prompt sets using active prompt tuning approaches.

The invention relates to the technical field of medical image intelligent diagnosis, and provides an interpretable lung CT image intelligent diagnosis method and device, and the device comprises an image calling module, an image processing module, an image analysis module and a result display module. According to the method, identity information of a patient is read to automatically verify and authorize, and an electronic CT image is called and stored in a data storage card through an encryption transmission channel. And the image processing module performs denoising, enhancement and interpretability analysis on the original image to generate a thermodynamic diagram to assist in decision making. The image analysis module completes lesion detection and classification, verifies the consistency between a model decision and doctor experience through a thermodynamic diagram, and optimizes network parameters in combination with feedback data. And the result display module displays a diagnosis result and a thermodynamic diagram in real time, and supports multi-terminal cooperation and data archiving. The device supports data sharing and remote consultation, the efficiency and convenience of diagnosis are improved, and the wide application prospect of artificial intelligence in the field of medical diagnosis is shown.

The invention relates to the field of biomedicine, particularly provides a copper-based nano-enzyme as well as a preparation method and application thereof, and aims to solve the problems that in the prior art, clinical treatment on primary sclerosing cholangitis (PSC) is difficult in diagnosis and lacks of effective treatment drugs. The copper-based nano-enzyme comprises a nano-enzyme carrier and a copper-based nano-enzyme, wherein the nano-enzyme carrier is a two-dimensional nanosheet formed by copper ions, gallic acid and ursodesoxycholic acid through coordinate bonds; the probe molecule is a cyanine dye molecule connected to the surface of the nano-enzyme carrier through a covalent bond; wherein the fluorescence intensity of the copper-based nano enzyme is enhanced after the action of the alkaline phosphatase. The three functions of alkaline phosphatase responsive diagnosis, nano-enzyme catalytic treatment and ursodesoxycholic acid hepatic targeting are innovatively and synergistically integrated into one nano platform, the treatment function can be executed while specific imaging diagnosis is performed on diseases, and a new strategy is provided for diagnosis and treatment of diseases such as PSC.

The invention provides a comprehensive solution for a medical ultrasonic image diagnosis task, which comprises two innovative parts, namely a CAPT parameter efficient fine tuning method based on a convolution adapter and embedded prompt tuning and a model lightweight method based on evolution pruning. Aiming at double challenges of scarcity of data and difficulty in model adaptation in the field of medical ultrasonic images, on one hand, the CAPT method introduces a convolution module into a traditional adapter to enhance the fine-grained feature extraction capability by fusing adapter tuning and prompt tuning mechanisms, and dynamically generates a prompt vector embedded with attention calculation by using adapter learning context information; the adaptability and the small sample generalization ability of the Transform model in an ultrasonic image classification task are obviously improved; on the other hand, aiming at a model edge deployment requirement, designing an evolution pruning algorithm, taking DenseNet-121 as a basic framework, inheriting parent strong neuron connection by adopting an asexual reproduction mechanism, and through a channel importance evaluation method for coupling a BN layer scaling factor and a weight L1 norm and in combination with a sparse evolution strategy guided by environmental constraint, constructing a model edge deployment model. And the classification precision is maintained, and meanwhile, the model parameters and the calculated amount are greatly compressed. The two technologies respectively break through the technical bottlenecks of medical image diagnosis from two dimensions of parameter efficient fine tuning and model structured pruning, the former improves the adaptability of the model field through feature extraction optimization, and the latter balances network lightweight and performance stability by means of an evolutionary algorithm. And an innovative technical path is provided for efficient deployment of an ultrasonic image auxiliary diagnosis system in a data limited scene and an edge computing environment.

The invention discloses a chest image diagnosis method and system based on multi-modal sign collection, and relates to the technical field of medical image.The method comprises the steps that a chest image of a patient is obtained, electrocardiosignals and blood oxygen saturation data are synchronously collected, and an associated radiology report is obtained; the image lesion features and the frequency domain rhythm template are combined for processing, motion artifacts are eliminated through a frequency domain decoupling equation, and refined image features are output; inputting the refined image features and the text pathological semantic features into a bidirectional attention mechanism to generate fusion features, and splicing the oxyhemoglobin saturation data and the text pathological semantic features into a sign-text vector; and inputting the fusion feature and the sign-text joint vector into a multi-task loss function, and outputting a structured diagnosis report. According to the method, accurate elimination of motion artifacts is achieved through a frequency domain decoupling equation, and coupling calculation is conducted on an electrocardio rhythm template and image lesion features in a frequency domain space.

