Abstract: 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.

Abstract: 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.

Abstract: The present disclosure is directed to a method and device for managing medical data. The method may include receiving medical image data of a plurality of patient cases acquired by at least one image acquisition device. The method may further include determining diagnosis results, by a processor, of the medical image data using an artificial intelligence method. The method may also include determining, by the processor, priority scores for the medical image data based on the respective diagnosis results, and sorting, by the processor, the medical image data based on the priority score. The method may yet further include presenting a queue of the medical image data on a display according to the sorted order.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure is directed to a method and device for managing medical data. The method may include receiving medical image data of a plurality of patient cases acquired by at least one image acquisition device. The method may further include determining diagnosis results, by a processor, of the medical image data using an artificial intelligence method. The method may also include determining, by the processor, priority scores for the medical image data based on the respective diagnosis results, and sorting, by the processor, the medical image data based on the priority score. The method may yet further include presenting a queue of the medical image data on a display according to the sorted order.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments of the disclosure provide systems and methods for generating a report based on medical images of a patient. An exemplary system includes a communication interface configured to receive the medical images acquired by an image acquisition device. The system may further include at least one processor. The at least one processor is configured to receive a user selection of at least one medical image in at least one view. The at least one processor is further configured to automatically generate keywords describing the selected medical image based on a learning network including a convolutional neural network and a recursive neural network connected in series. The at least one processor is also configured to receive a keyword selection among the generated keywords and generate the report based on the keyword selection. The exemplary system additionally includes a display configured to display the selected medical image and the report.

Abstract: Embodiments of the disclosure provide systems and methods for segmenting a medical image. An exemplary system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system further includes a memory configured to store a multi-level learning network including at least a first convolution block and a second convolution block. The second convolution block has at least one convolution layer. The system also includes a processor. The processor is configured to determine a first feature map by applying the first convolution block to the medical image, and determine a second feature map by applying the second convolution block to the first feature map. The processor is further configured to determine a first level feature map by concatenating the first feature map and the second feature map. The processor is also configured to obtain a first level segmented image based on the first level feature map.

Abstract: Embodiments of the disclosure provide systems and methods for segmenting a medical image. The system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system also includes a memory configured to store a plurality of learning networks jointly trained using first training images of a first imaging modality and second training images of a second imaging modality. The system further includes a processor, configured to segment the medical image using a segmentation network selected from the plurality of learning networks.

Abstract: Embodiments of the disclosure provide systems and methods for generating a diagnosis report based on a medical image of a patient. The system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system further includes at least one processor. The at least one processor is configured to detect a medical condition of the patient and parameters associated with the medical condition based on the medical image. The at least one processor is further configured to construct the diagnosis report based on the medical image, wherein the diagnosis report includes at least one view of the medical image and a description of the medical condition using the parameters. The system also includes a display configured to display the diagnosis report.

Abstract: 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.