Publications by authors named "Pin-Yu Lee"
Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. We hypothesize that deep learning applied to a structural neuroimaging dataset could detect disease-related alteration and improve classification and diagnostic accuracy. We tested this hypothesis using a single, widely available, and conventional T1-weighted MRI scan, from which we extracted the 3D whole-brain structure using standard post-processing methods.
View Article and Find Full Text PDFImproving across-dataset brain tissue segmentation for MRI imaging using transformer.
Front Neuroimaging
November 2022
Article Synopsis
- This study addresses the challenges of manual brain tissue segmentation in MRI data analysis and highlights the limitations of existing automated methods, especially CNNs, in achieving reliable results.
- The authors introduce a new hybrid CNN-Transformer architecture that enhances performance for 3D medical image segmentation, demonstrating its effectiveness on diverse T1-weighted MRI datasets.
- The model's robustness is validated across multiple sites and conditions, showing superior generality and reliability, making it a promising tool for brain research and available for public access via GitHub.
Focused ultrasound (FUS) can be used to open the blood-brain barrier (BBB), and MRI with contrast agents can detect that opening. However, repeated use of gadolinium-based contrast agents (GBCAs) presents safety concerns to patients. This study is the first to propose the idea of modeling a volume transfer constant (Ktrans) through deep learning to reduce the dosage of contrast agents.
View Article and Find Full Text PDF