  Brain extraction from images is a common pre-processing step. Many approaches
exist, but they are frequently only designed to perform brain extraction from
images without strong pathologies. Extracting the brain from images with strong
pathologies, for example, the presence of a tumor or of a traumatic brain
injury, is challenging. In such cases, tissue appearance may deviate from
normal tissue and violates algorithmic assumptions for these approaches; hence,
the brain may not be correctly extracted. This paper proposes a brain
extraction approach which can explicitly account for pathologies by jointly
modeling normal tissue and pathologies. Specifically, our model uses a
three-part image decomposition: (1) normal tissue appearance is captured by
principal component analysis, (2) pathologies are captured via a total
variation term, and (3) non-brain tissue is captured by a sparse term.
Decomposition and image registration steps are alternated to allow statistical
modeling in a fixed atlas space. As a beneficial side effect, the model allows
for the identification of potential pathologies and the reconstruction of a
quasi-normal image in atlas space. We demonstrate the effectiveness of our
method on four datasets: the IBSR and LPBA40 datasets which show normal images,
the BRATS dataset containing images with brain tumors and a dataset containing
clinical TBI images. We compare the performance with other popular models:
ROBEX, BEaST, MASS, BET, BSE and a recently proposed deep learning approach.
Our model performs better than these competing methods on all four datasets.
Specifically, our model achieves the best median (97.11) and mean (96.88) Dice
scores over all datasets. The two best performing competitors, ROBEX and MASS,
achieve scores of 96.23/95.62 and 96.67/94.25 respectively. Hence, our approach
is an effective method for high quality brain extraction on a wide variety of
images.
