Paper Info
Title
Adversarial Domain Adaptation For Multi-Device Retinal Oct Segmentation
Abstract
Deep networks provide excellent image segmentation results given copious amounts of supervised training data (source data). However, when a trained network is applied to data acquired at a different clinical center or on a different imaging device (target data), a significant drop in performance can occur due to the domain shift between the test data and the network training data. To solve this problem, unsupervised domain adaptation methods retrain the model with labeled source data and unlabeled target data. In real practice, retraining the model is time consuming and the labeled source data may not be available for people deploying the model. In this paper, we propose a straightforward unsupervised domain adaptation method for multi-device retinal OCT image segmentation which does not require labeled source data and does not require retraining of the segmentation model. The segmentation network is trained with labeled Spectralis images and tested on Cirrus images. The core idea is to use a domain adaptor to convert target domain images (Cirrus) to a domain that can be segmented well by the already trained segmentation network. Unlabeled Spectralis and Cirrus images are used to train this domain adaptor. The domain adaptation block is used before the trained network and a discriminator is used to differentiate the segmentation results from Spectralis and Cirrus. The domain adaptation portion of our network is fully unsupervised and does not change the previously trained segmentation network.
Year
DOI
Venue
2020
10.1117/12.2549839
MEDICAL IMAGING 2020: IMAGE PROCESSING
Keywords
DocType
Volume
unsupervised domain adaptation, OCT, deep learning, segmentation
Conference
11313
ISSN
Citations 
PageRank 
0277-786X
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Yufan He193.92
Aaron Carass238343.15
Yihao Liu3215.15
shiv saidha453.49
Peter A. Calabresi523220.40
Jerry L. Prince64990488.42