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 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: 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: Embodiments of the disclosure provide systems and methods for generating a report based on a medical image of a patient. An exemplary system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system may further include at least one processor. The at least one processor is configured to automatically determine keywords from a natural language description of the medical image generated by applying a learning network to the medical image. The at least one processor is further configured to generate the report describing the medical image of the patient based on the keywords. The at least one processor is also configured to provide the report for display.

Abstract: A method and system can be used for disease quantification modeling of an anatomical tree structure. The method may include obtaining a centerline of an anatomical tree structure and generating a graph neural network including a plurality of nodes based on a graph. Each node corresponds to a centerline point and edges are defined by the centerline, with an input of each node being a disease related feature or an image patch for the corresponding centerline point and an output of each node being a disease quantification parameter. The method also includes obtaining labeled data of one or more nodes, the number of which is less than a total number of the nodes in the graph neural network. Further, the method includes training the graph neural network by transferring information between the one or more nodes and other nodes based on the labeled data of the one or more nodes.

Abstract: Embodiments of the disclosure provide systems and methods for segmenting an image. An exemplary system includes a communication interface configured to receive the image acquired by an image acquisition device. The system further includes a memory configured to store a multi-level learning network comprising a plurality of convolution blocks cascaded at multiple levels. The system also includes a processor configured to apply a first convolution block and a second convolution block of the multi-level learning network to the image in series. The first convolution block is applied to the image and the second convolution block is applied to a first output of the first convolution block. The processor is further configured to concatenate the first output of the first convolution block and a second output of the second convolution block to obtain a feature map and obtain a segmented image based on the feature map.

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 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: 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.