85 results about "Clinical imaging" patented technology

The invention discloses a rigidity self-adaptive intravascular stent based on a three-dimensional virtual woven structure and a control method thereof. The method comprises the steps of reconstructing a preoperative tumor blood vessel three-dimensional model based on clinical iconography data, constructing a stent initialization model, starting a hexagonal knitting three-dimensional virtual algorithm, performing intravascular stent knitting modeling, realizing structural parameter optimization and adaptive stent expansion in combination with preoperative image data of a patient, and completing personalized deployment. The wire winding angle and the mesh density of each area of the stent are adjustable, the stent is adaptive to the complex intracranial aneurysm blood vessel form, accurate fitting and risk guiding are achieved, the treatment effect is improved, the postoperative complication risk is reduced, and the stent has good engineering suitability and clinical application prospects.

The invention relates to the technical field of image processing, in particular to a clinical focus auxiliary segmentation system based on a nuclear magnetic resonance image. The system comprises an MRI image processing module, a lesion auxiliary segmentation module, a probability segmentation correction module and a lesion boundary smoothing module, a corresponding clinical nuclear magnetic resonance image set of a patient can be obtained, image position alignment and gray level adjustment processing can be carried out, and meanwhile a corresponding clinical image lesion segmentation model is constructed to carry out multi-scale fusion auxiliary segmentation. Generating a clinical focus region segmentation fusion image; obtaining a focus confidence probability corresponding to each pixel point in the segmentation image through the clinical focus region segmentation fusion image, and carrying out probability segmentation boundary correction on the clinical focus region segmentation fusion image to obtain a clinical focus region segmentation correction result image; and performing focus edge shape smoothing processing on the clinical focus region segmentation correction result map to generate a clinical focus edge shape segmentation optimization result. According to the invention, high-precision segmentation of the focus in the MRI image can be realized.

The invention discloses a multi-modal image registration processing method and system based on deep learning, and relates to the technical field of medical image processing, and the method comprises the steps: calculating a mutual information loss value after registration through a mutual information loss method, optimizing a CNN model and UNet model parameters in combination with the total loss generated by the fusion of global and local deformation fields, and obtaining a registered sCT image. A CNN model and a UNet model are combined to generate global and local deformation fields, overall rigid transformation and local nonlinear deformation are effectively captured, the spatial alignment precision of sCT and reference CT is improved, model parameters are optimized through mutual information loss and total loss, the intensity distribution consistency is ensured, feature weights and treatment plan parameters are automatically adjusted through registration quality feedback, and the accuracy of the treatment plan is improved. The coverage precision and efficiency of the radiotherapy plan are remarkably improved, automatic and high-precision image registration and treatment plan optimization are realized, and the reliability and practicability of clinical image processing are enhanced.

The embodiment of the invention discloses a surgical navigation method and device based on a C-Arm image and electronic equipment. According to one specific embodiment, the method comprises the steps that the position of a surgical instrument is tracked in real time in the surgical process, and the surgical instrument comprises C-Arm equipment; breathing and movement of a target person are tracked in real time in the operation process; a mapping relation among a clinical image coordinate system, a C-Arm system coordinate system and an optical positioning coordinate system is established, a unified coordinate system is constructed, and the optical positioning coordinate system is a coordinate system of optical equipment for tracking a surgical instrument and a target person in real time; based on the unified coordinate system, performing registration fusion on the preoperative image, the intra-operative image and pre-planned navigation information, the intra-operative image including real-time positioning images of a surgical instrument and a target person; and rendering the intraoperative image and the navigation information matched with the intraoperative image on a display device so as to instruct an operation participating object to perform an operation based on the navigation information. Therefore, a novel surgical navigation mode based on the C-Arm image is provided.

The invention discloses an intelligent medical management method and system based on malignant tumor radiotherapy. The method comprises the following steps: acquiring holographic image data corresponding to clinical image data of a patient; inputting the holographic image data into a trained deep learning model to identify a malignant tumor target region and attributes thereof; calculating the dose of radioactive particles based on the malignant tumor target region and the attributes thereof; generating a 3D printing instruction according to the malignant tumor target region; obtaining dose distribution monitoring data of radioactive particles in the treatment area; analyzing the uniformity of radioactive particles according to the dose distribution monitoring data; and according to the malignant tumor target region, the attributes and the uniformity of the radioactive particles, generating a treatment scheme and performing treatment risk assessment. Effective connection of all links can be achieved, the treatment time of a patient is shortened, and the treatment effect is improved.

