Abstract: Aspects an autonomous driving system with air support are described herein. The aspects may include an unmanned aerial vehicle (UAV) in the air and a land vehicle on the ground communicatively connected to the UAV. The UAV may include at least one UAV camera configured to collect first ground traffic information and a UAV communication module configured to transmit the collected first ground traffic information. The land vehicle may include one or more vehicle sensors configured to collect second ground traffic information surrounding the land vehicle, a land communication module configured to receive the first ground traffic information from the UAV, and a processor configured to combine the first ground traffic information and the second ground traffic information to generate a world model.

Abstract: Aspects an autonomous driving system with air support are described herein. The aspects may include an unmanned aerial vehicle (UAV) in the air and a land vehicle on the ground communicatively connected to the UAV. The UAV may include at least one UAV camera configured to collect first ground traffic information and a UAV communication module configured to transmit the collected first ground traffic information. The land vehicle may include one or more vehicle sensors configured to collect second ground traffic information surrounding the land vehicle, a land communication module configured to receive the first ground traffic information from the UAV, and a processor configured to combine the first ground traffic information and the second ground traffic information to generate a world model.

Abstract: Described herein is a novel method and system for segmentation of the spine using 3D volumetric data. In embodiments, a method includes an extracting step, localization step, and segmentation step. The extracting step comprises detecting the spine centerline and the spine canal centerline. The localization step comprises localizing the vertebra and intervertebral disc centers. Background and foreground constraints are created for each vertebra digit. Segmentation is performed for each vertebra digit and based on the hard constraints.

Abstract: Described herein is a novel method and system for segmentation of the spine using 3D volumetric data. In embodiments, a method includes an extracting step, localization step, and segmentation step. The extracting step comprises detecting the spine centerline and the spine canal centerline. The localization step comprises localizing the vertebra and intervertebral disc centers. Background and foreground constraints are created for each vertebra digit. Segmentation is performed for each vertebra digit and based on the hard constraints.

Abstract: Described herein is a novel method and system for segmentation of the spine using 3D volumetric data. In embodiments, a method includes an extracting step, localization step, and segmentation step. The extracting step comprises detecting the spine centerline and the spine canal centerline. The localization step comprises localizing the vertebra and intervertebral disc centers. Background and foreground constraints are created for each vertebra digit. Segmentation is performed for each vertebra digit and based on the hard constraints.

Abstract: Described herein is a novel method and system for segmentation of the spine using 3D volumetric data. In embodiments, a method includes an extracting step, localization step, and segmentation step. The extracting step comprises detecting the spine centerline and the spine canal centerline. The localization step comprises localizing the vertebra and intervertebral disc centers. Background and foreground constraints are created for each vertebra digit. Segmentation is performed for each vertebra digit and based on the hard constraints.

Abstract: An automated method for aneurysm analysis including: extracting shape descriptors from test vessel data; generating an aneurysm probability map for the test vessel data using the shape descriptors; detecting the presence of the aneurysm on the test vessel data; localizing the aneurysm in the probability map; and separating the aneurysm from the probability map.

Abstract: An automated method for aneurysm analysis including: extracting shape descriptors from test vessel data; generating an aneurysm probability map for the test vessel data using the shape descriptors; detecting the presence of the aneurysm on the test vessel data; localizing the aneurysm in the probability map; and separating the aneurysm from the probability map.

Abstract: A method for segmenting intracranial aneurysms in digital medical images includes extracting a mesh representing a vessel surface from a volumetric digital image, the mesh comprising a set of points {pi} and edges {eij} respective points, and finding a set of binary labelings that minimizes an energy functional of the labelings and of shape descriptors of each point in the mesh, where the binary labelings segments an aneurysm from the surface by associating one of two labels with each point pi, where a label indicates whether an associated point is on a vessel or on an aneurysm, where the energy functional includes a unary potential term summed over all points that represent a posterior distribution of the labels over the points, and a pairwise potential term summed over all edges that represents neighborhood labeling relationships.

Abstract: A method for segmenting intracranial aneurysms in digital medical images includes extracting a mesh representing a vessel surface from a volumetric digital image, the mesh comprising a set of points {pi} and edges {eij} respective points, and finding a set of binary labelings that minimizes an energy functional of the labelings and of shape descriptors of each point in the mesh, where the binary labelings segments an aneurysm from the surface by associating one of two labels with each point pi, where a label indicates whether an associated point is on a vessel or on an aneurysm, where the energy functional includes a unary potential term summed over all points that represent a posterior distribution of the labels over the points, and a pairwise potential term summed over all edges that represents neighborhood labeling relationships.