US 11,954,854 B2
Retina vessel measurement
Wynne Hsu, Singapore (SG); Mong Li Lee, Singapore (SG); Dejiang Xu, Singapore (SG); Tien Yin Wong, Singapore (SG); and Yim Lui Cheung, Hong Kong (CN)
Assigned to National University of Singapore, Singapore (SG); and Singapore Health Services Pte Ltd, Singapore (SG)
Appl. No. 17/429,109
Filed by National University of Singapore, Singapore (SG); and Singapore Health Services Pte Ltd, Singapore (SG)
PCT Filed Feb. 11, 2020, PCT No. PCT/SG2020/050067
§ 371(c)(1), (2) Date Aug. 6, 2021,
PCT Pub. No. WO2020/167251, PCT Pub. Date Aug. 20, 2020.
Claims priority of application No. 10201901218S (SG), filed on Feb. 12, 2019.
Prior Publication US 2022/0130037 A1, Apr. 28, 2022
Int. Cl. G06T 7/12 (2017.01); G06T 7/00 (2017.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 40/18 (2022.01)
CPC G06T 7/0012 (2013.01) [G06V 10/454 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 40/193 (2022.01); G06V 40/197 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30041 (2013.01); G06V 2201/03 (2022.01)] 19 Claims
OG exemplary drawing
 
1. A method for training a neural network for automated retina vessel measurement, comprising:
receiving a plurality of fundus images;
pre-processing the fundus images to normalise images features of the fundus images; and
training a multi-layer neural network on the pre-processed fundus images, the neural network comprising convolutional unit, multiple dense blocks alternating with transition layers or transition units for down-sampling image features determined by the neural network, and a fully-connected unit, wherein each dense block comprises a series of cAdd units packed with multiple convolutions, and each transition layer or transition unit comprises a convolution with pooling.