Cai Jinzheng, Lu Le, Zhang Zizhao, Xing Fuyong, Yang Lin, Yin Qian
Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA.
Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892, USA.
Med Image Comput Comput Assist Interv. 2016 Oct;9901:442-450. doi: 10.1007/978-3-319-46723-8_51. Epub 2016 Oct 2.
Automated pancreas segmentation in medical images is a prerequisite for many clinical applications, such as diabetes inspection, pancreatic cancer diagnosis, and surgical planing. In this paper, we formulate pancreas segmentation in magnetic resonance imaging (MRI) scans as a graph based decision fusion process combined with deep convolutional neural networks (CNN). Our approach conducts pancreatic detection and boundary segmentation with two types of CNN models respectively: 1) the tissue detection step to differentiate pancreas and non-pancreas tissue with spatial intensity context; 2) the boundary detection step to allocate the semantic boundaries of pancreas. Both detection results of the two networks are fused together as the initialization of a conditional random field (CRF) framework to obtain the final segmentation output. Our approach achieves the mean dice similarity coefficient (DSC) 76.1% with the standard deviation of 8.7% in a dataset containing 78 abdominal MRI scans. The proposed algorithm achieves the best results compared with other state of the arts.
医学图像中的胰腺自动分割是许多临床应用的前提条件，如糖尿病检查、胰腺癌诊断和手术规划。在本文中，我们将磁共振成像（MRI）扫描中的胰腺分割表述为基于图的决策融合过程，并结合深度卷积神经网络（CNN）。我们的方法分别使用两种类型的CNN模型进行胰腺检测和边界分割：1）组织检测步骤，利用空间强度上下文区分胰腺和非胰腺组织；2）边界检测步骤，确定胰腺的语义边界。两个网络的检测结果融合在一起，作为条件随机场（CRF）框架的初始化，以获得最终的分割输出。在一个包含78例腹部MRI扫描的数据集上，我们的方法实现了平均骰子相似系数（DSC）为76.1%，标准差为8.7%。与其他现有技术相比，该算法取得了最佳结果。
