CoreFormer high fidelity pulmonary nodule segmentation with structural core priors and geodesic implicit fields

Accurate delineation of pulmonary nodules in chest computed tomography (CT) is essential for early lung cancer diagnosis and treatment planning. However, voxel-wise segmentation methods often produce fragmented masks and inconsistent topology due to low contrast, anatomical variability, and imaging noise. We propose CoreFormer, a segmentation framework that models nodules through structural core anchoring and geodesic shape decoding. CoreFormer identifies the intrinsic topological core of each nodule and generates continuous boundaries guided by anatomy-aware geodesic paths. It is built upon a Swin Transformer backbone and a dual-branch decoder consisting of a Structural Core Predictor and a Context-Aware Shape Decoder, enhanced by Feature Manifold Regularization for discriminative feature learning. Extensive experiments on four public datasets-LIDC-IDRI, LNDb, Tianchi-Lung (MosMedData), and NSCLC-Radiomics-demonstrate that CoreFormer achieves state-of-the-art boundary accuracy and topological fidelity, offering robust and high-fidelity pulmonary nodule segmentation.