Abstract
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.
Highlights
The volume of the ventricle has always been closely related to degenerative brain diseases and traumatic brain injury
There is no obvious deformation of their ventricle structure, and it is easier for the automatic ventricle segmentation of the normal elderly and the elderly with brain atrophy
We investigated the performance of U-Net (Ronneberger et al, 2015) and U-Net++ (Zhou et al, 2018) on thin-slice and thick-slice images
Summary
The volume of the ventricle has always been closely related to degenerative brain diseases and traumatic brain injury. Automated Ventricle Segmentation for Elderly disease diagnosis guidelines, EI > 0.3 is often defined as ventricular enlargement (Relkin et al, 2005; Mori et al, 2012). Taking EI = 0.3 as the cut-off value, it is difficult to effectively distinguish between normal and enlarged ventricles (Brix et al, 2017). 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
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