ABSTRACT
Background: Disproportionately enlarged subarachnoid-space hydrocephalus (DESH) is a key feature for Hakim disease (idiopathic normal pressure hydrocephalus: iNPH), but subjectively evaluated. To develop automatic quantitative assessment of DESH with automatic segmentation using combined deep learning models. Methods: This study included 180 participants (42 Hakim patients, 138 healthy volunteers; 78 males, 102 females). Overall, 159 three-dimensional (3D) T1-weighted and 180 T2-weighted MRIs were included. As a semantic segmentation, 3D MRIs were automatically segmented in the total ventricles, total subarachnoid space (SAS), high-convexity SAS, and Sylvian fissure and basal cistern on the 3D U-Net model. As an image classification, DESH, ventricular dilatation (VD), tightened sulci in the high convexities (THC), and Sylvian fissure dilatation (SFD) were automatically assessed on the multimodal convolutional neural network (CNN) model. For both deep learning models, 110 T1- and 130 T2-weighted MRIs were used for training, 30 T1- and 30 T2-weighted MRIs for internal validation, and the remaining 19 T1- and 20 T2-weighted MRIs for external validation. Dice score was calculated as (overlapping area) × 2/total area. Results: Automatic region extraction from 3D T1- and T2-weighted MRI was accurate for the total ventricles (mean Dice scores: 0.85 and 0.83), Sylvian fissure and basal cistern (0.70 and 0.69), and high-convexity SAS (0.68 and 0.60), respectively. Automatic determination of DESH, VD, THC, and SFD from the segmented regions on the multimodal CNN model was sufficiently reliable; all of the mean softmax probability scores were exceeded by 0.95. All of the areas under the receiver-operating characteristic curves of the DESH, Venthi, and Sylhi indexes calculated by the segmented regions for detecting DESH were exceeded by 0.97. Conclusion: Using 3D U-Net and a multimodal CNN, DESH was automatically detected with automatically segmented regions from 3D MRIs. Our developed diagnostic support tool can improve the precision of Hakim disease (iNPH) diagnosis.
ABSTRACT
OBJECTIVE: The presence of tightened sulci in the high-convexities (THC) is a key morphological feature for the diagnosis of idiopathic normal pressure hydrocephalus (iNPH), but the exact localization of THC has yet to be defined. The purpose of this study was to define THC and compare its volume, percentage, and index between iNPH patients and healthy controls. METHODS: According to the THC definition, the high-convexity part of the subarachnoid space was segmented and measured the volume and percentage from the 3D T1-weighted and T2-weighted magnetic resonance images in 43 patients with iNPH and 138 healthy controls. RESULTS: THC was defined as a decrease in the high-convexity part of the subarachnoid space located above the body of the lateral ventricles, with anterior end on the coronal plane perpendicular to the anterior commissure-posterior commissure (AC-PC) line passing through the front edge of the genu of corpus callosum, the posterior end in the bilateral posterior parts of the callosomarginal sulci, and the lateral end at 3 cm from the midline on the coronal plane perpendicular to the AC-PC line passing through the midpoint between AC and PC. Compared to the volume and volume percentage, the high-convexity part of the subarachnoid space volume per ventricular volume ratio < 0.6 was the most detectable index of THC on both 3D T1-weighted and T2-weighted magnetic resonance images. CONCLUSIONS: To improve the diagnostic accuracy of iNPH, the definition of THC was clarified, and high-convexity part of the subarachnoid space volume per ventricular volume ratio <0.6 proposed as the best index for THC detection in this study.
