  In this paper, we present SonoSAMTrack - that combines a promptable
foundational model for segmenting objects of interest on ultrasound images
called SonoSAM, with a state-of-the art contour tracking model to propagate
segmentations on 2D+t and 3D ultrasound datasets. Fine-tuned and tested
exclusively on a rich, diverse set of objects from $\approx200$k ultrasound
image-mask pairs, SonoSAM demonstrates state-of-the-art performance on 7 unseen
ultrasound data-sets, outperforming competing methods by a significant margin.
We also extend SonoSAM to 2-D +t applications and demonstrate superior
performance making it a valuable tool for generating dense annotations and
segmentation of anatomical structures in clinical workflows. Further, to
increase practical utility of the work, we propose a two-step process of
fine-tuning followed by knowledge distillation to a smaller footprint model
without comprising the performance. We present detailed qualitative and
quantitative comparisons of SonoSAM with state-of-the-art methods showcasing
efficacy of the method. This is followed by demonstrating the reduction in
number of clicks in a dense video annotation problem of adult cardiac
ultrasound chamber segmentation using SonoSAMTrack.
