Change Details

MedSAM aims to fulfill the role of a foundation model for universal medical image segmentation. It is trained on diverse and large-scale medical image segmentation dataset with 1,570,263 medical image-mask pairs, covering 10 imaging modalities, over 30 cancer types, and a multitude of imaging protocols. Paper: https://www.nature.com/articles/s41467-024-44824-z Code: https://github.com/bowang-lab/MedSAM Also interesting to checkout: Slicer has another plugin called MedSAM-Lite. Prima facie, it seems like a toned down UI for MedSAM without change in the backend models. Link: https://github.com/rasakereh/medsam-3dslicer An overall feasibility test should be done to check if it's worth (both interest-wise & effort-wise) integrating into MITK.

MedSAM aims to fulfill the role of a foundation model for universal medical image segmentation. It is trained on diverse and large-scale medical image segmentation dataset with 1,570,263 medical image-mask pairs, covering 10 imaging modalities, over 30 cancer types, and a multitude of imaging protocols. Paper: https://www.nature.com/articles/s41467-024-44824-z Code: https://github.com/bowang-lab/MedSAM Also interesting to checkout: MedSAM-Lite: A lightweight version of MedSAM for fast training and inference. https://github.com/bowang-lab/MedSAM/tree/LiteMedSAM An overall feasibility test should be done to check if it's worth (both interest-wise & effort-wise) integrating into MITK.

MedSAM aims to fulfill the role of a foundation model for universal medical image segmentation. It is trained on diverse and large-scale medical image segmentation dataset with 1,570,263 medical image-mask pairs, covering 10 imaging modalities, over 30 cancer types, and a multitude of imaging protocols. Paper: https://www.nature.com/articles/s41467-024-44824-z Code: https://github.com/bowang-lab/MedSAM Also interesting to checkout: Slicer has another plugin called MedSAM-Lite. Prima facie, it seems like a toned down UI for MedSAM without change in the backend modelsMedSAM-Lite: A lightweight version of MedSAM for fast training and inference. Link: https://github.com/rasakereh/medsam-3dslicerbowang-lab/MedSAM/tree/LiteMedSAM An overall feasibility test should be done to check if it's worth (both interest-wise & effort-wise) integrating into MITK.