Segment Anything in Medical Images

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Enforcing Geometry in Machine Learning for Computational Neuroimaging


Jun Ma


Medical imaging plays an indispensable role in clinical practice. Accurate and efficient medical image segmentation provides a means of delineating regions of interest and quantifying various clinical metrics. However, building customized segmentation models for each medical imaging task can be a daunting and time-consuming process, limiting the widespread adoption in clinical practice. In this talk, Jun will introduce MedSAM, a segmentation foundation model that enables universal segmentation across a wide range of medical imaging tasks and modalities. MedSAM achieved remarkable improvements in 30 segmentation tasks, surpassing the existing segmentation foundation model by a large margin. MedSAM also demonstrated zero-shot and few-shot capabilities to segment unseen tumor types and adapt to new imaging modalities with minimal effort. The results validate the versatility of MedSAM compared to existing customized segmentation models, emphasizing its potential to transform medical image segmentation and enhance clinical practice.


  • Cancer


  • Resource Sharing
  • Reproducibility