Spatio-temporal motion correction and iterative reconstruction of in-utero fetal fMRI

no code implementations • 17 Sep 2022 • Athena Taymourtash, Hamza Kebiri, Ernst Schwartz, Karl-Heinz Nenning, Sebastien Tourbier, Gregor Kasprian, Daniela Prayer, Meritxell Bach Cuadra, Georg Langs

Resting-state functional Magnetic Resonance Imaging (fMRI) is a powerful imaging technique for studying functional development of the brain in utero.

A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation

1 code implementation • 5 Apr 2022 • Lucas Fidon, Michael Aertsen, Florian Kofler, Andrea Bink, Anna L. David, Thomas Deprest, Doaa Emam, Frédéric Guffens, András Jakab, Gregor Kasprian, Patric Kienast, Andrew Melbourne, Bjoern Menze, Nada Mufti, Ivana Pogledic, Daniela Prayer, Marlene Stuempflen, Esther Van Elslander, Sébastien Ourselin, Jan Deprest, Tom Vercauteren

Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels.

Image Segmentation MRI segmentation +1

Motion Correction and Volumetric Reconstruction for Fetal Functional Magnetic Resonance Imaging Data

1 code implementation • 11 Feb 2022 • Daniel Sobotka, Michael Ebner, Ernst Schwartz, Karl-Heinz Nenning, Athena Taymourtash, Tom Vercauteren, Sebastien Ourselin, Gregor Kasprian, Daniela Prayer, Georg Langs, Roxane Licandro

Here, we propose a novel framework, which estimates a high-resolution reference volume by using outlier-robust motion correction, and by utilizing Huber L2 regularization for intra-stack volumetric reconstruction of the motion-corrected fetal brain fMRI.

Functional Connectivity L2 Regularization +3

Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI

1 code implementation • 9 Aug 2021 • Lucas Fidon, Michael Aertsen, Nada Mufti, Thomas Deprest, Doaa Emam, Frédéric Guffens, Ernst Schwartz, Michael Ebner, Daniela Prayer, Gregor Kasprian, Anna L. David, Andrew Melbourne, Sébastien Ourselin, Jan Deprest, Georg Langs, Tom Vercauteren

The performance of deep neural networks typically increases with the number of training images.

Anatomy

Distributionally Robust Deep Learning using Hardness Weighted Sampling

1 code implementation • 8 Jan 2020 • Lucas Fidon, Michael Aertsen, Thomas Deprest, Doaa Emam, Frédéric Guffens, Nada Mufti, Esther Van Elslander, Ernst Schwartz, Michael Ebner, Daniela Prayer, Gregor Kasprian, Anna L. David, Andrew Melbourne, Sébastien Ourselin, Jan Deprest, Georg Langs, Tom Vercauteren

In order to improve the robustness of machine learning systems, Distributionally Robust Optimization (DRO) has been proposed as a generalization of Empirical Risk Minimization (ERM).

BIG-bench Machine Learning Brain Tumor Segmentation +3