分割
人工智能
胰腺癌
计算机科学
胰腺
模式识别(心理学)
掷骰子
相似性(几何)
Sørensen–骰子系数
图像分割
计算机视觉
图像(数学)
癌症
数学
医学
统计
内科学
作者
Haiyan Zheng,Yufei Chen,Xiaodong Yue,Ma C,Xianhui Liu,Panpan Yang,Jianping Lu
标识
DOI:10.1016/j.mri.2020.01.008
摘要
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.
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