Computer-aided pancreas segmentation based on 3D GRE Dixon MRI: a feasibility study
Computer-aided pancreas segmentation based on 3D GRE Dixon MRI: a feasibility study
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基于 3D GRE Dixon MRI 的计算机辅助胰腺分割：可行性研究。
DOI：
10.1177/2058460119834690
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发表时间：
2019-03-27
影响因子：
1.1
通讯作者：
Lu, Jian-Ping
中科院分区：
文献类型：
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作者：
Gong, Xiaoliang;Ma, Chao;Lu, Jian-Ping
背景胰腺分割对于胰腺癌放疗定位、胰腺结构和功能评价具有重要意义。目的探讨基于优化三维狄克逊磁共振成像（MRI）图像的计算机辅助胰腺分割的可行性。材料与方法17例健康志愿者（男13例，女4例;平均年龄53.4 ± 13.2岁;年龄范围28-76岁）在3.0 T下接受常规和优化的3D梯度回波（GRE）狄克逊MRI。胰腺的计算机辅助分割是通过医学成像交互工具包（MITK）使用传统的分割算法流水线（阈值法和形态学方法）对狄克逊的反相和水图像执行的。通过Dice系数和二维（2D）/三维可视化图形对我们提出的计算机分割方法的性能进行了评估，并对常规和优化的狄克逊序列的反相和水图像进行了比较。结果常规狄克逊MRI的反相图像和水相图像的计算机辅助胰腺分割的Dice系数分别为0.633 ± 0.080和0.716 ± 0.033，优化后的狄克逊MRI的Dice系数分别为0.415 ± 0.143和0.779 ± 0.048。基于优化狄克逊序列的水图像的Dice指数显著高于其他三组（均P < 0.001），包括常规狄克逊MRI的水图像以及常规和优化狄克逊序列的反相图像。结论基于狄克逊MRI的计算机辅助胰腺分割是可行的。优化后的狄克逊图像相似度最高，稳定性较好。
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.