The invention discloses a hepatocellular carcinoma postoperative early recurrence prediction method based on multi-modal fusion. The method comprises the following steps: firstly, integrating clinical data of a training set, a preoperative enhanced CT image and a postoperative full-view digital pathological image, and carrying out standardized correction; then, traditional image omics features and deep learning features are extracted from the CT image, cell nucleus morphological features and tumor microenvironment spatial configuration features are extracted from the pathological image, and key feature signatures are screened out through a maximum correlation minimum redundancy algorithm (mRMR) and LASSO regression in combination with clinical features. And then carrying out progressive model construction by adopting an XGBoost algorithm, sequentially establishing a clinical single-mode model, an image single-mode model, a pathological single-mode model and a multi-mode fusion model, and explaining and visualizing the models by utilizing an SHAP value and a Grad-CAM technology. Finally, the performance of the model is evaluated in a multi-dimensional mode through internal cross validation, foresight and external independent validation, risk layering is carried out based on the prediction probability, and individualized postoperative management is guided.

The invention discloses a model-based cardiovascular image analysis, comparison and recognition system, which relates to the technical field of intelligent diagnosis and comprises an artifact preliminary detection module, an artifact recognition module, a boundary reconstruction module, an authenticity verification module, an interference learning module and a closed-loop regulation and control module, based on frequency domain analysis and spatial feature deconstruction, a multi-scale artifact analysis model is constructed, and preliminary positioning and signal separation of an artifact region are realized; and the artifact identification module is used for executing gray gradient aggregation and structural symmetry comparison based on a positioning result of the artifact analysis model, and generating an artifact mode label containing interference intensity, spatial distribution and morphological difference information. According to the invention, accurate separation of artifacts and real tissues and dual verification of lesions are realized through cooperation of multiple modules, a feedback optimization mechanism is constructed to improve recognition stability, misdiagnosis and excessive intervention risks are effectively reduced, and intelligent recognition capability and application security in clinical images are enhanced.

The invention discloses a method for determining brain PET standardized reconstruction parameters based on clinical brain MRI and PET data, and relates to the field of medical image processing. The method comprises the following steps: firstly, collecting clinically paired MRI (Magnetic Resonance Imaging) and PET (Positron Emission Tomography) images for preprocessing, carrying out virtual reconstruction by combining a partial volume correction algorithm and system parameters of equipment to estimate'real 'brain activity distribution so as to obtain a first simulated PET image, and calculating difference or similarity between the first simulated PET image and a clinical PET image; through multiple iterations, obtaining an optimal'real 'brain activity distribution diagram, and processing the optimal'real' brain activity distribution diagram into a'standardized PET image 'according to standardized Gaussian filtering; then, constructing different candidate combinations of reconstruction parameters, inputting an optimal'real 'brain activity distribution diagram, traversing a second simulated PET image generated by simulation reconstruction under each combination, and respectively performing difference or similarity calculation with the'standardized PET image', so as to obtain an optimal'real 'brain activity distribution diagram; and selecting the group of candidate reconstruction parameters with the minimum difference or the highest similarity as final standardized reconstruction parameters. The method gets rid of dependence on a physical motif.

The invention relates to the technical field of clinical medicine, and discloses an AI prediction system for relapse after radiotherapy of local advanced rectal cancer, and the system comprises a multi-modal biological image database building module which is used for collecting clinical data, image data, radiotherapy target region and dose distribution data, radiobiological data and genomics information of a patient, integrating the data to establish a database; a feature extraction and screening module; a prediction model construction module; a model verification and optimization module; and a clinical application evaluation module. According to the invention, information of patients in multiple aspects such as clinic, image, radiotherapy target area and dose distribution, radiobiology and genomics is widely collected, and the multi-modal biological image database is integrated and established, so that various factors influencing rectum cancer relapse after radiotherapy can be comprehensively covered; a rich, accurate and comprehensive data basis is provided for subsequent accurate analysis and prediction, and the accuracy and reliability of prediction of relapse conditions after radiotherapy are improved.

The invention discloses a biological characteristic analysis system based on multi-modal data and a residual image attention network. The biological characteristic analysis system comprises a multi-modal data processing module, an individual similarity network construction module, a biological characteristic extraction module and a biological characteristic analysis module. Clinical image data and multi-modal data of gene expression are collected, on one hand, a comprehensive biological individual similarity network is fused and constructed, on the other hand, feature extraction is carried out based on a multi-modal auto-encoder fusion method, and finally, a constructed residual image attention network model is input for feature processing. Therefore, accurate classification and analysis of various biological characteristics and samples can be realized. The biological potential feature analysis accuracy and reliability are effectively improved, powerful reference is provided for biological scientific research and health management, and early discovery of potential changes of biological features and implementation of personalized health management are facilitated.

