Zhou Xi, Ye Qinghao, Yang Xiaolin, Chen Jiakun, Ma Haiqin, Xia Jun, Del Ser Javier, Yang Guang
Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People's Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China.
Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA USA.
Neural Comput Appl. 2022 Feb 24;35(22):1-10. doi: 10.1007/s00521-022-07048-0.
Based on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multimodal and high-performance automatic ventricle segmentation method to achieve an efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 143 definite NPH patients. Second, we manually label the ventricular volume (VV) and intracranial volume (ICV). Then, we use the machine learning method to extract features and establish automatic ventricle segmentation model. Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV. In CT images, the Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), Pearson correlation, and Bland-Altman analysis of the automatic and manual segmentation result of the VV were 0.95, 0.99, 0.99, and 4.2 ± 2.6, respectively. The results of ICV were 0.96, 0.99, 0.99, and 6.0 ± 3.8, respectively. The whole process takes 3.4 ± 0.3 s. In MRI images, the DSC, ICC, Pearson correlation, and Bland-Altman analysis of the automatic and manual segmentation result of the VV were 0.94, 0.99, 0.99, and 2.0 ± 0.6, respectively. The results of ICV were 0.93, 0.99, 0.99, and 7.9 ± 3.8, respectively. The whole process took 1.9 ± 0.1 s. We have established a multimodal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume of NPH patients. This can help clinicians quickly and accurately understand the situation of NPH patient's ventricles.
基于从正常压力脑积水（NPH）患者获取的CT和MRI图像，利用机器学习方法，我们旨在建立一种多模态、高性能的自动脑室分割方法，以实现对脑室体积的高效、准确自动测量。首先，我们提取了143例确诊NPH患者的脑部CT和MRI图像。其次，我们手动标记脑室体积（VV）和颅内体积（ICV）。然后，我们使用机器学习方法提取特征并建立自动脑室分割模型。最后，我们验证了模型的可靠性，并实现了对VV和ICV的自动测量。在CT图像中，VV自动分割与手动分割结果的骰子相似系数（DSC）、组内相关系数（ICC）、皮尔逊相关性和布兰德-奥特曼分析分别为0.95、0.99、0.99和4.2±2.6。ICV的结果分别为0.96、0.99、0.99和6.0±3.8。整个过程耗时3.4±0.3秒。在MRI图像中，VV自动分割与手动分割结果的DSC、ICC、皮尔逊相关性和布兰德-奥特曼分析分别为0.94、0.99、0.99和2.0±0.6。ICV的结果分别为0.93、0.99、0.99和7.9±3.8。整个过程耗时1.9±0.1秒。我们建立了一种多模态、高性能的自动脑室分割方法，以实现对NPH患者脑室体积的高效、准确自动测量。这有助于临床医生快速、准确地了解NPH患者脑室的情况。
