Automated Coarse-to-Fine Segmentation of Thoracic Duct Using Anatomy Priors and Topology-Guided Curved Planar Reformation

Recent studies have emphasized the importance of protecting thoracic duct during radiation therapy (RT), as dose distributions in thoracic duct may be associated with the development radiation-induced lymphopenia. Because of its thin/slim size, curved geometry and extremely poor (intensity) contrast of thoracic duct, manual delineation of thoracic duct in RT planning CT is time-consuming and with large inter-observer variations. In this work, we aim to automatically and accurately segment thoracic duct in RT planning CT, as the first attempt to tackle this clinically critical yet under-studied task. A two-stage coarse-to-fine segmentation approach is proposed. At the first stage, we automatically segment six chest organs and combine these organ predictions with the input planning CT to better infer and localize the thoracic duct. Given the coarse initial segmentation from first stage, we subsequently extract the topology-corrected centerline of initial thoracic duct segmentation at stage two where curved planar reformation (CPR) is applied to transform the planning CT into a new 3D volume representation that provides a spatially smoother reformation of thoracic duct in its elongated medial axis direction. Thus the CPR-transformed CT is employed as input to the second stage deep segmentation network, and the output segmentation mask is transformed back to the original image space, as the final segmentation. We evaluate our approach on 117 lung cancer patients with RT planning CT scans. Our approach significantly outperforms a strong baseline model based on nnUNet, by reducing 57% relative Hausdorff distance error (from 49.9mm to 21.2mm) and improving 1.8% absolute Jaccard Index.