US 12,067,698 B2
Machine learning processing of contiguous slice image data
Jerry Prince, Timonium, MD (US); Can Zhao, Baltimore, MD (US); and Aaron Carass, Towson, MD (US)
Assigned to THE JOHNS HOPKINS UNIVERSITY, Baltimore, MD (US)
Filed by THE JOHNS HOPKINS UNIVERSITY, Baltimore, MD (US)
Filed on Jul. 12, 2023, as Appl. No. 18/350,816.
Application 18/350,816 is a continuation of application No. 17/274,901, granted, now 11,741,580, previously published as PCT/US2019/051035, filed on Sep. 13, 2019.
Claims priority of provisional application 62/731,537, filed on Sep. 14, 2018.
Prior Publication US 2023/0360179 A1, Nov. 9, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 5/00 (2024.01); G06F 18/214 (2023.01); G06N 3/045 (2023.01); G06T 5/73 (2024.01); G06T 7/00 (2017.01); G06V 10/44 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01)
CPC G06T 5/73 (2024.01) [G06F 18/2148 (2023.01); G06N 3/045 (2023.01); G06T 7/0012 (2013.01); G06V 10/454 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06T 2207/10088 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method of electronically image processing three-dimensional image data using machine learning, the method comprising:
obtaining image data representing a plurality of slices parallel to a first plane in three-dimensional space;
constructing machine learning training data from the image data, wherein the constructing comprises, for each of a plurality of angles, at least one of rotating, blurring, and introducing aliasing;
training an anti-aliasing machine learning system with at least a portion of the machine learning training data, wherein a trained anti-aliasing machine learning system is produced;
training a super-resolution machine learning system with at least a portion of the machine learning training data, wherein a trained super-resolution machine learning system is produced;
processing the image data using the trained anti-aliasing machine learning system and the trained super-resolution machine learning system to produce processed image data, wherein the processing comprises:
applying the trained anti-aliasing machine learning system to the image data in a first plurality of planes perpendicular to the first plane, and
applying the trained super-resolution machine learning system to the image data in a second plurality of planes perpendicular to the first plane and perpendicular to the first plurality of planes; and
outputting the processed image data.