Abstract: A correspondence between frames of a set of medical image data is determined where the set of medical image data includes at least one frame acquired without contrast medium and at least one frame acquired with contrast medium. First data representing a first image frame acquired without contrast medium is received. Second data representing a second image frame acquired with contrast medium is received. A position of a feature of a medical device in the second image frame is determined at least partly on the basis of a position of the feature determined from the first image frame.

Abstract: Systems and methods for expert-assisted classification are described herein. An example method for evaluating an expert-assisted classifier can include providing a cascade classifier including a plurality of classifier stages; and providing a simulated expert stage between at least two of the classifier stages. The simulated expert stage can be configured to validate or contradict an output of one of the at least two classifier stages. The method can also include classifying each of a plurality of records into one of a plurality of categories using the cascade classifier combined with the simulated expert stage; and determining whether the simulated expert stage improves performance of the cascade classifier.

Abstract: A method of deriving one or more medical scene model characteristics for use by one or more software applications is disclosed. The method includes receiving one or more sensor data streams. Each sensor data stream of the one or more sensor data steams includes position information relating to a medical scene. A medical scene model including a three-dimensional representation of a state of the medical scene is dynamically updated based on the one or more sensor data streams. Based on the medical scene model, the one or more medical scene model characteristics are derived.

Abstract: A cloud security system and method designed to protect users' data in case of accidental leaks in a cloud computing environment. Secured hashing of the names of folders stored on the cloud data storage are generated and persisted using multiple iterations of cryptographic hash functions along with a concatenated random number for each of the folder names, thereby providing protection against vulnerability of the folder names. The proposed system is a dual-layer framework consisting of a control layer and a data layer. The control layer is responsible for cryptographic hashing and persistence of the folder name, hashed name, salt, and iterations in a database. The control layer communicates with the data layer and provides the hashed folder names to persist the user data cloud storage.

Abstract: Synthetic CT is estimated for planning or other purposes from surface data (e.g., depth camera information). The estimation uses parameterization, such as landmark and/or segmentation information, in addition to the surface data. In training and/or application, the parameterization may be used to correct the predicted CT volume. The CT volume may be predicted as a sub-part of the patient, such as estimating the CT volume for scanning one system, organ, or type of tissue separately from other system, organ, or type of tissue.

Abstract: The present disclosure is related to a cyber-security system that includes a Supervisory Control and Data Acquisition (SCADA) network monitor configured to receive a data set from a power system network, an event manager, and a mitigation system, where the SCADA network monitor includes an anomaly detector.

Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Abstract: For anomaly detection based on topogram predication from surface data, a sensor captures the outside surface of a patient. A generative adversarial network (GAN) generates a topogram representing an interior anatomy based on the outside surface of the patient. An X-ray image of the patient is acquired and compared to the generated topogram. By quantifying the difference between the real X-ray image and the predicted one, anatomical anomalies may be detected.

Abstract: For training for and performance of patient modeling from surface data in a medical system, a progressive multi-task model is used. Different tasks for scanning are provided, such as landmark estimation and patient pose estimation. One or more features learned for one task are used as fixed or constant features in the other task. This progressive approach based on shared features increases efficiency while avoiding reductions in accuracy for any given task.

Abstract: A method of obtaining a medical image includes obtaining, via a camera, at least one surface image of a patient. A pose of the patient is determined from the at least one surface image of the patient using at least one spatial information module. The patient is positioned, via a moveable bed, to an imaging start position and a medical image of the patient is obtained using a medical imaging modality.

Abstract: For x-ray detector pose estimation, a machine-learned model is used to estimate locations of markers, including occluded or other non-visible markers, from an image. The locations of the markers, including the non-visible markers are used to determine the pose of the X-ray detector for aligning an X-ray tube with the X-ray detector.

Abstract: For x-ray detector pose estimation, a machine-learned model is used to estimate locations of markers, including occluded or other non-visible markers, from an image. The locations of the markers, including the non-visible markers are used to determine the pose of the X-ray detector for aligning an X-ray tube with the X-ray detector.

Abstract: Systems and methods are provided for automatic segmentation of a vessel. A sequence of image slices containing a vessel is acquired. Features maps are generated for each of the image slices using a trained fully convolutional neural network. A trained bi-directional recurrent neural network generates a segmented image based on the feature maps.

Abstract: For patient weight estimation in a medical imaging system, a patient model, such as a mesh, is fit to a depth image. One or more feature values are extracted from the fit patient model, reducing the noise and clutter in the values. The weight estimation is regressed from the extracted features.

Abstract: A method for training a learning-based medical scanner including (a) obtaining training data from demonstrations of scanning sequences, and (b) learning the medical scanner's control policies using deep reinforcement learning framework based on the training data.

Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: The embodiments herein provide an audio electronic shelf label (AESL) configured for providing information related to a product associated with the audio electronic shelf label. The AESL comprises a communication interface for communicating information related to the AESL; a control unit coupled to the communication interface and an audio interface unit coupled to the control unit. The control unit is configured for processing the information related to the AESL. The audio interface unit is configured for generating audio signals encoded with a characteristic audio tag upon receiving an indication from a user device.

Abstract: Embodiments include a medical imaging device and a method controlling one or more parameters of a medical imaging device. In one embodiment, a method includes receiving image data representing a first image of an object to be imaged using the radiation source and detecting a plurality of positions of respective predetermined features in the first image. Based upon the detected positions, a boundary of an imaging area of the object to be imaged is determined. Based on the determined boundary, one or more parameters of the radiation source unit are controlled.

Abstract: Robust calcification tracking is provided in fluoroscopic imagery. A patient with an inserted catheter is scanned over time. A processor detects the catheter in the patient from the scanned image data. The processor tracks the movement of the catheter. The processor also detects a structure represented in the data. The structure is detected as a function of movement with a catheter. The processor tracks the movement of the structure using sampling based on a previous location of the structure in the patient. The processor may output an image of the structure.