Disentangled Representation Learning for OCTA Vessel Segmentation With Limited Training Data-Reference-Cited by-同舟云学术

Disentangled Representation Learning for OCTA Vessel Segmentation With Limited Training Data

Published:2022-12 Issue:12 Volume:41 Page:3686-3698
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Liu Yihao¹^ORCID,Carass Aaron¹^ORCID,Zuo Lianrui¹,He Yufan¹,Han Shuo²^ORCID,Gregori Lorenzo¹,Murray Sean¹,Mishra Rohit¹,Lei Jianqin³^ORCID,Calabresi Peter A.⁴^ORCID,Saidha Shiv⁴,Prince Jerry L.¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA

2. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

3. Ophthalmology Department, First Affiliated Hospital, Xi’an Jiaotong University, Xi’an, China

4. Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, USA

Funder

NIH/National Eye Institute

NIH/National Institute of Neurological Disorders and Stroke

Intramural Research Program of the NIH, National Institute on Aging

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/9969446/09834971.pdf?arnumber=9834971

Reference55 articles.

1. Disentangled representation learning in cardiac image analysis

2. HybridNet: Classification and Reconstruction Cooperation for Semi-supervised Learning

3. 3D Vessel Reconstruction In Oct-Angiography Via Depth Map Estimation

4. LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation

5. PAENet: A Progressive Attention-Enhanced Network for 3D to 2D Retinal Vessel Segmentation

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