  Brain midline delineation can facilitate the clinical evaluation of brain
midline shift, which plays an important role in the diagnosis and prognosis of
various brain pathology. Nevertheless, there are still great challenges with
brain midline delineation, such as the largely deformed midline caused by the
mass effect and the possible morphological failure that the predicted midline
is not a connected curve. To address these challenges, we propose a
context-aware refinement network (CAR-Net) to refine and integrate the feature
pyramid representation generated by the UNet. Consequently, the proposed
CAR-Net explores more discriminative contextual features and a larger receptive
field, which is of great importance to predict largely deformed midline. For
keeping the structural connectivity of the brain midline, we introduce a novel
connectivity regular loss (CRL) to punish the disconnectivity between adjacent
coordinates. Moreover, we address the ignored prerequisite of previous
regression-based methods that the brain CT image must be in the standard pose.
A simple pose rectification network is presented to align the source input
image to the standard pose image. Extensive experimental results on the CQ
dataset and one inhouse dataset show that the proposed method requires fewer
parameters and outperforms three state-of-the-art methods in terms of four
evaluation metrics. Code is available at
https://github.com/ShawnBIT/Brain-Midline-Detection.
