Year: 2019
Type: conference-paper
Abstract: Skull stripping for brain MR images is a basic segmentation task. Although many methods have been proposed, most of them focused mainly on the adult MR images. Skull stripping for infant MR images is more challenging due to the small size and dynamic intensity changes of brain tissues during the early ages. In this paper, we propose a novel CNN based framework to robustly extract brain region from infant MR image without any human assistance. Specifically, we propose a simplified but more robust flattened residual network architecture (FRnet). We also introduce a new boundary loss function to highlight ambiguous and low contrast regions between brain and non-brain regions. To make the whole framework more robust to MR images with different imaging quality, we further introduce an artifact simulator for data augmentation. We have trained and tested our proposed framework on a large dataset (N=343), covering newborns to 48-month-olds, and obtained performance better than the state-of-the-art methods in all age groups.
Institution: University of North Carolina at Chapel Hill
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G2391590095, Volume-based Analysis of 6-month Infant Brain MRI for Autism Biomarker Identification and Early Diagnosis, G4847335975, G597442328, Constructing 4-dimensional Infant Cortical Surface Atlases, Infant Brain and Behavioral Signatures of Later Emerging Risk for Psychopathology, G886436862, G904464750, G913149829
OpenAlex ID: W2964036888