Shenzhen Keya Medical Technology Corporation

Beijing Keya Medical Technology Co., Ltd.

Keya Medical Technology Co., Ltd.

Beijing Curacloud Technology Co., Ltd.

Yuwei Li

Youbing Yin

Kunlin Cao

Qi Song

Xin Wang

Junjie Bai

Shubao Liu

Xiaoxiao Liu

Bin Ma

Xiaoyang Xu

Yi Lu

Bin Ouyang

Pengfei Zhao

Ying Xuan Zhi

Yujie Zhou

Junhuan Li

Dan Wu

Ruoping Li

Hanbo Chen

Liwei Wang

Tong Zheng

Shuyi Liang

Qian Zhao

Xiaomeng Huang

Shaofeng Yuan

Ling Hou

The present disclosure relates to a method, a device and a medium for performing vessel segmentation in a medical image. The method may comprise acquiring a medical image for vessel segmentation containing multiple parts, each of which contains vessels with different structural attributes. The method may comprise dividing the medical image into sub-medical images according to the parts by using a processor. The method may comprise determining individual vessel segmentation result for each part by means of using the vessel segmentation model corresponding to the part based on the sub-medical image of the part by using the processor. The method may comprise obtaining a vessel segmentation result of the medical image by means of fusing the individual vessel segmentation results of the sub-medical images of the parts by the processor. By applying the method and the device, vessel segmentation models are adapted differentially for the individual parts so as to segment the vessels of the individual parts respectively, so that the entire vessel segmentation process is fast, effective, and accurate.

Method and system for performing vessel segmentation in a medical image

The present disclosure relates to a training method and a training system for training a learning network for medical image analysis. The training method includes: acquiring an original training data set for a learning network with a predetermined structure; performing, by a processor, a pre-training on the learning network using the original training data set to obtain a pre-trained learning network; evaluating, by the processor, the pre-trained learning network to determine whether the pre-trained learning network has an evaluation defect; when the pre-trained learning network has the evaluation defect, performing, by the processor, a data augmentation on the original training data set for the existing evaluation defect; and performing, by the processor, a refined training on the pre-trained learning network using a data augmented training data set. The present disclosure can evaluate and train the learning network in stages, therefore, the complexity of medical image processing is reduced, and the efficiency and accuracy of medical image analysis are improved.

Methods and systems for training learning network for medical image analysis

A computer-implemented method for predicting a blood vessel stenosis is disclosed. The method may include extracting a blood vessel path and its centerline based on the image of the blood vessel. The method may further include determining a candidate stenosis for the blood vessel path and identifying image blocks along the centerline of the blood vessel path within a range of candidate stenosis for the blood vessel path determined based on the candidate stenosis. The method may also include determining a degree of stenosis for the blood vessel path by applying a trained learning network comprising a convolutional neural network and a recurrent neural network on the image blocks within the range of candidate stenosis.

Prediction method for healthy radius of blood vessel path, prediction method for candidate stenosis of blood vessel path, and blood vessel stenosis degree prediction device

Method, system, device and medium for determining a blood flow velocity in a vessel are provided. An example method includes receiving a 3D model of the vessel, which is reconstructed based on X-ray angiography images of the vessel. The method further includes specifying a segment of the 3D model by a start landmark and a termination landmark. Moreover, the method includes determining the blood flow velocity based on length of the segment and perfusion time for the segment by normalizing the blood flow velocity to correspond to a cardiac cycle. The method has a better accuracy in calculating blood flow velocity, and requires no additional modalities other than the original X-ray angiogram sequences used to visualize coronary arteries.

Method, system, device and medium for determining a blood flow velocity in a vessel

The disclosure relates to systems and methods for evaluating a blood vessel. The method includes receiving image data of the blood vessel acquired by an image acquisition device, and predicting, by a processor, blood vessel condition parameters of the blood vessel by applying a deep learning model to the acquired image data of the blood vessel. The deep learning model maps a sequence of image patches on the blood vessel to blood vessel condition parameters on the blood vessel, where in the mapping the entire sequence of image patches contribute to the blood vessel condition parameters. The method further includes providing the blood vessel condition parameters of the blood vessel for evaluating the blood vessel.

