# SAM & SAM 2 in 3D Slicer: SegmentWithSAM Extension for Annotating Medical Images

arXiv: https://arxiv.org/abs/2408.15224 · HTML: https://mlsanity.com/paper/2408.15224/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 27 Aug 2024.

## Builds on (most cited first, 12 of 12)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation](https://mlsanity.com/paper/1908.10454/) (2019)
- [Medical Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2009.13120/) (2020)
- [SAM3D: Segment Anything Model in Volumetric Medical Images](https://mlsanity.com/paper/2309.03493/) (2023)
- [MeLo: Low-rank Adaptation is Better than Fine-tuning for Medical Image Diagnosis](https://mlsanity.com/paper/2311.08236/) (2023)
- [SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAM](https://mlsanity.com/paper/2304.05622/) (2023)
- [Segment anything model 2: an application to 2D and 3D medical images](https://mlsanity.com/paper/2408.00756/) (2024)
- [SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI](https://mlsanity.com/paper/2401.12974/) (2024)
- [FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images](https://mlsanity.com/paper/2403.09827/) (2024)
- [TomoSAM: a 3D Slicer extension using SAM for tomography segmentation](https://mlsanity.com/paper/2306.08609/) (2023)
- [FastSAM-3DSlicer: A 3D-Slicer Extension for 3D Volumetric Segment Anything Model with Uncertainty Quantification](https://mlsanity.com/paper/2407.12658/) (2024)

## Built on by (most cited first, 3 of 3)

- [SAM2 for Image and Video Segmentation: A Comprehensive Survey](https://mlsanity.com/paper/2503.12781/) (2025)
- [Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2](https://mlsanity.com/paper/2505.01854/) (2025)
- [Towards Better Ultrasound Video Segmentation Foundation Model: An Empirical study on SAM2 Finetuning from Data Perspective](https://mlsanity.com/paper/2511.05731/) (2025)

## Abstract (arXiv)

Creating annotations for 3D medical data is time-consuming and often requires highly specialized expertise. Various tools have been implemented to aid this process. Segment Anything Model 2 (SAM 2) offers a general-purpose prompt-based segmentation algorithm designed to annotate videos. In this paper, we adapt this model to the annotation of 3D medical images and offer our implementation in the form of an extension to the popular annotation software: 3D Slicer. Our extension allows users to place point prompts on 2D slices to generate annotation masks and propagate these annotations across entire volumes in either single-directional or bi-directional manners. Our code is publicly available on https://github.com/mazurowski-lab/SlicerSegmentWithSAM and can be easily installed directly from the Extension Manager of 3D Slicer as well.