Pancreatic duct dilation indicates a high risk of various pancreatic diseases. Segmentation of dilated pancreatic ducts on computed tomography (CT) images shows the potential to assist the early diagnosis, surgical planning and prognosis. Because of the ducts' tiny sizes, slender tubular structures and the surrounding distractions, most current researches on pancreatic duct segmentation achieve low accuracy and always have segmentation errors on the terminal parts of the ducts. To address these problems, we propose a terminal guidance mechanism called cascaded terminal guidance network (CTG-Net). Firstly, a terminal attention mechanism is established on the skeletons extracted from the coarse predictions. Then, to get fine terminal segmentation, a subnetwork is designed for jointly learning the local intensity from the original images, feature cues from coarse predictions and global anatomy information from the pancreas distance transform maps. Finally, a terminal distraction attention module which explicitly learns the distribution of the terminal distraction is proposed to reduce the false positive and false negative predictions. We also propose a new metric called tDice to measure the terminal segmentation accuracy for targets with tubular structures and two segmentation metrics for distractions. We collect our dilated pancreatic duct segmentation dataset with 150 CT scans from patients with 5 types of pancreatic tumors. Experimental results on our dataset show that our proposed approach boosts dilated pancreatic duct segmentation accuracy by nearly 20% compared with the existing results, and achieves more than 9% improvement for the terminal segmentation accuracy compared with the state-of-the-art methods.

翻译：胰管扩张提示多种胰腺疾病的高风险。在计算机断层扫描（CT）图像上分割扩张性胰管，有助于辅助早期诊断、手术规划和预后。由于胰管体积微小、管状结构纤细以及周围干扰因素，当前大多数胰管分割研究精度较低，且管末端常出现分割错误。为解决这些问题，我们提出一种名为级联终端引导网络（CTG-Net）的终端引导机制。首先，在粗粒度预测结果提取的骨架上建立终端注意力机制；其次，为获得精细的末端分割，设计子网络联合学习原始图像的局部强度、粗粒度预测的特征线索以及胰腺距离变换图的全局解剖信息；最后，提出明确学习终端干扰分布的终端干扰注意力模块，以减少假阳性和假阴性预测。我们还提出一种新的度量标准tDice，用于评估管状结构目标的末端分割精度，以及两种用于干扰物的分割度量标准。我们收集了来自5种胰腺肿瘤患者的150例CT扫描数据，构建了扩张性胰管分割数据集。实验结果表明，所提方法在数据集上将扩张性胰管分割精度提升近20%，与现有最优方法相比，末端分割精度提升超过9%。