Systems, devices and methods are provided for facilitating surgical guidance using a surface detection device. In some example embodiments, a trackable surface detection device is disclosed that includes, in a spatially-fixed relationship, a surface detection subsystem, one or more reference markers that are detectable by a tracking system, and an integrated reference feature that is detectable by the surface detection subsystem for calibration thereof. The trackable surface detection device, which may be handheld, facilitates the determination of a calibration transform that relates a frame of reference of the surface detection subsystem to a frame of reference of the tracking system, which in turn may be employed, in combination with a transform obtained by performing surface-to-surface registration of intraoperatively detected surface data and pre-operative image data pertaining to a subject, when generating an intraoperative display, in a common frame of reference, of the pre-operative image data and a tracked surgical tool.

A subject support (14) is configured to dock with a medical imaging device (50) with a fixed spatial relationship between the docked subject support and the medical imaging device. A patient positioning device includes a range camera (10) that acquires a two-dimensional (2D) range image of a human imaging subject (12) disposed on a subject support (14). The range image has pixel values corresponding to distances from the range camera. An electronic processor (16) is programmed to perform a positioning method to determine a reference point on or in the human subject in a frame of reference (F.sub.S) of the subject support from the 2D range image. This reference point is translated to a frame of reference (F.sub.D) of the imaging device based on a priori known spatial relationship of the medical imaging device and the docked subject support. Using 3D models, the loading process may be simulated.

It is possible to determine a geometric transformation matrix representing geometric transformation between an input image and a template image with high precision. A geometric transformation matrix/inlier estimation section 32 determines a corresponding point group serving as inliers, and estimates the geometric transformation matrix representing the geometric transformation between the input image and the template image. A scatter degree estimation section 34 estimates scatter degree of the corresponding points based on the corresponding point group serving as inliers. A plane tracking convergence determination threshold calculation section 36 calculates a threshold used in convergence determination when iterative update of the geometric transformation matrix in a plane tracking section 38 is performed based on the estimated scatter degree. The plane tracking section 38 iterates update of the geometric transformation matrix so as to minimize a difference in value to pixels of a geometric transformation image obtained by transforming one of the input image and the templated image using the geometric transformation matrix and corresponding pixels until it is determined that convergence has been completed using the calculated threshold.

A method (100) for referencing an optical image (19) including: obtaining (110, 120) a stereoscopic image pair (19, 23) of the optical image (19) and a SAR image (35), the surface areas covered by the images (19, 23, 35) on the ground having an overlapping area (39); selecting (130) an area of interest (42) in the overlapping area (39); from the area of interest (42): obtaining (140) a 3D model (40); calculating (150) a simulated radar image (44); estimating (160) an offset (di, dj) between the simulated image (44) and the radar image (35); selecting (170) a reference point (46); projecting (180) and shifting (di, dj) the reference point (46) in the radar image (35) to correct the radar connection point (46′″); determining (175) a pair of connection points (46′, 46″) in the image pair; and referencing the optical image (19) based on the connection points (46′, 46″, 46′″).

A robot configured to perceive a model of an environment, including: a chassis; a set of wheels; a plurality of sensors; a processor; and memory storing instructions that when executed by the processor effectuates operations including: capturing a plurality of data while the robot moves within the environment; perceiving the model of the environment based on at least a portion of the plurality of data, the model being a top view of the environment; storing the model of the environment in a memory accessible to the processor; and transmitting the model of the environment and a status of the robot to an application of a smartphone previously paired with the robot.

A method for registering a two-dimensional image data set with a three-dimensional image data set of a body of interest is discloses herein. The method includes the following steps: obtaining a first spatial parameter of a first registered virtual camera, wherein the first registered virtual camera is positioned corresponding to a first two-dimensional image of the two-dimensional image data set; and adjusting a second spatial parameter of the first unregistered virtual camera with the first spatial parameter of the first registered virtual camera, wherein the first unregistered virtual camera is failed to be positioned corresponding to the first two-dimensional image of the two-dimensional image data set.

In a process for generating garment model data representative of a piece of garment, input image data containing a view of the piece of garment are processed. A type of wearing condition is determined as at least one of a first type of worn garment and of a second type of not-worn garment. If the first type is determined, a shape of the piece of garment and a shape of the person wearing the garment are identified utilizing an active contour modelling approach based on a preset body model. The identified shapes are adapted based on a garment template model. The garment model data are determined from the input image data based on the adapted identified shapes. If the second type is determined, a shape of the piece of garment is identified. The input image data are iteratively compared with a respective garment template model to identify at least one matching garment template model. The identified shape is aligned with a shape of the at least one matching garment template model and the garment model data are determined from the input image data based on the identified shape and on results of the aligning.

Disclosed are systems and methods of lymphatic specimen tracking, visualization, and lymph node drainage pathway determination. An exemplary method includes receiving computed tomographic (CT) image data corresponding to a CT scan, generating a three-dimensional (3D) model of at least a portion of a patient's body based on the CT image data, identifying one or more lymph nodes in the 3D model, performing a registration of the 3D model with one or more physical locations in the patient's body, determining an expected lymph node drainage pathway away from a region of interest through one or more lymph nodes, and displaying the 3D model and the expected lymph node drainage pathway.

A method for registering image data sets of a target region of a patient includes selecting a first anatomical structure only or at least partially only visible in the first image data set, and a second anatomical structure only or at least partially only visible in the second image data set, such that there is a known geometrical relationship between extended segments of the anatomical structures; automatically determining a first geometry information describing the geometry of at least a part of the first anatomical structure and a second geometry information describing the geometry of at least a part of the second anatomical structure, neither information being sufficient to enable registration of the image data sets on its own; automatically optimizing transformation parameters describing a rigid transformation of one of the anatomical structures with respect to the other and geometrical correspondences; and determining registration information from the optimized transformation parameters.

A method and system for adjusting a digital elevation model (DEM). A satellite image with one or more roads and the DEM are received. The DEM includes an elevation of each pixel of the satellite image. Recorded GPS data corresponding to the one or more roads is received. A pixel in the satellite image corresponding to the one or more roads is determined. A determination is made as to whether the location of the pixel corresponds to the GPS data. After determining that the location of the pixel does not correspond to the GPS data, a set of pixels is determined in the DEM that surround the pixel. The elevation of each pixel of the set of pixels of the DEM is adjusted so that the location of the pixel in the satellite image corresponds to the one or more roads.