63 citations · 98 across the 12 of their papers we have counts for
12 papers
Preserving Marker Specificity with Lightweight Channel-Independent Representation Learning
Simon Gutwein, Arthur Longuefosse, Jun Seita +2
Multiplexed tissue imaging measures dozens of protein markers per cell, yet most deep learning models still apply early channel fusion, assuming shared structure across markers. We…
Conditional Fetal Brain Atlas Learning for Automatic Tissue Segmentation
Johannes Tischer, Patric Kienast, Marlene Stümpflen +3
Magnetic Resonance Imaging (MRI) of the fetal brain has become a key tool for studying brain development in vivo. Yet, its assessment remains challenging due to variability in brai…
Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge
Vladyslav Zalevskyi, Thomas Sanchez, Misha Kaandorp +67
Accurate fetal brain tissue segmentation and biometric analysis are essential for studying brain development in utero. The FeTA Challenge 2024 advanced automated fetal brain MRI an…
Towards contrast- and pathology-agnostic clinical fetal brain MRI segmentation using SynthSeg
Ziyao Shang, Misha Kaandorp, Kelly Payette +9
Magnetic resonance imaging (MRI) has played a crucial role in fetal neurodevelopmental research. Structural annotations of MR images are an important step for quantitative analysis…
Interpretable Embeddings for Segmentation-Free Single-Cell Analysis in Multiplex Imaging
Simon Gutwein, Daria Lazic, Thomas Walter +2
Multiplex Imaging (MI) enables the simultaneous visualization of multiple biological markers in separate imaging channels at subcellular resolution, providing valuable insights int…
Evaluating Synthetic Data Generation for Domain Generalization in Fetal Brain MRI Segmentation
Vladyslav Zalevskyi, Thomas Sanchez, Margaux Roulet +10
Fetal brain tissue segmentation from magnetic resonance imaging (MRI) is crucial for studying neurodevelopment, but remains challenging due to data heterogeneity and limited annota…
FISHing in Uncertainty: Synthetic Contrastive Learning for Genetic Aberration Detection
Simon Gutwein, Martin Kampel, Sabine Taschner-Mandl +1
Detecting genetic aberrations is crucial in cancer diagnosis, typically through fluorescence in situ hybridization (FISH). However, existing FISH image classification methods face…
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
Kelly Payette, Céline Steger, Roxane Licandro +64
Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and th…
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Kelly Payette, Hongwei Li, Priscille de Dumast +55
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital ste…
Motion Correction and Volumetric Reconstruction for Fetal Functional Magnetic Resonance Imaging Data
Daniel Sobotka, Michael Ebner, Ernst Schwartz +8
Motion correction is an essential preprocessing step in functional Magnetic Resonance Imaging (fMRI) of the fetal brain with the aim to remove artifacts caused by fetal movement an…
4D iterative reconstruction of brain fMRI in the moving fetus
Athena Taymourtash, Hamza Kebiri, Sébastien Tourbier +8
Resting-state functional Magnetic Resonance Imaging (fMRI) is a powerful imaging technique for studying functional development of the brain in utero. However, unpredictable and exc…
Asymmetric Cascade Networks for Focal Bone Lesion Prediction in Multiple Myeloma
Roxane Licandro, Johannes Hofmanninger, Matthias Perkonigg +7
The reliable and timely stratification of bone lesion evolution risk in smoldering Multiple Myeloma plays an important role in identifying prime markers of the disease's advance an…