# AI-based Medical e-Diagnosis for Fast and Automatic Ventricular Volume Measurement in the Patients with Normal Pressure Hydrocephalus

arXiv: https://arxiv.org/abs/2202.00650 · HTML: https://mlsanity.com/paper/2202.00650/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 39 references that are in the map.
- First posted on arXiv 31 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization](https://mlsanity.com/paper/2104.05833/) (2021)
- [Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond](https://mlsanity.com/paper/2102.01998/) (2021)
- [Learning Based Segmentation of CT Brain Images: Application to Post-Operative Hydrocephalic Scans](https://mlsanity.com/paper/1712.03993/) (2017)
- [Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation](https://mlsanity.com/paper/1907.00695/) (2019)

## Abstract (arXiv)

Based on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multi-modal and high-performance automatic ventricle segmentation method to achieve 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 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 seconds. 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 seconds. We have established a multi-modal 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.