US 12,217,361 B1
Apparatus and method for generating a three-dimensional (3D) model of cardiac anatomy based on model uncertainty
Abhijith Chunduru, Bengaluru (IN); Uddeshya Upadhyay, Bengaluru (IN); Suthirth Vaidya, Bengaluru (IN); Sai Saketh Chennamsetty, Bengaluru (IN); and Arjun Puranik, San Jose, CA (US)
Assigned to Anumana, Inc., Cambridge, MA (US)
Filed by Anumana, Inc., Cambridge, MA (US)
Filed on Jan. 30, 2024, as Appl. No. 18/426,604.
Int. Cl. G06T 17/00 (2006.01); G06T 7/00 (2017.01)
CPC G06T 17/00 (2013.01) [G06T 7/0012 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30021 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method of generating a three-dimensional (3D) model of cardiac anatomy, the method comprising:
using at least a processor, receiving a first set of ultrasound images of cardiac anatomy;
using at least a processor, generating a first 3D model of the cardiac anatomy by:
inputting into at least a trained neural network the first set of ultrasound images of cardiac anatomy;
receiving as an output from the at least a trained neural network a first set of shape parameters; and
using a statistical shape model to determine the first 3D model of the cardiac anatomy as a function of the set of shape parameters;
using at least a processor, calculating a level of uncertainty at a plurality of locations on the first 3D model;
using at least a processor, receiving a second set of ultrasound images of the cardiac anatomy corresponding to a high uncertainty region of the first 3D model, wherein receiving the second set of ultrasound images comprises, by a user, positioning for capturing an image of the high uncertainty region a cardiac image capture device configured to capture ultrasound images; and
using at least a processor, generating a second 3D model by:
inputting into the at least a trained neural network the first set of ultrasound images of cardiac anatomy and the second set of ultrasound images of cardiac anatomy;
receiving as an output from the at least a trained neural network a second set of shape parameters; and
using the statistical shape model to determine the second 3D model of the cardiac anatomy as a function of the second set of shape parameters.