Direct Rating Estimation of Enlarged Perivascular Spaces (EPVS) in Brain MRI Using Deep Neural Network
Source abstract
Abstract
In this article, we propose a deep-learning-based estimation model for rating enlarged perivascular spaces (EPVS) in the brain’s basal ganglia region using T2-weighted magnetic resonance imaging (MRI) images. The proposed method estimates the EPVS rating directly from the T2-weighted MRI without using either the detection or the segmentation of EVPS. The model uses the cropped basal ganglia region on the T2-weighted MRI. We formulated the rating of EPVS as a multi-class classification problem. Model performance was evaluated using 96 subjects’ T2-weighted MRI data that were collected from two hospitals. The results show that the proposed method can automatically rate EPVS—demonstrating great potential to be used as a risk indicator of dementia to aid early diagnosis.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Cerebrospinal fluid and hydrocephalus · Fetal and Pediatric Neurological Disorders · Intracerebral and Subarachnoid Hemorrhage Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Medicine, Sungkyunkwan University, Seoul 06351, Korea
Department of Radiology, Konkuk University Medical Center, School of Medicine, Konkuk University, Seoul 05030, Korea
Department of Neurology, Konkuk University Medical Center, School of Medicine, Konkuk University, Seoul 05030, Korea
Department of Neurology, College of Medicine, Hanyang University, Seoul 04763, Korea
Department of Radiology, Gangnam Severance Hospital, College of Medicine, Yonsei University, Seoul 06273, Korea
Department of Radiology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Irwon-dong #50, Gangnam-Gu, Seoul 138-700, Korea
Funding
The Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI)
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