Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1552-1555. doi: 10.1109/EMBC48229.2022.9871188.
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.
多期计算机断层扫描 (CT) 图像被广泛用于肝脏疾病的诊断。由于每个阶段都有不同的对比度增强（即不同的域），因此需要对多期 CT 图像的所有阶段进行注释，以进行肝脏或肿瘤分割，这是一项耗时费力的任务。在本文中，我们提出了一种基于双判别器的无监督域自适应（DD-UDA）方法，用于在没有注释的多期 CT 图像上进行肝脏分割。我们的框架由三个模块组成：一个特定任务的生成器和两个判别器。我们在两个层次上进行了域自适应：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分布的差异来提高准确性。使用公共数据（仅 PV 期）作为源域和私有多期 CT 数据作为目标域的实验结果表明，我们提出的 DD-UDA 方法是有效的。临床相关性- 本研究有助于在多期 CT 图像上高效、准确地分割肝脏，这是诊断和手术支持的重要预处理步骤。与未进行 UDA 的情况相比，使用所提出的 DD-UDA 方法，分割准确性分别提高了 5%、8%和 6%。
