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

CTG-Net: An Efficient Cascaded Framework Driven by Terminal Guidance Mechanism for Dilated Pancreatic Duct Segmentation