Abstract: A method to automatically align magnetic resonance (MR) scans for diagnostic scan planning includes acquiring a three-dimensional (3D) localizer image of an anatomical object. One or more initial landmarks are identified in the 3D localizer image using a landmarking engine. One or more main axes associated with the anatomical object are identified based on the one or more initial landmarks. The 3D localizer image is registered to a canonical space based on the main axes associated to yield a registered 3D localizer image. The landmarking engine is applied to the registered 3D localizer image to yield one or more updated landmarks. A plurality of reference points for performing a MR scan are computed based on the one or more updated landmarks.

Abstract: A framework for automated measurement. In accordance with one aspect, the framework detects a centerline point of a structure of interest in an image. A centerline of the structure of interest may be traced based on the detected centerline point. A trained segmentation learning structure may be used to generate one or more contours of the structure of interest along the centerline. One or more measurements may then be extracted from the one or more contours.

Abstract: A framework for coarse orientation detection in image data. In accordance with one aspect, the framework trains a learning structure to recognize a coarse orientation of the anatomical structure of interest based on training images. The framework may then pass one or more current images through the trained learning structure to generate a coarse orientation of the anatomical structure of interest. The framework then outputs the generated coarse orientation of the anatomical structure of interest.

Abstract: A framework for pedicle screw positioning is described herein. In accordance with one aspect, the framework segments at least one vertebra of interest in image data. The framework then automatically determines a pedicle region within the segmented vertebra of interest, and a safe region within the segmented vertebra of interest. An optimal insertion path passing through the pedicle region may then be generated within the safe region.

Abstract: A method to automatically align magnetic resonance (MR) scans for diagnostic scan planning includes acquiring a three-dimensional (3D) localizer image of an anatomical object. One or more initial landmarks are identified in the 3D localizer image using a landmarking engine. One or more main axes associated with the anatomical object are identified based on the one or more initial landmarks. The 3D localizer image is registered to a canonical space based on the main axes associated to yield a registered 3D localizer image. The landmarking engine is applied to the registered 3D localizer image to yield one or more updated landmarks. A plurality of reference points for performing a MR scan are computed based on the one or more updated landmarks.

Abstract: A framework for automated measurement. In accordance with one aspect, the framework detects a centerline point of a structure of interest in an image. A centerline of the structure of interest may be traced based on the detected centerline point. A trained segmentation learning structure may be used to generate one or more contours of the structure of interest along the centerline. One or more measurements may then be extracted from the one or more contours.

Abstract: A framework for coarse orientation detection in image data. In accordance with one aspect, the framework trains a learning structure to recognize a coarse orientation of the anatomical structure of interest based on training images. The framework may then pass one or more current images through the trained learning structure to generate a coarse orientation of the anatomical structure of interest. The framework then outputs the generated coarse orientation of the anatomical structure of interest.

Abstract: A framework for anatomy orientation detection is described herein. In accordance with one aspect, a pre-trained regressor is applied to appearance features of the image volume to predict a colatitude of the structure of interest. An optimal longitude corresponding to the predicted colatitude is then determined. In response to the colatitude being more than a pre-determined threshold, the image volume is re-oriented based on the predicted colatitude and the optimal longitude, and the predicted colatitude and optimal longitude determination is repeated for the re-oriented image volume.

Abstract: A framework for pedicle screw positioning is described herein. In accordance with one aspect, the framework segments at least one vertebra of interest in image data. The framework then automatically determines a pedicle region within the segmented vertebra of interest, and a safe region within the segmented vertebra of interest. An optimal insertion path passing through the pedicle region may then be generated within the safe region.

Abstract: A framework for anatomy orientation detection is described herein. In accordance with one aspect, a pre-trained regressor is applied to appearance features of the image volume to predict a colatitude of the structure of interest. An optimal longitude corresponding to the predicted colatitude is then determined. In response to the colatitude being more than a pre-determined threshold, the image volume is re-oriented based on the predicted colatitude and the optimal longitude, and the predicted colatitude and optimal longitude determination is repeated for the re-oriented image volume.

Abstract: Disclosed herein is a framework for segmenting articulated structures. In accordance with one aspect, the framework receives a target image, a reference image, statistical shape models, local appearance models and a learned landmark detector. The framework may automatically detect first centerline landmarks along centerlines of articulated structures in the target image using the learned landmark detector. The framework may then determine a non-rigid transformation function that registers second centerline landmarks along centerlines of articulated structures in the reference image with the first centerline landmarks. Mean shapes of the statistical shape models may then be deformed to the target image space by applying the non-rigid transformation function on the mean shapes. The framework may further search for candidate points in the mean shapes using the local appearance models. The mean shapes may be fitted to the candidate points to generate a segmentation mask.

Abstract: Disclosed herein is a framework for segmenting articulated structures. In accordance with one aspect, the framework receives a target image, a reference image, statistical shape models, local appearance models and a learned landmark detector. The framework may automatically detect first centerline landmarks along centerlines of articulated structures in the target image using the learned landmark detector. The framework may then determine a non-rigid transformation function that registers second centerline landmarks along centerlines of articulated structures in the reference image with the first centerline landmarks. Mean shapes of the statistical shape models may then be deformed to the target image space by applying the non-rigid transformation function on the mean shapes. The framework may further search for candidate points in the mean shapes using the local appearance models. The mean shapes may be fitted to the candidate points to generate a segmentation mask.