Search Results for author: Tobias Kober

Found 6 papers, 1 papers with code

Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis

no code implementations10 Sep 2018 Francesco La Rosa, Mário João Fartaria, Tobias Kober, Jonas Richiardi, Cristina Granziera, Jean-Philippe Thiran, Meritxell Bach Cuadra

In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patients.

Lesion Segmentation Segmentation

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

Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI

no code implementations29 Jan 2022 Thomas Yu, Tom Hilbert, Gian Franco Piredda, Arun Joseph, Gabriele Bonanno, Salim Zenkhri, Patrick Omoumi, Meritxell Bach Cuadra, Erick Jorge Canales-Rodríguez, Tobias Kober, Jean-Philippe Thiran

In this paper, we investigate important aspects of the validation of self-supervised algorithms for reconstruction of undersampled MR images: quantitative evaluation of prospective reconstructions, potential differences between prospective and retrospective reconstructions, suitability of commonly used quantitative metrics, and generalizability.

Anatomy Denoising +1

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

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