作者
Georgios A Maragkos, Aristotelis S Filippidis, Sasank Chilamkurthy, Mohamed M Salem, Swetha Tanamala, Santiago Gomez-Paz, Pooja Rao, Justin M Moore, Efstathios Papavassiliou, David Hackney, Ajith J Thomas
发表日期
2021/4/1
期刊
World Neurosurgery
卷号
148
页码范围
e363-e373
出版商
Elsevier
简介
Background
No large dataset–derived standard has been established for normal or pathologic human cerebral ventricular and cranial vault volumes. Automated volumetric measurements could be used to assist in diagnosis and follow-up of hydrocephalus or craniofacial syndromes. In this work, we use deep learning algorithms to measure ventricular and cranial vault volumes in a large dataset of head computed tomography (CT) scans.
Methods
A cross-sectional dataset comprising 13,851 CT scans was used to deploy U-Net deep learning networks to segment and quantify lateral cerebral ventricular and cranial vault volumes in relation to age and sex. The models were validated against manual segmentations. Corresponding radiologic reports were annotated using a rule-based natural language processing framework to identify normal scans, cerebral atrophy, or hydrocephalus.
Results
U-Net models had high …
学术搜索中的文章
GA Maragkos, AS Filippidis, S Chilamkurthy… - World Neurosurgery, 2021
G Maragkos, AS Filippidis, S Chilamkurthy, MM Salem… - Available at SSRN 3501042, 2019