Computer-aided pancreas segmentation based on 3D GRE Dixon MRI: a feasibility study

Abstract

Background Pancreas segmentation is of great significance for pancreatic cancer radiotherapy positioning, pancreatic structure, and function evaluation. Purpose To investigate the feasibility of computer-aided pancreas segmentation based on optimized three-dimensional (3D) Dixon magnetic resonance imaging (MRI). Material and Methods Seventeen healthy volunteers (13 men, 4 women; mean age\u2009=\u200953.4\u2009±\u200913.2 years; age range\u2009=\u200928–76 years) underwent routine and optimized 3D gradient echo (GRE) Dixon MRI at 3.0 T. The computer-aided segmentation of the pancreas was executed by the Medical Imaging Interaction ToolKit (MITK) with the traditional segmentation algorithm pipeline (a threshold method and a morphological method) on the opposed-phase and water images of Dixon. The performances of our proposed computer segmentation method were evaluated by Dice coefficients and two-dimensional (2D)/3D visualization figures, which were compared for the opposed-phase and water images of routine and optimized Dixon sequences. Results The dice coefficients of the computer-aided pancreas segmentation were 0.633\u2009±\u20090.080 and 0.716\u2009±\u20090.033 for opposed-phase and water images of routine Dixon MRI, respectively, while they were 0.415\u2009±\u20090.143 and 0.779\u2009±\u20090.048 for the optimized Dixon MRI, respectively. The Dice index was significantly higher based on the water images of optimized Dixon than those in the other three groups (all P values\u2009<\u20090.001), including water images of routine Dixon MRI and both of the opposed-phase images of routine and optimized Dixon sequences. Conclusion Computer-aided pancreas segmentation based on Dixon MRI is feasible. The water images of optimized Dixon obtained the best similarity with a good stability.