207 results about "Lesion detection" patented technology

Method and system for lesion detection is disclosed. A lesion is detected from each data set in the spatial domain based on an organ intensity subtracted (OIST) image. In the temporal domain, the lesion is detected based on a maximum frequency-magnitude difference (MFMD) map across a plurality of data sets. The detection result from the spatial domain and the detection result from the temporal domain are integrated to produce an integrated lesion detection result.

A system, and computer implemented method are provided for interactively displaying three-dimensional structures. Three-dimensional volume data (34) is formed from a series of two-dimensional images (33) representing at least one physical property associated with the three-dimensional structure, such as a body organ having a lumen. A wire frame model of a selected region of interest is generated (38b). The wireframe model is then deformed or reshaped to more accurately represent the region of interest (40b). Vertices of the wire frame model may be grouped into regions having a characteristic indicating abnormal structure, such as a lesion. Finally, the deformed wire frame model may be rendered in an interactive three-dimensional display.

The invention discloses a pulmonary nodule automatic detection method and system based on a chest CT image; the method comprises the following steps: receiving a chest CT image, using a preset 3D convolution nerve network to carry out pulmonary nodule focus detection for the chest CT image, and outputting a detection result; receiving the CT image and the detection result, and loading the detection result with the corresponding chest CT image to form a fused file; obtaining the fused file and carrying out visualization treatment, providing browse auxiliary operations for users on the fused file with the visualization treatment, and providing a calculation and measuring function. The method and system can isolate lung areas, extract candidate pulmonary nodules and remove false positives, thus improving focus detection efficiency, and the false positive efficiency can reach 97.7%. the method can use visualization three dimensional forms to directly assist a doctor to determine the pulmonary nodule forms.

Methods are provided for close-range intraoperative, endoscopic and intravascular detection and treatment of lesions, including tumors and non-malignant lesions. The methods use antibody fragments or subfragments labeled with isotopic and non-isotopic agents. Also provided are methods for detection and treatment of lesions with photodynamic agents and methods of treating lesions with a protein conjugated to an agent capable of being activated to emit Auger electron or other ionizing radiation. Compositions and kits useful in the above methods are also provided.

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 diabetic retinopathy detection system based on serial structure segmentation. wherein the fundus image acquisition device is used for acquiring a retina fundus image; the data processing device is used for analyzing and processing the acquired fundus image; A data processing apparatus includes: a data processor; Preprocessing function module, Blood vessel segmentation function module, Visual disc segmentation function module, Centrally recessed determination function module, Exudation segmentation function module, and the statistical calculation function module and the doctor diagnosis function module. The data processing device is used for counting the exudation area and calculating the probability of diabetic macular edema lesions in the input fundus image, andfinally, a final diagnosis and treatment scheme is given by combining a statistical calculation result and the fundus doctor according to the divided exudation area and disease probability and combining with the specialty of the fundus doctor. Various related physiological structures of the fundus are systematically considered, a lesion area is segmented, then a diagnosis report is given by a fundus doctor, detection is efficient, lesion detection is more accurate, the workload of the doctor can be greatly reduced, and the working efficiency is improved.

A computer aided bone scan assessment system and method provide automated lesion detection and quantitative assessment of bone disease burden changes.

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.