Abstract #2159

Infant Brain Segmentation based on Multi-dMRI contrast Attention (BISMA)

Tianjia Zhu^1,2, Minhui Ouyang¹, Lei Feng³, Kay Sindabizera¹, Jessica Hyland¹, and Hao Huang^1,4

¹Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, United States, ²Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States, ³Shandong University Cheeloo College of Medicine, Jinan, China, ⁴Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States

Synopsis

On widely used relaxation-based T1-weighted (T1w) and T2-weighted (T2w) MRI images, 0-2-year-old infant brain images exhibit poor gray and white matter contrasts due to dynamic and poor myelination in this stage. Such poor T1w and T2w contrasts hinder accurate segmentation of infant brains based on T1w or T2w images. Instead, diffusion MRI (dMRI)-derived maps offer high contrasts throughout infancy. To leverage rich dMRI contrasts, we established an Infant Brain Segmentation based on Multi-dMRI contrast Attention (BISMA) technique. BISMA fills the gap in accurate deep learning segmentation of infant brain by fusing multi-contrasts of dMRI-derived maps.

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