Hepatocellular Carcinoma Segmentation fromDigital Subtraction Angiography Videos usingLearnable Temporal Difference

Automatic segmentation of hepatocellular carcinoma (HCC)in Digital Subtraction Angiography (DSA) videos can assist radiologistsin efficient diagnosis of HCC and accurate evaluation of tumors in clinical practice. Few studies have investigated HCC segmentation from DSAvideos. It shows great challenging due to motion artifacts in filming, ambiguous boundaries of tumor regions and high similarity in imaging toother anatomical tissues. In this paper, we raise the problem of HCCsegmentation in DSA videos, and build our own DSA dataset. We alsopropose a novel segmentation network called DSA-LTDNet, including asegmentation sub-network, a temporal difference learning (TDL) moduleand a liver region segmentation (LRS) sub-network for providing additional guidance. DSA-LTDNet is preferable for learning the latent motioninformation from DSA videos proactively and boosting segmentation performance. All of experiments are conducted on our self-collected dataset.Experimental results show that DSA-LTDNet increases the DICE scoreby nearly 4% compared to the U-Net baseline.