A novel MRI segmentation method using CNN‐based correction network for MRI‐guided adaptive radiotherapy
Medical Physics2018Vol. 45(11), pp. 5129–5137
Citations Over TimeTop 1% of 2018 papers
Yabo Fu, Thomas R. Mazur, Xuefeng Wu, Shi Liu, Chang Xiao, Yonggang Lu, H. Harold Li, Hyun Kim, Michael Roach, Lauren E. Henke, Deshan Yang
Abstract
The proposed method can automatically segment the liver, kidneys, stomach, bowel, and duodenum in 3D MR images with good accuracy. It is useful to expedite the manual contouring for MR-IGART.
Related Papers
- → Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy(2019)192 cited
- → Automatic detection of contouring errors using convolutional neural networks(2019)100 cited
- → Fully Automatic Adaptive Meshing Based Segmentation of the Ventricular System for Augmented Reality Visualization and Navigation(2021)17 cited
- → TU‐CD‐BRA‐02: Comparing Mutual Information and Gradient Magnitude Metrics for Deformable Image Registration(2015)
- → Cone Beam Computed Tomography Contouring Variability on Derivative Contours Generated With an Automated Deformable Registration Workflow for Prostate Cancer(2013)