SAM-Med3D [Paper]

💪 Recommendation

If you are interested in computer vision, we recommend checking out OpenGVLab for more exciting projects like SAM-Med2D!

🌟 Highlights

📚 Curated the most extensive volumetric medical dataset to date for training, boasting 131K 3D masks and 247 categories.
🚤 Achieved efficient promptable segmentation, requiring 10 to 100 times fewer prompt points for satisfactory 3D outcomes.
🏆 Conducted a thorough assessment of SAM-Med3D across 15 frequently used volumetric medical image segmentation datasets.

🔨 Usage

Training

To train the SAM-Med3D model on your own data, follow these steps:

Prepare Your Training Data: Ensure that your training data is organized according to the structure shown in the data/validation directories. Here is an example of how your file structure should look:

data/train
      ├── adrenal
      │ ├── ct_WORD
      │ │ ├── imagesTr
      │ │ │ ├── word_0025.nii.gz
      │ │ │ ├── ...
      │ │ ├── labelsTr
      │ │ │ ├── word_0025.nii.gz
      │ │ │ ├── ...
      ├── ...

Then modify the utils/data_paths.py according to your own data.

Run the Training Script: Execute the following command in your terminal:

bash train.sh

This will start the training process of the SAM-Med3D model on your prepared data.

Evaluation

Prepare your own dataset and refer to the samples in data/validation to replace them according to your specific scenario. Then you can simply run bash infer.sh to test SAM-Med3D on your data. Make sure the masks are processed into the one-hot format (have only two values: the main image (foreground) and the background).

python validation.py --seed 2023\
 -vp ./results/vis_sam_med3d \
 -cp ./ckpt/sam_med3d.pth \
 -tdp ./data/validation -nc 1 \
 --save_name ./results/sam_med3d.py

vp: visualization path, dir to save the final visualization files
cp: checkpoint path
tdp: test data path, where your data is placed
nc: number of clicks of prompt points
save_name: filename to save evaluation results

For validation of SAM and SAM-Med2D on 3D volumetric data, you can refer to infer_sam.sh and infer_med2d.sh for details.

Hint: We also provide a simple script sum_result.py to help summarize the results from file like ./results/sam_med3d.py.

🔗 Checkpoint

Our checkpoint of SAM-Med3D can be downloaded from Google Drive and Baidu NetDisk (pwd:r5o3).

Other checkpoints are available with their official link: SAM and SAM-Med2D.

🗼 Method

🏆 Results

💡 Overall Performance

Model	Prompt	Resolution	Inference Time (s)	Overall Dice
SAM	N points	1024×1024×N	13	17.01
SAM-Med2D	N points	256×256×N	4	42.75
SAM-Med3D	1 point	128×128×128	2	49.91
SAM-Med3D	10 points	128×128×128	6	60.94

Note: Quantitative comparison of different methods on our evaluation dataset. Here, N denotes the count of slices containing the target object (10 ≤ N ≤ 200). Inference time is calculated with N=100, excluding the time for image processing and simulated prompt generation.

💡 Dice on Different Anatomical Architecture and Lesions

Model	Prompt	A&T	Bone	Brain	Cardiac	Gland	Muscle	Seen Lesion	Unseen Lesion
SAM	N points	17.19	22.32	17.68	2.82	11.62	3.50	12.03	8.88
SAM-Med2D	N points	46.79	47.52	19.24	32.23	43.55	35.57	26.08	44.87
SAM-Med3D	1 point	46.80	54.77	34.48	46.51	57.28	53.28	42.02	40.53
SAM-Med3D	10 points	55.81	69.13	40.71	52.86	65.01	67.28	50.52	48.44

Note: Comparison from the perspective of anatomical structure and lesion. A&T represents Abdominal and Thorax targets. N denotes the count of slices containing the target object (10 ≤ N ≤ 200).

💡 Visualization

🗓️ Ongoing

🎫 License

This project is released under the Apache 2.0 license.

💬 Discussion Group

BTW, welcome to follow our Zhihu official account, we will share more information on medical imaging there.

🙏 Acknowledgement

We thank all medical workers and dataset owners for making public datasets available to the community.
Thanks to the open-source of the following projects:
- Segment Anything
- SAM-Med2D

👋 Hiring & Global Collaboration

Hiring: We are hiring researchers, engineers, and interns in General Vision Group, Shanghai AI Lab. If you are interested in Medical Foundation Models and General Medical AI, including designing benchmark datasets, general models, evaluation systems, and efficient tools, please contact us.
Global Collaboration: We're on a mission to redefine medical research, aiming for a more universally adaptable model. Our passionate team is delving into foundational healthcare models, promoting the development of the medical community. Collaborate with us to increase competitiveness, reduce risk, and expand markets.
Contact: Junjun He(hejunjun@pjlab.org.cn), Jin Ye(yejin@pjlab.org.cn), and Tianbin Li (litianbin@pjlab.org.cn).

openmedlab/SAM-Med3D