  Normal pressure hydrocephalus~(NPH) is a brain disorder associated with
enlarged ventricles and multiple cognitive and motor symptoms. The degree of
ventricular enlargement can be measured using magnetic resonance images~(MRIs)
and characterized quantitatively using the Evan's ratio (ER). Automatic
computation of ER is desired to avoid the extra time and variations associated
with manual measurements on MRI. Because shunt surgery is often used to treat
NPH, it is necessary that this process be robust to image artifacts caused by
the shunt and related implants. In this paper, we propose a 3D
regions-of-interest aware (ROI-aware) network for segmenting the ventricles.
The method achieves state-of-the-art performance on both pre-surgery MRIs and
post-surgery MRIs with artifacts. Based on our segmentation results, we also
describe an automated approach to compute ER from these results. Experimental
results on multiple datasets demonstrate the potential of the proposed method
to assist clinicians in the diagnosis and management of NPH.
