Infervision Medical Technology Co., Ltd.

Intervision Medical Technology Co., Ltd.

Kuan Chen

Yu Wang

Shaokang Wang

Mengmeng Sun

Rongguo Zhang

Chunxia Qin

Yanfeng Sun

Enyou Liu

Disclosed are a method, a system, and an apparatus for generating guide information, and an electronic device. The method includes: obtaining image data to be identified of a detection target; segmenting the image data to be identified by using a trained image segmentation model to obtain segmented target image data; determining image data of positioning patches arranged on a body surface of the detection target in the target image data as feature points, and establishing a mapping relationship between a preoperative spatial three-dimensional image coordinate system and an intraoperative spatial positioning coordinate system; and generating guide information based on the mapping relationship, to guide a puncture needle to perform puncturing on the detection target. An operation time of a puncture operation can be greatly shortened, and puncture efficiency and accuracy can be improved.

Method and system for generating guide information, and computer-readable storage medium

Disclosed are a method and an apparatus of processing image. The method includes: obtaining an initial bone segmentation result; and fusing the initial bone segmentation result based on characteristics of and correspondences between a plurality of bone segmentation results in the initial bone segmentation result, to obtain a target bone segmentation result. The initial bone segmentation result includes the plurality of bone segmentation results generated by a plurality of different deep learning models. Methods in the embodiments of the present application can improve precision of a fusion result of the plurality of bone segmentation results.

Method and apparatus of processing image

The method for image segmentation includes: acquiring, according to an image to be segmented including a background, a mediastinum, an artery and a vein, a first segmentation result of the mediastinum, the artery, the vein and the background in a mediastinum region of the image to be segmented; acquiring, according to the image to be segmented, a second segmentation result of a blood vessel and the background in an epitaxial region of the image to be segmented; and acquiring, according to the first segmentation result and the second segmentation result, a segmentation result of the mediastinum, the artery, the vein and the background of the image to be segmented, so that the segmentation accuracy and the segmentation efficiency of the artery and the vein may be improved.

Method for image segmentation, method for training image segmentation model

Disclosed are an image matching method, an image matching device, and a storage medium. A first image sequence and a second image sequence are acquired, and thus a first object and a second object are reconstructed and generated based on the first image sequence and the second image sequence respectively. The registration of the first object and the second object are further performed, and a mapping relationship obtained according to a registration result may indicate a correspondence between image frames in the first image sequence and image frames in the second image sequence. Compared with setting a difference value artificially, obtaining the correspondence between image frames in the first image sequence and image frames in the second image sequence by using the image matching method improves the matching accuracy.

Image matching method and device, and storage medium

A method for analysing medical treatment data based on deep learning via an intelligent analyser thereof, effectively alleviates work stress for doctors in a hospital or medical researchers and performs scientific analysis on a large amount of medical treatment data or medical data and obtain an analysis result matched therewith. The core content of the method is the establishment of a model in a computer using a deep convolution neuron algorithm in deep learning. The model assists the doctors to make correct judgments and effective decisions for the large amount of medical treatment data using mass medical treatment data selection and optimization model parameters by 'training' the model to automatically learn a pathology analysis process of the doctors or the medical researchers and then helping them in processing the large amount of medical treatment data or medical data.

Method for analysing medical treatment data based on deep learning and intelligence analyser thereof

A method and device for performing based learning on a medical image includes reading raw data of a medical image, performing transformation processing on the data by analyzing a data attribute, and integrating the same into a data format capable of being received by a model to be trained; selecting a transformation method by comparing parameters of the model to be trained and a trained model, so as to perform parameter transformation and apply transformation-based learning to training of the model to be trained for the medical image; and upon finishing model training, applying a parameter of a trained model to image category analysis. The invention further includes a device for performing transformation-based learning on a medical image, including: a data processing module; a transformation-based learning module; and an application module.

Method and device for performing transformation-based learning on medical image

A computed tomography (CT) pulmonary nodule detection method based on deep learning is provided. The method comprises the steps of: acquiring 3D pulmonary CT sequence images of a user; processing the acquired 3D pulmonary CT sequence images into 2D image data; inputting 2D image data into a preset deep learning network model for training to obtain a trained pulmonary nodule detection model; inputting a set of 3D pulmonary CT sequence images to be tested into the trained pulmonary nodule detection model to obtain a preliminary pulmonary nodule detection result; applying a pulmonary region segmentation algorithm based on deep learning to the preliminary pulmonary nodule detection result to remove false positive pulmonary nodules, so as to obtain a final pulmonary nodule detection result.

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Beijing, China

Kuan Chen