The invention discloses a medical image privacy protection-oriented structure perception adaptive steganography method, which comprises the following steps of: in a training stage, generating a diagnosis related region mask based on a pre-trained IMIS-Net, extracting an embeddable suppression mask, and obtaining an initial embedding probability under the minimum distortion constraint; and the self-adaptive adjustment of structure perception is carried out on the pixel-level embedding probability in combination with a reinforcement learning normal form, so that the ROI is protected preferentially and a high-risk region is inhibited on the premise of meeting the set capacity, and the steganalysis resistance and perception quality are improved. And after the training is completed, in a reasoning stage, the masks and probabilities are firstly generated or quoted, then action probabilities are obtained through a trained strategy network, then probability consistency is mapped into STC cost, real embedding is completed by the STC, and a secret-containing medical image is obtained. According to the invention, on the premise that the availability of image diagnosis is not reduced, safe embedding and hidden transmission of sensitive information are realized.

The invention relates to the technical field of computer vision and industrial equipment fault diagnosis, in particular to a fault image diagnosis system for a remote controller of a bolter miner driven by a CMC chip. Comprising a data sensing module for synchronously acquiring a CMC chip internal logic state vector and a real-time observation image, and preprocessing to obtain target visual data; the generative reconstruction module maps the logic state vector to a high-dimensional space to generate an ideal feature map; the visual analysis module is used for extracting an observation feature map through a convolutional neural network, generating an interference distribution mask based on frequency domain texture statistics, and identifying an unstructured environmental noise region; the difference analysis module is used for calculating a semantic residual image by combining the ideal feature map, the observation feature map and the interference distribution mask, and generating an image difference analysis result; according to the method, ideal features are generated by using the internal logic state vector of the CMC, and the residual calculation of the coal dust noise area is shielded by the interference mask, so that the false alarm caused by the environmental noise is effectively eliminated, and the closed-loop verification that what you see is what you control is realized.

The invention provides a pneumonia CT image diagnosis model training method, diagnosis method and equipment, and the training method comprises the steps: inputting a 3D chest CT image into a multi-task deep learning model, enabling a shared encoder in the model to extract multi-scale feature data, and enabling a connection module and a decoder to obtain pneumonia focus region prediction result data according to the multi-scale feature data, the classification head obtains pneumonia diagnosis type prediction result data according to the multi-scale feature data; determining the joint loss of the model in the current iteration round and updating model parameters; and if the current multi-task deep learning model satisfies a training termination condition, outputting the current model as a pneumonia CT image diagnosis model. According to the method, the problems of low model feature utilization rate and low pneumonia diagnosis process efficiency caused by incapability of simultaneously completing focus segmentation and type classification due to task simplification of an existing pneumonia diagnosis model can be solved.

The invention provides a concrete structure integrity evaluation method and device based on wave velocity image diagnosis, and is applied to the technical field of geophysical exploration, and the method comprises the steps: obtaining a vibration signal, monitored by a vibration sensor in real time, of a concrete structure; when the vibration signal exceeds a preset vibration threshold value, determining the vibration signal as a target vibration signal; identifying the target vibration signal, and when the target vibration signal is an internal micro-seismic signal of the concrete structure, determining the arrival time of a first arrival wave of the target vibration signal; on the basis of a speed model generated by preorder iteration, arrival time inversion positioning is carried out on arrival time of the first arrival waves, and focus coordinates of the target vibration signals are obtained; constructing a dynamic speed model based on the arrival time of the first arrival wave and the focus coordinates; and performing real-time evaluation and damage trend analysis on the interior of the concrete structure based on the dynamic speed model. According to the invention, high-precision real-time monitoring of the wave velocity distribution of the concrete structure can be realized.

