SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts

SynPo addresses weak negative prompts in training-free few-shot medical image segmentation. It combines SAM and DINOv2 features to construct a synergistic confidence map, then selects informative positive and negative points through confidence-aware sampling and clustering.

SynPo

Training-free few-shot medical segmentation with confidence-map synergy and informative negative prompts

Method	CHAOS-MRI					Synapse-CT
	Spleen	Liver	LK	RK	Mean	Spleen	Liver	LK	RK	Mean
SSL-ALPNettrained	67.02	73.05	73.63	78.39	73.02	60.25	73.65	63.34	54.82	63.02
ADNettrained	75.92	80.81	75.28	83.28	78.82	63.48	77.24	72.13	79.06	72.97
Q-Nettrained	75.99	81.74	78.36	87.98	81.02	74.86	71.21	75.26	74.79	74.03
RPTtrained	76.37	82.86	80.72	89.82	82.44	79.13	82.57	77.05	72.58	77.83
GMRDtrained	76.09	81.42	83.96	90.12	82.90	78.31	79.60	81.70	74.46	78.52
PerSAMtraining-free	69.14	42.44	64.84	71.36	61.12	65.03	65.55	58.47	60.31	62.34
ProtoSAMtraining-free	76.51	81.94	71.46	81.43	77.83	65.50	87.84	69.44	71.04	73.45
SynPo (ours)training-free	80.30	77.32	77.32	83.04	79.50	83.76	81.32	75.00	79.63	79.91

Qualitative results on abdominal MRI. Colored overlays show the predicted organ masks.