  While Magnetic Resonance Imaging (MRI) has played an essential role in infant
brain analysis, segmenting MRI into a number of tissues such as gray matter
(GM), white matter (WM), and cerebrospinal fluid (CSF) is crucial and complex
due to the extremely low intensity contrast between tissues at around 6-9
months of age as well as amplified noise, myelination, and incomplete volume.
In this paper, we tackle those limitations by developing a new deep learning
model, named DAM-AL, which contains two main contributions, i.e., dilated
attention mechanism and hard-case attention loss. Our DAM-AL network is
designed with skip block layers and atrous block convolution. It contains both
channel-wise attention at high-level context features and spatial attention at
low-level spatial structural features. Our attention loss consists of two terms
corresponding to region information and hard samples attention. Our proposed
DAM-AL has been evaluated on the infant brain iSeg 2017 dataset and the
experiments have been conducted on both validation and testing sets. We have
benchmarked DAM-AL on Dice coefficient and ASD metrics and compared it with
state-of-the-art methods.
