Search Results for author: Cristina Granziera

Found 11 papers, 6 papers with code

Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation

1 code implementation15 Nov 2023 Nataliia Molchanova, Vatsal Raina, Andrey Malinin, Francesco La Rosa, Adrien Depeursinge, Mark Gales, Cristina Granziera, Henning Muller, Mara Graziani, Meritxell Bach Cuadra

The results from a multi-centric MRI dataset of 334 patients demonstrate that our proposed measures more effectively capture model errors at the lesion and patient scales compared to measures that average voxel-scale uncertainty values.

Lesion Segmentation Uncertainty Quantification

Towards contrast-agnostic soft segmentation of the spinal cord

1 code implementation23 Oct 2023 Sandrine Bédard, Naga Karthik Enamundram, Charidimos Tsagkas, Emanuele Pravatà, Cristina Granziera, Andrew Smith, Kenneth Arnold Weber II, Julien Cohen-Adad

Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis.


GAMER-MRIL identifies Disability-Related Brain Changes in Multiple Sclerosis

no code implementations15 Aug 2023 Po-Jui Lu, Benjamin Odry, Muhamed Barakovic, Matthias Weigel, Robin Sandkühler, Reza Rahmanzadeh, Xinjie Chen, Mario Ocampo-Pineda, Jens Kuhle, Ludwig Kappos, Philippe Cattin, Cristina Granziera

To fully utilize the qMRI, GAMER-MRIL extended a gated-attention-based CNN (GAMER-MRI), which was developed to select patch-based qMRI important for a given task/question, to the whole-brain image.

Diffusion Models for Contrast Harmonization of Magnetic Resonance Images

no code implementations14 Mar 2023 Alicia Durrer, Julia Wolleb, Florentin Bieder, Tim Sinnecker, Matthias Weigel, Robin Sandkühler, Cristina Granziera, Özgür Yaldizli, Philippe C. Cattin

We map images from the source contrast to the target contrast for both directions, from 3 T to 1. 5 T and from 1. 5 T to 3 T. As we only want to change the contrast, not the anatomical information, our method uses the original image to guide the image-to-image translation process by adding structural information.

Image-to-Image Translation

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

Learn to Ignore: Domain Adaptation for Multi-Site MRI Analysis

1 code implementation13 Oct 2021 Julia Wolleb, Robin Sandkühler, Florentin Bieder, Muhamed Barakovic, Nouchine Hadjikhani, Athina Papadopoulou, Özgür Yaldizli, Jens Kuhle, Cristina Granziera, Philippe C. Cattin

The limited availability of large image datasets, mainly due to data privacy and differences in acquisition protocols or hardware, is a significant issue in the development of accurate and generalizable machine learning methods in medicine.

BIG-bench Machine Learning Classification +1

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

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