Object keypoint detection

A method for obtaining object keypoints for an object in a medical scanner, wherein the object keypoints are three dimensional (3D) coordinates with respect to the medical scanner and wherein the object keypoints are defined inside object parts of the object, the method comprising:

• obtaining two dimensional (2D) images of the object in the medical scanner from a camera system, wherein the camera system comprises one or more cameras;
• obtaining scanner variables from at least one of the medical scanner or the camera system, wherein the medical scanner contains a first moving part and/or wherein the camera system contains a second moving part, and wherein the scanner variables include a position of the first moving part of the medical scanner and/or a position of the second moving part of the camera system;
• determining object keypoint projections based on inputting the 2D images into a neural network trained to learn spatial relationships between object keypoint projections corresponding to different parts of the object, wherein the neural network is configured to output the object keypoint projections in each one of the input 2D images, wherein the object keypoint projections are 2D coordinates projected on to the 2D images, respectively;
• determining the object keypoints in 3D coordinates by triangulating the object keypoint projections with respect to the camera system based on the scanner variables;
• and
• determining imaging parameters for the medical scanner based on the object keypoints for imaging a subject.

A non-transitory computer readable medium storing computer program code for obtaining object keypoints for an object in a medical scanner, wherein when executed on a computing device having at least one processor, the computer program code causes the at least one processor to:

• obtain two dimensional (2D) images of the object in the medical scanner from a camera system, wherein the camera system comprises one or more cameras;
• obtain scanner variables from at least one of the medical scanner or the camera system, wherein the medical scanner contains a first moving part and/or wherein the camera system contains a second moving part, and wherein the scanner variables include a position of the first moving part of the medical scanner and/or a position of the second moving part of the camera system;
• determine object keypoint projections based on inputting the 2D images of the object into a neural network trained to learn spatial relationships between object keypoint projections corresponding to different parts of the object, wherein the neural network is configured to output the object keypoint projections in each one of the input 2D images, wherein the object keypoint projections are 2D coordinates projected on to the 2D images, respectively;
• determine the object keypoints by triangulating the object keypoint projections with respect to the camera system based on the scanner variables, wherein the object keypoints are three dimensional (3D) coordinates with respect to the medical scanner and wherein the object keypoints are defined in an object part of the object;
• and
• determine imaging parameters for the medical scanner based on the object keypoints for imaging a subject.