Automated Segmentation and Connectivity Analysis for NPH

Angela Zhang, Amil Khan, Saisidharth Majeti, Judy Pham, Christopher Nguyen, Peter Tran, Vikram Iyer, Ashutosh Shelat, Jefferson Chen, BS Manjunath

Abstract

We propose an automated method of predicting Normal Pressure Hydrocephalus (NPH) from CT scans. A deep convolutional network segments regions of interest from the scans. These regions are then combined with MRI information to predict NPH. To our knowledge, this is the first method which automatically predicts NPH from CT scans and incorporates diffusion tractography information for prediction.

Introduction

Due to their low cost and high versatility, CT scans are often used in NPH diagnosis. No well-defined and effective protocol currently exists for analysis of CT scans for NPH. Evans’ index, an approximation of the ventricle to brain volume using one 2D image slice, has been proposed but is not robust. The proposed approach is an effective way to quantify regions of interest and offers a computational method for predicting NPH.

Modified 3D UNet. The 3D CT scan is fed into a UNet, with the standard downsampling and upsampling layers. The layer with aggregated probability maps improves the performance of the network for the ventricle and especially for the subarachnoid class.

Methods

We propose a novel method to predict NPH by combining regions of interest segmented from CT scans with connectome data to compute features which capture the impact of enlarged ventricles by excluding fiber tracts passing through these regions. The segmentation and network features are used to train a model for NPH prediction.

Results.

Our method outperforms the current state-of-the-art by 9 precision points and 29 recall points. Our segmentation model outperforms the current state-of-the-art in segmenting the ventricle, gray-white matter, and subarachnoid space in CT scans.

Conclusion

Our experimental results demonstrate that fast and accurate volumetric segmentation of CT brain scans can help improve the NPH diagnosis process, and network properties can increase NPH prediction accuracy.