Abstract: Systems and methods for automatic assessment of a vessel are provided. A temporal sequence of medical images of a vessel of a patient is received. A plurality of sets of output embeddings is generated using a machine learning based model trained using multi-task learning. The plurality of sets of output embeddings is generated based on shared features extracted from the temporal sequence of medical images. A plurality of vessel assessment tasks is performed by modelling each of the plurality of sets of output embeddings in a respective probabilistic distribution. Results of the plurality of vessel assessment tasks are output.

Abstract: System and methods for determining and implementing optimized reconstruction parameters for computer-aided diagnosis applications. A simulator generates image data using different combinations of reconstruction parameters. The image data is used to evaluate or train machine learned networks that are configured for computer-aided diagnosis applications to determine which reconstruction parameters are optimal for application or training.

Abstract: Angiography angles are determined. Patient information and target vessel information are obtained, wherein the patient information defines individual medical information of a patient and wherein the target vessel information defines at least one target vessel to be imaged. At least one angiography angle is determined based on the patient information and the target vessel information. Angiograms obtained using the at least one angiography angle are analyzed to determine a vessel coverage of the target vessel and based on the vessel coverage determines additional angiography angles.

Abstract: Systems and methods for performing a medical imaging analysis task are provided. One or more input medical images of a patient are received. The one or more input medical images are encoded into embeddings using a machine learning based encoder network. A medical imaging analysis task is performed based on the embeddings. Results of the medical imaging analysis task are output.

Abstract: Systems and methods are provided for three dimensional depth reconstruction of vessels in two dimensional medical images. A medical image comprising braches of one or more vessels is received. A branch overlap image channel that represents a pixelwise probability that the branches overlap is generated. A set of branch orientation image channels are generated. Each branch orientation image channel is associated with one of a plurality of orientations. Each branch orientation image channel representing a image channel represents a pixelwise probability that the branches are oriented in its associated orientation. A multi-channel depth image is generated based on the branch overlap image channel and the set of branch orientation image channels. Each channel of the multi-channel depth image comprises portions of the branches corresponding to a respective depth.

Abstract: Systems and methods for determining corresponding locations of points of interest in a plurality of input medical images are provided. A plurality of input medical images comprising a first input medical image and one or more additional input medical images is received. The first input medical image identifies a location of a point of interest. A set of features is extracted from each of the plurality of input medical images. Features between each of the sets of features are related using a machine learning based relational network. A location of the point of interest in each of the one or more additional input medical images that corresponds to the location of the point of interest in the first input medical image is identified based on the related features. The location of the point of interest in each of the one or more additional input medical images is output.

Abstract: Various examples of the disclosure pertain to determining a label set for an anatomical structure such as a complex blood vessel, e.g., the coronary artery. The determining of the label set takes into account multiple inputs, such as the rule set of anatomical relationship between sub structures of the anatomical structure and a list of candidate labels and associated probabilities obtained for each one of the anatomical substructures.

Abstract: One or more example embodiments of the present invention relates to a method for the automated determination of examination results in an image sequence from multiple chronologically consecutive frames, the method comprising determining diagnostic candidates in the form of contiguous image regions in the individual frames for a predefined diagnostic finding; and for a number of the diagnostic candidates, determining which candidate image regions in other frames correspond to the particular diagnostic candidate, determining whether the candidate image regions of the particular diagnostic candidate in the other frames overlap with other diagnostic candidates, generating a graph containing the determined diagnostic candidates of the frames as nodes and the determined overlaps as edges, and generating communities from nodes connected via edges.

Abstract: Systems and methods for graph based assessment of a patient are provided. Medical imaging data and non-imaging medical data of a patient are received. The medical imaging data and the non-imaging medical data are encoded into encoded features using a graph based machine learning network by comparing the patient with patients of a patient population based on a graph of the patient population. An assessment of the patient is determined based on the encoded features using a machine learning classifier network. The assessment of the patient is output.

Abstract: Systems and methods for automated assessment of a vessel are provided. One or more input medical images of a vessel of a patient are received. A plurality of vessel assessment tasks for assessing the vessel is performed using a machine learning based model trained using multi-task learning. The plurality of vessel assessment tasks comprises segmentation of reference walls of the vessel from the one or more input medical images and segmentation of lumen of the vessel from the one or more input medical images. Results of the plurality of vessel assessment tasks are output.

