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This repository provides the official implementation of Universal Model.

CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
Rank First in Medical Segmentation Decathlon (MSD) Competition
Jie Liu¹, Yixiao Zhang², Jie-Neng Chen², Junfei Xiao², Yongyi Lu²,
Yixuan Yuan¹, Alan Yuille², Yucheng Tang³, Zongwei Zhou^2
1 City University of Hong Kong, ² Johns Hopkins University, ³ NVIDIA
paper | code | slides | poster | talk | blog

• 01 Multi-Atlas Labeling Beyond the Cranial Vault - Workshop and Challenge (BTCV)
• 02 Pancreas-CT TCIA
• 03 Combined Healthy Abdominal Organ Segmentation (CHAOS)
• 04 Liver Tumor Segmentation Challenge (LiTS)
• 05 Kidney and Kidney Tumor Segmentation (KiTS)
• 06 Liver segmentation (3D-IRCADb)
• 07 WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image
• 08 AbdomenCT-1K
• 09 Multi-Modality Abdominal Multi-Organ Segmentation Challenge (AMOS)
• 10 Decathlon (Liver, Lung, Pancreas, HepaticVessel, Spleen, Colon
• 11 CT volumes with multiple organ segmentations (CT-ORG)
• 12 AbdomenCT 12organ

Main Requirements

connected-components-3d
h5py==3.6.0
monai==0.9.0
torch==1.11.0
tqdm
fastremap

python3 -m venv universal
source /data/zzhou82/environments/universal/bin/activate

git clone https://github.com/ljwztc/CLIP-Driven-Universal-Model.git
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113
pip install 'monai[all]'
pip install -r requirements.txt
cd pretrained_weights/
wget https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/swin_unetr.base_5000ep_f48_lr2e-4_pretrained.pt
cd ../

Dataset Pre-Process

1. Download the dataset according to the dataset link and arrange the dataset according to the dataset/dataset_list/PAOT.txt.
2. Modify the ORGAN_DATASET_DIR value in label_transfer.py (line 51) and NUM_WORKER (line 53)
3. python -W ignore label_transfer.py

Current Template

How expand to new dataset with new organ?

1. Set the following index for new organ. (e.g. 33 for vermiform appendix)
2. Check if there are any organs that are not divided into left and right in the dataset. (e.g. kidney, lung, etc.) The RL_Splitd in label_transfer.py is used to processed this case.
3. Set up a new transfer list for new dataset in TEMPLATE (line 58 in label_transfer.py). (If a new dataset with Intestine labeled as 1 and vermiform appendix labeled as 2, we set the transfer list as [19, 33])
4. Run the program label_transfer.py to get new post-processing labels.
   More details please take a look at common questions

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -W ignore -m torch.distributed.launch --nproc_per_node=8 --master_port=1234 train.py --dist True --data_root_path /mnt/zzhou82/PublicAbdominalData/ --num_workers 12 --num_samples 4 --cache_dataset --cache_rate 0.6 --uniform_sample

CUDA_VISIBLE_DEVICES=0 python -W ignore validation.py --data_root_path /mnt/zzhou82/PublicAbdominalData/ --start_epoch 10 --end_epoch 40 --epoch_interval 10 --cache_dataset --cache_rate 0.6

CUDA_VISIBLE_DEVICES=0 python -W ignore test.py --resume ./out/epoch_61.pth --data_root_path /mnt/zzhou82/PublicAbdominalData/ --store_result --cache_dataset --cache_rate 0.6

• Code release
• Dataset link
• Support different backbones (SwinUNETR, Unet, DiNTS, Unet++)
• Model release
• Pesudo label release
• Tutorials for generalizability, transferability, and extensibility

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

A lot of code is modified from monai.

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

@article{liu2023clip,
  title={CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection},
  author={Liu, Jie and Zhang, Yixiao and Chen, Jie-Neng and Xiao, Junfei and Lu, Yongyi and Landman, Bennett A and Yuan, Yixuan and Yuille, Alan and Tang, Yucheng and Zhou, Zongwei},
  journal={arXiv preprint arXiv:2301.00785},
  year={2023}
}