The invention relates to the technical field of rectal cancer, and discloses an AI-based local advanced rectal cancer neoadjuvant therapy curative effect prediction system, which comprises a data collection and processing module, a prediction model construction and training module, a prediction model verification and optimization module and a clinical application module, the data collecting and processing module is used for collecting clinical data, image data, radiotherapy target area data, radiotherapy dose data, radiobiology data and genomics data of a patient and then transmitting the data to the prediction model building and training module, and the prediction model building and training module extracts and selects key features in the data. And constructing a tumor segmentation multi-modal fusion and calibration network prediction model based on the PET / CT image. According to the invention, the data of the patient is collected through the data collection and processing module, so that the accuracy of radiotherapy effect and side reaction prediction is improved through the constructed prediction model, and the problem of feature capture limitation caused by a single center and a single mode of an existing AI model is solved.

The invention discloses a prediction method for kidney stone prognosis based on CT imaging omics, and relates to the field of medicine. Comprising the following steps that CT images and clinical data are collected, retrieval is conducted through a hospital medical record system, clinical information is collected, and the CT images are scanned through a scanner; cT image processing and feature extraction are carried out, calculus areas are sketched layer by layer on all cross sections of the CT image, information is recorded, and then feature extraction is carried out; and image omics feature screening and model construction: extracting image omics features from each original CT image. According to the method, CT image features and clinical prediction factors are combined, a clinical-image omics model is developed and verified, a repeatable, non-invasive, non-invasive and safe preoperative prediction method is provided, the calculus removal success rate of a kidney stone patient after flexible ureteroscope lithotripsy can be accurately evaluated, and the calculus removal success rate of the kidney stone patient after flexible ureteroscope lithotripsy is improved. And a clinician can be assisted to make a clinical decision and implement precise medical treatment.

The invention relates to the technical field of medical image artificial intelligence, in particular to an auxiliary interpretation method for medical image rapid diagnosis of acute abdominal disease in combination with deep learning, and the method comprises the steps: 1, organ targeted segmentation: obtaining CT sequences of an arterial phase, a venous phase and a delay phase, and carrying out the dynamic calculation of an interpolation interval according to the layer thickness parameter of scanning equipment, and carrying out the spatial standardization; synchronously generating an intestinal canal mask, a blood vessel mask and a peritoneum mask by adopting an acute abdominal disease directional segmentation network, wherein the network introduces an anatomical size adaptive multi-scale cavity convolution group into a deep layer of an encoder; 2, quantifying dynamic pathological signs; step 3, performing clinical-image gating fusion; and 4, multi-task cooperative diagnosis: inputting the fusion features into a pre-trained multi-task network, and outputting an acute abdominal disease cause classification probability and an operation urgency evaluation value in parallel. The multi-dimensional analysis capability of acute abdominal disease diagnosis is improved through multi-task cooperative diagnosis, and accurate classification of disease causes and evaluation of operation urgency can be carried out at the same time.

The invention discloses a liver cancer prognosis risk assessment method and system based on data analysis, and relates to the technical field of intelligent medical treatment. The method is realized through the following steps: firstly, acquiring and standardizing clinical, image, genome and other multi-modal data of a patient; then constructing an individualized dynamic causal graph based on the medical knowledge base, and dynamically adjusting a causal edge weight by using a graph attention network; inputting the causal diagram into a prognosis evaluation model to obtain a basic risk trajectory, performing intervention simulation by modifying a treatment node state, and calculating an anti-factual risk trajectory to quantify an individual treatment effect; and finally, outputting a risk assessment result and generating natural language interpretation containing the quantitative contribution degree. The corresponding system comprises a data sensing layer, a causal calculation layer, an intelligent decision-making layer and an interactive presentation layer. According to the method, causal reasoning and deep learning are creatively combined, and dynamic, accurate and interpretable prognosis evaluation and intervention guidance of the liver cancer are realized.

