Search Results for author:
Found 19 papers, 3 papers with code
A self-attention model for robust rigid slice-to-volume registration of functional MRI
Furthermore, our approach exhibits significantly faster registration speed compared to conventional iterative methods ($0. 096$ sec.
Anatomically Constrained Tractography of the Fetal Brain
Diffusion-weighted Magnetic Resonance Imaging (dMRI) is increasingly used to study the fetal brain in utero.
IVIM-Morph: Motion-compensated quantitative Intra-voxel Incoherent Motion (IVIM) analysis for functional fetal lung maturity assessment from diffusion-weighted MRI data
IVIM-morph combines two sub-networks, a registration sub-network, and an IVIM model fitting sub-network, enabling simultaneous estimation of IVIM model parameters and motion.
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.
CORPS: Cost-free Rigorous Pseudo-labeling based on Similarity-ranking for Brain MRI Segmentation
With this motivation, we propose CORPS, a semi-supervised segmentation framework built upon a novel atlas-based pseudo-labeling method and a 3D deep convolutional neural network (DCNN) for 3D brain MRI segmentation.
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.
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.
Physics-Based Iterative Reconstruction for Dual Source and Flying Focal Spot Computed Tomography
Our algorithm uses precise physics models to reconstruct from the native cone-beam geometry and interleaved dual source helical trajectory of a DS-FFS CT. To do so, we construct a noise physics model to represent data acquisition noise and a prior image model to represent image noise and texture.
ODE-based Deep Network for MRI Reconstruction
Our results with undersampled data demonstrate that our method can deliver higher quality images in comparison to the reconstruction methods based on the standard UNet network and Residual network.
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 Plug-and-Play Prior for Parallel MRI Reconstruction
Our proposed reconstruction enables an increase in acceleration factor, and a reduction in acquisition time while maintaining high image quality.
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.
Non-Learning based Deep Parallel MRI Reconstruction (NLDpMRI)
For different MRI scanner configurations using these approaches, the network must be trained from scratch every time with new training dataset, acquired under new configurations, to be able to provide good reconstruction performance.
Missing Slice Recovery for Tensors Using a Low-rank Model in Embedded Space
The higher-order tensor is then recovered by Tucker-based low-rank tensor factorization.
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.
Automatic Renal Segmentation in DCE-MRI using Convolutional Neural Networks
In this paper, we propose a time and memory efficient fully automated segmentation method which achieves high segmentation accuracy with running time in the order of seconds in both normal kidneys and kidneys with hydronephrosis.
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.
