One Model to Rule them All: Towards Universal Segmentation for Medical Images with Text Prompts

arXiv:2312.17183v2 Announce Type: replace-cross
Abstract: In this study, we focus on building up a model that aims to Segment Anything in medical scenarios, driven by Text prompts, termed as SAT. Our main contributions are three folds: (i) for dataset construction, we combine multiple knowledge sources to construct the first multi-modal knowledge tree on human anatomy, including 6502 anatomical terminologies; Then we build up the largest and most comprehensive segmentation dataset for training, by collecting over 22K 3D medical image scans …

abstract arxiv building construction cs.cv dataset eess.iv focus images knowledge medical multiple one model prompts segment segment anything segmentation study text them type universal