57 results about "Lesion Identification" patented technology

The invention discloses a coronary artery virtual stent implantation method and system based on haemodynamics analysis. The coronary artery virtual stent implantation method comprises the steps that CTA image data of a patient are read, plaque is identified, a vascular three-dimensional image model is constructed, the plaque is removed, and the position of the plaque is recorded; the physiological parameters of the patient is received, a CFD boundary condition is set, and CFD calculating is conducted; according to a three-dimensional vascular form, a plaque characteristic and FFR values throughout blood vessels, a lesion is identified; according to the identified lesion, a stent specification database and a virtual stent implantation strategy, an implantation scheme is generated, and a new vascular three-dimensional geometric model is generated; under the new three-dimensional geometric model, fluid calculation is conducted again, according to a preset selecting standard, an optimal stent implantation scheme is output. According to the coronary artery virtual stent implantation method and system, blood vessel stenosis degree calculating, stent implantation strategy generating and quantitative evaluating of the virtual stent implantation effect are completed automatically, the operation scheme is planned accurately, the doctor decision efficiency is improved effectively, and the risk of dependence on the artificial judgment is reduced.

The invention provides a lung detection method and device based on PET / CT image features. The method comprises the steps that the local image of a region corresponding to a lesion is acquired from a PET / CT image library as a training image; the PET / CT image library comprises the lung lesion images of multiple types of lesions; the training image is transformed and filtered to acquire a processed training image; the feature information of the processed training image is extracted, wherein the feature information comprises one or combination of texture feature information and color feature information; a lesion detection model is established according to the feature information; and a lung image to be detected is detected based on the lesion detection model to determine the type of the lesion in the lung image to be detected. An image processing technology is used to carry out lesion analysis and identification on a PET / CT image, which improves the accuracy and sensitivity of lesion identification.

The invention discloses a rice lesion detection method and system based on deep learning, belonging to the image processing field, the method comprising: providing a photo sample set and a manual labeling sample set, and cutting the photo sample set and the manual labeling sample set according to a proportion to form a second photo sample set and a second manual labeling sample set; inputting thesecond photo sample set and the second label sample set into the Linknet network model, and obtaining the optimal model by training the Linknet network model based on the Pytorch deep learning framework; using the optimal model to identify the rice lesion images needed to be detected at present, and calculating the proportion of rice lesion area and classifying the disease status. Through the Linknet network model of Pytorch deep learning framework, the generalization ability and field practicability of rice leaf lesion identification can be improved, and the utilization rate of information can be improved, which is conducive to the subsequent quantitative application of pesticides and reduce environmental pollution.

The invention discloses a fundus illumination multi-disease detection system based on a regional feature set neural network. The system comprises a computer memory, a computer processor and a computerprogram which is stored in the computer memory and can be executed on the computer processor, wherein a trained multi-fundus disease detection network model is stored in the computer memory, and themulti-fundus disease detection network model comprises a feature extraction network, a semantic segmentation sub-network and a plurality of classifiers; when the computer processor executes the computer program, the following steps are realized: obtaining a to-be-detected original fundus picture, inputting the to-be-detected original fundus picture into the multi-fundus-disease detection network model, obtaining the probability of each type of lesion, and realizing fundus lesion identification. According to the invention, a single model can be used for detecting various fundus diseases, and the detection precision is high.

Method and system for grading a medical image. For example, a system for grading a medical image comprising a grading network configured to provide a grading result corresponding to the medical image based on at least the medical image and / or a list of lesion candidates generated by a lesion identification network.

A cancerous lesion identifying method via hyper-spectral imaging technique comprises steps of: acquiring a plurality of first pathology images via an endoscopy, wherein the first pathology images are cancerous lesion images respectively; importing the first pathology images into an image processing module to acquire a plurality of first simulating spectra of the first pathology images so as to generate a principle component score diagram in accordance with the first simulating spectra; defining a plurality of triangle areas in the principle component score diagram in accordance with the first simulating spectra; determining whether a principle component score of a second simulating spectrum of a second pathology image is within any one of the triangle areas; and confirming the second pathology image belongs to one of the cancerous lesion images when the principle component score of the second simulating spectrum is within any one of the triangle areas.

The invention relates to the technical field of medical image processing, in particular to an artificial intelligence system for identifying high myopia retinopathy, comprising: an image acquisition module for acquiring an optical coherence tomography image and a fundus reference of a patient to be identified; the first recognition module is used for inputting the optical coherence tomography image into a high myopia retinopathy recognition model, judging whether the optical coherence tomography image has lesion or not and obtaining a lesion type result; the second recognition module is used for inputting the fundus color photograph into a high myopia retinal lesion staging model, judging the retinal lesion staging of the fundus color photograph and obtaining a lesion staging judgment result; and the report generation module is used for generating a diagnosis and treatment suggestion report according to the lesion type result and the lesion staging judgment result. According to the method, the optical coherence tomography image and the fundus color photograph of the patient can be quickly and effectively recognized to recognize common retinopathy of high myopia.

A lesion identification system for surgical operations has a light marker adapted for placement in the vicinity of a lesion inside an organ of an organism, and a location-identifying device detecting a light emitted from the light marker at an outside of the organ for identifying a location of the lesion. The marker includes a light emitter and an engagement member associated with the emitter to be engageable with a wall of the organ. The engagement member includes a clip. The light emitter emits light with a wavelength in a near-infrared range. The device includes an endoscope comprising an inserter section having an image pickup element picking up a reflected light, involving the light from the light marker, at an outside of the organ, and an image pickup unit allowing the reflected light, picked by the image pickup element, to be generated as an image for display on a monitor.

Method and system for grading a medical image. For example, a system for grading a medical image comprising a grading network configured to provide a grading result corresponding to the medical image based on at least the medical image and / or a list of lesion candidates generated by a lesion identification network.

The invention discloses a blood vessel tree generation and lesion recognition method, device and equipment and a readable storage medium. The method comprises the following steps: acquiring a medicalimage, wherein the medical image comprises a plurality of voxels; segmenting a plurality of blood vessel regions from the medical image, wherein the blood vessel regions comprise blood vessels and voxels having similar characteristics with the blood vessels; establishing an incidence matrix of the plurality of blood vessel areas according to the proximity relation among the plurality of blood vessel areas; respectively acquiring a blood vessel section and a blood vessel bifurcation point of each blood vessel area; according to the incidence matrix, the blood vessel sections, the blood vessel bifurcation points and the connection relations between the blood vessel sections and the blood vessel bifurcation points contained in the blood vessel areas are constructed into blood vessel graphs ofthe blood vessel areas; and establishing a plurality of corresponding blood vessel trees based on the plurality of connected sub-graphs in the blood vessel graph. According to the method, the form, the structure and the characteristics of the blood vessel image and the connection relationship between the blood vessels can be analyzed, and an accurate blood vessel tree is constructed.

The invention discloses an intelligent film reading system for tumor image data. One-key film reading report writing is realized, steps such as image data preprocessing, lesion identification, quantitative parameter measurement, diagnostic scoring and visualization are completed automatically by a computer, the operation procedure and the diagnostic process are simplified, the advantages of a doctor and the computer are executed to the maximum extent, the current image film reading working efficiency is improved significantly, the film reading result is objective and accurate, and the repeatability is high.