Structure-Aware Brain Tissue Segmentation for Isointense Infant MRI Data Using Multi-Phase Multi-Scale Assistance Network

Accurate and automatic brain tissue segmentation is crucial for tracking brain development and diagnosing brain disorders. However, due to inherently ongoing myelination and maturation during the first postnatal year, the intensity distributions of gray matter and white matter in the infant brain MRI at the age of around 6 months old (a.k.a. isointense phase) are highly overlapped, which makes tissue segmentation very challenging, even for experts. To address this issue, in this study, we propose a multi-phase multi-scale assistance segmentation framework, which comprises a structure-preserved generative adversarial network (SPGAN) and a multi-phase multi-scale assisted segmentation network (M$^{2}$ASN). SPGAN bi-directionally synthesizes isointense and adult-like data. The synthetic isointense data essentially augment the training dataset, combined with high-quality annotations transferred from its adult-like counterpart. By contrast, the synthetic adult-like data offers clear tissue structures and is concatenated with isointense data to serve as the input of M$^{2}$ASN. In particular, M$^{2}$ASN is designed with two-branch networks, which simultaneously segment tissues with two phases (isointense and adult-like) and two scales by also preserving their correspondences. We further propose a boundary refinement module to extract maximum gradients from local feature maps to indicate tissue boundaries, prompting M$^{2}$ASN to focus more on boundaries where segmentation errors are prone to occur. Extensive experiments on the National Database for Autism Research and Baby Connectome Project datasets quantitatively and qualitatively demonstrate the superiority of our proposed framework compared with seven state-of-the-art methods.