A variant form of 3D-UNet for infant brain segmentation

Abstract Magnetic Resonance Imaging (MRI) is dominant modality for infant brain analysis. Segmenting the whole infant MRI brain into number of tissues such as Cerebrospinal fluid (CSF), White matter (WM), and Gray Matter (GM) are highly desirable in the clinical environment. However, traditional methods tend to be degrading due to low contrast between GM and WM in isointense phase (about 6–8 months of early life). Recently, Convolutional Neural Network (CNN) emerged as a robust intelligent approach to examine medical image. The UNet model is among the preferred CNN models that have been widely used for medical imaging applications and achieved excellent results. The UNet architecture is a combination of convolutional, pooling, and up-sampling layers. Recently, 3D-UNet architecture used to exploit 3D-contextual information of volumetric data in many applications. However, CNN faces challenge to distinguish the similar brain tissues. In this paper, we present a variant of 3D-UNet to extract the volumetric contextual information of medical data. We propose a novel combined architecture of dense connection, residual connection, and inception module. The proposed architecture contains three stages, namely the densely connected stage, a residual inception stage, and an up-sampling stage. Our proposed approach provides state-of-art results in comparison to other existing approaches. This suggested approach achieves dice scores of 0.95, 0.905, and 0.92 in CSF, WM, and GM tissues respectively.