Uncertainty-Guided Lung Tumor Segmentation via Coarse-to-Fine Refinement

This research paper introduces a novel two-stage framework for more accurately identifying lung tumors in CT scans. The approach first performs a coarse, full-volume localization of potential tumors, followed by an anatomy-aware post-processing step to refine these regions. Subsequently, a second stage focuses on detailed segmentation of the identified regions of interest, utilizing uncertainty information to improve accuracy, especially in challenging areas. The study demonstrates that this uncertainty-guided, coarse-to-fine method, combined with anatomical knowledge, leads to better tumor delineation and reduces false positives compared to traditional methods.