Unsupervised Domain Adaptation with Target-Only Margin Disparity Discrepancy

TL;DR

This paper introduces a novel unsupervised domain adaptation framework based on Margin Disparity Discrepancy to improve liver segmentation in interventional CBCT scans by leveraging annotated CT data.

Contribution

It proposes a reformulated MDD-based UDA method specifically designed for medical image modality adaptation, achieving state-of-the-art results.

Findings

Achieves superior liver segmentation accuracy on CBCT data.

Effective in few-shot learning scenarios.

Outperforms existing domain adaptation methods.

Abstract

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.