Abstract: A method and system for detecting a spatial and temporal location of a contrast injection in a fluoroscopic image sequence is disclosed. Training volumes generated by stacking a sequence of 2D fluoroscopic images in time order are annotated with ground truth contrast injection points. A heart rate is globally estimated for each training volume, and local frequency and phase is estimated in a neighborhood of the ground truth contrast injection point for each training volume. Frequency and phase invariant features are extracted from each training volume based on the heart rate, local frequency and phase, and a detector is trained based on the training volumes and the features extracted for each training volume. The detector can be used to detect the spatial and temporal location of a contrast injection in a fluoroscopic image sequence.

Abstract: A method and system for detecting a spatial and temporal location of a contrast injection in a fluoroscopic image sequence is disclosed. Training volumes generated by stacking a sequence of 2D fluoroscopic images in time order are annotated with ground truth contrast injection points. A heart rate is globally estimated for each training volume, and local frequency and phase is estimated in a neighborhood of the ground truth contrast injection point for each training volume. Frequency and phase invariant features are extracted from each training volume based on the heart rate, local frequency and phase, and a detector is trained based on the training volumes and the features extracted for each training volume. The detector can be used to detect the spatial and temporal location of a contrast injection in a fluoroscopic image sequence.

Abstract: A method and system for detecting a spatial and temporal location of a contrast injection in a fluoroscopic image sequence is disclosed. Training volumes generated by stacking a sequence of 2D fluoroscopic images in time order are annotated with ground truth contrast injection points. A heart rate is globally estimated for each training volume, and local frequency and phase is estimated in a neighborhood of the ground truth contrast injection point for each training volume. Frequency and phase invariant features are extracted from each training volume based on the heart rate, local frequency and phase, and a detector is trained based on the training volumes and the features extracted for each training volume. The detector can be used to detect the spatial and temporal location of a contrast injection in a fluoroscopic image sequence.

Abstract: A decision construct provides a system and method of configuring/managing/controlling a networked computing environment, such as a learning technology/platform. The construct provides one or more permission levels associated with one or more users of the system, such as independent permission levels for a provost, a dean, a department chair, a course director, and a faculty member, wherein the permission levels allow differentiation amongst the users of the system as to various decisions that are made to configure the networked computing environment. The decisions are made by the user through interaction with decision cells that the user(s) is allowed access to based on their associated permission level. The access and decisions that the user is allowed to make affect various component features, such as the display and functional capabilities, of the networked computing environment.