  Segmentation of brain magnetic resonance images (MRI) is crucial for the
analysis of the human brain and diagnosis of various brain disorders. The
drawbacks of time-consuming and error-prone manual delineation procedures are
aimed to be alleviated by atlas-based and supervised machine learning methods
where the former methods are computationally intense and the latter methods
lack a sufficiently large number of labeled data. With this motivation, we
propose CORPS, a semi-supervised segmentation framework built upon a novel
atlas-based pseudo-labeling method and a 3D deep convolutional neural network
(DCNN) for 3D brain MRI segmentation. In this work, we propose to generate
expert-level pseudo-labels for unlabeled set of images in an order based on a
local intensity-based similarity score to existing labeled set of images and
using a novel atlas-based label fusion method. Then, we propose to train a 3D
DCNN on the combination of expert and pseudo labeled images for binary
segmentation of each anatomical structure. The binary segmentation approach is
proposed to avoid the poor performance of multi-class segmentation methods on
limited and imbalanced data. This also allows to employ a lightweight and
efficient 3D DCNN in terms of the number of filters and reserve memory
resources for training the binary networks on full-scale and full-resolution 3D
MRI volumes instead of 2D/3D patches or 2D slices. Thus, the proposed framework
can encapsulate the spatial contiguity in each dimension and enhance
context-awareness. The experimental results demonstrate the superiority of the
proposed framework over the baseline method both qualitatively and
quantitatively without additional labeling cost for manual labeling.
