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CLIP-Driven Universal Model

Paper

This repository provides the official implementation of Universal Model.

CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
${\color{red} {\textbf{Rank First in Medical Segmentation Decathlon (MSD) Competition}}}$ (see leaderboard)
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
ICCV, 2023
paper | code | slides | poster | talk | blog

Large Language-Image Model for Multi-Organ Segmentation and Cancer Detection from Computed Tomography
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
RSNA, 2023
abstract | code | slides

Model

Architecture	Param	Download
U-Net	19.08M	link
Swin UNETR	62.19M	link

Dataset

• 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

0. Preliminary

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
wget wget https://www.dropbox.com/s/lh5kuyjxwjsxjpl/Genesis_Chest_CT.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 ORGAN_DATASET_DIR and NUM_WORKER in label_transfer.py
3. python -W ignore label_transfer.py

Current Template

Index	Organ	Index	Organ
1	Spleen	17	Left Lung
2	Right Kidney	18	Colon
3	Left Kidney	19	Intestine
4	Gall Bladder	20	Rectum
5	Esophagus	21	Bladder
6	Liver	22	Prostate
7	Stomach	23	Left Head of Femur
8	Aorta	24	Right Head of Femur
9	Postcava	25	Celiac Trunk
10	Portal Vein and Splenic Vein	26	Kidney Tumor
11	Pancreas	27	Liver Tumor
12	Right Adrenal Gland	28	Pancreas Tumor
13	Left Adrenal Gland	29	Hepatic Vessel Tumor
14	Duodenum	30	Lung Tumor
15	Hepatic Vessel	31	Colon Tumor
16	Right Lung	32	Kidney Cyst

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

1. Training

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

2. Validation

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

3. Evaluation

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

Todo

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

Acknowledgement

A lot of code is modified from . This work was supported by the Lustgarten Foundation for Pancreatic Cancer Research and partially by the Patrick J. McGovern Foundation Award. We appreciate the effort of the MONAI Team to provide open-source code for the community.

Citation

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}
}