Systems, methods, and computer program products are provided for segmenting a brain tumor from various MRI sequencing techniques. A plurality of MRI sequences of a head of a patient are received. Each MRI sequence includes a T1-weighted with contrast image, a Fluid Attenuated Inversion Recovery (FLAIR) image, a T1-weighted image, and a T2-weighted image. Each image of the plurality of MRI sequences is registered to an anatomical atlas. A plurality of modified MRI sequences are generated by removing a skull from each image in the plurality of MRI sequences. A tumor segmentation map is determined by segmenting a tumor within a brain in each image in the plurality of modified MRI sequences. The tumor segmentation map is applied to each of the plurality of MRI sequences to thereby generate a plurality of labelled MRI sequences.

Systems, methods, and computer program products are provided for segmenting a brain tumor from various MRI sequencing techniques. A plurality of MRI sequences of a head of a patient are received. Each MRI sequence includes a T1-weighted with contrast image, a Fluid Attenuated Inversion Recovery (FLAIR) image, a T1-weighted image, and a T2-weighted image. Each image of the plurality of MRI sequences is registered to an anatomical atlas. A plurality of modified MRI sequences are generated by removing a skull from each image in the plurality of MRI sequences. A tumor segmentation map is determined by segmenting a tumor within a brain in each image in the plurality of modified MRI sequences. The tumor segmentation map is applied to each of the plurality of MRI sequences to thereby generate a plurality of labelled MRI sequences.

Techniques are disclosed for aligning fiducial markers that commonly exist in each of multiple different N-dimensional (N-D) data sets. Notably, the N-D data sets are at least three-dimensional (3D) data sets. A first set and a second set of N-D data are accessed. A set of one or more fiducial markers that commonly exist in both those sets are identified. Based on the fiducial markers, one or more transformations are performed to align the two sets. Performing this alignment process results in at least a selected number of the common fiducial markers that exist in the two sets being within a threshold alignment relative to one another.

A data input device includes: a controller that: acquires position data indicating position coordinates of a facility, estimates, based on the position data, one or more facility candidates, and causes a display to display an input screen showing the estimated one or more facility candidates as options for selection by a user, and creates inspection data relating to gas inspection of the one or more facility candidates selected by the user.

A data input device includes: a controller that: acquires position data indicating position coordinates of a facility, estimates, based on the position data, one or more facility candidates, and causes a display to display an input screen showing the estimated one or more facility candidates as options for selection by a user, and creates inspection data relating to gas inspection of the one or more facility candidates selected by the user.

A method for 3D reconstruction includes: acquiring an image sequence of an object to be reconstructed continuously acquired by a monocular image collector; extracting depth information of an image to be processed in the image sequence; estimating translation attitude information of the image to be processed based on world coordinate information of each feature point in a reference image, image coordinate information of each feature point in the image to be processed, and rotation attitude information of the image to be processed, the reference image being an adjacent image whose acquisition time point in the image sequence is located before the image to be processed; generating a point cloud image based on the depth information, the rotation attitude information and the translation attitude information of each image; and performing 3D reconstruction on the object to be reconstructed based on the point cloud image.

A method for 3D reconstruction includes: acquiring an image sequence of an object to be reconstructed continuously acquired by a monocular image collector; extracting depth information of an image to be processed in the image sequence; estimating translation attitude information of the image to be processed based on world coordinate information of each feature point in a reference image, image coordinate information of each feature point in the image to be processed, and rotation attitude information of the image to be processed, the reference image being an adjacent image whose acquisition time point in the image sequence is located before the image to be processed; generating a point cloud image based on the depth information, the rotation attitude information and the translation attitude information of each image; and performing 3D reconstruction on the object to be reconstructed based on the point cloud image.

Facial skin analysis methods and systems for improving facial skin conditions using vehicle cameras of vehicles having onboard communication modules. Each vehicle is equipped to analyze the facial skin images over time periods and compare the images to determine skin conditions. Based on the facial skin conditions, a treatment recommendation can be transmitted to a seat occupant. Each vehicle is operatively connected to a facial skin analysis application in a data center. The facial skin analysis application includes a registration module which registers each vehicle. Additionally, a vehicle user may register with the facial skin analysis application to have his/her facial skin analyzed when travelling in any of the plurality of vehicles. The facial skin analysis application is operatively connected to a skin data AI analytics module and data lake and searches a plurality of databases for information related to the facial skin conditions to improve the treatment recommendation.

Facial skin analysis methods and systems for improving facial skin conditions using vehicle cameras of vehicles having onboard communication modules. Each vehicle is equipped to analyze the facial skin images over time periods and compare the images to determine skin conditions. Based on the facial skin conditions, a treatment recommendation can be transmitted to a seat occupant. Each vehicle is operatively connected to a facial skin analysis application in a data center. The facial skin analysis application includes a registration module which registers each vehicle. Additionally, a vehicle user may register with the facial skin analysis application to have his/her facial skin analyzed when travelling in any of the plurality of vehicles. The facial skin analysis application is operatively connected to a skin data AI analytics module and data lake and searches a plurality of databases for information related to the facial skin conditions to improve the treatment recommendation.

Systems and methods of optical registration in a computer-aided system includes a control system. The control system is configured to provide a first registration indicator having a first indicator pose in a workspace; determine a first robotic device pose in a robotic device reference frame; obtain a first image of the first registration indicator; determine, based on the first image, the first indicator pose in an imaging device reference frame; provide a second registration indicator having a second indicator pose in the workspace; determine a second robotic device pose in the robotic device reference frame; obtain a second image of the second registration indicator; determine, based on the second image, the second indicator pose in the imaging device reference frame; and determine a registration transform between the robotic device reference frame and the imaging device reference frame based on correspondences between the robotic device poses and the indicator poses.