3D Slicer Plugin for Segment anything in medical images
302
stars
47
commits
Python
primary language
Apr 7, 2025
updated
This is the official repository for 3D Slicer Plugin for MedSAM: Segment Anything in Medical Images.
https://github.com/bowang-lab/MedSAMSlicer/assets/19947331/c7ef20f1-7e23-4e6c-8be1-e39931eeb841
You can watch a video tutorial of installation steps here.
Install Extension tab lookup PyTorch and install the extension. Our plugin currently relies on this. Restart 3D Slicer afterward.Welcome to Slicer drop-down menu in the toolbar at the top and navigate to Developer Tools > Extension Wizard.select Extension and locate the MedSAM folder among the extracted files and directories at step 2. Accept if asked to add the new modules.Welcome to Slicer drop-down menu, under the Segmentation sub-menu, MedSAMLite option is added. By choosing it, you get to the final steps.Choose a folder to install module dependencies and click on Install dependencies. It can take several minutes.Remove all pre-existing files from step#2 and install the new version as instructed before.
You can watch a video guide for usage here.
Welcome to Slicer drop-down menu, under the Segmentation sub-menu, MedSAMLite option is added. By choosing it, you get to the final steps.Prepare Data accordion menu you can choose from preset preprocessing methods (optional, but extremely helpful)Select the Region of Interest accordion menu, you can click on Attach ROI to select the region for segmentation. It is possible to use Set Current Frame As Selection's Start and Set Current Frame As Selection's End to set ROI boundaries on the L-R slice.Start Segmentation accordion menu, you have two different ways to infer the segmentation:
Send Image button. It will prepare the whole 3D image for the module to process. It will take several minutes. You will need to do this step only once for each image. As long as the image is not changed (new preprocessing / cropping / etc.) you do not have to redo this step.Segmentation button to get the segmentation results. You can replace your ROI or delete and re-attach it (step 4) as many times as you need and repeat step 5.b without going over step 5.a again.Single Segmentation. After confirming, the segmentation would be inferred.Segmentation and Source volume drop-down menus you can choose the right segmentation group and manually refine the segmentation results. You can also assign different color to each segmentation mask.Python
99.7%
3D Slicer Plugin for Segment anything in medical images
302
stars
47
commits
Python
primary language
Apr 7, 2025
updated
This is the official repository for 3D Slicer Plugin for MedSAM: Segment Anything in Medical Images.
https://github.com/bowang-lab/MedSAMSlicer/assets/19947331/c7ef20f1-7e23-4e6c-8be1-e39931eeb841
You can watch a video tutorial of installation steps here.
Install Extension tab lookup PyTorch and install the extension. Our plugin currently relies on this. Restart 3D Slicer afterward.Welcome to Slicer drop-down menu in the toolbar at the top and navigate to Developer Tools > Extension Wizard.select Extension and locate the MedSAM folder among the extracted files and directories at step 2. Accept if asked to add the new modules.Welcome to Slicer drop-down menu, under the Segmentation sub-menu, MedSAMLite option is added. By choosing it, you get to the final steps.Choose a folder to install module dependencies and click on Install dependencies. It can take several minutes.Remove all pre-existing files from step#2 and install the new version as instructed before.
You can watch a video guide for usage here.
Welcome to Slicer drop-down menu, under the Segmentation sub-menu, MedSAMLite option is added. By choosing it, you get to the final steps.Prepare Data accordion menu you can choose from preset preprocessing methods (optional, but extremely helpful)Select the Region of Interest accordion menu, you can click on Attach ROI to select the region for segmentation. It is possible to use Set Current Frame As Selection's Start and Set Current Frame As Selection's End to set ROI boundaries on the L-R slice.Start Segmentation accordion menu, you have two different ways to infer the segmentation:
Send Image button. It will prepare the whole 3D image for the module to process. It will take several minutes. You will need to do this step only once for each image. As long as the image is not changed (new preprocessing / cropping / etc.) you do not have to redo this step.Segmentation button to get the segmentation results. You can replace your ROI or delete and re-attach it (step 4) as many times as you need and repeat step 5.b without going over step 5.a again.Single Segmentation. After confirming, the segmentation would be inferred.Segmentation and Source volume drop-down menus you can choose the right segmentation group and manually refine the segmentation results. You can also assign different color to each segmentation mask.Python
99.7%