The invention discloses a method and a device for predicting and evaluating early-stage ovarian insufficiency. The method comprises the following steps: acquiring clinical and image parameters of a patient; the ovarian image data of the patient is input into an ovarian matrix extraction network model, morphological texture features are obtained, and the ovarian matrix extraction network model comprises an MRI sequence input layer, a slics scribing layer, a matrix feature fusion layer and a feedback network composed of a plurality of convolution layers and fusion layers; the morphological texture features comprise a gray-level co-occurrence matrix, a gray-level run-length matrix, a gray-level dependent matrix and a neighborhood gray-level difference matrix; inputting the clinical parameter data and the morphological texture features into an early-stage ovarian insufficiency prediction model, and outputting a prediction result of the early-stage ovarian insufficiency; the early ovarian insufficiency prediction model comprises a plurality of internal residual networks, an EMP model, an LMP model and a VGG-16 network. Through the ovarian matrix extraction network model and the early ovarian insufficiency prediction model, the early ovarian insufficiency prediction precision is further improved.

The invention relates to an automatic generation method, system and device for a clinical image reference map and a medium, and the method comprises the steps: a doctor determines the anatomical positioning of a required reference map according to a browsed image, and extracts the image examination category, examination items and basic information of a patient of the patient, and the relative positions of the layer where the anatomical positioning is located in all scanning layers; the method comprises the following steps: matching same image examination items and categories in a hospital image diagnosis report database, matching basic information of a patient, examination equipment and the like according to a priority set by a doctor, determining a retrieval keyword according to anatomical positioning, determining a retrieval structured medical image report statement based on the retrieval keyword, and querying the hospital image report database. Matching the report statement and judging the report as a normal report; and calling the report image, matching a reference atlas layer based on the relative position of the anatomical positioning layer in all scanning layers, automatically generating a reference atlas, and displaying the reference atlas to a physician for reference. According to the invention, accurate and abundant imaging reference images can be provided for imaging diagnosis doctors, and the working efficiency can be improved.

The invention discloses a method for establishing a column diagram model for diagnosis and prediction of different types of temporal lobe epilepsy. The method comprises the following steps: acquiring clinical data and an MRI image of a temporal lobe epilepsy patient; constructing a clinical feature prediction model by utilizing Logistic regression; preprocessing the MRI image, segmenting a hippocampus region of interest (ROI) and extracting radiomics features, establishing a Radscore model based on screened image features, and combining the Radscore model with clinical features to construct a joint model; performing performance evaluation on the clinical model, the radiomics model and the joint model; and constructing a temporal lobe epilepsy typing prediction column diagram based on the joint model, and realizing prediction result visualization and individualized risk assessment. According to the method, the clinical-image combined model is developed by integrating high-throughput radiomics characteristics and clinical independent factors, high-precision prediction of the temporal lobe epilepsy type is achieved, and the method can provide a reliable basis for clinical early-stage typing diagnosis, precise intervention and personalized treatment.

An electronic device is disclosed. The device receives retinal images and patient data corresponding to the retinal images. The device can train a first machine learning model (“model”) based on a first group of the retinal images and patient data corresponding to the first group and a second model based on a second group of the retinal images and patient data corresponding to the second group. The electronic device can generate a first prediction based on the first subset of a third group of the retinal images and a second prediction based on the second subset of the third group. After training the first model and the second model, the device can train a third model to predict a geographic atrophy progression in an eye of a patient based on the first and second predictions, the first and second subsets, and patient data corresponding to the first and second subset.

The invention provides a clinical image-based cancellous bone mechanical property prediction method and system, and the method comprises the steps: carrying out the medical CT scanning of a plurality of prepared cancellous bone cubic samples, and obtaining the medical CT image data; carrying out segmentation processing on the medical CT image data, extracting a sample area and calculating an average HU value of a sample CT image; performing a uniaxial compression experiment on the sample, accurately measuring strain by using a digital image correlation method, and further obtaining a stress-strain curve to calculate an elastic modulus and compression strength; according to the average HU value and the corresponding mechanical index data, a linear relation model of the average HU value of the cancellous bone CT image and the elastic modulus and the compression strength of the cancellous bone is established, and then a personalized treatment scheme is formulated according to the characteristics of the cancellous bone of the patient. The method has high universality and accuracy, and the elastic modulus and the compression strength of the cancellous bone sample can be accurately measured.

