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Proceedings Paper

Adversarial domain adaptation for multi-device retinal OCT segmentation
Author(s): Yufan He; Aaron Carass; Yihao Liu; Shiv Saidha; Peter A. Calabresi; Jerry L. Prince
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Paper 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.

Paper Details

Date Published: 10 March 2020
PDF: 6 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131309 (10 March 2020); doi: 10.1117/12.2549839
Show Author Affiliations
Yufan He, Johns Hopkins Univ. (United States)
Aaron Carass, Johns Hopkins Univ. (United States)
Yihao Liu, Johns Hopkins Univ. (United States)
Shiv Saidha, Johns Hopkins Univ. School of Medicine (United States)
Peter A. Calabresi, Johns Hopkins Univ. School of Medicine (United States)
Jerry L. Prince, Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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