Gong Xiaoliang, Ma Chao, Yang Panpan, Chen Yufei, Du Chaolin, Fu Caixia, Lu Jian-Ping
College of Electronic and Information Engineering, Tongji University, Shanghai, PR China.
Department of Radiology, Changhai Hospital of Shanghai, The Second Medical University, Shanghai, PR China.
Acta Radiol Open. 2019 Mar 27;8(3):2058460119834690. doi: 10.1177/2058460119834690. eCollection 2019 Mar.
Pancreas segmentation is of great significance for pancreatic cancer radiotherapy positioning, pancreatic structure, and function evaluation.
To investigate the feasibility of computer-aided pancreas segmentation based on optimized three-dimensional (3D) Dixon magnetic resonance imaging (MRI).
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.
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 values < 0.001), including water images of routine Dixon MRI and both of the opposed-phase images of routine and optimized Dixon sequences.
Computer-aided pancreas segmentation based on Dixon MRI is feasible. The water images of optimized Dixon obtained the best similarity with a good stability.
胰腺分割对于胰腺癌放射治疗定位、胰腺结构及功能评估具有重要意义。
探讨基于优化三维（3D）狄克逊磁共振成像（MRI）的计算机辅助胰腺分割的可行性。
17名健康志愿者（13名男性，4名女性；平均年龄=53.4±13.2岁；年龄范围=28 - 76岁）在3.0 T条件下接受常规及优化的3D梯度回波（GRE）狄克逊MRI检查。采用医学影像交互工具包（MITK）及传统分割算法流程（阈值法和形态学法），在狄克逊反相位图像和水图像上进行胰腺的计算机辅助分割。通过骰子系数及二维（2D）/三维（3D）可视化图评估所提出的计算机分割方法的性能，并对常规及优化狄克逊序列的反相位图像和水图像进行比较。
常规狄克逊MRI反相位图像和水图像的计算机辅助胰腺分割骰子系数分别为0.633±0.080和0.716±0.033，而优化狄克逊MRI的相应系数分别为0.415±0.143和0.779±0.048。基于优化狄克逊水图像的骰子指数显著高于其他三组（所有P值<0.001），包括常规狄克逊MRI水图像以及常规和优化狄克逊序列的反相位图像。
基于狄克逊MRI的计算机辅助胰腺分割是可行的。优化狄克逊的水图像具有最佳相似性且稳定性良好。
