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Medical image segmentation aims to delineate the anatomical or pathologicalstructures of interest, playing a crucial role in clinical diagnosis. Asubstantial amount of high-quality annotated data is crucial for constructinghigh-precision deep segmentation models. However, medical annotation is highlycumbersome and time-consuming, especially for medical videos or 3D volumes, dueto the huge labeling space and poor inter-frame consistency. Recently, afundamental task named Moving Object Segmentation (MOS) has made significantadvancements in natural images. Its objective is to delineate moving objectsfrom the background within image sequences, requiring only minimal annotations.In this paper, we propose the first foundation model, named iMOS, for MOS inmedical images. Extensive experiments on a large multi-modal medical datasetvalidate the effectiveness of the proposed iMOS. Specifically, with theannotation of only a small number of images in the sequence, iMOS can achievesatisfactory tracking and segmentation performance of moving objects throughoutthe entire sequence in bi-directions. We hope that the proposed iMOS can helpaccelerate the annotation speed of experts, and boost the development ofmedical foundation models.

A Foundation Model for General Moving Object Segmentation in Medical Images