The invention relates to the technical field of medical image artificial intelligence, in particular to a multi-prototype cross-modal contrast learning fetal heart ultrasound image intelligent diagnosis system which comprises the steps that a to-be-detected fetal heart ultrasound image is obtained through an obtaining module, and a processing module inputs the to-be-detected fetal heart ultrasound image into a pre-trained image diagnosis model; the method comprises the following steps: performing feature extraction on a to-be-detected fetal heart ultrasound image, performing cross-modal multi-prototype comparison according to a feature extraction result to obtain a diagnosis category and a most matched image-text prototype pair, outputting the diagnosis category and the most matched image-text prototype pair by an output module, and finally performing CHD classification and screening according to the diagnosis category and the most matched image-text prototype pair. The problem that the model generalization ability is insufficient due to the fact that a single prototype center is mainly relied on to represent each category and the significant difference of the same category under different conditions is neglected is solved, and multi-category CHD intelligent screening is achieved by utilizing unlabeled images and a small amount of text priori on the premise that a large number of graph-text pairs are not needed.

The invention relates to the technical field of medical image processing, and discloses a temporomandibular joint three-dimensional reconstruction system based on joint imaging diagnosis. The system utilizes CBCT equipment to collect temporal-mandibular joint tomographic image data, and extracts facial feature sites and maxillofacial joint standard model approval sites through a multi-scale residual network. And carrying out space registration by adopting an affine transformation and nonlinear optimization algorithm to generate a synchronous correction parameter, further carrying out deformation interpolation on the standard model of the maxillofacial joint to construct a maxillofacial model, and adjusting the position of a condylar process. And based on the deformed maxillofacial model, generating an occlusal plate structure model by using an isogeometric analysis method, and iteratively optimizing the degree of fit through an edge calculation frame. The system can accurately reconstruct the three-dimensional model of the temporomandibular joint, optimizes the adaptation of the biteplate, provides powerful support for the diagnosis and treatment of temporomandibular joint diseases, and facilitates the improvement of the diagnosis and treatment level.

This invention relates to aggregation-induced emission probes with specific responses to tumor markers and their applications, belonging to the field of biochemistry. This invention utilizes molecular structure modulation to prepare a series of molecules possessing large Stokes shifts, resistance to photobleaching, near-infrared II (NIR-II) emission, and aggregation-induced emission properties. Different self-eliminating groups are attached to the dimethylamino group of the molecule via quaternization or to the pyridine group via pyridinium salting. By controlling the structure of these self-eliminating groups, activatable luminescent probes responding to different tumor markers can be constructed. These probes integrate deep NIR-II imaging, aggregation-point luminescence, large Stokes shifts, and marker-specific responses, providing a new approach for precise imaging diagnosis of tumors.

The invention discloses a preparation method and application of near-infrared fluorescent dye DOM-O for acute alcoholic liver injury detection. The specific chemical structural formula of the near-infrared fluorescent dye DOM-O is shown in the specification. The near-infrared fluorescent dye prepared by the invention has excellent fluorescence imaging performance, can realize specific fluorescence imaging diagnosis of the acute alcoholic liver injury, and provides an imaging dye with application prospect for early detection and clinical diagnosis of the acute alcoholic liver injury.

This invention provides a detachable, interchangeable imaging diagnostic device that allows for the use of various sizes of scopes, enabling easy image diagnosis of various affected areas. [Solution] The interchangeable imaging diagnostic device comprises a scope module including a fluoroscopy tube 110, a scope body 120 surrounding the rear of the fluoroscopy tube, an LED 130 providing light into the fluoroscopy tube, a heat sink 140, and an LED connection jack 150 protruding from the rear surface; a main body including a focus lens 210 for observing the condition of the affected area through the fluoroscopy tube, an adjustment lever 220 for magnifying or reducing the condition of the affected area as seen by the focus lens, a camera 230 for saving images of the body cavity, and an imaging trigger 240 for capturing images of the captured images. The main body has an LED power jack into which the LED connection jack is inserted and a coupling groove into which the rear end of the fluoroscopy tube is inserted, and is characterized by a structure that allows for the selection of scope modules with different fluoroscopy tube sizes and their detachable connection to the main body.

The application provides an ophthalmic image diagnosis method and system based on multi-modal imaging cooperation, and relates to the technical field of medical image diagnosis. First, the OCT, fundus camera and ultrasonic original image data of an ophthalmic examination object are acquired, and multi-modal dynamic correlation mapping results are obtained through dynamic correlation and trend correlation processing. Cross-modal lesion feature progressive mining and interactive verification are performed to obtain a cross-modal lesion correlation feature set. Multi-modal cooperative diagnosis reasoning and weight feedback optimization are used to generate an ophthalmic disease reasoning result containing disease types, lesion dynamic distribution and reasoning confidence. Finally, an ophthalmic diagnosis report with dynamic labeling and confidence explanation is generated. The application comprehensively utilizes the advantages of various image technologies to improve the accuracy of ophthalmic image diagnosis.

