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

DAM-AL: Dilated Attention Mechanism with Attention Loss for 3D Infant Brain Image Segmentation