  In this paper, we introduce Medical SAM 2 (MedSAM-2), an advanced
segmentation model that utilizes the SAM 2 framework to address both 2D and 3D
medical image segmentation tasks. By adopting the philosophy of taking medical
images as videos, MedSAM-2 not only applies to 3D medical images but also
unlocks new One-prompt Segmentation capability. That allows users to provide a
prompt for just one or a specific image targeting an object, after which the
model can autonomously segment the same type of object in all subsequent
images, regardless of temporal relationships between the images. We evaluated
MedSAM-2 across a variety of medical imaging modalities, including abdominal
organs, optic discs, brain tumors, thyroid nodules, and skin lesions, comparing
it against state-of-the-art models in both traditional and interactive
segmentation settings. Our findings show that MedSAM-2 not only surpasses
existing models in performance but also exhibits superior generalization across
a range of medical image segmentation tasks. Our code will be released at:
https://github.com/MedicineToken/Medical-SAM2
