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 int
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 53%Deep Learning Advances Lung Cancer Segmentation and Volumetric Analysis in CT Scans - Bioengineer.org →
- PossiblePossibly related (embedding) · 48%Integrated data and machine learning transform lung cancer diagnosis and treatment - Bioengineer.org →
- FuzzySimilar title/name (fuzzy) · 59%linshenkx/prompt-optimizer →
“Fuzzy title match (0.73): “Prompt-Guided Interactive Segmentation of Interstitial Lung ” ≈ “linshenkx/prompt-optimizer””
- FuzzySimilar title/name (fuzzy) · 59%NirDiamant/Prompt_Engineering →
“Fuzzy title match (0.73): “Prompt-Guided Interactive Segmentation of Interstitial Lung ” ≈ “NirDiamant/Prompt_Engineering””
- LinkedLinked via arxiv author · 85%Vasilis Dedousis →
“Prompt-Guided Interactive Segmentation of Interstitial Lung Disease in Thoracic CT”
- LinkedLinked via arxiv author · 85%Lubnaa Abdur Rahman →
“Prompt-Guided Interactive Segmentation of Interstitial Lung Disease in Thoracic CT”
- LinkedLinked via arxiv author · 85%Lorenzo Brigatο →
“Prompt-Guided Interactive Segmentation of Interstitial Lung Disease in Thoracic CT”
- LinkedLinked via arxiv author · 85%Ethan Dack →
“Prompt-Guided Interactive Segmentation of Interstitial Lung Disease in Thoracic CT”