Abstract: A computer-implemented method for providing a see-through visualization of a patient includes receiving an image dataset representative of anatomical features of the patient acquired using a medical image scanner and acquiring a body surface model of the patient using an RGB-D sensor. The body surface model is aligned with the image dataset in a canonical/common coordinate system to yield an aligned body surface model. A relative pose of a mobile device is determined with respect to the RGB-D sensor and a pose dependent visualization of the patient is created by rendering the image dataset at a viewpoint corresponding to the relative pose of the mobile device. Then, the pose dependent visualization of the patient may be presented on the mobile device.

Abstract: A computer-implemented method for automatically calibrating an RGB-D sensor and an imaging device using a transformation matrix includes using a medical image scanner to acquire a first dataset representative of an apparatus attached to a downward facing surface of a patient table, wherein corners of the apparatus are located at a plurality of corner locations. The plurality of corner locations are identified based on the first dataset and the RGB-D sensor is used to acquire a second dataset representative of a plurality of calibration markers displayed on an upward facing surface of the patient table at the corner locations. A plurality of calibration marker locations are identified based on the second dataset and the transformation matrix is generated by aligning the first dataset and the second dataset using the plurality of corner locations and the plurality of calibration marker locations.

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: A method and apparatus for generating a 3D personalized mesh of a person from a depth camera image for medical imaging scan planning is disclosed. A depth camera image of a subject is converted to a 3D point cloud. A plurality of anatomical landmarks are detected in the 3D point cloud. A 3D avatar mesh is initialized by aligning a template mesh to the 3D point cloud based on the detected anatomical landmarks. A personalized 3D avatar mesh of the subject is generated by optimizing the 3D avatar mesh using a trained parametric deformable model (PDM). The optimization is subject to constraints that take into account clothing worn by the subject and the presence of a table on which the subject in lying.

Abstract: A method and system for approximating a deep neural network for anatomical object detection is discloses. A deep neural network is trained to detect an anatomical object in medical images. An approximation of the trained deep neural network is calculated that reduces the computational complexity of the trained deep neural network. The anatomical object is detected in an input medical image of a patient using the approximation of the trained deep neural network.

Abstract: A method for estimating a body surface model of a patient includes: (a) segmenting, by a computer processor, three-dimensional sensor image data to isolate patient data from environmental data; (b) categorizing, by the computer processor, a body pose of the patient from the patient data using a first trained classifier; (c) parsing, by the computer processor, the patient data to an anatomical feature of the patient using a second trained classifier, wherein the parsing is based on a result of the categorizing; and (d) estimating, by the computer processor, the body surface model of the patient based on a result of the parsing. Systems for estimating a body surface model of a patient are described.

Abstract: A method and system for anatomical object detection using marginal space deep neural networks is disclosed. The pose parameter space for an anatomical object is divided into a series of marginal search spaces with increasing dimensionality. A respective sparse deep neural network is trained for each of the marginal search spaces, resulting in a series of trained sparse deep neural networks. Each of the trained sparse deep neural networks is trained by injecting sparsity into a deep neural network by removing filter weights of the deep neural network.

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: Object detection uses a deep or multiple layer network to learn features for detecting the object in the image. Multiple features from different layers are aggregated to train a classifier for the object. In addition or as an alternative to feature aggregation from different layers, an initial layer may have separate learnt nodes for different regions of the image to reduce the number of free parameters. The object detection is learned or a learned object detector is applied.

Abstract: A method and system for anatomical object detection using marginal space deep neural networks is disclosed. The pose parameter space for an anatomical object is divided into a series of marginal search spaces with increasing dimensionality. A respective deep neural network is trained for each of the marginal search spaces, resulting in a series of trained deep neural networks. Each of the trained deep neural networks can evaluate hypotheses in a current parameter space using discriminative classification or a regression function. An anatomical object is detected in a medical image by sequentially applying the series of trained deep neural networks to the medical image.

Abstract: Object detection includes providing an image and determining at least one feature point. The at least one feature point defines a location of an image patch used for determining a feature descriptor, and the image patch defines an image area of the image. The feature descriptor is generated based on respective image intensities of a number of respective pairs of pixels with two dimensional coordinates located inside the image patch. An n-th component of the feature descriptor for an n-th pair of pixels is derived. A threshold is set depending on the number. The feature descriptor is generated by an arrangement of the M components. An indication signal is generated for a detected object when the feature descriptor is within a predefined distance to a reference feature descriptor.

Abstract: A method and apparatus for generating a 3D personalized mesh of a person from a depth camera image for medical imaging scan planning is disclosed. A depth camera image of a subject is converted to a 3D point cloud. A plurality of anatomical landmarks are detected in the 3D point cloud. A 3D avatar mesh is initialized by aligning a template mesh to the 3D point cloud based on the detected anatomical landmarks. A personalized 3D avatar mesh of the subject is generated by optimizing the 3D avatar mesh using a trained parametric deformable model (PDM). The optimization is subject to constraints that take into account clothing worn by the subject and the presence of a table on which the subject in lying.

Abstract: A method for viewing a collection of images or videos, includes analyzing the collection to determine properties of the images or videos and using the determined properties to produce icons corresponding to such properties; providing a time-varying display of the images or videos in the collection following an ordering of the images or videos in the collection and at least one of the corresponding icons; receiving a user selection of an icon; and changing the display of the images or videos in the collection following a reordering of the images or videos in the collection in response to the user selection.

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: A method for viewing a collection of images or videos, includes analyzing the collection to determine properties of the images or videos and using the determined properties to produce icons corresponding to such properties; providing a time-varying display of the images or videos in the collection following an ordering of the images or videos in the collection and at least one of the corresponding icons; receiving a user selection of an icon; changing the time-varying display of the images or videos in the collection following a reordering of the images or videos in the collection in response to the user selection; storing the sequence of the user selections and associated timing in a script in a processor accessible memory; and playing back the viewing of the collection of images or videos using the script.

Abstract: Automated person re-identification may be assisted by consideration of attributes of the person in a joint classification with matching of the person. By both solving for similarities in a plurality of attributes and identities, discriminative interactions may be captured. Automated person re-identification may be assisted by consideration of a semantic color name. Rather than a color histogram, probability distributions are mapped to color terms of the semantic color name. Using other descriptors as well, similarity measures for the various descriptors are weighted and combined into a score. Either or both considerations may be used.

Abstract: A method for viewing a collection of images or videos, includes analyzing the collection to determine properties of the images or videos and using the determined properties to produce icons corresponding to such properties; providing a time-varying display of the images or videos in the collection following an ordering of the images or videos in the collection and at least one of the corresponding icons; receiving a user selection of an icon; changing the time-varying display of the images or videos in the collection following a reordering of the images or videos in the collection in response to the user selection; storing the sequence of the user selections and associated timing in a script in a processor accessible memory; and playing back the viewing of the collection of images or videos using the script.

Abstract: A method for viewing a collection of images or videos, includes analyzing the collection to determine properties of the images or videos and using the determined properties to produce icons corresponding to such properties; providing a time-varying display of the images or videos in the collection following an ordering of the images or videos in the collection and at least one of the corresponding icons; receiving a user selection of an icon; and changing the display of the images or videos in the collection following a reordering of the images or videos in the collection in response to the user selection.

Abstract: The present invention relates to a system and a method for analysis of a DNA sequence by converting the DNA sequence into a unique number string using a genomic number system in order to extract and/or analyze biological information. The invention is particularly useful in the development of new drugs or active chemical agents.