216 citations · 443 across the 20 of their papers we have counts for
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qDWI-Morph: Motion-compensated quantitative Diffusion-Weighted MRI analysis for fetal lung maturity assessment
Yael Zaffrani-Reznikov, Onur Afacan, Sila Kurugol +2
Quantitative analysis of fetal lung Diffusion-Weighted MRI (DWI) data shows potential in providing quantitative imaging biomarkers that indirectly reflect fetal lung maturation. Ho…
SUPER-IVIM-DC: Intra-voxel incoherent motion based Fetal lung maturity assessment from limited DWI data using supervised learning coupled with data-consistency
Noam Korngut, Elad Rotman, Onur Afacan +5
Intra-voxel incoherent motion (IVIM) analysis of fetal lungs Diffusion-Weighted MRI (DWI) data shows potential in providing quantitative imaging bio-markers that reflect, indirectl…
CORPS: Cost-free Rigorous Pseudo-labeling based on Similarity-ranking for Brain MRI Segmentation
Can Taylan Sari, Sila Kurugol, Onur Afacan +1
Segmentation of brain magnetic resonance images (MRI) is crucial for the analysis of the human brain and diagnosis of various brain disorders. The drawbacks of time-consuming and e…
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…
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…
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…
Non-Learning based Deep Parallel MRI Reconstruction (NLDpMRI)
Ali Pour Yazdanpanah, Onur Afacan, Simon K. Warfield
Fast data acquisition in Magnetic Resonance Imaging (MRI) is vastly in demand and scan time directly depends on the number of acquired k-space samples. Recently, the deep learning-…
Missing Slice Recovery for Tensors Using a Low-rank Model in Embedded Space
Tatsuya Yokota, Burak Erem, Seyhmus Guler +2
Let us consider a case where all of the elements in some continuous slices are missing in tensor data. In this case, the nuclear-norm and total variation regularization methods usu…
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…
Automatic Renal Segmentation in DCE-MRI using Convolutional Neural Networks
Marzieh Haghighi, Simon K. Warfield, Sila Kurugol
Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of rena…
Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning
Seyed Sadegh Mohseni Salehi, Seyed Raein Hashemi, Clemente Velasco-Annis +5
Brain segmentation is a fundamental first step in neuroimage analysis. In the case of fetal MRI, it is particularly challenging and important due to the arbitrary orientation of th…