Zhou Xi, Ye Qinghao, Jiang Yinghui, Wang Minhao, Niu Zhangming, Menpes-Smith Wade, Fang Evandro Fei, Liu Zhi, Xia Jun, Yang Guang
Department of Radiology, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China.
Hangzhou Ocean's Smart Boya Co., Ltd., Hangzhou, China.
Front Aging Neurosci. 2020 Dec 16;12:618538. doi: 10.3389/fnagi.2020.618538. eCollection 2020.
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. 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. 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). 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.
脑室容积与脑积水、脑萎缩、阿尔茨海默病、帕金森综合征等疾病密切相关。为准确测量老年患者的脑室容积，我们利用深度学习建立了一个系统、全面的自动脑室分割框架。该研究纳入了20名正常老年人、20名脑萎缩患者、64名正常压力脑积水患者和51名后天性脑积水患者。其次，通过图像存档与通信系统（PACS）系统获取他们的影像数据。然后使用ITK软件手动标记参与者的脑室结构。最后，通过机器学习提取影像特征。这种自动脑室分割方法不仅可以应用于CT和MRI图像，还可以应用于不同扫描层厚的图像。更重要的是，它产生了优异的分割结果（Dice>0.9）。这种自动脑室分割方法具有广泛的适用性和临床实用性。它可以帮助临床医生发现早期疾病、诊断疾病、了解患者的疾病进展以及评估患者的治疗效果。
