  Automatic segmentation of hepatocellular carcinoma (HCC) in Digital
Subtraction Angiography (DSA) videos can assist radiologists in efficient
diagnosis of HCC and accurate evaluation of tumors in clinical practice. Few
studies have investigated HCC segmentation from DSA videos. It shows great
challenging due to motion artifacts in filming, ambiguous boundaries of tumor
regions and high similarity in imaging to other anatomical tissues. In this
paper, we raise the problem of HCC segmentation in DSA videos, and build our
own DSA dataset. We also propose a novel segmentation network called
DSA-LTDNet, including a segmentation sub-network, a temporal difference
learning (TDL) module and a liver region segmentation (LRS) sub-network for
providing additional guidance. DSA-LTDNet is preferable for learning the latent
motion information 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 score by nearly 4%
compared to the U-Net baseline.