The application discloses a kind of based on CBCT image data evaluation implant stability classification model and the construction method and device thereof.First, through the Mobilenetv2-DeepLabV3+ network after training in cross-sectional image implant is segmented, then in combination with the knowledge of oral implantology and segmentation result corresponding implant around bone image is extracted on cross-sectional image, finally using the deep residual network Resnet-50 after training is completed classification, obtains implant stability evaluation result.Test evaluation, the model of the application has higher diagnostic performance, the time consumption of evaluation is short, accuracy is high (>90%), can be effectively used for implant stability evaluation.The classification model for evaluating implant stability of the application has important significance for better guiding further implant restoration treatment, and provides important reference for artificial intelligence used for image diagnosis.

The invention discloses an acute kidney injury nuclear magnetic resonance detection contrast agent as well as a preparation method and application thereof. The lipidosome comprises a first lipid component and a second lipid component, the first lipid component is any one of 1, 2-dipalmitoyl-sn-glycerol-3-phosphorylcholine, 1, 2-dioleoyl-sn-glycerol-3-phosphorylcholine and distearoyl phosphatidylcholine, and the second lipid component is a sterol derivative, and the first lipid component is any one of 1, 2-dipalmitoyl-sn-glycerol-3-phosphorylcholine, 1, 2-dioleoyl-sn-glycerol-3-phosphorylcholine and distearoyl phosphatidylcholine. The molar ratio of the first lipid component to the second lipid component is (1-3): 1. According to the invention, all organic free radicals are used for replacing gadolinium ions, so that renal toxicity and systemic fibrosis risks caused by metal accumulation are eliminated, and the method is especially suitable for imaging of patients with renal insufficiency. The material synthesis process is simple, and the cost is controllable. In addition, the system is seamlessly compatible with clinical 3.0 T MRI equipment, additional hardware transformation is not needed, and popularization and application of AKI bedside rapid imaging diagnosis can be directly promoted.

The invention belongs to the technical field of biological medicine. More specifically, the invention provides the annular RNACircMAP2, and the sequence of the annular RNACircMAP2 is as shown in SEQ ID NO. 1 or SEQ ID NO. 2. The invention proves that CircMAP2 can be used as a novel biomarker for diagnosing acute ischemic stroke for the first time. Compared with a traditional imaging diagnosis method, the acute ischemic stroke can be diagnosed by detecting the expression level of the CircMAP2 in the plasma and the plasma exosome of the patient, the cerebral infarction area of the acute ischemic stroke can be speculated through the expression level of the CircMAP2, and the method has the advantages of being easy and convenient to operate, high in detection speed and low in cost; the system is especially suitable for popularization and use in primary medical institutions. The product for diagnosing acute ischemic stroke provided by the invention can quickly and accurately diagnose acute ischemic stroke, and has good clinical popularization prospect and application value.

The invention relates to the field of CT image diagnosis, in particular to a comparison device for CT image diagnosis, which comprises a base, an observation assembly arranged at the upper end of the base, a placement assembly arranged in the observation assembly, a fixing assembly arranged on the inner wall of the observation assembly, an irradiation assembly arranged at the upper end of the base, and a rotating groove arranged in the rotating groove. A rotating groove is formed in the upper surface of the base, a fixing ring is fixedly connected to the inner wall of the bottom of the rotating groove, a lantern ring is connected to the outer wall of the fixing ring in a sleeving mode, a rotating box is fixedly connected to the outer wall of the lantern ring in a sleeving mode, and openings are formed in the outer wall of the rotating box. Then a transparent inserting plate is pushed to move towards the interior of the rotating box, the transparent inserting plate drives a push rod to approach an L-shaped rod, the inclined face of the L-shaped rod pushes a push plate, the push plate pushes the push rod, the push rod pushes a clamping block to extrude the CT film to be attached to a baffle, then the CT film is fixed, and the CT film is prevented from sliding off.

