Part-aware Personalized Segment Anything Model for Patient-Specific Segmentation

arXiv:2403.05433v1 Announce Type: new
Abstract: Precision medicine, such as patient-adaptive treatments utilizing medical images, poses new challenges for image segmentation algorithms due to (1) the large variability across different patients and (2) the limited availability of annotated data for each patient. In this work, we propose a data-efficient segmentation method to address these challenges, namely Part-aware Personalized Segment Anything Model (P^2SAM). Without any model fine-tuning, P^2SAM enables seamless adaptation to any new patients relying only on one-shot patient-specific data. We …

abstract algorithms annotated data arxiv availability challenges cs.cv data image images medical medicine part patient patients personalized precision precision medicine segment segment anything segment anything model segmentation type work