Abstract: For training for and performance of LGE analysis, multi-task machine-learning model is trained to output various cardiac tissue characteristics based on input of LGE MR data. The use of segmentation may be avoided or limited, resulting in a greater number of available training data samples, by using radiology clinical reports with LGE information as a source for samples. The multi-task model may be trained to output cardiac tissue characteristics using radiology clinical reports with LGE information with no segmentation or with segmentation for only a subset of the training samples. By training for multiple tasks, the accuracy of prediction for each task benefits from the information for other tasks. The trained model outputs values of characteristics for multiple tasks, such as extent of enhancement, type of enhancement, and localization of enhancement. Other tasks may be included, such as disease classification. Other inputs may be used, such as also including sensor data and/or cardiac motion.

Abstract: Systems and methods for automated assessment of a vessel are provided. One or more input medical images of a vessel of a patient are received. A plurality of vessel assessment tasks for assessing the vessel is performed using a machine learning based model trained using multi-task learning. The plurality of vessel assessment tasks are performed by the machine learning based model based on shared features extracted from the one or more input medical images. Results of the plurality of vessel assessment tasks or a combination of the results of the plurality of vessel assessment tasks are output.

Abstract: Systems and methods for automatic assessment of a lesion are provided. One or more input medical images of a vessel of a patient is received. A lesion is defined in the one or more input medical images. A region of interest around the lesion is defined in the one or more input medical images. Radiomic features are extracted from the region of interest. An assessment of the lesion is determined using a machine learning based classifier network based on the radiomic features. The assessment of the lesion is output.

Abstract: At least one example embodiment provides a computer-implemented method for evaluating at least one image data set of an imaging region of a patient, wherein at least one evaluation information describing at least one medical condition in an anatomical structure of the imaging region is determined.

Abstract: Systems and methods for automated assessment of a vessel are provided. One or more input medical images of a vessel of a patient are received. A plurality of vessel assessment tasks for assessing the vessel is performed using a machine learning based model trained using multi-task learning. The plurality of vessel assessment tasks are performed by the machine learning based model based on shared features extracted from the one or more input medical images. Results of the plurality of vessel assessment tasks or a combination of the results of the plurality of vessel assessment tasks are output.

Abstract: Systems and methods for determining a 3D centerline of a vessel are provided. A current state observation of an artificial agent is determined based on one or more image view sets, each including 2D medical images of a vessel, a current position of the artificial agent in the 2D medical images, and a start position and a target position in the 2D medical images. Policy values are calculated for a plurality of actions for moving the artificial agent in 3D based on the current state observation using a trained machine learning model. The artificial agent is moved according to a particular action based on the policy values. The steps of determining, calculating, and moving are repeated for a plurality of iterations to move the artificial agent along a 3D path between the start position and the target position. The 3D centerline of the vessel is determined as the 3D path.

Abstract: Methods and systems for artificial intelligence based medical image segmentation are disclosed. In a method for autonomous artificial intelligence based medical image segmentation, a medical image of a patient is received. A current segmentation context is automatically determined based on the medical image and at least one segmentation algorithm is automatically selected from a plurality of segmentation algorithms based on the current segmentation context. A target anatomical structure is segmented in the medical image using the selected at least one segmentation algorithm.

Abstract: Systems and methods for determining a 3D centerline of a vessel are provided. A current state observation of an artificial agent is determined based on one or more image view sets, each including 2D medical images of a vessel, a current position of the artificial agent in the 2D medical images, and a start position and a target position in the 2D medical images. Policy values are calculated for a plurality of actions for moving the artificial agent in 3D based on the current state observation using a trained machine learning model. The artificial agent is moved according to a particular action based on the policy values. The steps of determining, calculating, and moving are repeated for a plurality of iterations to move the artificial agent along a 3D path between the start position and the target position. The 3D centerline of the vessel is determined as the 3D path.

Abstract: Anomalies in images are detected. A generative network and/or an autoencoder (“G/A-Network”), a Siamese network, a first training-dataset of normal images and a second training-dataset of abnormal images are provided. The G/A-network is trained to produce latent data from input images and output images from the latent data, wherein the training is performed with images of the first training-dataset, wherein a loss function is used for training at least at the beginning of training, and the loss function enhances the similarity of the input images and respective output images. The Siamese network is trained to generate similarity measures between input images and respective output images, wherein the training is performed with images of the first training-dataset and the second training-dataset in that images of both training-datasets are used as input images for the G/A-network and output images of the G/A-network are compared with their respective input images by the Siamese network.

Abstract: Systems and methods for computing a transformation for correction motion between a first medical image and a second medical image are provided. One or more landmarks are detected in the first medical image and the second medical image. A first tree of the anatomical structure is generated from the first medical image based on the one or more landmarks detected in the first medical image and a second tree of the anatomical structure is generated from the second medical image based on the one or more landmarks detected in the second medical image. The one or more landmarks detected in the first medical image are mapped to the one or more landmarks detected in the second medical image based on the first tree and the second tree. A transformation to align the first medical image and the second medical image is computed based on the mapping.