The invention discloses a tumor specific signal analysis device and equipment for uncertain pulmonary nodules and a storage medium, and relates to the technical field of electrical digital data processing.The device constructs a LungTCR database containing lung cancer tissue enrichment type and blood enrichment type CDR3 sequences through TCR beta chain sequencing and IMGT database comparison, and the LungTCR database is used for analyzing the lung cancer tissue enrichment type and blood enrichment type CDR3 sequences. A basis is provided for tumor specific signal recognition; a Needleman-Wunsch algorithm and an editing distance threshold value are adopted for screening, so that high-precision sequence matching is realized; through quantification of lung cancer tissue scores, blood scores and mutation specificity scores, a TCR feature group with biological significance is formed; and finally, the TCRnodseek plus model trained by clinical and image features is combined, a reliable malignant probability prediction value can be output, the model AUC can reach 0.90 or above, and the model is obviously superior to traditional imaging diagnosis and tumor markers.

The application belongs to the field of medical instrument imaging diagnosis equipment quality detection, and particularly relates to a phantom and a method for coronal plane transmission detection of a three-dimensional ultrasonic imaging device. The phantom comprises: an upper panel, an acoustic window, a target group, and an aggregate formed by background ultrasonic tissue simulation material. The upper panel is located above the acoustic window, and the upper panel and the acoustic window form an inner cavity after being connected. The target group and the aggregate formed by the background ultrasonic tissue simulation material are located in the inner cavity. When the coronal plane transmission imaging performance is detected, the phantom is suspended in the imaging area of a hemispherical ultrasonic array, and a degassed water body is also contained in the imaging area. Each target in the target group extends along the vertical direction and is perpendicular to the coronal plane of the breast to be imaged. In this way, the convenience and accuracy of the coronal plane transmission imaging performance detection are improved, and the utility is good.

This invention discloses a cross-modal medical image segmentation method based on pathological anchoring. Using pathological priors as semantic anchors, this method integrates cross-scale collaboration and uncertainty perception in a closed-loop prompting refinement process. This achieves interpretable suppression and robust learning of cross-modal domain shifts, forming a closed-loop path from structure to semantics to noise control. Under strong cross-modal zero-sample settings such as multi-source ultrasound cross-domain segmentation and color dermoscopy, stable and consistent improvements are achieved, manifested in more precise boundaries, better calibration, and more robust generalization. This provides technical support for disease image diagnosis.

To provide a diagnostic imaging device, a diagnostic imaging method, a program, and a storage medium, which are not easily affected by unnecessary matters other than a diagnosis object part existing in an image extracted from a crop image when acquiring a growth index and the resolution of the crop image.SOLUTION: A diagnostic imaging device includes an object pixel determination unit configured to divide a crop image into a plurality of divided images and determine object pixels including at least a part of the crop with each of the plurality of divided images as an input, and a feature quantity diagnostic unit configured to output a feature quantity relating to the growth state of the crop using the object pixels.SELECTED DRAWING: Figure 3

The present invention discloses a semi-supervised coronary artery segmentation method based on frequency domain information fusion. The method first obtains a coronary computed tomography angiography (CCTA) image and stores it in NIFTI format; then manually annotates the NIFTI format data to obtain the specific structure of the coronary artery and constructs a coronary artery dataset; inputs the coronary artery dataset into a semi-supervised segmentation network based on frequency domain information fusion for training to obtain a training model; and uses the obtained training model to predict new coronary artery data to obtain a coronary artery segmentation mask. The present invention proposes a semi-supervised coronary artery segmentation method based on frequency domain information fusion, which can obtain highly accurate output results with a small amount of manual annotation, while solving the problem of difficult coronary artery segmentation. The present invention can directly obtain a coronary artery segmentation mask through a trained deep learning segmentation model, providing technical support for clinicians' imaging diagnosis work.

The invention discloses a radioactive diagnosis medicine for targeting OPN as well as a preparation method and application of the radioactive diagnosis medicine. The radioactive diagnosis medicine comprises linear polypeptide and radionuclide, wherein the linear polypeptide is composed of 20 L-type amino acids and one D-type amino acid, and the radionuclide marks the linear polypeptide through a bifunctional chelating agent; the sequence of the linear polypeptide is as shown in SEQ ID NO. 1. In vivo, radionuclides in the medicine are concentrated to an atherosclerotic vulnerable plaque part through the targeting effect of the amino acid linear polypeptide, and imaging diagnosis is carried out on the atherosclerotic vulnerable plaque by utilizing a positron emission computed tomography technology of nuclear medicine.