  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. Several
proposals have been designed throughout the years comprising conventional
machine learning strategies as well as convolutional neural networks (CNN)
approaches. In particular, in this paper, we analyse a sub-group of deep
learning methods producing dense predictions. This branch, referred in the
literature as Fully CNN (FCNN), is of interest as these architectures can
process an input volume in less time than CNNs and local spatial dependencies
may be encoded since several voxels are classified at once. Our study focuses
on understanding architectural strengths and weaknesses of literature-like
approaches. Hence, we implement eight FCNN architectures inspired by robust
state-of-the-art methods on brain segmentation related tasks. We evaluate them
using the IBSR18, MICCAI2012 and iSeg2017 datasets as they contain infant and
adult data and exhibit varied voxel spacing, image quality, number of scans and
available imaging modalities. The discussion is driven in three directions:
comparison between 2D and 3D approaches, the importance of multiple modalities
and overlapping as a sampling strategy for training and testing models. To
encourage other researchers to explore the evaluation framework, a public
version is accessible to download from our research website.
