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Found 9 papers, 2 papers with code
Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge
The FeTA Challenge 2024 advanced automated fetal brain MRI analysis by introducing biometry prediction as a new task alongside tissue segmentation.
Towards contrast- and pathology-agnostic clinical fetal brain MRI segmentation using SynthSeg
Magnetic resonance imaging (MRI) has played a crucial role in fetal neurodevelopmental research.
DRIFTS: Optimizing Domain Randomization with Synthetic Data and Weight Interpolation for Fetal Brain Tissue Segmentation
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.
Interpretable Embeddings for Segmentation-Free Single-Cell Analysis in Multiplex Imaging
Multiplex Imaging (MI) enables the simultaneous visualization of multiple biological markers in separate imaging channels at subcellular resolution, providing valuable insights into cell-type heterogeneity and spatial organization.
FISHing in Uncertainty: Synthetic Contrastive Learning for Genetic Aberration Detection
Detecting genetic aberrations is crucial in cancer diagnosis, typically through fluorescence in situ hybridization (FISH).
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
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.
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.
Motion Correction and Volumetric Reconstruction for Fetal Functional Magnetic Resonance Imaging Data
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.
4D iterative reconstruction of brain fMRI in the moving fetus
Resting-state functional Magnetic Resonance Imaging (fMRI) is a powerful imaging technique for studying functional development of the brain in utero.
