Using deep learning convolutional neural networks to automatically perform cerebral aqueduct CSF flow analysis

Abstract

Since the development of phase-contrast magnetic resonance imaging (PC-MRI), quantification of cerebrospinal fluid (CSF) flow across the cerebral aqueduct has been utilized for diagnosis of conditions such as normal pressure hydrocephalus (NPH). This study aims to develop an automated method of aqueduct CSF flow analysis using convolution neural networks (CNNs), which can replace the current standard involving manual segmentation of aqueduct region of interest (ROI). Retrospective analysis was performed on 333 patients who underwent PC-MRI, totaling 353 imaging studies. Aqueduct flow measurements using manual ROI placement was performed independently by two radiologists. Two types of CNNs, MultiResUNet and UNet, were trained using ROI data from the senior radiologist, with PC-MRI studies being randomly divided into training (80%) and validation (20%) datasets. Segmentation performance was assessed using Dice similarity coefficient (DSC), and CSF flow parameters were calculated from both manual and CNN-derived ROIs. MultiResUNet, UNet and second radiologist (Rater 2) had DSCs of 0.933, 0.928, and 0.867, respectively, with p < 0.001 between CNNs and Rater 2. Comparison of CSF flow parameters showed excellent intraclass correlation coefficients (ICCs) for MultiResUNet, with lowest correlation being 0.67. For UNet, lower ICCs of −0.01 to 0.56 were observed. Only 3/353 (0.8%) studies failed to have appropriate ROIs placed by MultiResUNet, compared to 12/353 (3.4%) failed cases for UNet. In conclusion, CNNs were able to measure aqueductal CSF flow with similar performance to a senior neuroradiologist. MultiResUNet demonstrated fewer cases of segmentation failure and more consistent flow measurements compared to the widely adopted UNet.