Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge

no code implementations • 5 May 2025 • Vladyslav Zalevskyi, Thomas Sanchez, Misha Kaandorp, Margaux Roulet, Diego Fajardo-Rojas, Liu Li, Jana Hutter, Hongwei Bran Li, Matthew Barkovich, Hui Ji, Luca Wilhelmi, Aline Dändliker, Céline Steger, Mériam Koob, Yvan Gomez, Anton Jakovčić, Melita Klaić, Ana Adžić, Pavel Marković, Gracia Grabarić, Milan Rados, Jordina Aviles Verdera, Gregor Kasprian, Gregor Dovjak, Raphael Gaubert-Rachmühl, Maurice Aschwanden, Qi Zeng, Davood Karimi, Denis Peruzzo, Tommaso Ciceri, Giorgio Longari, Rachika E. Hamadache, Amina Bouzid, Xavier Lladó, Simone Chiarella, Gerard Martí-Juan, Miguel Ángel González Ballester, Marco Castellaro, Marco Pinamonti, Valentina Visani, Robin Cremese, Keïn Sam, Fleur Gaudfernau, Param Ahir, Mehul Parikh, Maximilian Zenk, Michael Baumgartner, Klaus Maier-Hein, Li Tianhong, Yang Hong, Zhao Longfei, Domen Preloznik, Žiga Špiclin, Jae Won Choi, Muyang Li, Jia Fu, Guotai Wang, Jingwen Jiang, Lyuyang Tong, Bo Du, Andrea Gondova, Sungmin You, Kiho Im, Abdul Qayyum, Moona Mazher, Steven A Niederer, Andras Jakab, Roxane Licandro, Kelly Payette, Meritxell Bach Cuadra

The FeTA Challenge 2024 advanced automated fetal brain MRI analysis by introducing biometry prediction as a new task alongside tissue segmentation.

MRI segmentation Segmentation +1

Towards contrast- and pathology-agnostic clinical fetal brain MRI segmentation using SynthSeg

no code implementations • 14 Apr 2025 • Ziyao Shang, Misha Kaandorp, Kelly Payette, Marina Fernandez Garcia, Roxane Licandro, Georg Langs, Jordina Aviles Verdera, Jana Hutter, Bjoern Menze, Gregor Kasprian, Meritxell Bach Cuadra, Andras Jakab

Magnetic resonance imaging (MRI) has played a crucial role in fetal neurodevelopmental research.

Data Augmentation MRI segmentation

DRIFTS: Optimizing Domain Randomization with Synthetic Data and Weight Interpolation for Fetal Brain Tissue Segmentation

no code implementations • 11 Nov 2024 • Vladyslav Zalevskyi, Thomas Sanchez, Margaux Roulet, Hélène Lajous, Jordina Aviles Verdera, Roxane Licandro, Georg Langs, Gregor Kasprian, Jana Hutter, Hamza Kebiri, Meritxell Bach Cuadra

Fetal brain tissue segmentation in magnetic resonance imaging (MRI) is a crucial tool that supports understanding of neurodevelopment, yet it faces challenges due to the heterogeneity of data coming from different scanners and settings, as well as data scarcity.

Image Segmentation Semantic Segmentation +2

Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results

no code implementations • 8 Feb 2024 • Kelly Payette, Céline Steger, Roxane Licandro, Priscille de Dumast, Hongwei Bran Li, Matthew Barkovich, Liu Li, Maik Dannecker, Chen Chen, Cheng Ouyang, Niccolò McConnell, Alina Miron, Yongmin Li, Alena Uus, Irina Grigorescu, Paula Ramirez Gilliland, Md Mahfuzur Rahman Siddiquee, Daguang Xu, Andriy Myronenko, Haoyu Wang, Ziyan Huang, Jin Ye, Mireia Alenyà, Valentin Comte, Oscar Camara, Jean-Baptiste Masson, Astrid Nilsson, Charlotte Godard, Moona Mazher, Abdul Qayyum, Yibo Gao, Hangqi Zhou, Shangqi Gao, Jia Fu, Guiming Dong, Guotai Wang, ZunHyan Rieu, HyeonSik Yang, Minwoo Lee, Szymon Płotka, Michal K. Grzeszczyk, Arkadiusz Sitek, Luisa Vargas Daza, Santiago Usma, Pablo Arbelaez, Wenying Lu, WenHao Zhang, Jing Liang, Romain Valabregue, Anand A. Joshi, Krishna N. Nayak, Richard M. Leahy, Luca Wilhelmi, Aline Dändliker, Hui Ji, Antonio G. Gennari, Anton Jakovčić, Melita Klaić, Ana Adžić, Pavel Marković, Gracia Grabarić, Gregor Kasprian, Gregor Dovjak, Milan Rados, Lana Vasung, Meritxell Bach Cuadra, Andras Jakab

The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms.

Brain Segmentation Segmentation +1

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 +4