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.

通过引入新的局部感知提示机制，本研究提出了一种数据高效的个性化图像分割方法（P^2SAM），能够在不进行模型微调的情况下，仅利用一次性病人特定数据实现对新病人的无缝适应，大大提高了性能并在不同应用领域展示出了出色的通用性。