The invention belongs to the field of medical image processing, particularly relates to a general medical image segmentation method and system based on semantic aggregation, and aims to solve the problem that an existing model is poor in generalization ability when facing a new data field. The core of the method is that an expectation maximization semantic aggregation mechanism is adopted, and iterative purification is performed on visual features extracted from an image and language features extracted from a text so as to enhance semantic compactness of the features and form stable text semantic anchor points; then, through a depth alignment strategy, carrying out cross-attention fusion on the text semantic anchor point and the most abstract feature of the visual network; and finally, accurately guiding pixel-level segmentation decoding by using the fused features. According to the method, the segmentation performance and robustness of the model on unseen data are remarkably improved by forcing the model to learn an invariant internal semantic structure in the field, and the method can be widely applied to multi-center and cross-equipment clinical image intelligent analysis.

Systems and methods for machine learning assisted authenticity enhanced virtual clinical imaging preserving baseline truth values. Non-paired images are trained to an image network to migrate a realistic style derived from a real patient image to a simulated image generated using a phantom.

The present invention discloses a method for simulating aneurysm virtual stent implantation based on a spring analogy method. The method comprises: reconstructing a three-dimensional model of the preoperative parent vessel and the parent vessel after aneurysm restoration based on clinical imaging data; extracting the centerline of the first vessel to construct a virtual stent initialization model; deploying the virtual stent initialization model using a spring analogy virtual stent fast algorithm to determine the three-dimensional model of the virtual stent after deployment in the individualized vessel; and calculating efficacy evaluation parameters using a fast algorithm based on the arc surface porosity of the virtual stent neck. The present invention can simulate the actual motion of stent deployment and significantly shorten the simulation calculation time and efficacy evaluation time of simulated stent deployment within a vessel, thereby achieving real-time guidance during virtual aneurysm intervention.

The application discloses a clinical image lesion classification method based on adaptive frequency domain learning and anti-aliasing complex convolution, comprising: carrying out pretreatment and normalization treatment on an original clinical image to obtain a time domain image; converting the time domain image into a frequency domain image through a position consistency module to generate a frequency spectrum capable of preserving local and position information; carrying out masking and supervision on the generated frequency spectrum through a high-frequency self-encoder module to obtain a self-encoding frequency spectrum; inputting the self-encoding frequency spectrum into an anti-aliasing complex convolution model for training to learn specific features of different clinical lesions in the image; and integrating and classifying the specific features learned by the anti-aliasing complex convolution model to obtain a clinical image lesion classification result. The method can better utilize frequency domain information of the image on the basis of preserving time domain features of the clinical image, solves the problems of brightness imbalance and inter-class confusion in lesion classification, and greatly improves the accuracy of automatic classification.

The invention relates to the technical field of medical artificial intelligence prediction, and discloses a method for predicting the total lifetime of bladder cancer through a multi-modal fusion deep learning model. The method includes quality control and normalized preprocessing by collecting clinical, image and genetic data of a patient. Then multi-modal features are extracted, and high-value features are screened out through feature importance evaluation to form an enhanced feature set; a feature interaction network is adopted to learn a complex dependency relationship among different modal features, and deep fusion is realized to generate integrated feature representation. And finally, constructing a depth prediction model based on the representation, and capturing a mapping relation between the depth prediction model and the total lifetime. Through the deep fusion and feature screening technology, multi-modal information is integrated, the defect that a traditional method neglects interaction between modals is overcome, and the accuracy and reliability of predicting the total lifetime of the bladder cancer patient are improved.

The invention discloses an artificial intelligence medical image quality control method applied to clinical images, and relates to the technical field of medical image quality control. The artificial intelligence medical image quality control method applied to the clinical image comprises the following five steps: improving on the basis of a conventional word sequence training model by using a BiLSTM (Bidirectional Circulating Long Short Term Memory) neural network sequence prediction model, and improving input data of sequence prediction from a mode of predicting a next word by word input into a mode of predicting a next word by word input. According to the method, a sentence is input according to sentences, learning, recognition and prediction of a combination sequence between characters are carried out in a mode of predicting a word sequence, a model is trained in a mode of simulating word sequence prediction by taking the sentences as a whole, and the problem that difference between different prediction report groups cannot be unifiedly applicable to error screening index thresholds in practical application is solved; the occurrence of misdiagnosis is reduced, and the efficiency and accuracy of quality control marking are improved.

The invention relates to the technical field of laryngeal cancer treatment, in particular to a middle-advanced laryngeal cancer neoadjuvant chemotherapy sensitivity prediction system which comprises a multi-modal data acquisition and preprocessing module, a dynamic feature fusion engine module, a chemotherapy response dynamic prediction core module, an interpretable output interface module and a system verification closed loop module. Through multi-modal dynamic fusion (clinical / image / molecular data cross-modal attention allocation), a time sequence LSTM-Bayesian correction prediction model, an SHAP-t-SNE interpretable decision space and an incremental learning closed loop verification system, precise prediction of laryngeal cancer chemosensitivity is realized, and clinical response evaluation precision and intervention targeting are improved.

