Journal article
End-to-End Adversarial Retinal Image Synthesis
IEEE Transactions on Medical Imaging, Vol.37(3), pp.781-791
03/2018
DOI: 10.1109/TMI.2017.2759102
PMID: 28981409
Abstract
In medical image analysis applications, the availability of the large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing retinal color images by applying recent techniques based on adversarial learning. In this setting, a generative model is trained to maximize a loss function provided by a second model attempting to classify its output into real or synthetic. In particular, we propose to implement an adversarial autoencoder for the task of retinal vessel network synthesis. We use the generated vessel trees as an intermediate stage for the generation of color retinal images, which is accomplished with a generative adversarial network. Both models require the optimization of almost everywhere differentiable loss functions, which allows us to train them jointly. The resulting model offers an end-to-end retinal image synthesis system capable of generating as many retinal images as the user requires, with their corresponding vessel networks, by sampling from a simple probability distribution that we impose to the associated latent space. We show that the learned latent space contains a well-defined semantic structure, implying that we can perform calculations in the space of retinal images, e.g., smoothly interpolating new data points between two retinal images. Visual and quantitative results demonstrate that the synthesized images are substantially different from those in the training set, while being also anatomically consistent and displaying a reasonable visual quality.
Details
- Title: Subtitle
- End-to-End Adversarial Retinal Image Synthesis
- Creators
- Pedro Costa - Institute for Systems and Computer Engineering, Technology and Science, Porto, PortugalAdrian Galdran - Institute for Systems and Computer Engineering, Technology and Science, Porto, PortugalMaria Ines Meyer - Institute for Systems and Computer Engineering, Technology and Science, Porto, PortugalMeindert Niemeijer - IDx LLC, Iowa City, IA, USAMichael Abramoff - Stephen A. Wynn Institute for Vision Research, University of Iowa, Iowa City, IA, USAAna Maria Mendonca - Institute for Systems and Computer Engineering, Technology and Science, Porto, PortugalAurelio Campilho - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
- Resource Type
- Journal article
- Publication Details
- IEEE Transactions on Medical Imaging, Vol.37(3), pp.781-791
- DOI
- 10.1109/TMI.2017.2759102
- PMID
- 28981409
- NLM abbreviation
- IEEE Trans Med Imaging
- ISSN
- 0278-0062
- eISSN
- 1558-254X
- Publisher
- IEEE
- Grant note
- NORTE-01-0145-FEDER-000016 / North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement within the project NanoSTIMA: Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics (10.13039/501100001871) ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme (10.13039/501100008530) CMUP-ERI/TIC/0028/2014 / National Funds through the FCT Fundação para a Ciência e a Tecnologia Portuguese Foundation for Science and Technology (10.13039/501100001871)
- Language
- English
- Date published
- 03/2018
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Ophthalmology and Visual Sciences
- Record Identifier
- 9983806382302771
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