Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data
Yuankai Huo, Zhoubing Xu, Katherine S. Aboud, Prasanna Parvathaneni, Shunxing Bao, Camilo L Bermudez, Susan M. Resnick, Laurie E. Cutting, Bennett A. Landman · arXiv (Cornell University) · 2018
Whole brain segmentation on a structural magnetic resonance imaging (MRI) is essential in non-invasive investigation for neuroanatomy. Historically, multi-atlas segmentation (MAS) has been regarded as the de facto standard method for whole brain segmentation. Recently, deep neural network approaches have been applied to whole brain segmentation by learning random patches or 2D slices. Yet, few previous efforts have been made on detailed whole brain segmentation using 3D networks due to the following challenges: (1) fitting entire whole brain volume into 3D networks is restricted by the current GPU memory, and (2) the large number of targeting labels (e.g., > 100 labels) with limited number of training 3D volumes (e.g., 30 hours using MAS to ~15 minutes using the proposed method. The source code is available online https://github.com/MASILab/SLANTbrainSeg