Pancreas segmentation is a challenging task in medical image analysis especially for the patients with pancreatic cancer. First, the images often have poor contrast and blurred boundaries. Second, there exist large variations in gray scale, texture, location, shape and size among pancreas images. It becomes even worse with cases of pancreatic cancer. Besides, as an inevitable phenomenon, some of the slices have disconnected topology in pancreas part. All these problems lead to high segmentation uncertainties and make the results inaccurate. Existing pancreas segmentation methods rarely achieve sufficiently accurate and robust results especially for cancer cases. To tackle these problems, we propose a 2D deep learning-based method which can involve uncertainties in the process of segmentation iteratively. The proposed method describes the uncertain regions of pancreatic MRI images based on shadowed sets theory. The results are further corrected through increasing the weights of uncertain regions in iterative training. We evaluate our approach on a challenging pancreatic cancer MRI images dataset collected from the Changhai Hospital, and also validate our approach on the NIH pancreas segmentation dataset. The experimental results demonstrate that our proposed method outperforms the state-of-the-art methods in terms of the Dice similarity coefficient of 73.88% on cancer MRI dataset and 84.37% on NIH dataset respectively.

中文翻译：

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深部胰腺分割，阴影区域不确定。

胰腺分割在医学图像分析中是一项艰巨的任务，特别是对于胰腺癌患者。首先，图像经常具有较差的对比度和模糊的边界。其次，胰腺图像之间在灰度，纹理，位置，形状和大小方面存在很大差异。对于胰腺癌，情况甚至更糟。此外，作为一种不可避免的现象，一些切片在胰腺部分已断开拓扑连接。所有这些问题导致了较高的分割不确定性，并使结果不准确。现有的胰腺分割方法很少能获得足够准确和可靠的结果，尤其是对于癌症病例。为了解决这些问题，我们提出了一种基于2D深度学习的方法，该方法可能会在迭代分割过程中涉及不确定性。该方法基于阴影集理论描述了胰腺MRI图像的不确定区域。通过在迭代训练中增加不确定区域的权重，可以进一步校正结果。我们评估了从长海医院收集的具有挑战性的胰腺癌MRI图像数据集的方法，并验证了NIH胰腺分割数据集的方法。实验结果表明，我们提出的方法在癌症MRI数据集上的Dice相似系数分别为73.88％和NIH数据集上的84.37％方面优于最新方法。我们评估了从长海医院收集的具有挑战性的胰腺癌MRI图像数据集的方法，并验证了NIH胰腺分割数据集的方法。实验结果表明，我们提出的方法在癌症MRI数据集上的Dice相似系数分别为73.88％和NIH数据集上的84.37％方面优于最新方法。我们评估了从长海医院收集的具有挑战性的胰腺癌MRI图像数据集的方法，并验证了NIH胰腺分割数据集的方法。实验结果表明，我们提出的方法在癌症MRI数据集上的Dice相似系数分别为73.88％和NIH数据集上的84.37％方面优于最新方法。