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MedSAM2 adapted for interactive ILD segmentation in CT scans

By PulseAugur Editorial · [1 sources] ·

Researchers have adapted MedSAM2, a foundation model, for interactive segmentation of interstitial lung disease (ILD) in thoracic CT scans. This adaptation aims to improve quantitative disease assessment by allowing refinement of initial predictions using various prompt types, including bounding boxes, points, lasso, and scribbles. Full model fine-tuning demonstrated the best performance, enhancing the Dice score by 4.7 percentage points, with bounding box prompts showing the strongest results, though other interactive prompts also proved effective. AI

IMPACT This research could lead to more accurate and efficient diagnosis and monitoring of interstitial lung diseases through improved medical image analysis.

RANK_REASON The item is an academic paper detailing a new adaptation of a model for a specific medical imaging task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

MedSAM2 adapted for interactive ILD segmentation in CT scans

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The item is an academic paper detailing a new adaptation of a model for a specific medical imaging task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Vasilis Dedousis, Lubnaa Abdur Rahman, Lorenzo Brigat{\omicron}, Ethan Dack, Andreas Christe, Christoph Frank, Manuela Funke-Chambour, Justus Roos, Adrian Huber, Lukas Ebner, Stavroula Mougiakakou ·

    Prompt-Guided Interactive Segmentation of Interstitial Lung Disease in Thoracic CT

    arXiv:2608.28453v1 Announce Type: new Abstract: 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 producin…

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