FastSAM3D_slicer

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FastSAM3D_slicer

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A 3D Slicer extension for FastSAM3D

FastSAM3D_slicer 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# FastSAM-3DSlicer: A 3D-Slicer Extension for 3D Volumetric Segment Anything Model with Uncertainty Quantification

[paper](https://arxiv.org/abs/2403.09827)
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[FastSAM3D code](https://github.com/arcadelab/FastSAM3D)
\
[video](https://youtu.be/oJ9ZhnPWqSs)
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[ARCADE lab](https://arcade.cs.jhu.edu/), [Johns Hopkins University](https://www.jhu.edu/)
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![Results](https://raw.githubusercontent.com/arcadelab/FastSAM3D_slicer/main/utils/results.PNG)

# Table of contents
- [Fast Segment-anything model in 3D medical image (FastSAM3D): A 3D Slicer extension to FastSAM3D](#fast-segment-anything-model-in-3d-medical-image-fastsam3d-a-3d-slicer-extension-for-fastsam3d)
- [Table of contents](#table-of-contents)
  - [Introduction ](#introduction-)
  - [Before You Try ](#before-you-try-)
  - [How to Use ](#how-to-use-)
    - [Features ](#features-)
  - [Citation ](#citation-)

## Introduction <a name="introduction"></a>
What are SAM, SAMMed3D, FastSAM3D and FastSAM3D_slicer?
* SAM is the vision foundation model developed by Meta, [Segment Anything](https://segment-anything.com).
* SAMMed3D is the 3D version of SAM based on medical image.
* FastSAM3D is the faster version of SAMMed3D.
* FastSAM3D_slicer is the 3D slicer extension based on FastSAM3D and SAMMed3D.
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Why FastSAM3D and FastSAM3D_slicer?
* FastSAM3D is about 10 times faster compare with SAMMed3D, with small accuracy lose. 
* FastSAM3D_slicer provide an interface for user to do segmentation based on FastSAM3D for medical image intutively.
* And it's really fast! 
## Before You Try <a name="before-you-try"></a>
Make sure you have more than 3GB storage to download model weights and install pytorch.
Don't forget to use the provided resample.py file to do resample for medical image. 
## How to Use <a name="how-to-use"></a>
step 1: Download the file and compress it.

step 2: open the 3D slicer and open the extension manager, download the pytorch extension, and restart the slicer.
![step21](https://raw.githubusercontent.com/arcadelab/FastSAM3D_slicer/main/utils/step21.PNG)
![step22](https://raw.githubusercontent.com/arcadelab/FastSAM3D_slicer/main/utils/step22.PNG)
step 3: open the extension wizard in the 3D slicer.
![step3](https://raw.githubusercontent.com/arcadelab/FastSAM3D_slicer/main/utils/step3.PNG)
step 4: click the select extension and choose the compressed file in step 1.
![step4](https://raw.githubusercontent.com/arcadelab/FastSAM3D_slicer/main/utils/step4.PNG)
step 5: the extension will now be available here
![step5](https://raw.githubusercontent.com/arcadelab/FastSAM3D_slicer/main/utils/step5.PNG)
### Features <a name="features"></a>
- 3 View Inference
- Data type
  - NIFTI file
  - volume
- models
  - FastSAM3D
  - SAMMed3D
- interactions
  - include and exclude points
## Citation <a name="citation"></a>
If you use FastSAM3D_slicer in your research, please consider use the following BibTeX entry.

```bibtex
@misc{shen2024fastsam3d,
      title={FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images}, 
      author={Yiqing Shen and Jingxing Li and Xinyuan Shao and Blanca Inigo Romillo and Ankush Jindal and David Dreizin and Mathias Unberath},
      year={2024},
      eprint={2403.09827},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

@article{shen2024fastsam,
  title={FastSAM-3DSlicer: A 3D-Slicer Extension for 3D Volumetric Segment Anything Model with Uncertainty Quantification},
  author={Shen, Yiqing and Shao, Xinyuan and Romillo, Blanca Inigo and Dreizin, David and Unberath, Mathias},
  journal={arXiv preprint arXiv:2407.12658},
  year={2024}
}
```

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