To the best of our knowledge, our work is the first to study subcortical structure segmentation on such large-scale and heterogeneous data.
Fully convolutional neural networks (F-CNNs) have set the state-of-the-art in image segmentation for a plethora of applications.
To address the first challenge, multiple spatially distributed networks were used in the SLANT method, in which each network learned contextual information for a fixed spatial location.
Whole brain segmentation on a structural magnetic resonance imaging (MRI) is essential in non-invasive investigation for neuroanatomy.
The proposed network architecture provides a dense connection between layers that aims to improve the information flow in the network.
Next to voxel-wise uncertainty, we introduce four metrics to quantify structure-wise uncertainty in segmentation for quality control.
Accurate brain tissue segmentation in Magnetic Resonance Imaging (MRI) has attracted the attention of medical doctors and researchers since variations in tissue volume help in diagnosing and monitoring neurological diseases.