DeepAI AI Chat
Log In Sign Up

Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images

by   Fei Yu, et al.
Peking University

Segmenting coronary arteries is challenging, as classic unsupervised methods fail to produce satisfactory results and modern supervised learning (deep learning) requires manual annotation which is often time-consuming and can some time be infeasible. To solve this problem, we propose a knowledge transfer based shape-consistent generative adversarial network (SC-GAN), which is an annotation-free approach that uses the knowledge from publicly available annotated fundus dataset to segment coronary arteries. The proposed network is trained in an end-to-end fashion, generating and segmenting synthetic images that maintain the background of coronary angiography and preserve the vascular structures of retinal vessels and coronary arteries. We train and evaluate the proposed model on a dataset of 1092 digital subtraction angiography images, and experiments demonstrate the supreme accuracy of the proposed method on coronary arteries segmentation.


page 6

page 7


Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images

Accurate segmentation of the optic disc from a retinal image is vital to...

Retinal Optic Disc Segmentation using Conditional Generative Adversarial Network

This paper proposed a retinal image segmentation method based on conditi...

Elastic Registration of Medical Images With GANs

Conventional approaches to image registration consist of time consuming ...

Retinal Vessel Segmentation under Extreme Low Annotation: A Generative Adversarial Network Approach

Contemporary deep learning based medical image segmentation algorithms r...

JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets

Automated and accurate segmentations of left atrium (LA) and atrial scar...