Synthesizing Filamentary Structured Images with GANs
This work addresses the challenge of data scarcity in medical image analysis by enabling synthetic data generation from as few as 10 examples, which is incremental but practical for specific domains.
The paper tackles the problem of synthesizing filamentary structured images like retinal fundus and neuronal images from limited training data, using a GAN-based method that generates realistic phantoms from binary segmentation maps, and demonstrates its utility in improving image analysis performance across benchmarks.
This paper aims at synthesizing filamentary structured images such as retinal fundus images and neuronal images, as follows: Given a ground-truth, to generate multiple realistic looking phantoms. A ground-truth could be a binary segmentation map containing the filamentary structured morphology, while the synthesized output image is of the same size as the ground-truth and has similar visual appearance to what have been presented in the training set. Our approach is inspired by the recent progresses in generative adversarial nets (GANs) as well as image style transfer. In particular, it is dedicated to our problem context with the following properties: Rather than large-scale dataset, it works well in the presence of as few as 10 training examples, which is common in medical image analysis; It is capable of synthesizing diverse images from the same ground-truth; Last and importantly, the synthetic images produced by our approach are demonstrated to be useful in boosting image analysis performance. Empirical examination over various benchmarks of fundus and neuronal images demonstrate the advantages of the proposed approach.