Objective and Impact Statement. 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&#x2019; 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. 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.

Angela Zhang

Amil Khan

Saisidharth Majeti

Judy Pham

Christopher Nguyen

Peter Tran

Vikram Iyer

Ashutosh Shelat

Jefferson Chen

B. S. Manjunath

Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus

About this journal

The open access journal BME Frontiers (BMEF), published in association with SIBET CAS, is a platform for the multidisciplinary community of biomedical engineering, publishing wide-ranging research in the field.

Journal profile

Meet the editorial board

BMEF's editorial board is led by Xingde Li&nbsp;(Johns Hopkins University), Yuguo Tang&nbsp;(Suzhou Institute of Biomedical Engineering and Technology), and&nbsp;Guoqi Zhang&nbsp;(Delft University of Technology) and is comprised of leading experts in the field of biomedical engineering.

Editorial board

More special issue details

• New name, same mission: As we seek to distinguish ourselves to the biomedical engineering community, we are taking the opportunity to refresh our logo and simplify our name. While our mission remains the same, we look forward to serving you as BMEF.

• Special Issues: <a data-entity-substitution="canonical" data-entity-type="node" data-entity-uuid="0570e52c-b405-43e0-9427-1ad7ff0a60bf" href="/node/24445">DNA Nanotechnology</a>

Announcements

Author guidelines

Databases and indexing

title

The open access journal BME Frontiers (BMEF), published in association with SIBET CAS, is a platform for the multidisciplinary community of biomedical engineering, publishing wide-ranging research in the field.

Table of Contents

Table of Contents: 2022

Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus