title: Feasibility of automated pancreas segmentation based on dynamic MRI.
abstract: OBJECTIVE
  MRI-guided radiotherapy is particularly attractive for abdominal targets with low CT contrast. To fully utilize this modality for pancreas tracking, automated segmentation tools are needed. A hybrid gradient, region growth and shape constraint (hGReS) method to segment two-dimensional (2D) upper abdominal dynamic MRI (dMRI) is developed for this purpose.
  
  
  METHODS
  2D coronal dynamic MR images of two healthy volunteers were acquired with a frame rate of 5 frames per second. The regions of interest (ROIs) included the liver, pancreas and stomach. The first frame was used as the source where the centres of the ROIs were manually annotated. These centre locations were propagated to the next dMRI frame. Four-neighborhood region transfer growth was performed from these initial seeds before refinement using shape constraints. RESULTS from hGReS and two other automated segmentation methods using integrated edge detection and region growth (IER) and level set, respectively, were compared with manual contours using Dice's index (DI).
  
  
  RESULTS
  For the first patient, the hGReS resulted in the organ segmentation accuracy as a measure by the DI (0.77) for the pancreas, superior to the level set method (0.72) and IER (0.71). The hGReS was shown to be reproducible on the second subject, achieving a DI of 0.82, 0.92 and 0.93 for the pancreas, stomach and liver, respectively. Motion trajectories derived from the hGReS were highly correlated to respiratory motion.
  
  
  CONCLUSIONS
  We have shown the feasibility of automated segmentation of the pancreas anatomy on dMRI.
  
  
  CONCLUSIONS
  Using the hybrid method improves segmentation robustness of low-contrast images.
authors.affiliation: 1 Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, the School of Electronic Engineering, Xidian University, Xi'an, China.
authors.forename: S
authors.initials: S
authors.lastname: Gou
authors.forename: J
authors.initials: J
authors.lastname: Wu
authors.forename: F
authors.initials: F
authors.lastname: Liu
authors.forename: P
authors.initials: P
authors.lastname: Lee
authors.forename: S
authors.initials: S
authors.lastname: Rapacchi
authors.forename: P
authors.initials: P
authors.lastname: Hu
authors.forename: K
authors.initials: K
authors.lastname: Sheng
mesh_terms: D001331:Automation / Q000379:methods*; D005240:Feasibility Studies; D064368:Healthy Volunteers; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009038:Motion; D010179:Pancreas / Q000033:anatomy & histology*