Abstract:
Recent past has seen an inexorable shift towards the use
of deep learning techniques to solve a myriad of problems
in the field of medical imaging. In this paper, a novel segmentation method involving a fully-connected deep neural
network called Deep Segmentation Network (DSN) is proposed to perform supervised regression for brain extraction
from T1-weighted magnetic resonance (MR) images. In contrast to the existing patch-based feature learning techniques,
DSN works on full 3D volumes, simplifying pre- and postprocessing operations, to efficiently provide a voxel-wise binary mask delineating the brain region. The model is evaluated using three publicly available datasets and is observed
to either outdo or perform comparably to the state-of-the-art
methods. DSN is able to achieve a maximum and minimum
Dice Similarity Coefficient (DSC) of 97.57 and 92.82 respectively across all the datasets. Experiments conducted in this
paper highlight the ability of the DSN model to automatically learn feature representations; making it a simple yet
highly effective approach for brain segmentation. Preliminary experiments also suggest that the proposed model has
the potential to segment sub-cortical structures accurately.
