公开(公告)号：US20210082116A1

公开(公告)日：2021-03-18

申请号：US17103058

申请日：2020-11-24

Applicant: Topcon Corporation

Inventor： Zaixing Mao ,  Zhenguo Wang ,  Kinpui Chan ,  Yasufumi Fukuma

IPC: G06T7/00 ,  G06N20/00 ,  G06N7/00

Abstract: A machine learning model is trained to identify the texture difference between the different layers of a multilayer object. By training with data in full 3D space, the resulting model is capable of predicting the probability that each pixel in a 3D image belongs to a certain layer. With the resulting probability map, comparing probabilities allows one to determine boundaries between layers, and/or other properties and useful information such as volume data.

公开(公告)号：US20200279352A1

公开(公告)日：2020-09-03

申请号：US16797848

申请日：2020-02-21

Applicant: Topcon Corporation

Inventor： Zaixing Mao ,  Zhenguo Wang ,  Kinpui Chan

IPC: G06T5/00 ,  G06T5/50

Abstract: Ophthalmological mages generated by coherent imaging modalities have multiple types of noise, including random noise caused by the imaging system and speckle noise caused by turbid objects such as living tissues. These noises can occur at different levels in different locations. A noise-reduction method and system of the present disclosure thus relates to applying different filters for different types of noise and/or different locations of images, sequentially or in parallel and combined, to produce a final noise-reduced image.

公开(公告)号：US20190206054A1

公开(公告)日：2019-07-04

申请号：US16212027

申请日：2018-12-06

Applicant: Topcon Corporation

Inventor： Zaixing Mao ,  Zhenguo Wang ,  Kinpui Chan ,  Jonathan Liu ,  Jongsik Kim

IPC: G06T7/00 ,  G06T7/73 ,  G06T11/00

CPC classification number: G06T7/0014 ,  A61B3/102 ,  A61B3/12 ,  G06K9/4628 ,  G06K9/627 ,  G06K2209/05 ,  G06T7/0012 ,  G06T7/73 ,  G06T11/008 ,  G06T2207/10101 ,  G06T2207/20081 ,  G06T2207/30041

Abstract: A multimodal imaging system and method is capable of taking fundus images, automatically identifying regions of interest (ROIs) of the eye from the fundus images, and performing OCT imaging in the identified ROIs, where the OCT images can provide clinically relevant information for screening purposes. By automatically identifying the ROIs, expert intervention is not required to perform specialized OCT imaging and thus, such imaging and analysis can be provided at more facilities and for more subjects for a lower cost.