US 11,903,793 B2
Machine learning dental segmentation methods using sparse voxel representations
Christopher E. Cramer, Durham, NC (US); Roman Gudchenko, Moscow (RU); Dmitrii Ischeykin, Moscow (RU); Vasily Paraketsov, Moscow (RU); Sergey Grebenkin, Moscow (RU); Denis Durdin, Novosibirsk (RU); Dmitry Guskov, Moscow (RU); Nikolay Zhirnov, Moscow (RU); Mikhail Gorodilov, Novosibirsk (RU); Ivan Potapenko, Berdsk (RU); Anton Baskanov, Novosibirsk (RU); Elizaveta Ulianenko, Novosibirsk (RU); Alexander Vovchenko, Moscow (RU); Roman Solovyev, Novosibirsk (RU); Aleksandr Sergeevich Karsakov, Moscow (RU); Aleksandr Anikin, Moscow (RU); and Mikhail Toporkov, Moscow (RU)
Assigned to Align Technology, Inc., San Jose, CA (US)
Filed by Align Technology, Inc., San Jose, CA (US)
Filed on Dec. 30, 2020, as Appl. No. 17/138,824.
Claims priority of provisional application 62/955,968, filed on Dec. 31, 2019.
Prior Publication US 2021/0196434 A1, Jul. 1, 2021
Int. Cl. A61C 9/00 (2006.01); A61C 13/34 (2006.01); G06T 7/143 (2017.01); G06T 17/20 (2006.01)
CPC A61C 9/0053 (2013.01) [A61C 13/34 (2013.01); G06T 7/143 (2017.01); G06T 17/20 (2013.01); G06T 2207/30036 (2013.01); G06T 2210/41 (2013.01)] 11 Claims
OG exemplary drawing
 
1. A method of segmenting a three-dimensional (3D) model of a patient's dentition, the method comprising:
receiving, in a computing device, the 3D model of the patient's dentition;
converting the 3D model of the patient's dentition into a sparse voxel representation comprising voxels having features mapped from the 3D model of the patient's dentition; and
convolving the sparse voxel representation to segment the 3D model of the patient's dentition using a convolutional neural network to form a segmented 3D model of the patient's dentition.