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

Federico Semeraro, Alexandre Quintart, Sergio Fraile Izquierdo, Joseph C. Ferguson
6/14/2023
cs.CVcs.AIcs.LG

No official code found. Best implementation: fedesemeraro/SlicerTomoSAM (68 stars, last commit 2025-07-11).

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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Code Implementations(1)

fedesemeraro/SlicerTomoSAMCommunity100%

An extension of 3D Slicer using the Segment Anything Model (SAM) to aid the segmentation of 3D data from tomography or other imaging techniques.

6811CMake, PythonJun 7, 20231 years agoNOASSERTION
3d-slicer-extensionsegment-anything-model

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Cite this paper

@article{semeraro2023tomosam,
  title  = {TomoSAM: a 3D Slicer extension using SAM for tomography segmentation},
  author = {Federico Semeraro and Alexandre Quintart and Sergio Fraile Izquierdo and Joseph C. Ferguson},
  year   = {2023},
  eprint = {2306.08609},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.08609v1}
}

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