  Medical image segmentation aims to delineate the anatomical or pathological
structures of interest, playing a crucial role in clinical diagnosis. A
substantial amount of high-quality annotated data is crucial for constructing
high-precision deep segmentation models. However, medical annotation is highly
cumbersome and time-consuming, especially for medical videos or 3D volumes, due
to the huge labeling space and poor inter-frame consistency. Recently, a
fundamental task named Moving Object Segmentation (MOS) has made significant
advancements in natural images. Its objective is to delineate moving objects
from the background within image sequences, requiring only minimal annotations.
In this paper, we propose the first foundation model, named iMOS, for MOS in
medical images. Extensive experiments on a large multi-modal medical dataset
validate the effectiveness of the proposed iMOS. Specifically, with the
annotation of only a small number of images in the sequence, iMOS can achieve
satisfactory tracking and segmentation performance of moving objects throughout
the entire sequence in bi-directions. We hope that the proposed iMOS can help
accelerate the annotation speed of experts, and boost the development of
medical foundation models.
