Welcome to the official repository for the TriALS challenge at MICCAI 2024. This repository is built on nnUNet, incorporating additional functionalities for training models such as SAMed ✂️, Mamba Variants 🐍, and MedNeXT ⚡. We aim to establish a benchmark for MICCAI 2024 challenges.
2024/8/8: TriALS is now open for submissions! Find detailed instructions here.2024/4/4: Pre-registration for the TriALS challenge is now open Pre-register here.2024/4/4: The TriALS repository now supports SAMed ✂️, Mamba Variants 🐍, and MedNeXT ⚡.
- nnU-Net V2 ✓
- Swin-UNETR ✓
- SegResNet ✓
- MedNeXt ✓
- LightM-UNet ✓
- U-Mamba ✓
- SAMed ✓
- SAM-3D (In Progress)
- Liver Tumor Segmentation 5-Fold (100 epoch) (LiTS) ✓
The Dice coefficient for lesions is calculated as an average across all lesions. For the evaluation code and a detailed list of metrics, please refer to the evaluation folder.
| Model | Lesion Dice | Lesion Jaccard | Lesion RMSD | Liver Dice | Liver Jaccard | Liver RMSD |
|---|---|---|---|---|---|---|
| nnUNet2D | 77.14 (±1.69) | 64.05 (±2.19) | 1.42 (±0.14) | 95.30 (±0.96) | 91.21 (±1.56) | 9.24 (±1.33) |
| nnUNet3D | 76.29 (±2.98) | 63.22 (±3.68) | 1.60 (±0.24) | 91.84 (±0.86) | 85.45 (±1.29) | 22.39 (±5.23) |
| SegResNet | 76.15 (±1.36) | 63.18 (±1.80) | 1.93 (±0.74) | 90.99 (±1.73) | 83.97 (±2.86) | 25.57 (±5.44) |
| SwinUNETR | 74.21 (±1.92) | 60.74 (±2.22) | 1.86 (±0.39) | 86.74 (±1.36) | 77.24 (±1.73) | 32.70 (±3.06) |
| UMambaBot | 77.31 (±2.34) | 64.41 (±3.04) | 1.74 (±0.42) | 92.30 (±0.98) | 86.12 (±1.60) | 21.58 (±5.64) |
| LightM-UNet | 76.77 (±1.58) | 63.70 (±1.77) | 1.97 (±0.71) | 91.23 (±1.26) | 84.37 (±1.89) | 24.76 (±3.67) |
| MedNext-B | 77.44 (±2.03) | 64.57 (±2.67) | 1.63 (±0.52) | 93.60 (±1.25) | 88.49 (±1.88) | 17.28 (±3.45) |
| SAMed-B | 76.99 (±2.12) | 63.84 (±2.62) | 1.81 (±0.38) | 94.24 (±0.49) | 89.26 (±0.82) | 20.79 (±4.70) |
| MedNext-L | 77.85 (2.79) | 65.02 (±3.64) | 1.48 (±0.30) | 94.55 (±1.07) | 90.00 (±1.77) | 14.69 (±3.08) |
| SAMed-H | 78.60 (±1.56) | 65.77 (±2.03) | 1.50 (±0.15) | 95.58 (±0.68) | 91.65 (±1.14) | 8.67 (±2.09) |
- Liver Tumor Segmentation 5-Fold (1000 epoch) (LiTS) (in-progress)
| Model | Lesion Dice | Lesion Jaccard | Lesion RMSD | Liver Dice | Liver Jaccard | Liver RMSD |
|---|---|---|---|---|---|---|
| nnUNet3D | 79.57 (±1.33) | 67.06 (±1.84) | 1.26 (±0.11) | 96.28 (±0.62) | 93.01 (±1.03) | 4.35 (±1.22) |
- TriALS (in-progress)
To get started, follow these steps:
- Clone the Repository
git clone https://github.com/xmed-lab/TriALS
- Create and Activate a Virtual Environment
conda create -n TriALS python=3.10 conda activate TriALS
- Install Pytorch: Follow the instructions here:
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
- Install the Repository
cd TriALS pip install -e .
We follow the nnU-Net V2 guideline for data preparation, detailed below and accessible here.
-
Download and Prepare the MSD Liver Dataset, and extract it into the
data/nnUNet_raw_data_basedirectory.gdown https://drive.google.com/uc?id=1jyVGUGyxKBXV6_9ivuZapQS8eUJXCIpu # or use wget wget https://msd-for-monai.s3-us-west-2.amazonaws.com/Task03_Liver.tar
tar -xvf Task03_Liver.tar -C data/nnUNet_raw_data_base
-
Export nnUNet-v2 evironment variables:
export nnUNet_raw=<path-to>/data/nnUNet_raw_data_base/ export nnUNet_preprocessed=<path-to>/data/nnUNet_preprocessed/ export nnUNet_results=<path-to>/data/nnUNet_results/
-
Convert the MSD Liver dataset to nnU-Net format:
python nnunetv2/dataset_conversion/convert_MSD_dataset.py -i data/nnUNet_raw_data_base/Task03_Liver/
Sanity Check: Please verify that the dataset is organized in the following structure:
data/
├── nnUNet_raw_data_base/
│ ├── Dataset003_Liver/
│ │ ├── imagesTr
│ │ │ ├── liver_1_0000.nii.gz
│ │ │ ├── liver_2_0000.nii.gz
│ │ │ ├── ...
