Zou Liwen, Cai Zhenghua, Qiu Yudong, Gui Luying, Mao Liang, Yang Xiaoping
Department of Mathematics, Nanjing University, Nanjing, 210093, People's Republic of China.
Medical School, Nanjing University, Nanjing, 210007, People's Republic of China.
Phys Med Biol. 2023 Oct 23;68(21). doi: 10.1088/1361-6560/acf110.
Pancreatic duct dilation indicates a high risk of various pancreatic diseases. Segmentation for dilated pancreatic duct (DPD) on computed tomography (CT) image shows the potential to assist the early diagnosis, surgical planning and prognosis. Because of the DPD's tiny size, slender tubular structure and the surrounding distractions, most current researches on DPD segmentation achieve low accuracy and always have segmentation errors on the terminal DPD regions. To address these problems, we propose a cascaded terminal guidance network to efficiently improve the DPD segmentation performance. Firstly, a basic cascaded segmentation architecture is established to get the pancreas and coarse DPD segmentation, a DPD graph structure is build on the coarse DPD segmentation to locate the terminal DPD regions. Then, a terminal anatomy attention module is introduced for jointly learning the local intensity from the CT images, feature cues from the coarse DPD segmentation and global anatomy information from the designed pancreas anatomy-aware maps. Finally, a terminal distraction attention module which explicitly learns the distribution of the terminal distraction regions 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 other metrics for segmentation error evaluation. We collect our dilated pancreatic duct segmentation dataset with 150 CT scans from patients with five types of pancreatic tumors. Experimental results on our dataset show that our proposed approach boosts DPD 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.
胰管扩张表明存在患各种胰腺疾病的高风险。在计算机断层扫描（CT）图像上对扩张的胰管（DPD）进行分割显示出有助于早期诊断、手术规划和预后的潜力。由于DPD尺寸微小、管状结构细长且周围存在干扰因素，目前大多数关于DPD分割的研究准确率较低，并且在DPD末端区域总是存在分割错误。为了解决这些问题，我们提出了一种级联末端引导网络，以有效提高DPD分割性能。首先，建立一个基本的级联分割架构来获得胰腺和粗略的DPD分割，在粗略的DPD分割上构建一个DPD图结构来定位DPD末端区域。然后，引入一个末端解剖注意力模块，用于联合学习来自CT图像的局部强度、来自粗略DPD分割的特征线索以及来自设计的胰腺解剖感知图的全局解剖信息。最后，提出一个末端干扰注意力模块，该模块明确学习末端干扰区域的分布，以减少假阳性和假阴性预测。我们还提出了一种名为tDice的新指标来衡量具有管状结构目标的末端分割准确率，以及另外两种用于分割误差评估的指标。我们从患有五种类型胰腺肿瘤的患者中收集了包含150次CT扫描的扩张胰管分割数据集。在我们的数据集上的实验结果表明，与现有结果相比，我们提出的方法将DPD分割准确率提高了近20%，与最先进的方法相比，末端分割准确率提高了超过9%。
