US 10,891,762 B2
Apparatus and method for medical image denoising based on deep learning
Hyun Sook Park, Seoul (KR); and Jong Hyo Kim, Seoul (KR)
Assigned to CLARIPI INC., Seoul (KR); and SEOUL NATIONAL UNIVERSITY R&DB FOUNDATION, Seoul (KR)
Filed by ClariPI Inc., Seoul (KR); and Seoul National University R&DB Foundation, Seoul (KR)
Filed on Nov. 19, 2018, as Appl. No. 16/194,941.
Claims priority of application No. 10-2017-0155115 (KR), filed on Nov. 20, 2017; and application No. 10-2018-0123786 (KR), filed on Oct. 17, 2018.
Prior Publication US 2019/0156524 A1, May 23, 2019
Int. Cl. G06T 11/00 (2006.01); G06T 5/00 (2006.01); G06T 7/00 (2017.01)
CPC G06T 11/003 (2013.01) [G06T 5/002 (2013.01); G06T 7/0012 (2013.01); G06T 2207/10072 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 11 Claims
OG exemplary drawing
 
1. A method for medical image denoising based on deep learning, the method comprising: generating multiple trained deep learning models, the multiple trained deep learning models being grouped by examination areas; extracting examination information from an input CT data, the examination information including examination area information; selecting at least one deep learning model corresponding to the examination information from the multiple trained deep learning models; and outputting a CT data denoised from the input CT data by feeding the input CT data into the selected at least one deep learning model wherein the generating comprises: generating a second training CT data set to which noises of multiple predetermined levels are added by applying a CT data image noise simulator to a first training CT data set; extracting examination information from the second training CT data set and grouping the second training CT data set into multiple groups according to a predetermined rule; and generating and training multiple training-target deep learning models so as to correspond to the respective groups of the second training CT data set by groups, wherein in the selecting, the multiple previously trained deep learning models are the multiple training-target deep learning models trained in the generating and training.