A multi-modal visualization system (MMVS) is provided, which may be used to analyze and visualize bioimaging data, objects, and pointers, such as neuroimaging data, surgical tools, and pointing rods. MMVS can integrate multiple bioimaging modalities to visualize a plurality of bioimaging datasets simultaneously, such as anatomical bioimaging data and functional bioimaging data.

Provided are a capsule endoscope apparatus for supporting a lesion diagnosis and a lesion diagnosis supporting method using the same. The capsule endoscope apparatus for supporting lesion diagnosis includes an imaging unit configured to capture one or more images of an inside of a body, a control unit configured to detect a suspected lesion region in the images and perform a precision diagnosis procedure when a suspected lesion region corresponding to a value equal to or greater than a certain threshold is detected, an image processing unit configured to process the images in the precision diagnosis procedure, and a communication module configured to transmit and receive processed images to another capsule endoscope apparatus or a terminal by using a wireless communication method.

Provided are a capsule endoscope apparatus for supporting a lesion diagnosis and a lesion diagnosis supporting method using the same. The capsule endoscope apparatus for supporting lesion diagnosis includes an imaging unit configured to capture one or more images of an inside of a body, a control unit configured to detect a suspected lesion region in the images and perform a precision diagnosis procedure when a suspected lesion region corresponding to a value equal to or greater than a certain threshold is detected, an image processing unit configured to process the images in the precision diagnosis procedure, and a communication module configured to transmit and receive processed images to another capsule endoscope apparatus or a terminal by using a wireless communication method.

Described herein are systems, methods, and non-transitory computer readable media for performing an alignment between a first vehicle sensor and a second vehicle sensor. Two-dimensional (2D) data indicative of a scene within an environment being traversed by a vehicle is captured by the first vehicle sensor such as a camera or a collection of multiple cameras within a sensor assembly. A three-dimensional (3D) representation of the scene is constructed using the 2D data. 3D point cloud data also indicative of the scene is captured by the second vehicle sensor, which may be a LiDAR. A 3D point cloud representation of the scene is constructed based on the 3D point cloud data. A rigid transformation is determined between the 3D representation of the scene and the 3D point cloud representation of the scene and the alignment between the sensors is performed based at least in part on the determined rigid transformation.

Aspects of the disclosure provide for generating a visualization of a three-dimensional (3D) world view from the perspective of a camera of a vehicle. For example, images of a scene captured by a camera of the vehicle and 3D content for the scene may be received. A virtual camera model for the camera of the vehicle may be identified. A set of matrices may be generated using the virtual camera model. The set of matrices may be applied to the 3D content to create a 3D world view. The visualization may be generated using the 3D world view as an overlay with the image, and the visualization provides a real-world image from the perspective of the camera of the vehicle with one or more graphical overlays of the 3D content.

The present disclosure provides an operating method of a dopamine transporter check system, and the operation method includes steps as follows. A scan image of a subject's brain is obtained from a scan machine, and the scan image is a three-dimensional image. The scan image is aligned to a standard brain space to obtain a standardized scan image. Intensity normalization is performed on the standardized scan image. The standardized scan image after the intensity normalization is converted into a two-dimensional image. A plurality of image data are got from at least one region of interest in the two-dimensional image, and the at least one region of interest includes a left caudate, a left putamen, a right caudate and a right putamen. A dopamine neuron loss degree measurement and evaluation model based on the image data is established through a transfer learning.

The present disclosure provides an operating method of a dopamine transporter check system, and the operation method includes steps as follows. A scan image of a subject's brain is obtained from a scan machine, and the scan image is a three-dimensional image. The scan image is aligned to a standard brain space to obtain a standardized scan image. Intensity normalization is performed on the standardized scan image. The standardized scan image after the intensity normalization is converted into a two-dimensional image. A plurality of image data are got from at least one region of interest in the two-dimensional image, and the at least one region of interest includes a left caudate, a left putamen, a right caudate and a right putamen. A dopamine neuron loss degree measurement and evaluation model based on the image data is established through a transfer learning.

A method includes generating a set of sub-images of a subsurface formation based on measurement values acquired by a plurality of sensors corresponding to one or more signals that have propagated through the subsurface formation, wherein each of the set of sub-images correspond to one of the plurality of sensors. The plurality of sensors are on a tool in a borehole, wherein each of the plurality of sensors are at different spatial positions with respect to each other. The method also includes generating a combined image by aligning the set of sub-images based on the measurement values, wherein the aligning of the set of sub-images is independent of acceleration of the tool during tool motion.

Systems and methods for collecting and processing physical space data for use while performing an image-guided surgical (IGS) procedure are provided. The system and method includes obtaining a computer model of a non-rigid structure of interest in a patient and performing a rigid alignment of the computer model and surface data in a patient space associated with at least a portion of the non-rigid structure. The system and method also include computing a deformation of the computer model that provides a non-rigid alignment of the computer model and surface data, the deformation computed using a set of boundary conditions defined for each node of the computer model based on the rigid alignment and a kernel function. Additionally, the system and method can include displaying data for facilitating the IGS procedure based on the deformation.

Systems and methods for collecting and processing physical space data for use while performing an image-guided surgical (IGS) procedure are provided. The system and method includes obtaining a computer model of a non-rigid structure of interest in a patient and performing a rigid alignment of the computer model and surface data in a patient space associated with at least a portion of the non-rigid structure. The system and method also include computing a deformation of the computer model that provides a non-rigid alignment of the computer model and surface data, the deformation computed using a set of boundary conditions defined for each node of the computer model based on the rigid alignment and a kernel function. Additionally, the system and method can include displaying data for facilitating the IGS procedure based on the deformation.