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Found 23 papers, 13 papers with code
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.
Zero-DeepSub: Zero-Shot Deep Subspace Reconstruction for Rapid Multiparametric Quantitative MRI Using 3D-QALAS
Using an ISMRM/NIST system phantom, the accuracy and reproducibility of the T1 and T2 maps estimated using the proposed methods were evaluated by comparing them with reference techniques.
Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets
Associations of SAM's accuracy with six factors were computed, independently and jointly, including segmentation difficulties as measured by segmentation ability score and by Dice overlap in U-Net, image dimension, size of the target region, image modality, and contrast.
U-Netmer: U-Net meets Transformer for medical image segmentation
The global-context information among local patches is learnt by the self-attention mechanism in Transformer and U-Net segments each local patch instead of flattening into tokens to solve the `token-flatten" problem.
SSL-QALAS: Self-Supervised Learning for Rapid Multiparameter Estimation in Quantitative MRI Using 3D-QALAS
Conclusion: The proposed SSL-QALAS method enabled rapid reconstruction of multiparametric maps from 3D-QALAS measurements without an external dictionary or labeled ground-truth training data.
Segmentation Ability Map: Interpret deep features for medical image segmentation
In addition, our method can provide a mean SA score which can give a performance estimation of the output on the test images without ground-truth.
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.
SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI
Experiments with real-world MRI data are also performed to demonstrate the ability of the proposed model to improve the quality of 3D reconstruction under severe fetal motion.
Deep Relation Learning for Regression and Its Application to Brain Age Estimation
In this paper, we propose deep relation learning for regression, aiming to learn different relations between a pair of input images.
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.
Global-Local Transformer for Brain Age Estimation
The fine-grained information from the local patches are fused with the global-context information by the attention mechanism, inspired by the transformer, to estimate the brain age.
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.
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.
Brain Age Estimation Using LSTM on Children's Brain MRI
Brain age prediction based on children's brain MRI is an important biomarker for brain health and brain development analysis.
Fast Infant MRI Skullstripping with Multiview 2D Convolutional Neural Networks
The three probabilistic segmentations in the three views are linearly fused and thresholded to produce a final brain mask.
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.
Temporal Registration in Application to In-utero MRI Time Series
To achieve accurate and robust alignment, we make a Markov assumption on the nature of motion and take advantage of the temporal smoothness in the image data.
Network Structural Dependency in the Human Connectome Across the Life-Span
Principles of network topology have been widely studied in the human connectome.
Neurons and Cognition
