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openmedlab / MedLSAM

MedLSAM: Localize and Segment Anything Model for 3D Medical Images
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MedLSAM: Localize and Segment Anything Model for 3D Medical Images

Key Features

Updates

The Segment Anything Model (SAM) has recently emerged as a groundbreaking model in the field of image segmentation. Nevertheless, both the original SAM and its medical adaptations necessitate slice-by-slice annotations, which directly increase the annotation workload with the size of the dataset. We propose MedLSAM to address this issue, ensuring a constant annotation workload irrespective of dataset size and thereby simplifying the annotation process. Our model introduces a few-shot localization framework capable of localizing any target anatomical part within the body. To achieve this, we develop a Localize Anything Model for 3D Medical Images (MedLAM), utilizing two self-supervision tasks: relative distance regression (RDR) and multi-scale similarity (MSS) across a comprehensive dataset of 14,012 CT scans. We then establish a methodology for accurate segmentation by integrating MedLAM with SAM. By annotating only six extreme points across three directions on a few templates, our model can autonomously identify the target anatomical region on all data scheduled for annotation. This allows our framework to generate a 2D bounding box for every slice of the image, which are then leveraged by SAM to carry out segmentations. We conducted experiments on two 3D datasets covering 38 organs and found that MedLSAM matches the performance of SAM and its medical adaptations while requiring only minimal extreme point annotations for the entire dataset. Furthermore, MedLAM has the potential to be seamlessly integrated with future 3D SAM models, paving the way for enhanced performance.

MedLSAM Image Fig.1 The overall segmentation pipeline of MedLSAM.

Feedback and Contact

Get Started

Main Requirements

torch>=1.11.0
tqdm
nibabel
scipy
SimpleITK
monai

Installation

  1. Create a virtual environment conda create -n medlsam python=3.10 -y and activate it conda activate medlsam
  2. Install Pytorch
  3. git clone https://github.com/openmedlab/MedLSAM
  4. Enter the MedSAM folder cd MedLSAM and run pip install -e .

Download Model

Download MedLAM checkpoint, SAM checkpoint, MedSAM checkpoint and place them at checkpoint/medlam.pth, checkpoint/sam_vit_b_01ec64.pth and checkpoint/medsam_vit_b.pth

Inference

GPU requirement

We recommend using a GPU with 12GB or more memory for inference.

Data preparation

Note: You can also download other CT datasets and place them any place you want. MedLSAM will automaticly apply the preprocessing procedure during the inference time, so please do not normalize the original CT images!!!

After downloading the datasets, you should sort the data into "support" and "query" groups. This does not require moving the actual image files. Rather, you need to create separate lists of file paths for each group.

For each group ("support" and "query"), perform the following steps:

  1. Create a .txt file listing the paths to the image files.
  2. Create another .txt file listing the paths to the corresponding label files.

Ensure that the ordering of images and labels aligns in both lists. These lists will direct MedLSAM to the appropriate files during the inference process.
The file names are not important, as long as the ordering of images and labels aligns in both lists.

Example format for the .txt files:

[vit] net_type = vit_b

[weight] medlam_load_path = checkpoint/medlam.pth vit_load_path = checkpoint/medsam_20230423_vit_b_0.0.1.pth

Each of the parameters is explained as follows:

- `support_image_ls`: The path to the list of support image files.  It is recommended to use between 3 and 10 support images.
- `support_label_ls`: The path to the list of support label files.
- `query_image_ls`: The path to the list of query image files.
- `query_label_ls`: The path to the list of query label files.
- `gt_slice_threshold`: The threshold value for ground truth slice selection.
- `bbox_mode`: The bounding box mode. It could be `SPL` (Sub-Patch Localization) or `WPL` (Whole-Patch Localization), as shown in Fig.2.
- `slice_interval`: Specifies the number of slices in a sub-patch. A smaller value results in more patches. This parameter should be of type `int`, and its value should be greater than 0. **Applicable only for Sub-Patch Localization (SPL), set to `False` for Whole-Patch Localization (WPL)**.
- `fg_class`: The list of foreground class to be used for localization and segmentation. This could be a list of integers indicating the class labels. You can only select a part of them as target classes.
- `seg_save_path`: The path to save the segmentation results in .npz format, **only required for MedLSAM**.
- `seg_png_save_path`: The path to save the segmentation results in .png format, **only required for MedLSAM**.
- `net_type`: The type of vision transformer model to be used, **only required for MedLSAM**. By default, this is set to vit_b.
- `medlam_load_path`: The path to load the pretrained MedLAM model weights.
- `vit_load_path`: The path to load the pretrained vision transformer model weights, **only required for MedLSAM**. You can change it to `checkpoint/sam_vit_b_01ec64.pth` to use the SAM model as segmentation basis.

<div align="center">
  <img src="https://github.com/openmedlab/MedLSAM/raw/main/fig/wpl_spl.png" width="80%">
</div>

*Fig.2 Comparison between Whole-Patch Localization (WPL) and Sub-Patch Localization (SPL) strategies.*

### Inference
- MedLAM (**Localize any anatomy target**)
```bash
CUDA_VISIBLE_DEVICES=0 python MedLAM_Inference.py --config_file path/to/your/test_medlam_config.txt

Example:

CUDA_VISIBLE_DEVICES=0 python MedLAM_Inference.py --config_file config/test_config/test_structseg_medlam.txt
CUDA_VISIBLE_DEVICES=0 python MedLAM_Inference.py --config_file config/test_config/test_word_medlam.txt

Results

Training

Training Data preparation

To do list

🛡️ License

This project is under the CC-BY-NC 4.0 license. See LICENSE for details.

🙏 Acknowledgement

📝 Citation

If you find this repository useful, please consider citing this paper:

@article{Lei2023medlam,
  title={MedLSAM: Localize and Segment Anything Model for 3D Medical Images},
  author={Wenhui Lei, Xu Wei, Xiaofan Zhang, Kang Li, Shaoting Zhang},
  journal={arXiv preprint arXiv:},
  year={2023}
}