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Found 14 papers, 9 papers with code
SE(3)-Equivariant and Noise-Invariant 3D Rigid Motion Tracking in Brain MRI
Here we propose EquiTrack, the first method that uses recent steerable SE(3)-equivariant CNNs (E-CNN) for motion tracking.
Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series
In this work, we propose a machine learning segmentation framework for placental BOLD MRI and apply it to segmenting each volume in a time series.
Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series
We apply our method to learning subject-specific atlases and motion stabilization of dynamic BOLD MRI time-series of fetuses in utero.
Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series
The placenta plays a crucial role in fetal development.
AnyStar: Domain randomized universal star-convex 3D instance segmentation
Star-convex shapes arise across bio-microscopy and radiology in the form of nuclei, nodules, metastases, and other units.
Automatic Segmentation of the Placenta in BOLD MRI Time Series
In this work, we propose a machine learning model based on a U-Net neural network architecture to automatically segment the placenta in BOLD MRI and apply it to segmenting each volume in a time series.
Volumetric Parameterization of the Placenta to a Flattened Template
However, due to the curved and highly variable in vivo shape of the placenta, interpreting and visualizing these images is difficult.
Rapid head-pose detection for automated slice prescription of fetal-brain MRI
In fetal-brain MRI, head-pose changes between prescription and acquisition present a challenge to obtaining the standard sagittal, coronal and axial views essential to clinical assessment.
STRESS: Super-Resolution for Dynamic Fetal MRI using Self-Supervised Learning
Fetal motion is unpredictable and rapid on the scale of conventional MR scan times.
Equivariant Filters for Efficient Tracking in 3D Imaging
The transformation is then derived in closed form from the outputs of the filters.
Enhanced detection of fetal pose in 3D MRI by Deep Reinforcement Learning with physical structure priors on anatomy
The proposed DRL for fetal pose landmark search demonstrates a potential clinical utility for online detection of fetal motion that guides real-time mitigation of motion artifacts as well as health diagnosis during MRI of the pregnant mother.
Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency
The proposed method is also implemented and evaluated on an MR scanner, which demonstrates the feasibility of online image quality assessment and image reacquisition during fetal MR scans.
Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Network
The performance and diagnostic utility of magnetic resonance imaging (MRI) in pregnancy is fundamentally constrained by fetal motion.
Placental Flattening via Volumetric Parameterization
We formulate our method as a map from the in vivo shape to a flattened template that minimizes the symmetric Dirichlet energy to control distortion throughout the volume.
