  Despite that the segment anything model (SAM) achieved impressive results on
general-purpose semantic segmentation with strong generalization ability on
daily images, its demonstrated performance on medical image segmentation is
less precise and not stable, especially when dealing with tumor segmentation
tasks that involve objects of small sizes, irregular shapes, and low contrast.
Notably, the original SAM architecture is designed for 2D natural images,
therefore would not be able to extract the 3D spatial information from
volumetric medical data effectively. In this paper, we propose a novel
adaptation method for transferring SAM from 2D to 3D for promptable medical
image segmentation. Through a holistically designed scheme for architecture
modification, we transfer the SAM to support volumetric inputs while retaining
the majority of its pre-trained parameters for reuse. The fine-tuning process
is conducted in a parameter-efficient manner, wherein most of the pre-trained
parameters remain frozen, and only a few lightweight spatial adapters are
introduced and tuned. Regardless of the domain gap between natural and medical
data and the disparity in the spatial arrangement between 2D and 3D, the
transformer trained on natural images can effectively capture the spatial
patterns present in volumetric medical images with only lightweight
adaptations. We conduct experiments on four open-source tumor segmentation
datasets, and with a single click prompt, our model can outperform domain
state-of-the-art medical image segmentation models on 3 out of 4 tasks,
specifically by 8.25%, 29.87%, and 10.11% for kidney tumor, pancreas tumor,
colon cancer segmentation, and achieve similar performance for liver tumor
segmentation. We also compare our adaptation method with existing popular
adapters, and observed significant performance improvement on most datasets.
