Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly

Numerous brain disorders are associated with ventriculomegaly, including both neuro-degenerative diseases and cerebrospinal fluid disorders. Detailed evaluation of the ventricular system is important for these conditions to help understand the pathogenesis of ventricular enlargement and elucidate no...

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Bibliographic Details
Published in:NeuroImage clinical 2019-01, Vol.23, p.101871-101871, Article 101871
Main Authors: Shao, Muhan, Han, Shuo, Carass, Aaron, Li, Xiang, Blitz, Ari M, Shin, Jaehoon, Prince, Jerry L, Ellingsen, Lotta M
Format: Article
Language:English
Subjects:
Adult
Aged
Aged, 80 and over
Cerebral Ventricles - diagnostic imaging
Cerebral Ventricles - pathology
Deep Learning
Female
Humans
Hydrocephalus, Normal Pressure - diagnostic imaging
Hydrocephalus, Normal Pressure - pathology
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Male
Middle Aged
Neuroimaging - methods
Regular
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Summary:Numerous brain disorders are associated with ventriculomegaly, including both neuro-degenerative diseases and cerebrospinal fluid disorders. Detailed evaluation of the ventricular system is important for these conditions to help understand the pathogenesis of ventricular enlargement and elucidate novel patterns of ventriculomegaly that can be associated with different diseases. One such disease is normal pressure hydrocephalus (NPH), a chronic form of hydrocephalus in older adults that causes dementia. Automatic parcellation of the ventricular system into its sub-compartments in patients with ventriculomegaly is quite challenging due to the large variation of the ventricle shape and size. Conventional brain labeling methods are time-consuming and often fail to identify the boundaries of the enlarged ventricles. We propose a modified 3D U-Net method to perform accurate ventricular parcellation, even with grossly enlarged ventricles, from magnetic resonance images (MRIs). We validated our method on a data set of healthy controls as well as a cohort of 95 patients with NPH with mild to severe ventriculomegaly and compared with several state-of-the-art segmentation methods. On the healthy data set, the proposed network achieved mean Dice similarity coefficient (DSC) of 0.895 ± 0.03 for the ventricular system. On the NPH data set, we achieved mean DSC of 0.973 ± 0.02, which is significantly (p 
ISSN:2213-1582
2213-1582
DOI:10.1016/j.nicl.2019.101871