US 12,376,909 B2
Training data collection for machine learning models
Grzegorz Andrzej Toporek, Cambridge, MA (US); and Aleksandra Popovic, Boston, MA (US)
Assigned to KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
Appl. No. 17/434,397
Filed by KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
PCT Filed Feb. 21, 2020, PCT No. PCT/EP2020/054572
§ 371(c)(1), (2) Date Aug. 26, 2021,
PCT Pub. No. WO2020/173814, PCT Pub. Date Sep. 3, 2020.
Claims priority of provisional application 62/825,914, filed on Mar. 29, 2019.
Claims priority of provisional application 62/825,905, filed on Mar. 29, 2019.
Claims priority of provisional application 62/811,705, filed on Feb. 28, 2019.
Prior Publication US 2022/0142712 A1, May 12, 2022
Int. Cl. A61B 34/20 (2016.01); A61B 8/12 (2006.01); A61B 34/30 (2016.01); A61B 90/00 (2016.01); B25J 9/16 (2006.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06N 3/08 (2023.01); G06N 3/084 (2023.01); G06T 7/246 (2017.01); G06T 7/70 (2017.01); G16H 20/40 (2018.01); G16H 30/20 (2018.01); A61B 34/10 (2016.01)
CPC A61B 34/20 (2016.02) [A61B 8/12 (2013.01); A61B 34/30 (2016.02); A61B 90/37 (2016.02); B25J 9/1607 (2013.01); B25J 9/1697 (2013.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06N 3/08 (2013.01); G06N 3/084 (2013.01); G06T 7/246 (2017.01); G06T 7/70 (2017.01); G16H 20/40 (2018.01); G16H 30/20 (2018.01); A61B 2034/107 (2016.02); A61B 2034/2061 (2016.02); A61B 2034/2065 (2016.02); A61B 2034/301 (2016.02); A61B 2090/378 (2016.02); G06T 2207/20084 (2013.01); G06T 2207/30244 (2013.01)] 20 Claims
OG exemplary drawing
 
17. A training data collection method for an interventional device, the training data collection method comprising:
controlling at least one motion variable of the interventional device in accordance with a pre-defined data point pattern, wherein the interventional device includes a device portion and one or more sensors configured to provide at least one of position information, orientation information, or shape information, and wherein the one or more sensors are affixed to-said the interventional device, wherein the at least one motion variable includes a set of joint variables associated with movement of the interventional device;
receive an image of a patient anatomy;
process the received image via an image predictive model to output image inference data;
determining positioning information based on the image inference data and at least one of the position information, the orientation information, or the shape information received from the one or more sensors;
at each data point of the pre-defined data point pattern, estimating a pose of the device portion based on the positioning information and estimating a positioning motion of the interventional device based on the positioning information, wherein estimating the positioning motion comprises generating of a temporal motion sequence of the set of joint variables at each former data point to reach the pose of the device portion; and
throughout the controlling the at least one motion variable of the interventional device in accordance with the pre-defined data point pattern, storing a temporal data sequence configured to train a model to infer kinematics for the interventional device, wherein the temporal data sequence derived from the estimated pose of the device portion at each data point and the temporal motion sequence of the set of joint variables to reach the estimated pose of the device portion at each data point.