  The midline related pathological image features are crucial for evaluating
the severity of brain compression caused by stroke or traumatic brain injury
(TBI). The automated midline delineation not only improves the assessment and
clinical decision making for patients with stroke symptoms or head trauma but
also reduces the time of diagnosis. Nevertheless, most of the previous methods
model the midline by localizing the anatomical points, which are hard to detect
or even missing in severe cases. In this paper, we formulate the brain midline
delineation as a segmentation task and propose a three-stage framework. The
proposed framework firstly aligns an input CT image into the standard space.
Then, the aligned image is processed by a midline detection network (MD-Net)
integrated with the CoordConv Layer and Cascade AtrousCconv Module to obtain
the probability map. Finally, we formulate the optimal midline selection as a
pathfinding problem to solve the problem of the discontinuity of midline
delineation. Experimental results show that our proposed framework can achieve
superior performance on one in-house dataset and one public dataset.
