Prompt-Guided Interactive Segmentation of Interstitial Lung Disease in Thoracic CT
Accurate segmentation of interstitial lung disease (ILD) patterns is essential for quantitative disease assessment and longitudinal monitoring. However, existing approaches remain limited by relying on dense annotations and producing static predictions that cannot be refined, motivating interactive approaches. While promptable models show promise in interactive segmentation, their adaptation to ILDs remains largely unexplored. To address this gap, we investigate prompt-guided foundation models for ILD refinement and present, to the best of our knowledge, the first adaptation of MedSAM2 for interactive 3D ILD segmentation on thoracic CT. We investigate three fine-tuning strategies and mult...
| Authors | Vasilis Dedousis, Lubnaa Abdur Rahman, Lorenzo Brigatο et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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