Search Results for author: Ali Gholipour

Found 28 papers, 9 papers with code

Anatomically Constrained Tractography of the Fetal Brain

no code implementations4 Mar 2024 Camilo Calixto, Camilo Jaimes, Matheus D. Soldatelli, Simon K. Warfield, Ali Gholipour, Davood Karimi

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

no code implementations22 Dec 2023 Rizhong Lin, Ali Gholipour, Jean-Philippe Thiran, Davood Karimi, Hamza Kebiri, Meritxell Bach Cuadra

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.

Domain Adaptation

Fetal-BET: Brain Extraction Tool for Fetal MRI

1 code implementation2 Oct 2023 Razieh Faghihpirayesh, Davood Karimi, Deniz Erdoğmuş, Ali Gholipour

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.

Anatomy Data Augmentation

Characterizing normal perinatal development of the human brain structural connectivity

no code implementations22 Aug 2023 Yihan Wu, Lana Vasung, Camilo Calixto, Ali Gholipour, Davood Karimi

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

no code implementations7 Jul 2023 Davood Karimi, Hamza Kebiri, Ali Gholipour

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

no code implementations5 Jul 2023 Hamza Kebiri, Ali Gholipour, Meritxell Bach Cuadra, Davood Karimi

The new methods can serve many critically important clinical and scientific applications that require accurate and reliable non-invasive segmentation of white matter tracts.

Brain Segmentation Segmentation +1

3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation

no code implementations28 Apr 2023 Rui Nian, Guoyao Zhang, Yao Sui, Yuqi Qian, Qiuying Li, Mingzhang Zhao, Jianhui Li, Ali Gholipour, Simon K. Warfield

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.

Brain Tumor Segmentation Segmentation +1

Atlas-powered deep learning (ADL) -- application to diffusion weighted MRI

no code implementations5 May 2022 Davood Karimi, Ali Gholipour

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

1 code implementation2 May 2022 Razieh Faghihpirayesh, Davood Karimi, Deniz Erdogmus, Ali Gholipour

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.

Brain Segmentation Head Pose Estimation +1

Learning to segment fetal brain tissue from noisy annotations

no code implementations25 Mar 2022 Davood Karimi, Caitlin K. Rollins, Clemente Velasco-Annis, Abdelhakim Ouaalam, Ali Gholipour

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.

Brain Segmentation Image Segmentation +2

Diffusion Tensor Estimation with Transformer Neural Networks

no code implementations14 Jan 2022 Davood Karimi, Ali Gholipour

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

no code implementations21 Nov 2021 Davood Karimi, Simon K. Warfield, Ali Gholipour

Here, we propose a deep learning method to estimate the diffusion tensor and compute the estimation uncertainty.

Convolution-Free Medical Image Segmentation using Transformers

1 code implementation26 Feb 2021 Davood Karimi, Serge Vasylechko, Ali Gholipour

We show that the proposed model can achieve segmentation accuracies that are better than the state of the art CNNs on three datasets.

Image Segmentation Inductive Bias +4

A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging

1 code implementation19 Jun 2020 Davood Karimi, Lana Vasung, Camilo Jaimes, Fedel Machado-Rivas, Shadab Khan, Simon K. Warfield, Ali Gholipour

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.

A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI

2 code implementations27 Apr 2020 Haoran Dou, Davood Karimi, Caitlin K. Rollins, Cynthia M. Ortinau, Lana Vasung, Clemente Velasco-Annis, Abdelhakim Ouaalam, Xin Yang, Dong Ni, Ali Gholipour

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

1 code implementation12 Apr 2020 Davood Karimi, Ali Gholipour

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.

Image Segmentation Medical Image Segmentation +5

Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging

2 code implementations25 Sep 2019 Ayush Singh, Seyed Sadegh Mohseni Salehi, Ali Gholipour

Nevertheless, visual monitoring of fetal motion based on displayed slices, and navigation at the level of stacks-of-slices is inefficient.

3D Object Reconstruction Image Registration +2

Exclusive Independent Probability Estimation using Deep 3D Fully Convolutional DenseNets: Application to IsoIntense Infant Brain MRI Segmentation

no code implementations21 Sep 2018 Seyed Raein Hashemi, Sanjay P. Prabhu, Simon K. Warfield, Ali Gholipour

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.

Image Segmentation Infant Brain Mri Segmentation +3

Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection

no code implementations28 Mar 2018 Seyed Raein Hashemi, Seyed Sadegh Mohseni Salehi, Deniz Erdogmus, Sanjay P. Prabhu, Simon K. Warfield, Ali Gholipour

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.

Data Augmentation Image Segmentation +4

Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration

no code implementations15 Mar 2018 Seyed Sadegh Mohseni Salehi, Shadab Khan, Deniz Erdogmus, Ali Gholipour

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).

3D Pose Estimation Anatomy +2

Tversky loss function for image segmentation using 3D fully convolutional deep networks

2 code implementations18 Jun 2017 Seyed Sadegh Mohseni Salehi, Deniz Erdogmus, Ali Gholipour

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.

Image Segmentation Lesion Segmentation +2

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