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Found 28 papers, 9 papers with code
Anatomically Constrained Tractography of the Fetal Brain
Diffusion-weighted Magnetic Resonance Imaging (dMRI) is increasingly used to study the fetal brain in utero.
Cross-Age and Cross-Site Domain Shift Impacts on Deep Learning-Based White Matter Fiber Estimation in Newborn and Baby Brains
However, these models face domain shift challenges when test and train data are from different scanners and protocols, or when the models are applied to data with inherent variations such as the developing brains of infants and children scanned at various ages.
Fetal-BET: Brain Extraction Tool for Fetal MRI
Evaluations on independent test data show that our method achieves accurate brain extraction on heterogeneous test data acquired with different scanners, on pathological brains, and at various gestational stages.
Characterizing normal perinatal development of the human brain structural connectivity
The new computational method and results are useful for assessing normal and abnormal development of the structural connectome early in life.
TBSS++: A novel computational method for Tract-Based Spatial Statistics
Our method promises a drastic improvement in accuracy and reproducibility of cross-subject dMRI studies that are routinely used in neuroscience and medical research.
Direct segmentation of brain white matter tracts in diffusion MRI
The new methods can serve many critically important clinical and scientific applications that require accurate and reliable non-invasive segmentation of white matter tracts.
3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation
By the nature of limited receptive fields, however, those architectures are subject to representing long-range spatial dependencies of the voxel intensities in MRI images.
Subject-specific quantitative susceptibility mapping using patch based deep image priors
This problem of estimating the susceptibility map is ill posed.
Atlas-powered deep learning (ADL) -- application to diffusion weighted MRI
We use the biomarker atlas, atlas reliability map, and alignment error map, in addition to the dMRI signal, as inputs to a deep learning model for biomarker estimation.
Deep Learning Framework for Real-time Fetal Brain Segmentation in MRI
Fast and accurate segmentation of the fetal brain on fetal MRI is required to achieve real-time fetal head pose estimation and motion tracking for slice re-acquisition and steering.
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.
Learning to segment fetal brain tissue from noisy annotations
However, effective training of a deep learning model to perform this task requires a large number of training images to represent the rapid development of the transient fetal brain structures.
Diffusion Tensor Estimation with Transformer Neural Networks
Estimations produced by our method with six diffusion-weighted measurements are comparable with those of standard estimation methods with 30-88 diffusion-weighted measurements.
Calibrated Diffusion Tensor Estimation
Here, we propose a deep learning method to estimate the diffusion tensor and compute the estimation uncertainty.
Learning to estimate the fiber orientation distribution function from diffusion-weighted MRI
Our study demonstrates the potential of data-driven methods for improving the accuracy and robustness of fODF estimation.
Convolution-Free Medical Image Segmentation using Transformers
We show that the proposed model can achieve segmentation accuracies that are better than the state of the art CNNs on three datasets.
A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging
Existing methods for estimating the number and orientations of fascicles in an imaging voxel either depend on non-convex optimization techniques that are sensitive to initialization and measurement noise, or are prone to predicting spurious fascicles.
Critical Assessment of Transfer Learning for Medical Image Segmentation with Fully Convolutional Neural Networks
Here, we study the role of transfer learning for training fully convolutional networks (FCNs) for medical image segmentation.
A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI
Automatic segmentation of the cortical plate, on the other hand, is challenged by the relatively low resolution of the reconstructed fetal brain MRI scans compared to the thin structure of the cortical plate, partial voluming, and the wide range of variations in the morphology of the cortical plate as the brain matures during gestation.
Improving Calibration and Out-of-Distribution Detection in Medical Image Segmentation with Convolutional Neural Networks
We show that different datasets, representing different imaging modalities and/or different organs of interest, have distinct spectral signatures, which can be used to identify whether or not a test image is similar to the images used to train a model.
Deep learning with noisy labels: exploring techniques and remedies in medical image analysis
Then, we review studies that have dealt with label noise in deep learning for medical image analysis.
Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging
Nevertheless, visual monitoring of fetal motion based on displayed slices, and navigation at the level of stacks-of-slices is inefficient.
Exclusive Independent Probability Estimation using Deep 3D Fully Convolutional DenseNets: Application to IsoIntense Infant Brain MRI Segmentation
Using our proposed training strategy based on similarity loss functions and patch prediction fusion we decrease the number of parameters in the network, reduce the complexity of the training process focusing the attention on less number of tasks, while mitigating the effects of data imbalance between labels and inaccuracies near patch borders.
Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection
One of the major challenges in training such networks raises when data is unbalanced, which is common in many medical imaging applications such as lesion segmentation where lesion class voxels are often much lower in numbers than non-lesion voxels.
Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration
Our results show that in such registration applications that are amendable to learning, the proposed deep learning methods with geodesic loss minimization can achieve accurate results with a wide capture range in real-time (<100ms).
Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning
We aimed to develop a fully automatic segmentation method that independently segments sections of the fetal brain in 2D fetal MRI slices in real-time.
Tversky loss function for image segmentation using 3D fully convolutional deep networks
One of the main challenges in training these networks is data imbalance, which is particularly problematic in medical imaging applications such as lesion segmentation where the number of lesion voxels is often much lower than the number of non-lesion voxels.
Auto-context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging
Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines.
