CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI
This work addresses the challenge of reliable brain analysis in prenatal diagnosis for medical professionals, though it appears incremental as it builds on prior atlas-based methods.
The authors tackled the problem of accurate brain tissue segmentation in fetal MRI, which is prone to motion artifacts, by proposing CAS-Net, a network that simultaneously generates conditional atlases and predicts segmentation, achieving an overall Dice similarity coefficient of 85.2% for 9 tissue labels.
Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development. Accurate segmentation of the different brain tissues is a vital step in several brain analysis tasks, such as cortical surface reconstruction and tissue thickness measurements. Fetal MRI scans, however, are prone to motion artifacts that can affect the correctness of both manual and automatic segmentation techniques. In this paper, we propose a novel network structure that can simultaneously generate conditional atlases and predict brain tissue segmentation, called CAS-Net. The conditional atlases provide anatomical priors that can constrain the segmentation connectivity, despite the heterogeneity of intensity values caused by motion or partial volume effects. The proposed method is trained and evaluated on 253 subjects from the developing Human Connectome Project (dHCP). The results demonstrate that the proposed method can generate conditional age-specific atlas with sharp boundary and shape variance. It also segment multi-category brain tissues for fetal MRI with a high overall Dice similarity coefficient (DSC) of $85.2\%$ for the selected 9 tissue labels.