Year: 2022
Type: article
Abstract: Objective and Impact Statement . We propose an automated method of predicting Normal Pressure Hydrocephalus (NPH) from CT scans. A deep convolutional network segments regions of interest from the scans. These regions are then combined with MRI information to predict NPH. To our knowledge, this is the first method which automatically predicts NPH from CT scans and incorporates diffusion tractography information for prediction. Introduction . Due to their low cost and high versatility, CT scans are often used in NPH diagnosis. No well-defined and effective protocol currently exists for analysis of CT scans for NPH. Evans’ index, an approximation of the ventricle to brain volume using one 2D image slice, has been proposed but is not robust. The proposed approach is an effective way to quantify regions of interest and offers a computational method for predicting NPH. Methods . We propose a novel method to predict NPH by combining regions of interest segmented from CT scans with connectome data to compute features which capture the impact of enlarged ventricles by excluding fiber tracts passing through these regions. The segmentation and network features are used to train a model for NPH prediction. Results . Our method outperforms the current state-of-the-art by 9 precision points and 29 recall points. Our segmentation model outperforms the current state-of-the-art in segmenting the ventricle, gray-white matter, and subarachnoid space in CT scans. Conclusion . Our experimental results demonstrate that fast and accurate volumetric segmentation of CT brain scans can help improve the NPH diagnosis process, and network properties can increase NPH prediction accuracy.
Source: BME Frontiers
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funders: National Science Foundation, U.S. Department of Defense, Foundation for the National Institutes of Health, Alzheimer's Association, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, Biogen, University of Southern California, F. Hoffmann-La Roche, Meso Scale Diagnostics, Alzheimer's Disease Neuroimaging Initiative, BioClinica, Novartis Pharmaceuticals Corporation, Northern California Institute for Research and Education, Eisai, Servier, H. Lundbeck A/S, IXICO, National Institutes of Health, Genentech, GE Healthcare, Canadian Institutes of Health Research, National Institute on Aging, National Institute of General Medical Sciences, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering, Janssen Alzheimer Immunotherapy Research And Development
Awards: G1012222616, G1120803110, G1298807586, The connectome and neurobiology of a novel model chordate, Ciona intestinalis, G1554142629, G1593117279, G1650819308, G1880935189, Alzheimer's Disease Neuroimaging Initiative, G2100670906, G2152643862, G2189010064, G2340207221, G2423387114, G2471646051, G2522063444, G2835601961, G29951154, G3131374986, G3216574877, G3277900835, G3553156159, G3678370191, G3710456697, G3752451908, G3870774570, G3908963977, G3966393351, G4119534169, G43180419, G4475667458, G4501223628, G4800177743, G5207404921, G5209990037, G528601746, G5324093878, G5365730499, EFFECTS OF TRAUMATIC BRAIN INJURY (TBI) AND POST TRAUMATIC STRESS DISORDER (PTSD) ON ALZHEIMER'S DISEASE (AD) IN VETERANS USING IMAGING AND BIOMARKERS IN THE AD NEUROIMAGING INITIATIVE (ADNI)", G5654639750, G5740349702, G5850682332, G613619302, G6173816828, G6210868510, G6371410416, G6412110892, G6447063303, G6694908309, G6719828067, G6792506723, G6899163935, Medical Scientist Training Program, G6948409098, G72415811, G7268282379, G7298697282, G764699806, G7799614282, G781725486, SI2-SSI: LIMPID: Large-Scale IMage Processing Infrastructure Development, G8228018387, G8281378126, G8301434335, G839208290, G8852128998, G8895907669, G8897063270, G8957659042, G9000039018, G9000122335
OpenAlex ID: W4205249103