用无监督域适应提升介入式CBCT的肝脏分割效果
Unsupervised Domain Adaptation with Target-Only Margin Disparity Discrepancy
- 基于改进的边际差异不一致性框架，仅用目标域数据优化
- 在肝分割任务中达到当前最优的无监督域适应性能
- 适合缺乏标注数据的医学影像场景，尤其介入放射领域
在介入放射学中，锥束计算机断层扫描（CBCT）能为微创手术提供引导，但其重建视野有限、存在特定伪影且需动脉内注射对比剂，与传统CT差异显著。尽管CT有大量公开标注数据，介入CBCT数据稀缺且基本无标注，现有数据集也多聚焦于放疗应用。为此，我们利用自有未标注介入CBCT扫描数据，结合标注的CT数据，采用无监督域适应技术弥合模态差距，提升CBCT上的肝脏分割性能。提出一种基于边际差异不一致性（MDD）的新框架，通过重构原始优化形式提升目标域表现。在CT与CBCT数据集上的实验表明，该方法在无监督域适应及少样本设置下均达到当前最优性能。
原文摘要 · Abstract (English)
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.
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