Parameter-Efficient Adaptation of SAM 3 for Automated ITV Generation from 4DCT Images
Four-dimensional computed tomography (4DCT) captures the full respiratory cycle of thoracic anatomy, yet current Internal Target Volume contouring workflows process each phase in isolation, discarding temporal coherence and leaving contours vulnerable to phase-specific artifacts. We present a lightweight framework that applies parameter-efficient fine-tuning to the Segment Anything Model 3 (SAM 3) via low-rank adaptation (LoRA) to align its text-prompted segmentation with the medical domain using only seven annotated 3D CT volumes. Furthermore, the framework incorporates a hard negative mining strategy to improve boundary discrimination in low-contrast thoracic regions. At inference, phas...
| Authors | Changwoo Song |
|---|---|
| Primary category | eess.IV |
| Published | 14 June 2026 |
| Last updated at source | 14 June 2026 |
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