Hepatocellular Carcinoma Segmentation fromDigital Subtraction
Angiography Videos usingLearnable Temporal Difference
- URL: http://arxiv.org/abs/2107.04306v1
- Date: Fri, 9 Jul 2021 08:35:37 GMT
- Title: Hepatocellular Carcinoma Segmentation fromDigital Subtraction
Angiography Videos usingLearnable Temporal Difference
- Authors: Wenting Jiang, Yicheng Jiang, Lu Zhang, Changmiao Wang, Xiaoguang Han,
Shuixing Zhang, Xiang Wan, Shuguang Cui
- Abstract summary: 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 module and a liver region segmentation sub-network.
DSA-LTDNet is preferable for learning the latent motioninformation from DSA videos proactively and boosting segmentation performance.
- Score: 34.5652668406547
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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