Simultaneous super-resolution and segmentation using a generative adversarial network: Application to neonatal brain MRI
Résumé
The analysis of clinical neonatal brain MRI remains challenging due to low anisotropic resolution of the data. In most pipelines, images are first re-sampled using interpolation or single image super-resolution techniques and then segmented using (semi-)automated approaches. Image reconstruction and segmentation are then performed separately. In this paper, we propose an end-to-end generative adversarial network for simultaneous high-resolution reconstruction and segmentation of brain MRI data. This joint approach is first assessed on the simulated low-resolution images of the high-resolution neonatal dHCP dataset. Then, the learned model is used to enhance and segment real clinical low-resolution images. Results demonstrate the potential of our proposed method with respect to practical medical applications.
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