In this section
@article{Zhou:2020:10.3389/fnagi.2020.618538,
author = {Zhou, X and Ye, Q and Jiang, Y and Wang, M and Niu, Z and Menpes-Smith, W and Fang, EF and Liu, Z and Xia, J and Yang, G},
doi = {10.3389/fnagi.2020.618538},
journal = {Frontiers in Aging Neuroscience},
title = {Systematic and comprehensive automated ventricle segmentation on ventricle images of the elderly patients: A retrospective study},
url = {http://dx.doi.org/10.3389/fnagi.2020.618538},
volume = {12},
year = {2020}
}
TY - JOUR
AB - Background and Objective: Ventricle volume is closely related to hydrocephalus, brain atrophy, Alzheimer's, Parkinson's syndrome, and other diseases. To accurately measure the volume of the ventricles for elderly patients, we use deep learning to establish a systematic and comprehensive automated ventricle segmentation framework.Methods: The study participation included 20 normal elderly people, 20 patients with cerebral atrophy, 64 patients with normal pressure hydrocephalus, and 51 patients with acquired hydrocephalus. Second, get their imaging data through the picture archiving and communication systems (PACS) system. Then use ITK software to manually label participants' ventricular structures. Finally, extract imaging features through machine learning.Results: This automated ventricle segmentation method can be applied not only to CT and MRI images but also to images with different scan slice thicknesses. More importantly, it produces excellent segmentation results (Dice > 0.9).Conclusion: This automated ventricle segmentation method has wide applicability and clinical practicability. It can help clinicians find early disease, diagnose disease, understand the patient's disease progression, and evaluate the patient's treatment effect.
AU - Zhou,X
AU - Ye,Q
AU - Jiang,Y
AU - Wang,M
AU - Niu,Z
AU - Menpes-Smith,W
AU - Fang,EF
AU - Liu,Z
AU - Xia,J
AU - Yang,G
DO - 10.3389/fnagi.2020.618538
PY - 2020///
SN - 1663-4365
TI - Systematic and comprehensive automated ventricle segmentation on ventricle images of the elderly patients: A retrospective study
T2 - Frontiers in Aging Neuroscience
UR - http://dx.doi.org/10.3389/fnagi.2020.618538
UR - http://hdl.handle.net/10044/1/84927
VL - 12
ER -
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