Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 38, Issue 1, Pages 46-56Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2018.2854886
Keywords
Biomedical optical imaging; image segmentation; phantoms
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Funding
- A*STAR JCO Grants
- CSC Chinese Government Scholarship
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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.
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