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 supporting therapy planning is described herein. In accordance with one aspect, patient-specific characteristics are extracted from medical data associated with a given patient. The framework may then search a database for one or more other patients associated with personal characteristics that are similar to the patient-specific characteristics. Information associated with the one or more other patients may be presented to support therapy planning or diagnosis.

Abstract: Disclosed herein is a framework for facilitating automatic planning for medical imaging. In accordance with one aspect, the framework receives first image data of a subject. One or more imaging parameters may then be derived using a geometric model and at least one reference anatomical primitive detected in the first image data. The geometric model defines a geometric relationship between the detected reference anatomical primitive and the one or more imaging parameters. The one or more imaging parameters may be presented, via a user interface, for use in acquisition, reconstruction or processing of second image data of the subject.

Abstract: A framework for patient management based on anatomic measurements is described herein. In accordance with one aspect, patient records are clustered into a set of sub-populations based on first anatomic measurements and characteristics extracted from first patient data associated with a population of patients. A representative sub-population similar to a patient may be determined from the set of sub-populations based on the patient data of the patient. A report that presents the second anatomic measurements associated with the patient in relation to corresponding first anatomic measurements associated with the representative sub-population may then be generated.

Abstract: Disclosed herein is a framework for facilitating synchronized image navigation. In accordance with one aspect, at least first and second medical images are received. A non-linear mapping between the first and second medical images is generated. A selection of a given location in the first medical image is received in response to a user's navigational operation. Without deforming the second medical image, a target location in the second medical image is determined by using the non-linear mapping. The target location corresponds to the given location in the first medical image. An optimized deformation-free view of the second medical image is generated based at least in part on the target location.

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 facilitating adaptive anatomical region prediction. In accordance with one aspect, a set of exemplar images including annotated first landmarks is received. User definitions of first anatomical regions in the exemplar images are obtained. The framework may detect second landmarks in a subject image. It may further compute anatomical similarity scores between the subject image and the exemplar images based on the first and second landmarks, and predict a second anatomical region in the subject image by adaptively combining the first anatomical regions based on the anatomical similarity scores.

Abstract: A framework for visualization is described herein. In accordance with one implementation, one or more structures of interest are localized in a three-dimensional image. A position of an anatomical label may be determined using a positioning technique that is selected according to a view type of a visualization plane through the image, wherein the position of the anatomical label is outside the one or more structures of interest. The anatomical label may then be displayed at the determined position in the visualization plane.

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 localizing anatomical structures. In accordance with one aspect, the framework receives a learned regressor and image data of a subject. The learned regressor may be invoked to predict a first spatial metric from a seed voxel to a target anatomical structure in the image data. The learned regressor may further be invoked to predict second spatial metrics from candidate voxels to the target anatomical structure. The candidate voxels may be located around a search region defined by the first spatial metric. The candidate voxel associated with the smallest second spatial metric may then be output as a localized voxel.

Abstract: Disclosed herein is a framework for facilitating visualization. In accordance with one aspect, the framework localizes at least one anatomical structure of interest in image data. The structure of interest is then highlighted by reformatting the image data. The resulting reformatted image data is rendered for display to a user.

Abstract: A method for automatically scheduling tasks in whole-body computer aided detection/diagnosis (CAD), including: (a) receiving a plurality of tasks to be executed by a whole-body CAD system; (b) identifying a task to be executed, wherein the task to be executed has an expected information gain that is greater than that of each of the other tasks; (c) executing the task with the greatest expected information gain and removing the executed task from further analysis; and (d) repeating steps (b) and (c) for the remaining tasks.

Abstract: Systems and methods are provided for detecting anatomical components in images. In accordance with one implementation, at least one anchor landmark is detected in an image. The position of the anchor landmark is used to detect at least one bundle landmark in the image. In accordance with another implementation, at least two neighboring landmarks are detected in an image, and used to detect at least one anatomical primitive in the image.

Abstract: Disclosed herein is a framework for facilitating automatic planning for medical imaging. In accordance with one aspect, the framework receives first image data of a subject. One or more imaging parameters may then be derived using a geometric model and at least one reference anatomical primitive detected in the first image data. The geometric model defines a geometric relationship between the detected reference anatomical primitive and the one or more imaging parameters. The one or more imaging parameters may be presented, via a user interface, for use in acquisition, reconstruction or processing of second image data of the subject.

Abstract: Disclosed herein is a framework for localizing anatomical structures. In accordance with one aspect, the framework receives a learned regressor and image data of a subject. The learned regressor may be invoked to predict a first spatial metric from a seed voxel to a target anatomical structure in the image data. The learned regressor may further be invoked to predict second spatial metrics from candidate voxels to the target anatomical structure. The candidate voxels may be located around a search region defined by the first spatial metric. The candidate voxel associated with the smallest second spatial metric may then be output as a localized voxel.

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 facilitating image processing. In accordance with one aspect, the framework receives first image data acquired by a first modality and one or more articulated models. The one or more articulated models may include at least one section image acquired by the first modality and aligned with a local image acquired by a second modality. The framework may align an anatomical region of the first image data with the section image and non-rigidly register a first region of interest extracted from the section image with a second region of interest extracted from the aligned anatomical region. To generate a segmentation mask of the anatomical region, the registered first region of interest may be inversely mapped to a subject space of the first image data.

Abstract: Disclosed herein is a framework for facilitating synchronized image navigation. In accordance with one aspect, at least first and second medical images are received. A non-linear mapping between the first and second medical images is generated. A selection of a given location in the first medical image is received in response to a user's navigational operation. Without deforming the second medical image, a target location in the second medical image is determined by using the non-linear mapping. The target location corresponds to the given location in the first medical image. An optimized deformation-free view of the second medical image is generated based at least in part on the target location.

Abstract: Disclosed herein is a framework for facilitating adaptive anatomical region prediction. In accordance with one aspect, a set of exemplar images including annotated first landmarks is received. User definitions of first anatomical regions in the exemplar images are obtained. The framework may detect second landmarks in a subject image. It may further compute anatomical similarity scores between the subject image and the exemplar images based on the first and second landmarks, and predict a second anatomical region in the subject image by adaptively combining the first anatomical regions based on the anatomical similarity scores.

Abstract: Described herein is a technology for facilitating deformable model-based segmentation of image data. In one implementation, the technology includes receiving training image data (202) and automatically constructing a hierarchical structure (204) based on the training image data. At least one spatially adaptive boundary detector is learned based on a node of the hierarchical structure (206).