Computer-aided pancreas segmentation based on 3D GRE Dixon MRI: a feasibility study. (March 2019)

• Record Type: Journal Article
• Authors: Gong, Xiaoliang; Ma, Chao; Yang, Panpan; Chen, Yufei; Du, Chaolin; Fu, Caixia; Lu, Jian-Ping
• Is Part Of: Acta radiologica open. Volume 8:Number 3(2019)
• Publication Date: 2019-03
• Subjects: Magnetic resonance imaging -- pancreas -- segmentation -- Dixon; Radiology -- Periodicals; Diagnostic Imaging -- Periodicals; Radiology; Periodicals; 616.075705
• DOI: 10.1177/2058460119834690
• Languages: English
• ISSNs: 2058-4601

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 = 53.4 ± 13.2 years; age range = 28–76 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 ± 0.080 and 0.716 ± 0.033 for opposed-phase and water images of routine Dixon MRI, respectively, while they were 0.415 ± 0.143 and 0.779 ± 0.048 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 < 0.001), 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.