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, I 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. Our results validate the versatility of MedSAM compared to existing customized segmentation models, emphasizing its potential to transform medical image segmentation and enhance clinical practice. This work underscores the significance of creating adaptable and efficient segmentation tools that can meet the growing demands of personalized healthcare and contribute to the ongoing progress in medical imaging analysis.
This seminar will be held online via ZOOM .
Join Zoom Meeting: https://maths-cam-ac-uk.zoom.us/j/93331132587?pwd=MlpReFY3MVpyVThlSi85TmUzdTJxdz09