mlsanityData to 14 Oct 2026

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

arXiv 2306.08609 ↗First posted 14 Jun 2023cs.CV cs.AI cs.LGcited by 5

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Abstract (arXiv)

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

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 4 of its 16 references that are in the map. First posted on arXiv 14 Jun 2023.

Builds on

4 of 16 references.

References · 4

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2023Segment Anythingcited by 8,234
  3. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  4. 2023SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAMcited by 31

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Cited by year

Citing papers per year: 2023: 1; 2024: 4.

5 papers.

  1. 2024Foundation Models for Biomedical Image Segmentation: A Surveycited by 28
  2. 2023Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaulcited by 4
  3. 2024SAM & SAM 2 in 3D Slicer: SegmentWithSAM Extension for Annotating Medical Imagescited by 3
  4. 2024arcjetCV: an open-source software to analyze material ablationcited by 0
  5. 2024Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resectioncited by 0