Systems and methods for determining blood vessel conditions

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 present disclosure provides a prediction method for a healthy radius of a blood vessel path, a prediction method for candidate stenosis of a blood vessel path, and a blood vessel stenosis degree prediction device. The prediction method for a healthy radius includes: obtaining a blood vessel radius of the blood vessel path; by a processor, detecting a radius peak of the blood vessel radius of the blood vessel path; and by the processor, predicting the healthy radius of the blood vessel path by performing a regression on the radius peak of the blood vessel radius. The blood vessel stenosis degree prediction device can, in certain embodiments, automatically determine the candidate stenosis and detect the degree of stenosis for the candidate stenosis range, significantly reduce the computation load, improve the detection efficiency and effectively avoid missed detection.

The present disclosure relates to a method, storage medium, and system for analyzing an image sequence of a periodic physiological activity. In one implementation, the method includes receiving the image sequence acquired by an imaging device, the image sequence having a plurality of frames, and identifying a feature point in a first frame. The method further includes determining motion vectors for the feature point in the frames of the image sequence. Each motion vector for the feature point is determined based on respective locations of corresponding feature points in frames adjacent to the first frame. The method also includes determining a motion magnitude profile based on the determined motion vectors and determining a phase of each frame in the image sequence based on the motion magnitude profile.

Method, storage medium, and system for analyzing image sequences of periodic physiological activities

The disclosure relates to systems and methods for determining blood vessel conditions. The method includes receiving a sequence of image patches along a blood vessel path acquired by an image acquisition device. The method also includes predicting a sequence of blood vessel condition parameters on the blood vessel path by applying a trained deep learning model to the acquired sequence of image patches on the blood vessel path. The deep learning model includes a data flow neural network, a recursive neural network and a conditional random field model connected in series. The method further includes determining the blood vessel condition based on the sequence of blood vessel condition parameters. The disclosed systems and methods improve the calculation of the sequence of blood vessel condition parameters through an end-to-end training model, including improving the calculation speed, reducing manual intervention for feature extraction, increasing accuracy, and the like.

The present disclosure relates to a device, a system, and a computer-readable medium for calculating vessel flow parameters based on angiography. In one implementation, the device includes a processor and a memory storing computer-executable instructions that, when executed by the processor, cause the processor to perform the following operations: selecting a plurality of template frames from the angiographic images to generate a 3D model for a vessel; determining a start frame and an end frame in the plurality of angiographic images showing a contrast filling process; determining corresponding locations of front ends of the contrast in the start frame and the end frame in the 3D model of the vessel; calculating a vessel volume between the determined locations of the front ends in the 3D model; and determining an average blood flow rate based on the calculated volume, and a time interval between the start frame and the end frame.

System and method for calculating vessel flow parameters based on angiography

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 relates to a method, storage medium, and system for analyzing an image sequence of a periodic physiological activity. In one implementation, the method includes receiving the image sequence acquired by an imaging device, the image sequence having a plurality of frames and determining local motions for pixels in each frame of the image sequence. The local motion for a pixel may be determined using corresponding pixels in frames adjacent to the frame to which the pixel belongs. The method further includes determining principal motions for the plurality of frames based on the local motions; determining a motion magnitude profile based on the principal motions; and determining the phase of each frame in the image sequence based on the motion magnitude profile.

Methods and systems for segmenting images having sparsely distributed objects are disclosed. A method may include: predicting object potential areas in the image using a preliminary fully convolutional neural network; segmenting a plurality of sub-images corresponding to the object potential areas in the image using a refinement fully convolutional neural network, wherein the refinement fully convolutional neural network is trained to segment images on a higher resolution compared to a lower resolution utilized by the preliminary fully convolutional neural network; and combining the segmented sub-images to generate a final segmented image.

Method and system for performing segmentation of image having a sparsely distributed object

The present disclosure is directed to a method and system for automatically predicting a blood flow feature based on a medical image. The method may include acquiring, by a processor, image patches and a vessel related feature of a vessel tree. Then, the blood flow feature of the vessel tree may be calculated, by the processor, using a learning network based on both the image patches and the vessel related feature of the vessel tree. The learning network includes a multi-model neural network and a tree structure recurrent neural network connected in series. The method and system of present disclosure can perform a quick and accurate prediction for the blood flow feature, such as FFR, of the vessel tree of a target object (such as certain site of human body or animal body) based on both the medical images and vessel related features of the vessel tree of the target object. The predicted FFR may assist the user in pathological diagnosis or other treatment of the target object.

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Bellevue, WA, United States of America

Yuwei Li