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Using deep learning convolutional neural networks to automatically perform cerebral aqueduct CSF flow analysis
- Source :
- Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia. 90
- Publication Year :
- 2021
-
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.
- Subjects :
- Adult
Male
Adolescent
Aqueduct
Convolutional neural network
03 medical and health sciences
Young Adult
0302 clinical medicine
Deep Learning
Normal pressure hydrocephalus
Region of interest
Physiology (medical)
medicine
Humans
Segmentation
Child
Aged
Retrospective Studies
Aged, 80 and over
medicine.diagnostic_test
business.industry
Deep learning
Cerebral Aqueduct
Infant, Newborn
Infant
Magnetic resonance imaging
Pattern recognition
General Medicine
Middle Aged
medicine.disease
Magnetic Resonance Imaging
Hydrocephalus, Normal Pressure
Cross-Sectional Studies
Neurology
030220 oncology & carcinogenesis
Cerebral aqueduct
Child, Preschool
Surgery
Female
Neurology (clinical)
Artificial intelligence
Neural Networks, Computer
business
030217 neurology & neurosurgery
Subjects
Details
- ISSN :
- 15322653
- Volume :
- 90
- Database :
- OpenAIRE
- Journal :
- Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
- Accession number :
- edsair.doi.dedup.....76d0f6b98a7f036d429f37581bc8e2c8