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Found 6 papers, 5 papers with code
Extended 2D Consensus Hippocampus Segmentation
Segmentation done by experts is considered to be a gold-standard when evaluating automated methods, buts it is a time consuming and arduos task, requiring specialized personnel.
Hippocampus Segmentation on Epilepsy and Alzheimer's Disease Studies with Multiple Convolutional Neural Networks
We test this methodology alongside other recent deep learning methods, in two domains: The HarP test set and an in-house epilepsy dataset, containing hippocampus resections, named HCUnicamp.
Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil Configurations
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process.
Automated computed tomography and magnetic resonance imaging segmentation using deep learning: a beginner's guide
Medical image segmentation is an increasingly popular area of research in medical imaging processing and analysis.
MEDPSeg: Hierarchical polymorphic multitask learning for the segmentation of ground-glass opacities, consolidation, and pulmonary structures on computed tomography
The COVID-19 pandemic response highlighted the potential of deep learning methods in facilitating the diagnosis, prognosis and understanding of lung diseases through automated segmentation of pulmonary structures and lesions in chest computed tomography (CT).
FOD-Swin-Net: angular super resolution of fiber orientation distribution using a transformer-based deep model
In this work, we explore the use of automated angular super resolution from faster acquisitions to overcome this challenge.
