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.

翻译：医学图像分割旨在勾画出感兴趣的解剖或病理结构，在临床诊断中发挥着关键作用。大量高质量标注数据对于构建高精度深度分割模型至关重要。然而，医学标注极为繁琐且耗时，尤其对于医学视频或三维体数据而言，由于标注空间庞大且帧间一致性差，这一问题更为突出。近年来，一项名为运动目标分割（MOS）的基础任务在自然图像领域取得了显著进展，其目标是在图像序列中从背景中分割出运动物体，且仅需少量标注。本文首次提出针对医学图像MOS的基础模型，命名为iMOS。在大规模多模态医学数据集上的大量实验验证了所提出的iMOS的有效性。具体而言，仅需标注序列中少量图像，iMOS即可在双向全序列中实现运动目标的满意跟踪与分割性能。我们希望所提出的iMOS能加速专家标注速度，并推动医学基础模型的发展。