│ │ ├── labelsTr
│ │ │ ├── liver_1.nii.gz
│ │ │ ├── liver_2.nii.gz
│ │ │ ├── ...
│ │ ├── dataset.json
- Preprocess the LiTS Dataset: Replace
<DATASET_ID>in the command below with3:nnUNetv2_plan_and_preprocess -d <DATASET_ID> --verify_dataset_integrity
To train the models, follow these instructions:
- General Training Command on GPU 0. For the preliminary benchmark, all models are trained for 100 epochs. Please note that all model variants, with the exception of nnUNet, are trained without deep supervision.
CUDA_VISIBLE_DEVICES=0 nnUNetv2_train <DATASET_ID> <CONFIGURATION> <FOLD_NUM> -tr <TRAINER>
Model Configuration Trainer nnU-Net 2D 2d nnUNetTrainer_100epochs nnU-Net 3D 3d_fullres nnUNetTrainer_100epochs Swin-UNETR 3d_fullres nnUNetTrainerSwinUNETR_100epochs SegResNet 3d_fullres nnUNetTrainerSegResNet_100epochs U-Mamba 3d_fullres nnUNetTrainerUMambaBot_100epochs LightM-UNet 3d_fullres nnUNetTrainerLightMUNet_100epochs MedNext-B-5 3d_fullres nnUNetTrainerV2_MedNeXt_B_kernel5_100epochs MedNext-L-5 3d_fullres nnUNetTrainerV2_MedNeXt_L_kernel5_100epochs SAMed-B 2d_p256 nnUNetTrainerV2_SAMed_b_r_4_100epochs SAMed-H 2d_p512 nnUNetTrainerV2_SAMed_h_r_4_100epochs
- U-Mamba 🐍
CUDA_VISIBLE_DEVICES=0 nnUNetv2_train 3 3d_fullres 0 -tr nnUNetTrainerUMambaBot- MedNeXT ⚡
CUDA_VISIBLE_DEVICES=0 nnUNetv2_train 3 3d_fullres 0 -tr nnUNetTrainerV2_MedNeXt_B_kernel5- SAMed ✂️
Download the checkpoint of original SAM into checkpoint
# sam-b checkpoint
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth -O checkpoints/sam_vit_b_01ec64.pth
# sam-h checkpoint
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth -O checkpoints/sam_vit_h_4b8939.pthCUDA_VISIBLE_DEVICES=0 nnUNetv2_train 3 2d_p256 0 -tr nnUNetTrainerV2_SAMed_b_r_4- Validation Inference
To generate model validation outputs, execute the command below. To acquire probabilities for the optimal configuration, append the --npz flag, noting this requires significant disk space.
CUDA_VISIBLE_DEVICES=0 nnUNetv2_train DATASET_ID CONFIGURATION FOLD_NUM -tr TRAINER --val --npz- Inference on unseen
nnUNetv2_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -d DATASET_ID -c CONFIGURATION -tr TRAINER_NAME- Best Configuration and Ensembles (in progress)
If you utilize the TriALS dataset, please cite the relevant paper:
@misc{elbatel2026trialstriphasicaidedliverlesion,
title={TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT},
author={Marawan Elbatel and Mohamed Ghonim and Jiaji Mao and Zhuosheng Lin and Katharina Eckstein and Andrés Martínez Mora and Jonathan Deissler and Maximilian Rokuss and Constantin Ulrich and Zdravko Marinov and Wenhui Deng and Baoxun Li and Huijun Hu and Jun Shen and Mohanad Ghonim and Khadiga Omar Nassar and Mariam Elbakry and Menna Dyab and Amr Muhammad Abdo Salem and Nouran Elghitany and Noha Elghitany and Yi Qin and Xuanqi Huang and Haonan Wang and Shao-Woo Yen and Ahmed Elghamry Saba and Salma Ahmad and Xinyan Fang and Jiahao Zhang and Xiaodi Wang and Xinghua Ma and Gongning Luo and Jessica C. Delmoral and João Manuel R. S. Tavares and Ankan Deria and Adinath Dukre and Yutong Xie and Imran Razzak and Dongwook Kim and Matthew Choi and Hanxiao Zhang and Minghui Zhang and Xin You and Abdul Qayyum and Steven A. Niederer and Moona Mazher and Rachika E. Hamadache and Ricardo Montoya-del-Angel and Robert Martí and Xavier Lladó and Toufiq Musah and Livingstone Eli Ayivor and Enrique Almar-Munoz and Agnes Mayr and Kaouther Mouheb and Esther E. Bron and Stefan Klein and Ahmed Abouelhoda and Amira Adel and Susan Adil Ali and Rainer Stiefelhagen and Klaus H. Maier-Hein and Fabian Isensee and Aya Yassin and Xiaomeng Li},
year={2026},
eprint={2605.16572},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2605.16572},
}
If you utilize the baselines in this repository for your research, please consider citing the relevant papers for Swin-UNETR, SegResNet, LightM-UNet, U-Mamba, MedNext, SAMed, and nnU-Net.
We would like to acknowledge the contributions of nnU-Net and the authors of the baseline models: LightM-UNet, MedNeXT, and SAMed. This repository builds upon their foundational code and work.