This invention discloses a method and system for pontine infarct segmentation and END prediction based on multimodal joint learning. The method includes acquiring and preprocessing multimodal data; constructing a wavelet transform-based feature encoding network to perform multi-scale decomposition and detail preservation of features; constructing a dual-task guided fusion module to align the deep semantics of clinical variables and imaging features and generate task-specific representations; constructing a Mamba-based global feature aggregation module to model sequence dependencies using a state-space model; constructing a multimodal second-order fusion classifier to enhance the clinical-image interaction modeling using second-order statistics; and employing a two-stage joint training strategy for training and prediction, and outputting the prediction results. This invention utilizes the DWT / IWT mechanism to significantly improve the accuracy of capturing small pontine infarct lesions; it achieves explicit interaction between segmentation evidence and prediction signals, significantly improving the segmentation accuracy of small lesions and the reliability of stroke risk assessment.

An intelligent system for optimizing radiotherapy for personalized radiotherapy, consisting of: • a genome data acquisition module configured to acquire and process a patient's genomic, molecular, and biomarker information; • a multimodal medical imaging module configured to acquire medical image data from one or more imaging modalities; • an image processing and automatic segmentation module configured to preprocess the acquired medical images, register multimodal images, segment tumors and organs at risk, and extract quantitative image features; • an artificial intelligence and data integration module configured to integrate genome data, imaging data, radiomic features, and clinical information to generate a patient-specific predictive model;• A treatment planning and dose optimization module configured to automatically generate and optimize a personalized treatment plan based on the integrated patient-specific model; • A digital twin and adaptive therapy module configured to simulate the patient-specific treatment response and continuously adjust the treatment plan during therapy; • A treatment monitoring and outcome prediction module configured to predict treatment response, disease progression, and radiation-induced toxicity based on longitudinal patient data; • A clinical decision support module configured to generate personalized treatment recommendations and support clinical decision-making;• A communication and data management module configured for the secure exchange, synchronization, and storage of clinical, imaging, genomic, and treatment-related information; and • An autonomous learning and systems management module configured to continuously improve predictive models and treatment optimization algorithms based on collected treatment outcomes, with the modules working together to generate, optimize, monitor, and continuously adapt personalized radiotherapy based on integrated genomic and multimodal medical imaging information.

The present application relates to the technical field of image processing, and particularly relates to a clinical lesion auxiliary segmentation system based on nuclear magnetic resonance images. The system comprises an MRI image processing module, a lesion auxiliary segmentation module, a probability segmentation correction module and a lesion boundary smoothing module, can obtain corresponding patient clinical nuclear magnetic resonance image sets and perform image position alignment and gray scale adjustment processing, simultaneously construct corresponding clinical image lesion segmentation models for multi-scale fusion auxiliary segmentation, and generate a clinical lesion region segmentation fusion image; through the clinical lesion region segmentation fusion image, obtain the lesion confidence probability corresponding to each pixel point in the segmentation image, and correct the probability segmentation boundary of the clinical lesion region segmentation fusion image, to obtain a clinical lesion region segmentation correction result image; perform lesion edge shape smoothing processing on the clinical lesion region segmentation correction result image, to generate a clinical lesion edge shape segmentation optimization result. The present application can realize high-precision segmentation of lesions in MRI images.

The invention provides a molecule for inducing spontaneous calcification of tumor cells and application thereof, the molecule for inducing spontaneous calcification of tumor cells at least contains two basic units, one basic unit is a targeting functional unit and is a targeting functional molecule fragment of tumor cells / tissues / microenvironment, and the other basic unit is a targeting functional molecule fragment of tumor cells / tissues / microenvironment. The other basic unit is a calcification induction functional unit; or the molecule for inducing spontaneous calcification of the tumor cells at least contains one basic unit, and the basic unit is a targeting functional unit and a calcification induction unit at the same time. After tumor cells are calcified, on one hand, calcium salt deposition on the surfaces of the cells can influence the physiological processes such as metabolism of the tumor cells, tumor cell apoptosis is further induced, and the effects of tumor inhibition and treatment are achieved; on the other hand, the contrast ratio of calcified tumor cells and tissues in clinical images is improved, and early diagnosis and accurate diagnosis of tumor focuses are facilitated.