  We introduce SAM3D, a new approach to semi-automatic zero-shot segmentation
of 3D images building on the existing Segment Anything Model. We achieve fast
and accurate segmentations in 3D images with a four-step strategy involving:
user prompting with 3D polylines, volume slicing along multiple axes,
slice-wide inference with a pretrained model, and recomposition and refinement
in 3D. We evaluated SAM3D performance qualitatively on an array of imaging
modalities and anatomical structures and quantify performance for specific
structures in abdominal pelvic CT and brain MRI. Notably, our method achieves
good performance with zero model training or finetuning, making it particularly
useful for tasks with a scarcity of preexisting labeled data. By enabling users
to create 3D segmentations of unseen data quickly and with dramatically reduced
manual input, these methods have the potential to aid surgical planning and
education, diagnostic imaging, and scientific research.
