Dice comparison of 3D-UNet with BSE, ROBEX and Kleesiek's method on a
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Last updated 12 abril 2025


Human Brain Mapping, Neuroimaging Journal

Skull stripping MRI images of the brain using deep learning

Training instability with Dice Loss/Tversky Loss · Issue #807 · Project-MONAI/MONAI · GitHub

Frontiers Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg

Applied Sciences, Free Full-Text

State-of-the-Art Traditional to the Machine- and Deep-Learning-Based Skull Stripping Techniques, Models, and Algorithms. - Abstract - Europe PMC

Intracranial volume segmentation for neurodegenerative populations using multicentre FLAIR MRI - ScienceDirect

deep learning - High image segmentation metrics after training but poor results in prediction - Data Science Stack Exchange

Human Brain Mapping, Neuroimaging Journal
3 Deepmedic, PDF, Image Segmentation

State-of-the-Art Traditional to the Machine- and Deep-Learning-Based Skull Stripping Techniques, Models, and Algorithms

State-of-the-Art Traditional to the Machine- and Deep-Learning-Based Skull Stripping Techniques, Models, and Algorithms
Dice Cube, 3D CAD Model Library

Bayesian convolutional neural network based MRI brain extraction on nonhuman primates - ScienceDirect
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