This is the official repository for our paper: Segment anything model 2: an application to 2D and 3D medical images, where we evaluated Meta AI's Segment Anything Model 2 (SAM2) on many medical imaging datasets. The code will be ready momentarily.
The code requires installing SAM2's repository Segment Anything Model 2 (SAM2). The model and dependencies can be found in SA2M's repository, or you can install them with
git clone https://github.com/facebookresearch/segment-anything-2.git
cd segment-anything-2 & pip install -e .
First, download SAM's model checkpoint
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_large.pt
Note that our evaluation is conducted on sam2_hiera_large.pt, but you can switch to other models simply by changing the checkpoint path and the model configuration.
If you want to run SAM (and competing methods) with iterative prompts, run the code with:
python3 eval_sam2_3d.py --dataset DATASET_PATH --num_class NUM_OF_CLASS (--bidirectional)
By default, it will run with fmode=2 (selecting the middle slice), pmode=1 (1 point at the center of the largest component).
More choices are included in Figure 2, 
Although all the evaluations were conducted on publicly available datasets, we do not plan to share them directly since many of them require additional agreement before use. We kindly ask you to follow the official website for each dataset.
To evaluate your own dataset, you need to format the dataset as:
XXX:
images:
volume1:
00000.jpg
00001.jpg
...
volume2:
00000.jpg
00001.jpg
...
masks:
volume1:
00000.png
00001.png
...
volume2:
00000.png
00001.png
...
where images and masks should have the same name. Note that images have to be jpg format as required by SAM 2. You can also follow SAM 2's instructions on dataset format for details.
- We are planning to release the interactive mode.
- We are planning to integrate channel selection as an additional hyperparameter.
If you find our work to be useful for your research, please cite our paper:
@article{dong2024segment,
title={Segment anything model 2: an application to 2d and 3d medical images},
author={Dong, Haoyu and Gu, Hanxue and Chen, Yaqian and Yang, Jichen and Mazurowski, Maciej A},
journal={arXiv preprint arXiv:2408.00756},
year={2024}
}