Search Results for author: Till Huelnhagen

Found 2 papers, 1 papers with code

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency

1 code implementation26 May 2023 Nataliia Molchanova, Bénédicte Maréchal, Jean-Philippe Thiran, Tobias Kober, Till Huelnhagen, Jonas Richiardi

To evaluate the performance of the proposed de-identification tool, a comparative study was conducted between several existing defacing and refacing tools, with two different segmentation algorithms (FAST and Morphobox).

Brain Morphometry De-identification +2

Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: emerging machine learning techniques and future avenues

no code implementations19 Jan 2022 Francesco La Rosa, Maxence Wynen, Omar Al-Louzi, Erin S Beck, Till Huelnhagen, Pietro Maggi, Jean-Philippe Thiran, Tobias Kober, Russell T Shinohara, Pascal Sati, Daniel S Reich, Cristina Granziera, Martina Absinta, Meritxell Bach Cuadra

Recently, advanced MS lesional imaging biomarkers such as cortical lesions (CL), the central vein sign (CVS), and paramagnetic rim lesions (PRL), visible in specialized magnetic resonance imaging (MRI) sequences, have shown higher specificity in differential diagnosis.

Lesion Segmentation Specificity

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