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.
Keyphrases
- deep learning
- minimally invasive
- coronary artery bypass
- magnetic resonance
- pulmonary artery
- convolutional neural network
- surgical site infection
- endoplasmic reticulum
- estrogen receptor
- magnetic resonance imaging
- breast cancer cells
- contrast enhanced
- pulmonary hypertension
- white matter
- palliative care
- left ventricular
- optical coherence tomography
- functional connectivity
- resting state
- pulmonary arterial hypertension
- image quality
- rna seq
- physical activity
- congenital heart disease
- cone beam