  Recent advances in Vision Transformers (ViT) and Stable Diffusion (SD) models
with their ability to capture rich semantic features of the image have been
used for image correspondence tasks on natural images. In this paper, we
examine the ability of a variety of pre-trained ViT (DINO, DINOv2, SAM, CLIP)
and SD models, trained exclusively on natural images, for solving the
correspondence problems on medical images. While many works have made a case
for in-domain training, we show that the models trained on natural images can
offer good performance on medical images across different modalities
(CT,MR,Ultrasound) sourced from various manufacturers, over multiple anatomical
regions (brain, thorax, abdomen, extremities), and on wide variety of tasks.
Further, we leverage the correspondence with respect to a template image to
prompt a Segment Anything (SAM) model to arrive at single shot segmentation,
achieving dice range of 62%-90% across tasks, using just one image as
reference. We also show that our single-shot method outperforms the recently
proposed few-shot segmentation method - UniverSeg (Dice range 47%-80%) on most
of the semantic segmentation tasks(six out of seven) across medical imaging
modalities.
