ArticleFrontiers in medicine2026
Text-prompted foundation-model segmentation of intrapartum fetal ultrasound: a controlled methodological study with F-TPA-SAM2.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Segmenting intrapartum fetal anatomy on B-mode ultrasound supports labor-progress monitoring, but conventional workflows require task-specific models and manual geometric prompts. We investigate whether text-conditioned prompting can select anatomical targets under controlled-label budgets. Methods: We propose F-TPA-SAM2, a Text-to-Prompt Adapter with 0.871 M trainable parameters (0.135% of the model), which maps frozen FetalCLIP embeddings to a low-rank dense prompt for the frozen SAM 2 mask decoder. We trained the adapter on PSFHS using three seeds and label budgets Results: On the PSFHS test set, F-TPA-SAM2 achieved 0.811 ± 0.011 Dice at Conclusion: F-TPA-SAM2 uses a stable category-conditioned input to select between the two trained anatomical targets. However, matched no-text and random-code controls indicate that pre-trained language semantics are not necessary for this result. The findings do not establish open-vocabulary or multi-anatomy segmentation; clinical utility requires a sonographer-in-the-loop and prospective validation.
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