Background and objective

The size, shape, and position of the pancreas are affected by the patient characteristics such as age, sex, adiposity. Owing to more complex anatomical structures (size, shape, and position) of the pancreas, specialists have some difficulties in the analysis of pancreatic diseases (diabetes, pancreatic cancer, pancreatitis). Therefore, the treatment of the disease requires enormous time and depends on the experience of specialists. In order to decrease the rate of pancreatic disease deaths and to assist the specialist in the analysis of pancreatic diseases, automatic pancreas segmentation techniques have been actively developed in the research article for many years.

Methods

Although the rapid growth of deep learning and proving satisfactory performance in many medical image processing and computer vision applications, the maximum Dice Similarity Coefficients (DSC) value of these techniques related to automatic pancreas segmentation is only around 85% due to complex structure of the pancreas and other factors. Contrary to previous techniques which are required significantly higher computational power and memory, this paper suggests a novel two-phase approach for high-accuracy automatic pancreas segmentation in computed tomography (CT) imaging. The proposed approach consists of two phases; (1) Pancreas Localization, where the rough pancreas position is detected on the 2D CT slice by adopting Mask R-CNN model, (2) Pancreas Segmentation, where the segmented pancreas region is produced by refining the candidate pancreas region with 3D U-Net on the 2D sub-CT slices generated in the first phase. Both qualitative and quantitative assessments of the proposed approach are performed on the NIH data set.

Results

In order to evaluate the achievement of the recommended approach, a total of 16 different automatic pancreas segmentation techniques reported in the literature are compared by utilizing performance assessment procedures which are Dice Similarity Coefficient (DSC), Jaccard Index (JI), Precision (PRE), Recall (REC), Pixel Accuracy (ACC), Specificity (SPE), Receiver Operating Characteristics (ROC) and Area under ROC curve (AUC). The average values of DSC, JI, REC and ACC are computed as 86.15%, 75.93%, 86.27%, 86.27% and 99.95% respectively, which are the best values among well-established studies for automatic pancreas segmentation.

Conclusion

It is demonstrated with qualitative and quantitative results that our suggested two-phase approach creates more favorable results than other existing approaches.

中文翻译：

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使用 Mask R-CNN 和 3D U-Net 进行 CT 成像中胰腺的高精度自动分割的两阶段方法

背景和目的

胰腺的大小、形状和位置受患者特征的影响，例如年龄、性别、肥胖。由于胰腺的解剖结构（大小、形状和位置）更加复杂，专家在分析胰腺疾病（糖尿病、胰腺癌、胰腺炎）时存在一定的困难。因此，该疾病的治疗需要大量时间并取决于专家的经验。为了降低胰腺疾病的死亡率并协助专家分析胰腺疾病，多年来在研究文章中积极开发了自动胰腺分割技术。

方法

尽管深度学习的快速增长并在许多医学图像处理和计算机视觉应用中证明了令人满意的性能，但由于胰腺结构复杂，这些与自动胰腺分割相关的技术的最大骰子相似系数 (DSC) 值仅约为 85%和其他因素。与需要更高计算能力和内存的先前技术相反，本文提出了一种新的两阶段方法，用于计算机断层扫描 (CT) 成像中的高精度自动胰腺分割。提议的方法包括两个阶段；（1）Pancreas Localization，采用Mask R-CNN模型在2D CT切片上检测粗略的胰腺位置，（2）Pancreas Segmentation，其中分割的胰腺区域是通过在第一阶段生成的 2D sub-CT 切片上使用 3D U-Net 细化候选胰腺区域来产生的。所提出的方法的定性和定量评估都是在 NIH 数据集上进行的。

结果

为了评估推荐方法的实现，文献中报告的总共 16 种不同的自动胰腺分割技术通过利用骰子相似系数 (DSC)、Jaccard 指数 (JI)、精度 (PRE) 的性能评估程序进行了比较、召回率 (REC)、像素精度 (ACC)、特异性 (SPE)、接收器操作特性 (ROC) 和 ROC 曲线下面积 (AUC)。DSC、JI、REC 和 ACC 的平均值分别计算为 86.15%、75.93%、86.27%、86.27% 和 99.95%，这是自动胰腺分割的成熟研究中的最佳值。

结论

定性和定量结果表明，我们建议的两阶段方法比其他现有方法产生了更有利的结果。