Abstract
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.
Highlights
In 1965, Hakim and Adams [1] first proposed the concept of normal pressure hydrocephalus (NPH), that clinical symptoms are gait disorder, urinary incontinence, and dementia; the pressure of the cerebrospinal fluid during London, UKNeural Computing and Applications lumbar puncture is normal; the imaging manifestations are communicating hydrocephalus [2, 3]
We present a method how to automatically calculate the ventricular volume (VV) and intracranial volume (ICV) using the segmentation method described in the previous sections
The Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), and Pearson correlations of the VV generated by our model for automatic segmentation and the VV generated by manual segmentation are 0.95, 0.99, and 0.99, respectively
Summary
In 1965, Hakim and Adams [1] first proposed the concept of normal pressure hydrocephalus (NPH), that clinical symptoms are gait disorder, urinary incontinence, and dementia; the pressure of the cerebrospinal fluid during London, UKNeural Computing and Applications lumbar puncture is normal; the imaging manifestations are communicating hydrocephalus [2, 3]. In 1965, Hakim and Adams [1] first proposed the concept of normal pressure hydrocephalus (NPH), that clinical symptoms are gait disorder, urinary incontinence, and dementia; the pressure of the cerebrospinal fluid during London, UK. As a kind of dementia disease that can be treated in the elderly [2], NPH is of increasing clinical importance [6]. Early diagnosis and surgical treatment may increase the likelihood of a good prognosis for patients with NPH [7]. NPH has a spectrum of disease development, and radiological signs precede clinical symptoms [8]. Enlargement of the ventricle is an imaging feature of NPH [2]. Similar to NPH, enlarged ventricles are related to cognitive and gait disorders [9]. It is very necessary to evaluate the ventricles of patients with NPH
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