A Foundation Model for General Moving Object Segmentation in Medical Images

arXiv:2309.17264v4 Announce Type: replace-cross
Abstract: 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 …

abstract annotated data annotation arxiv clinical cs.ai cs.cv cs.lg data diagnosis foundation foundation model general image images medical moving playing precision quality role segmentation type