Shenzhen Keya Medical Technology Corporation

Beijing Keya Medical Technology Co., Ltd.

Keya Medical Technology Co., Ltd.

Beijing Curacloud Technology Co., Ltd.

Bin Ouyang

Qi Song

Kunlin Cao

Youbing Yin

Xin Wang

Junjie Bai

Yi Lu

Yuwei Li

Xiaoyang Xu

Zhihui Guo

Shubao Liu

Xiaoxiao Liu

Bin Ma

Pengfei Zhao

Bo Jiang

Junhuan Li

Dan Wu

Ruoping Li

Shuyi Liang

Systems and methods for generating a centerline for an object in an image are provided. An exemplary method includes receiving an image containing the object. The method also includes detecting at least one bifurcation of the object using a trained bifurcation learning network based on the image. The method further includes extracting the centerline of the object based on a constraint condition that the centerline passes through the detected bifurcation.

Method, device and system for generating a centerline for an object in an image

The present disclosure is directed to a computer-implemented method and system for anatomical tree structure analysis. The method includes receiving model inputs for a set of positions in an anatomical tree structure. The method further includes applying, by a processor, a set of encoders to the model inputs. Each encoder is configured to extract features from the model input at a corresponding position. The method also includes applying, by the processor, a tree structured network to the extracted features. The tree structured network has a plurality of nodes each connected to one or more of the encoders, and information propagates among the nodes of the tree structured network according to spatial constraints of the anatomical tree structure. The method additionally includes providing an output of the tree structured network as an analysis result of the anatomical tree structure analysis.

Method and system for anatomical tree structure analysis

The present disclosure relates to a medical image analysis method, a medical image analysis device, and a computer-readable storage medium. The medical image analysis method includes receiving a medical image acquired by a medical imaging device; determining a navigation trajectory by performing navigation processing on the medical image based on an analysis requirement, the analysis requirement indicating a disease to be analyzed; extracting an image block set along the navigation trajectory; extracting image features using a first learning network based on the image block set; and determining an analysis result using a second learning network based on the image features and the navigation trajectory.

Medical image analysis using navigation processing

The disclosure provides a method and system for determining a lumen volume of a target blood vessel. The method may include acquiring a temporal sequence of angiography images of the target blood vessel after a contract agent is injected in the target blood vessel. The method may further include identifying a region of interest containing the target blood vessel, by a processor, in each angiography image in the temporal sequence of angiography images. The method may also include integrating, by the processor, pixel values in each region of interest, and determining the lumen volume, by the processor, based on the integrated values of the regions of interest and a predetermined correlation between the integrated values and volumes of the contrast agent.

Methods and systems for determining lumen volume and coronary blood flow

Systems and methods for generating a centerline for an object in an image are provided. An exemplary method includes receiving an image containing the object. The method also includes generating a distance cost image using a trained first learning network based on the image. The method further includes detecting end points of the object using a trained second learning network based on the image. Moreover, the method includes extracting the centerline of the object based on the distance cost image and the end points of the object.

Lamp for ukulele

The present disclosure is directed to a method and device for generating anatomical labels for a physiological tree structure. The method may include receiving a 3D model and a 3D skeleton line of the physiological tree structure. The 3D model is restructured based on medical image data of the physiological tree structure acquired by an imaging device. The method further includes selecting at least one level from extracting geometrical features from a pool of selectable levels. The method also includes extracting, by a processor, geometrical features from the 3D model of the physiological tree structure along the 3D skeleton line at the selected at least one level. The method also includes generating, by the processor, anatomical labels for the physiological tree structure using a trained learning network based on the extracted geometrical features.

Method and device for generating anatomical labels for a physiological tree structure

The present disclosure is directed to a computer-implemented method and system for anatomical tree structure analysis. The method may begin with receiving a task of the anatomical tree structure analysis. Then, a set of positions in the anatomical tree structure may be set or received, by a processor, as the sampling positions for model inputs and model outputs. Then model inputs may be determined, by the processor, at the sampling positions on the basis of the task. An encoder may be selected, by the processor, for each position of the set of positions on the basis of the task. The encoder may be configured to receive a model input at each position and extract features for the corresponding position. After that, a tree structured recurrent neural network (RNN) may be constructed by the processor with nodes corresponding to the set of positions and connected with the respective encoders. An RNN unit for each node may be selected on the basis of the task and an information propagation among the nodes may be set on the basis of the spatial constraints of the set of positions in the anatomical tree structure. The generated treed structured RNN may be provided for performing the task of the anatomical tree structure analysis.

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Shenzhen, China

Bin Ouyang