activity
20202025
most citedCAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI

3 citations · 7 across the 11 of their papers we have counts for

collaborators

11 papers

eess.IV2025

Meta-learning Slice-to-Volume Reconstruction in Fetal Brain MRI using Implicit Neural Representations

Maik Dannecker, Thomas Sanchez, Meritxell Bach Cuadra +6

High-resolution slice-to-volume reconstruction (SVR) from multiple motion-corrupted low-resolution 2D slices constitutes a critical step in image-based diagnostics of moving subjec…

physics.med-ph2024

Real-time fetAl brain and placental T2* mapping at 0.55T low-field MRI (RAT)

Jordina Aviles Verdera, Sara Neves Silva, Raphael Tomi-Tricot +6

Purpose: To provide real-time quantitative organ-specific information - specifically placental and brain T2* - to allow optimization of the MR examination to the individual patient…

cs.LG2024

CINA: Conditional Implicit Neural Atlas for Spatio-Temporal Representation of Fetal Brains

Maik Dannecker, Vanessa Kyriakopoulou, Lucilio Cordero-Grande +3

We introduce a conditional implicit neural atlas (CINA) for spatio-temporal atlas generation from Magnetic Resonance Images (MRI) of the neurotypical and pathological fetal brain,…

physics.med-ph2024★ 2 cited

Fully automated planning for anatomical fetal brain MRI on 0.55T

Sara Neves Silva, Sarah McElroy, Jordina Aviles Verdera +11

Purpose: Widening the availability of fetal MRI with fully automatic real-time planning of radiological brain planes on 0.55T MRI. Methods: Deep learning-based detection of key bra…

eess.IV2023

The Challenge of Fetal Cardiac MRI Reconstruction Using Deep Learning

Denis Prokopenko, Kerstin Hammernik, Thomas Roberts +3

Dynamic free-breathing fetal cardiac MRI is one of the most challenging modalities, which requires high temporal and spatial resolution to depict rapid changes in a small fetal hea…

eess.IV2023★ 1 cited

An automated pipeline for quantitative T2* fetal body MRI and segmentation at low field

Kelly Payette, Alena Uus, Jordina Aviles Verdera +9

Fetal Magnetic Resonance Imaging at low field strengths is emerging as an exciting direction in perinatal health. Clinical low field (0.55T) scanners are beneficial for fetal imagi…

eess.IV2023

Conditional Temporal Attention Networks for Neonatal Cortical Surface Reconstruction

Qiang Ma, Liu Li, Vanessa Kyriakopoulou +4

Cortical surface reconstruction plays a fundamental role in modeling the rapid brain development during the perinatal period. In this work, we propose Conditional Temporal Attentio…

eess.IV2022★ 1 cited

Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View

Veronika A. Zimmer, Alberto Gomez, Emily Skelton +10

Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in…

eess.IV2022★ 3 cited

CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI

Liu Li, Qiang Ma, Matthew Sinclair +6

Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development. Accurate segmentation of the different brain tissues is a vital step in…

eess.IV2021

Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration: application to gestational age prediction

Lucilio Cordero-Grande, Juan Enrique Ortuño-Fisac, Alena Uus +4

Magnetic resonance imaging of whole fetal body and placenta is limited by different sources of motion affecting the womb. Usual scanning techniques employ single-shot multi-slice s…

eess.IV2020

Diffusion tensor driven image registration: a deep learning approach

Irina Grigorescu, Alena Uus, Daan Christiaens +6

Tracking microsctructural changes in the developing brain relies on accurate inter-subject image registration. However, most methods rely on either structural or diffusion data to…