Deep Cascade of Convolutional Neural Networks for Quantification of Enlarged Perivascular Spaces in the Basal Ganglia in Magnetic Resonance Imaging

• Published in: Diagnostics (Basel) Vol. 14; no. 14; p. 1504
• Main Authors: Chae, Seunghye, Yang, Ehwa, Moon, Won-Jin, Kim, Jae-Hun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.07.2024
• Subjects: Algorithms; Automation; Biomarkers; Cerebrospinal fluid; Cognitive ability; Datasets; Deep learning; Dementia; Efficiency; enlarged perivascular spaces; image enhancement; Magnetic resonance imaging; Medical imaging equipment; Medical research; Nervous system diseases; Neural networks; Neurological disorders; quantification; South Korea; deep learning; image enhancement; quantification; enlarged perivascular spaces
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics14141504

In this paper, we present a cascaded deep convolution neural network (CNN) for assessing enlarged perivascular space (ePVS) within the basal ganglia region using T2-weighted MRI. Enlarged perivascular spaces (ePVSs) are potential biomarkers for various neurodegenerative disorders, including dementia and Parkinson’s disease. Accurate assessment of ePVS is crucial for early diagnosis and monitoring disease progression. Our approach first utilizes an ePVS enhancement CNN to improve ePVS visibility and then employs a quantification CNN to predict the number of ePVSs. The ePVS enhancement CNN selectively enhances the ePVS areas without the need for additional heuristic parameters, achieving a higher contrast-to-noise ratio (CNR) of 113.77 compared to Tophat, Clahe, and Laplacian-based enhancement algorithms. The subsequent ePVS quantification CNN was trained and validated using fourfold cross-validation on a dataset of 76 participants. The quantification CNN attained 88% accuracy at the image level and 94% accuracy at the subject level. These results demonstrate significant improvements over traditional algorithm-based methods, highlighting the robustness and reliability of our deep learning approach. The proposed cascaded deep CNN model not only enhances the visibility of ePVS but also provides accurate quantification, making it a promising tool for evaluating neurodegenerative disorders. This method offers a novel and significant advancement in the non-invasive assessment of ePVS, potentially aiding in early diagnosis and targeted treatment strategies.