Nov 26, 2014 · Automatic Segmentation and Quantitative Analysis of Gray Matter on MR Images of Patients with Epilepsy Based on Unsupervised Learning Methods.
Automatic Segmentation and Quantitative Analysis of Gray Matter on MR Images of Patients with Epilepsy Based on Unsupervised Learning Methods · Rui Wang, Jie ...
Machine Learning Quantitative Analysis of FDG PET Images ... - NCBI
www.ncbi.nlm.nih.gov › PMC8884180
18F-FDG PET is widely used in epilepsy surgery. We established a robust quantitative algorithm for the lateralization of epileptogenic foci and examined the ...
Missing: Unsupervised | Show results with:Unsupervised
A Quantitative Imaging Biomarker Supporting Radiological ... - NCBI
www.ncbi.nlm.nih.gov › PMC8894898
Feb 18, 2022 · In this study, we selected two of the most frequently used freely available morphometry tools (11) to segment deep gray matter structures, ...
Missing: Unsupervised | Show results with:Unsupervised
摘要. The quantitative analysis of volume information about gray matter (GM) on magnetic resonance (MR) images is important.
To address these challenges, we must find innovative ways to automate medical processing and produce lower-cost medical imaging devices. Recent advances in deep ...
Jan 31, 2024 · Keywords: magnetic resonance imaging, temporal lobe epilepsy, gray matter volume, cortical thickness, cortical surface area, machine learning.
Missing: Unsupervised | Show results with:Unsupervised
Best accuracies were achieved using the gray matter based segmentation (90–100%) and mean diffusivity (95–97%). For the three-way classification, accuracies ...
Nov 2, 2021 · applied a registration method based on dictionary learning to segment the post-surgical brain surface from the fusion of CT and MRI data.
Mar 13, 2024 · The detailed MRI signs include cortex thickening, gray-white matter ... analysis program in the MRI-positive patients. ... people with epilepsy and ...