SegSRGAN: A software solution for super-resolution and segmentation using generative adversarial networks -- Application to neonatal brain MRI
Résumé
One of the main issues in the analysis of clinical neonatal brain MRI is the low anisotropic resolution of the data. In most MRI analysis pipelines, data are first re-sampled using interpolation or single image super-resolution techniques and then segmented using (semi-)automated approaches. In other words, image reconstruction and segmentation are then performed separately. In this article, we propose a methodology and a software solution for carrying out simultaneously high-resolution reconstruction and segmentation of brain MRI data. Our strategy mainly relies on generative adversarial networks. The network architecture is described in details, such as the associated software tool. We illustrate its behaviour for cortex analysis from neonatal MR images, both in a quantitative way on a research MRI dataset, and more qualitatively on real clinical data. Results emphasize the potential of our proposed method / software with respect to practical medical applications.
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