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：医学图像分割旨在描绘感兴趣的解剖或病理结构，在临床诊断中发挥着至关重要的作用。大量高质量的标注数据是构建高精度深度分割模型的关键。然而，医学标注非常繁琐且耗时，尤其是在医学视频或3D体积中，由于标注空间巨大且帧间一致性差，这一问题更为突出。近期，名为运动物体分割（MOS）的基础任务在自然图像中取得了显著进展，其目标是从图像序列的背景中分割出运动物体，且只需少量标注。在本文中，我们提出了首个用于医学图像中MOS的基础模型，命名为iMOS。在大规模多模态医学数据集上的广泛实验验证了所提出的iMOS的有效性。具体而言，仅需对序列中少量图像进行标注，iMOS即可在整个序列中双向实现跟踪和分割运动物体的令人满意的性能。我们希望所提出的iMOS能够加速专家的标注速度，并推动医学基础模型的发展。