Advancing deep learning-based segmentation for multiple lung cancer lesions in real-world multicenter CT scans.

ABSTRACT:

Background

Accurate segmentation of lung cancer lesions in computed tomography (CT) is essential for precise diagnosis, personalized therapy planning, and treatment response assessment. While automatic segmentation of the primary lung lesion has been widely studied, the ability to segment multiple lesions per patient remains underexplored. In this study, we address this gap by introducing a novel, automated approach for multi-instance segmentation of lung cancer lesions, leveraging a heterogeneous cohort with real-world multicenter data.

Materials and methods

We analyzed 1,081 retrospectively collected CT scans with 5,322 annotated lesions (4.92 ± 13.05 lesions per scan). The cohort was stratified into training (n = 868) and testing (n = 213) subsets. We developed an automat

SUBMITTER: Rafael-Palou X 

PROVIDER: S-EPMC12361585 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature