Search Results for author:
Found 23 papers, 6 papers with code
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.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
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.
Automatic Segmentation of the Prostate on 3D Trans-rectal Ultrasound Images using Statistical Shape Models and Convolutional Neural Networks
In this work we propose to segment the prostate on a challenging dataset of trans-rectal ultrasound (TRUS) images using convolutional neural networks (CNNs) and statistical shape models (SSMs).
Interpolation of CT Projections by Exploiting Their Self-Similarity and Smoothness
The proposed algorithm exploits the self-similarity and smoothness of the sinogram.
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.
Sparse and redundant signal representations for x-ray computed tomography
The goal of this paper is to explain the principles of patch-based methods and to review some of their recent applications in CT. We review the central concepts in patch-based image processing and explain some of the state-of-the-art algorithms, with a focus on aspects that are more relevant to CT. Then, we review some of the recent application of patch-based methods in CT.
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.
Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks
Based on these three methods for estimating HD, we suggest three loss functions that can be used for training to reduce HD.
A deep learning-based method for prostate segmentation in T2-weighted magnetic resonance imaging
A global CNN will determine a prostate bounding box, which is then resampled and sent to a local CNN for accurate delineation of the prostate boundary.
