In interventional radiology, Cone-Beam Computed Tomography (CBCT) is a helpful imaging modality that provides guidance to practicians during minimally invasive procedures. CBCT differs from traditional Computed Tomography (CT) due to its limited reconstructed field of view, specific artefacts, and the intra-arterial administration of contrast medium. While CT benefits from abundant publicly available annotated datasets, interventional CBCT data remain scarce and largely unannotated, with existing datasets focused primarily on radiotherapy applications. To address this limitation, we leverage a proprietary collection of unannotated interventional CBCT scans in conjunction with annotated CT data, employing domain adaptation techniques to bridge the modality gap and enhance liver segmentation performance on CBCT. We propose a novel unsupervised domain adaptation (UDA) framework based on the formalism of Margin Disparity Discrepancy (MDD), which improves target domain performance through a reformulation of the original MDD optimization framework. Experimental results on CT and CBCT datasets for liver segmentation demonstrate that our method achieves state-of-the-art performance in UDA, as well as in the few-shot setting.

翻译：在介入放射学中，锥形束计算机断层扫描（CBCT）是一种有用的成像模态，可在微创手术过程中为从业者提供引导。CBCT与传统计算机断层扫描（CT）不同，因其有限的重建视野、特定伪影以及动脉内造影剂给药方式。尽管CT受益于大量公开可用的标注数据集，但介入性CBCT数据仍然稀缺且大多未标注，现有数据集主要集中于放射治疗应用。为克服这一局限，我们利用专有的未标注介入性CBCT扫描数据与标注CT数据相结合，采用域自适应技术来弥合成像模态间的差异，并提升CBCT上的肝脏分割性能。我们提出了一种基于边际差异差异（MDD）形式的新型无监督域自适应（UDA）框架，该框架通过对原始MDD优化框架的重构来提升目标域性能。在CT和CBCT肝脏分割数据集上的实验结果表明，我们的方法在UDA以及少样本设置下均实现了最先进的性能。