Dual Discriminator-Based Unsupervised Domain Adaptation Using Adversarial Learning for Liver Segmentation on Multiphase CT Images.
Dual Discriminator-Based Unsupervised Domain Adaptation Using Adversarial Learning for Liver Segmentation on Multiphase CT Images.
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DOI：
10.1109/embc48229.2022.9871188
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发表时间：
2022-07-01
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通讯作者：
Chen, Yen-Wei
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作者：
Ananda, Swathi;Iwamoto, Yutaro;Chen, Yen-Wei
多相计算机断层扫描（CT）图像被广泛用于肝脏疾病的诊断。由于每个相位具有不同的对比度增强（即，不同的域），多相CT图像应该针对所有相位进行注释以执行肝脏或肿瘤分割，这是一项耗时且劳动力昂贵的任务。在本文中，我们提出了一种基于双鉴别器的无监督域自适应（DD-UDA）肝脏分割多相CT图像无注释。我们的框架由三个模块组成：一个特定于任务的生成器和两个判别器。我们已经在两个级别上进行了域自适应：一个是在特征级别，另一个是在输出级别，通过减少源域和目标域之间的分布差异来提高精度。以公共数据（仅PV相）为源域，以私有多相CT数据为目标域的实验结果表明了该方法的有效性。临床相关性-本研究有助于在多相CT图像上有效准确地分割肝脏，这是诊断和手术支持的重要预处理步骤。通过使用所提出的DD-UDA方法，分割精度分别提高了5%，8%和6%，与那些没有UDA的CT图像的所有相位相比。
Multiphase computed tomography (CT) images are widely used for the diagnosis of liver disease. Since each phase has different contrast enhancement (i.e., different domain), the multiphase CT images should be annotated for all phases to perform liver or tumor segmentation, which is a time-consuming and labor-expensive task. In this paper, we propose a dual discriminator-based unsupervised domain adaptation (DD-UDA) for liver segmentation on multiphase CT images without annotations. Our framework consists of three modules: a task-specific generator and two discriminators. We have performed domain adaptation at two levels: one is at the feature level, and the other is at the output level, to improve accuracy by reducing the difference in distributions between the source and target domains. Experimental results using public data (PV phase only) as the source domain and private multiphase CT data as the target domain show the effectiveness of our proposed DD-UDA method. Clinical relevance- This study helps to efficiently and accurately segment the liver on multiphase CT images, which is an important preprocessing step for diagnosis and surgical support. By using the proposed DD-UDA method, the segmentation accuracy has improved from 5%, 8%, and 6% respectively, for all phases of CT images with comparison to those without UDA.