Supervised Segmentation of Un-Annotated Retinal Fundus Images by Synthesis

Supervised Segmentation of Un-Annotated Retinal Fundus Images by Synthesis
Title:
Supervised Segmentation of Un-Annotated Retinal Fundus Images by Synthesis
Other Titles:
IEEE Transactions in Medical Imaging
DOI:
10.1109/TMI.2018.2854886
Keywords:
Publication Date:
01 January 2019
Citation:
H. Zhao, H. Li, S. Maurer-Stroh, Y. Guo, Q. Deng and L. Cheng, "Supervised Segmentation of Un-Annotated Retinal Fundus Images by Synthesis," in IEEE Transactions on Medical Imaging, vol. 38, no. 1, pp. 46-56, Jan. 2019.
Abstract:
We focus on the practical challenge of segmenting new retinal fundus images that are dissimilar to existing well-annotated data sets. It is addressed in this paper by a supervised learning pipeline, with its core being the construction of a synthetic fundus image data set using the proposed R-sGAN technique. The resulting synthetic images are realistic-looking in terms of the query images while maintaining the annotated vessel structures from the existing data set. This helps to bridge the mismatch between the query images and the existing well-annotated data set. As a consequence, any known supervised fundus segmentation technique can be directly utilized on the query images, after training on this synthetic data set. Extensive experiments on different fundus image data sets demonstrate the competitiveness of the proposed approach in dealing with a diverse range of mismatch settings.
License type:
http://creativecommons.org/licenses/by/4.0/
Funding Info:
Description:
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
0278-0062
1558-254X
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