no code implementations • 6 Apr 2024 • Samah Khawaled, Simon K. Warfield, Moti Freiman
Furthermore, our approach exhibits significantly faster registration speed compared to conventional iterative methods ($0. 096$ sec.
no code implementations • 4 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.
1 code implementation • 13 Jan 2024 • Noga Kertes, Yael Zaffrani-Reznikov, Onur Afacan, Sila Kurugol, Simon K. Warfield, Moti Freiman
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.
no code implementations • 28 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.
no code implementations • 19 May 2022 • Can Taylan Sari, Sila Kurugol, Onur Afacan, Simon K. Warfield
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.
no code implementations • 21 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.
no code implementations • NeuroImage 2021 • Davood Karimi, Lana Vasung, Camilo Jaimes, Fedel Machado-Rivas, Simon K. Warfield, Ali Gholipour
Our study demonstrates the potential of data-driven methods for improving the accuracy and robustness of fODF estimation.
1 code implementation • 19 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.
no code implementations • 30 May 2020 • Davood Karimi, Simon K. Warfield, Ali Gholipour
Here, we study the role of transfer learning for training fully convolutional networks (FCNs) for medical image segmentation.
no code implementations • 26 Jan 2020 • Xiao Wang, Robert D. MacDougall, Peng Chen, Charles A. Bouman, Simon K. Warfield
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.
no code implementations • 27 Dec 2019 • Ali Pour Yazdanpanah, Onur Afacan, Simon K. Warfield
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.
no code implementations • 5 Dec 2019 • Davood Karimi, Haoran Dou, Simon K. Warfield, Ali Gholipour
Then, we review studies that have dealt with label noise in deep learning for medical image analysis.
no code implementations • 30 Aug 2019 • Ali Pour Yazdanpanah, Onur Afacan, Simon K. Warfield
Our proposed reconstruction enables an increase in acceleration factor, and a reduction in acquisition time while maintaining high image quality.
no code implementations • 21 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.
no code implementations • 6 Aug 2018 • Ali Pour Yazdanpanah, Onur Afacan, Simon K. Warfield
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.
no code implementations • CVPR 2018 • Tatsuya Yokota, Burak Erem, Seyhmus Guler, Simon K. Warfield, Hidekata Hontani
The higher-order tensor is then recovered by Tucker-based low-rank tensor factorization.
no code implementations • 28 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.
no code implementations • 19 Dec 2017 • Marzieh Haghighi, Simon K. Warfield, Sila Kurugol
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.
1 code implementation • 25 Oct 2017 • Seyed Sadegh Mohseni Salehi, Seyed Raein Hashemi, Clemente Velasco-Annis, Abdelhakim Ouaalam, Judy A. Estroff, Deniz Erdogmus, Simon K. Warfield, Ali Gholipour
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.