Abstract: A method comprising: generating a parametrized three-dimensional (3D) body surface model on a training set comprising a plurality of 3D scans of subjects, wherein at least some of said 3D scans are of subjects having a skeletal deformity; receiving one or more target 3D scans of a target subject; optimizing said body surface model with respect to said one or more target 3D scans to calculate a target body surface model of said target subject; training a skeletal estimation model on a training set comprising: (i) body surface models of a plurality of subjects, and (ii) skeletal landmarks sets of said plurality of subjects; and applying said trained skeletal estimation model to said calculated target body surface model of said target subject, to estimate a skeletal shape of said target subject.

Abstract: A method comprising: receiving a radiographic image dataset representing a sequential radiographic scan of a region of a human subject; receiving three-dimensional (3D) image data representing an optical scan of a surface of said region, wherein said 3D image data is performed simultaneously with said sequential radiographic scan; estimating a time-dependent motion of said subject during said acquisition, relative to a specified position, based, at least in part, on said 3D image data; and using said estimating to determine corrections for said radiographic image dataset, based, at least in part, on a known transformation between corresponding coordinate systems of said radiographic image dataset and said 3D image data.

Abstract: A method comprising: generating a parametrized three-dimensional (3D) body surface model on a training set comprising a plurality of 3D scans of subjects, wherein at least some of said 3D scans are of subjects having a skeletal deformity; receiving one or more target 3D scans of a target subject; optimizing said body surface model with respect to said one or more target 3D scans to calculate a target body surface model of said target subject; training a skeletal estimation model on a training set comprising: (i) body surface models of a plurality of subjects, and (ii) skeletal landmarks sets of said plurality of subjects; and applying said trained skeletal estimation model to said calculated target body surface model of said target subject, to estimate a skeletal shape of said target subject.

Abstract: A method comprising: generating a parametrized three-dimensional (3D) body surface model on a training set comprising a plurality of 3D scans of subjects, wherein at least some of said 3D scans are of subjects having a skeletal deformity; receiving one or more target 3D scans of a target subject; optimizing said body surface model with respect to said one or more target 3D scans to calculate a target body surface model of said target subject; training a skeletal estimation model on a training set comprising: (i) body surface models of a plurality of subjects, and (ii) skeletal landmarks sets of said plurality of subjects; and applying said trained skeletal estimation model to said calculated target body surface model of said target subject, to estimate a skeletal shape of said target subject.

Abstract: A method comprising: receiving a radiographic image dataset representing a sequential radiographic scan of a region of a human subject; receiving three-dimensional (3D) image data representing an optical scan of a surface of said region, wherein said 3D image data is performed simultaneously with said sequential radiographic scan; estimating a time-dependent motion of said subject during said acquisition, relative to a specified position, based, at least in part, on said 3D image data; and using said estimating to determine corrections for said radiographic image dataset, based, at least in part, on a known transformation between corresponding coordinate systems of said radiographic image dataset and said 3D image data.

Abstract: A method comprising: receiving a radiographic image dataset representing a sequential radiographic scan of a region of a human subject; receiving three-dimensional (3D) image data representing an optical scan of a surface of said region, wherein said 3D image data is performed simultaneously with said sequential radiographic scan; estimating a time-dependent motion of said subject during said acquisition, relative to a specified position, based, at least in part, on said 3D image data; and using said estimating to determine corrections for said radiographic image dataset, based, at least in part, on a known transformation between corresponding coordinate systems of said radiographic image dataset and said 3D image data.

Abstract: A method comprising: generating a parametrized three-dimensional (3D) body surface model on a training set comprising a plurality of 3D scans of subjects, wherein at least some of said 3D scans are of subjects having a skeletal deformity; receiving one or more target 3D scans of a target subject; optimizing said body surface model with respect to said one or more target 3D scans to calculate a target body surface model of said target subject; training a skeletal estimation model on a training set comprising: (i) body surface models of a plurality of subjects, and (ii) skeletal landmarks sets of said plurality of subjects; and applying said trained skeletal estimation model to said calculated target body surface model of said target subject, to estimate a skeletal shape of said target subject.