Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus  

作　　者：Angela Zhang Amil Khan Saisidharth Majeti Judy Pham Christopher Nguyen Peter Tran Vikram Iyer Ashutosh Shelat Jefferson Chen B.S.Manjunath 

机构地区：[1]Vision Research Laboratory,Electrical and Computer Engineering,University of California,Santa Barbara,Santa Barbara,CA,USA [2]Chen Lab,Department of Neurosurgery,University of California,Irvine Medical Center,Orange,CA,USA [3]Santa Barbara Cottage Hospital,Santa Barbara,CA,USA

出　　处：《Biomedical Engineering Frontiers》2022年第1期1-13,共13页生物医学工程前沿（英文）

基　　金：funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904);DOD ADNI (Department of Defense award number W81XWH-12-2-0012);supported by the following awards:National Institutes of Health (grant numbers T32-GM08620 and 5R01NS103774);National Science Foundation (grant number 1664172).

摘　　要：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.

关 键 词：DIAGNOSIS NETWORK CONNECTIVITY 

分 类 号：R318[医药卫生—生物医学工程] TP183[医药卫生—基础医学]