US 12,220,246 B2
Detecting spinal shape from optical scan
Ron Kimmel, Haifa (IL); Benjamin Groisser, Haifa (IL); Alon Wolf, Haifa (IL); Roger F. Widmann, New York, NY (US); and Howard J. Hillstrom, New York, NY (US)
Assigned to TECHNION RESEARCH & DEVELOPMENT FOUNDATION LIMITED, Haifa (IL); and NEW YORK SOCIETY FOR THE RELIEF OF THE RUPTURED AND CRIPPLED, MAINTAINING THE HOSPITAL FOR SPECIAL SURGERY, New York, NY (US)
Filed by TECHNION RESEARCH & DEVELOPMENT FOUNDATION LIMITED, Haifa (IL); and NEW YORK SOCIETY FOR THE RELIEF OF THE RUPTURED AND CRIPPLED, MAINTAINING THE HOSPITAL FOR SPECIAL SURGERY, New York, NY (US)
Filed on May 1, 2024, as Appl. No. 18/652,432.
Application 18/652,432 is a continuation of application No. 17/271,687, granted, now 12,016,697, previously published as PCT/IL2019/050969, filed on Aug. 28, 2019.
Claims priority of provisional application 62/723,598, filed on Aug. 28, 2018.
Prior Publication US 2024/0277283 A1, Aug. 22, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. A61B 5/00 (2006.01); G06T 7/00 (2017.01); G06T 17/20 (2006.01)
CPC A61B 5/4566 (2013.01) [G06T 7/0012 (2013.01); G06T 17/20 (2013.01); G06T 2200/08 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20101 (2013.01); G06T 2207/30012 (2013.01); G06T 2210/41 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method comprising:
generating a parametrized three-dimensional (3D) body surface model, based, at least in part, on a training set comprising a plurality of 3D scans of subjects;
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, based, at least in part, on minimizing a loss function which registers said body surface model to said target 3D scans, to calculate a target body surface model of said target subject;
training a skeletal estimation model, based, at least in part, 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.