Automatic Quantitative Identification of Disproportionately Enlarged Subarachnoid-Space Hydrocephalus in iNPH Using Deep Learning Models

Motivation: Automated detection for disproportionately enlarged subarachnoid-space hydrocephalus (DESH) using 3D MRIs. Goal(s): We developed robust deep learning models for accurate DESH detection by automatically segmenting regions. Approach: Utilized 3D U-Net for segmentation and multimodal convolutional neural network for classification. Achieved high accuracy, with mean Dice scores ranging 0.60 – 0.84 and softmax probability scores exceeding 0.95. All of the area under the curves exceeded 0.97. Results: Successfully developed the highly accurate deep learning models in automatically segmentation of ventricles and regional subarachnoid spaces and in the detecting DESH, ventricular dilatation, tightened sulci in the high convexities, and Sylvian fissure dilatation. Impact: Combining a 3D U-Net model and a multi-modal convolutional neural network model, disproportionately enlarged subarachnoid-space hydrocephalus (DESH) for idiopathic normal pressure hydrocephalus (iNPH) was automatically detected with automatically segmented regions from 3D T1- and T2-weighted MRIs.