arXiv Computer Vision
Anatomy-Aware Prediction of Bronchoscopic Accessibility from 3D CT
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Prompt-Guided Interactive Segmentation of Interstitial Lung Disease in Thoracic CT
The paper introduces the first adaptation of the MedSAM2 foundation model for interactive 3D segmentation of interstitial lung disease (ILD) on thoracic CT scans. It evaluates three fine‑tuning strategies and four prompt types—bounding‑boxes, points, lassos, and scribbles—finding that full model fine‑tuning yields the best performance, improving Dice scores by 4.7 percentage points over the baseline. A proof‑of‑concept workflow is presented where MedSAM2 is first initialized with an automatic prior and then refined by radiologist prompts, with all resources released on GitHub.
arXiv Machine Learning
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MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation
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arXiv Computer Vision
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ARC-CT: Anatomy-Routed Contrastive Vision-Language Learning for 3D Chest CT
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BronchoTop: Bronchoscopy Navigation via RGB-Only Topological Localization
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arXiv Computer Vision
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DALE-CT: Depth-Aware 2D Slice Encoders Learn an Anatomical World Model of Chest CT
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arXiv AI
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