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.

翻译：数字减缩血管成像(DSA)视频中的肝细胞癌自动分离(HCC)可以帮助放射学家高效地诊断HCC和准确评估临床实践中的肿瘤。很少有研究调查DSA视频中的HCC分解。它显示出了巨大的挑战性,因为电影中的运动性制品、肿瘤区域的模糊界限以及成像与其他解剖组织高度相似。在本文中,我们在DSA视频中提出了HCC分解问题,并建立了我们自己的DSA数据集。我们还提议建立一个名为DSA-LTDNet的新型分解网络,包括一个分解子网络、一个时间差异学习模块和一个肝脏区域分解子网络,以提供额外的指导。DSA-LTDNet对于主动地从DSA视频中学习潜在运动信息以及增强分解性表现来说是可取的。所有实验都是在我们的自收集数据集上进行的。实验结果表明,DSA-LTDNet将DIC的得分数比UNet基线增加了近4%。