63 citations · 99 across the 19 of their papers we have counts for
14 papers · 1 filter
TBSS++: A novel computational method for Tract-Based Spatial Statistics
Davood Karimi, Hamza Kebiri, Ali Gholipour
Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. One of the most common computations in dMRI involves cross-subject tract-speci…
Direct segmentation of brain white matter tracts in diffusion MRI
Hamza Kebiri, Ali Gholipour, Meritxell Bach Cuadra +1
The brain white matter consists of a set of tracts that connect distinct regions of the brain. Segmentation of these tracts is often needed for clinical and research studies. Diffu…
Subject-specific quantitative susceptibility mapping using patch based deep image priors
Arvind Balachandrasekaran, Davood Karimi, Camilo Jaimes +1
Quantitative Susceptibility Mapping is a parametric imaging technique to estimate the magnetic susceptibilities of biological tissues from MRI phase measurements. This problem of e…
Deep Learning Framework for Real-time Fetal Brain Segmentation in MRI
Razieh Faghihpirayesh, Davood Karimi, Deniz Erdogmus +1
Fetal brain segmentation is an important first step for slice-level motion correction and slice-to-volume reconstruction in fetal MRI. Fast and accurate segmentation of the fetal b…
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Kelly Payette, Hongwei Li, Priscille de Dumast +55
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital ste…
Learning to segment fetal brain tissue from noisy annotations
Davood Karimi, Caitlin K. Rollins, Clemente Velasco-Annis +2
Automatic fetal brain tissue segmentation can enhance the quantitative assessment of brain development at this critical stage. Deep learning methods represent the state of the art…
Diffusion Tensor Estimation with Transformer Neural Networks
Davood Karimi, Ali Gholipour
Diffusion tensor imaging (DTI) is a widely used method for studying brain white matter development and degeneration. However, standard DTI estimation methods depend on a large numb…
Automatic Segmentation of the Prostate on 3D Trans-rectal Ultrasound Images using Statistical Shape Models and Convolutional Neural Networks
Golnoosh Samei, Davood Karimi, Claudia Kesch +1
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 m…
Interpolation of CT Projections by Exploiting Their Self-Similarity and Smoothness
Davood Karimi, Rabab K. Ward
As the medical usage of computed tomography (CT) continues to grow, the radiation dose should remain at a low level to reduce the health risks. Therefore, there is an increasing ne…
Convolution-Free Medical Image Segmentation using Transformers
Davood Karimi, Serge Vasylechko, Ali Gholipour
Like other applications in computer vision, medical image segmentation has been most successfully addressed using deep learning models that rely on the convolution operation as the…
A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging
Davood Karimi, Lana Vasung, Camilo Jaimes +4
Multi-compartment modeling of diffusion-weighted magnetic resonance imaging measurements is necessary for accurate brain connectivity analysis. Existing methods for estimating the…
Sparse and redundant signal representations for x-ray computed tomography
Davood Karimi
Image models are central to all image processing tasks. The great advancements in digital image processing would not have been made possible without powerful models which, themselv…
Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks
Davood Karimi, Septimiu E. Salcudean
The Hausdorff Distance (HD) is widely used in evaluating medical image segmentation methods. However, existing segmentation methods do not attempt to reduce HD directly. In this pa…
A deep learning-based method for prostate segmentation in T2-weighted magnetic resonance imaging
Davood Karimi, Golnoosh Samei, Yanan Shao +1
We propose a novel automatic method for accurate segmentation of the prostate in T2-weighted magnetic resonance imaging (MRI). Our method is based on convolutional neural networks…