  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 introduce a novel part-aware prompt mechanism to
select multiple-point prompts based on part-level features of the one-shot
data. To further promote the robustness of the selected prompt, we propose a
retrieval approach to handle outlier prompts. Extensive experiments demonstrate
that P^2SAM improves the performance by +8.0% and +2.0% mean Dice score within
two patient-specific segmentation settings, and exhibits impressive generality
across different application domains, e.g., +6.4% mIoU on the PerSeg benchmark.
Code will be released upon acceptance.
