TomoSAM: a 3D Slicer extension using SAM for tomography segmentation

• URL: http://arxiv.org/abs/2306.08609v1
• Date: Wed, 14 Jun 2023 16:13:27 GMT
• Title: TomoSAM: a 3D Slicer extension using SAM for tomography segmentation
• Authors: Federico Semeraro, Alexandre Quintart, Sergio Fraile Izquierdo, Joseph C. Ferguson
• Abstract summary: TomoSAM has been developed to integrate the cutting-edge Segment Anything Model (SAM) into 3D Slicer. SAM is a promptable deep learning model that is able to identify objects and create image masks in a zero-shot manner. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography or other imaging techniques.
• Score: 62.997667081978825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: TomoSAM has been developed to integrate the cutting-edge Segment Anything Model (SAM) into 3D Slicer, a highly capable software platform used for 3D image processing and visualization. SAM is a promptable deep learning model that is able to identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography or other imaging techniques, which would otherwise require a laborious manual segmentation process. The source code associated with this article can be found at https://github.com/fsemerar/SlicerTomoSAM

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