  Detailed whole brain segmentation is an essential quantitative technique,
which provides a non-invasive way of measuring brain regions from a structural
magnetic resonance imaging (MRI). Recently, deep convolution neural network
(CNN) has been applied to whole brain segmentation. However, restricted by
current GPU memory, 2D based methods, downsampling based 3D CNN methods, and
patch-based high-resolution 3D CNN methods have been the de facto standard
solutions. 3D patch-based high resolution methods typically yield superior
performance among CNN approaches on detailed whole brain segmentation (>100
labels), however, whose performance are still commonly inferior compared with
multi-atlas segmentation methods (MAS) due to the following challenges: (1) a
single network is typically used to learn both spatial and contextual
information for the patches, (2) limited manually traced whole brain volumes
are available (typically less than 50) for training a network. In this work, we
propose the spatially localized atlas network tiles (SLANT) method to
distribute multiple independent 3D fully convolutional networks (FCN) for
high-resolution whole brain segmentation. To address the first challenge,
multiple spatially distributed networks were used in the SLANT method, in which
each network learned contextual information for a fixed spatial location. To
address the second challenge, auxiliary labels on 5111 initially unlabeled
scans were created by multi-atlas segmentation for training. Since the method
integrated multiple traditional medical image processing methods with deep
learning, we developed a containerized pipeline to deploy the end-to-end
solution. From the results, the proposed method achieved superior performance
compared with multi-atlas segmentation methods, while reducing the
computational time from >30 hours to 15 minutes
(https://github.com/MASILab/SLANTbrainSeg).

Computer Vision and Pattern Recognition