216 citations · 520 across the 27 of their papers we have counts for
28 papers
White matter tract crossing and bottleneck regions in the fetal brain
Camilo Calixto, Matheus D. Soldatelli, Bo Li +4
There is a growing interest in using diffusion MRI to study the white matter tracts and structural connectivity of the fetal brain. Recent progress in data acquisition and processi…
HAITCH: A Framework for Distortion and Motion Correction in Fetal Multi-Shell Diffusion-Weighted MRI
Haykel Snoussi, Davood Karimi, Onur Afacan +2
Diffusion magnetic resonance imaging (dMRI) is pivotal for probing the microstructure of the rapidly-developing fetal brain. However, fetal motion during scans and its interaction…
Cross-Age and Cross-Site Domain Shift Impacts on Deep Learning-Based White Matter Fiber Estimation in Newborn and Baby Brains
Rizhong Lin, Ali Gholipour, Jean-Philippe Thiran +3
Deep learning models have shown great promise in estimating tissue microstructure from limited diffusion magnetic resonance imaging data. However, these models face domain shift ch…
Fetal-BET: Brain Extraction Tool for Fetal MRI
Razieh Faghihpirayesh, Davood Karimi, Deniz Erdoğmuş +1
Fetal brain extraction is a necessary first step in most computational fetal brain MRI pipelines. However, it has been a very challenging task due to non-standard fetal head pose,…
Characterizing normal perinatal development of the human brain structural connectivity
Yihan Wu, Lana Vasung, Camilo Calixto +2
Early brain development is characterized by the formation of a highly organized structural connectome. The interconnected nature of this connectome underlies the brain's cognitive…
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…
3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation
Rui Nian, Guoyao Zhang, Yao Sui +6
Magnetic resonance imaging (MRI) is critically important for brain mapping in both scientific research and clinical studies. Precise segmentation of brain tumors facilitates clinic…
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…
Atlas-powered deep learning (ADL) -- application to diffusion weighted MRI
Davood Karimi, Ali Gholipour
Deep learning has a great potential for estimating biomarkers in diffusion weighted magnetic resonance imaging (dMRI). Atlases, on the other hand, are a unique tool for modeling th…
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…
Diffusion Tensor Estimation with Uncertainty Calibration
Davood Karimi, Simon K. Warfield, Ali Gholipour
It is highly desirable to know how uncertain a model's predictions are, especially for models that are complex and hard to understand as in deep learning. Although there has been a…
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…
Critical Assessment of Transfer Learning for Medical Image Segmentation with Fully Convolutional Neural Networks
Davood Karimi, Simon K. Warfield, Ali Gholipour
Transfer learning is widely used for training machine learning models. Here, we study the role of transfer learning for training fully convolutional networks (FCNs) for medical ima…
A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI
Haoran Dou, Davood Karimi, Caitlin K. Rollins +7
Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding. Manual segmentation of the cortical plate, or manual refinem…
Improving Calibration and Out-of-Distribution Detection in Medical Image Segmentation with Convolutional Neural Networks
Davood Karimi, Ali Gholipour
Convolutional Neural Networks (CNNs) have shown to be powerful medical image segmentation models. In this study, we address some of the main unresolved issues regarding these model…
Deep learning with noisy labels: exploring techniques and remedies in medical image analysis
Davood Karimi, Haoran Dou, Simon K. Warfield +1
Supervised training of deep learning models requires large labeled datasets. There is a growing interest in obtaining such datasets for medical image analysis applications. However…
Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging
Ayush Singh, Seyed Sadegh Mohseni Salehi, Ali Gholipour
Fetal magnetic resonance imaging (MRI) is challenged by uncontrollable, large, and irregular fetal movements. It is, therefore, performed through visual monitoring of fetal motion…
Exclusive Independent Probability Estimation using Deep 3D Fully Convolutional DenseNets: Application to IsoIntense Infant Brain MRI Segmentation
Seyed Raein Hashemi, Sanjay P. Prabhu, Simon K. Warfield +1
The most recent fast and accurate image segmentation methods are built upon fully convolutional deep neural networks. In this paper, we propose new deep learning strategies for Den…
Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection
Seyed Raein Hashemi, Seyed Sadegh Mohseni Salehi, Deniz Erdogmus +3
Fully convolutional deep neural networks have been asserted to be fast and precise frameworks with great potential in image segmentation. One of the major challenges in training su…