Fig.1: Different paradigms for brain lesion segmentation and an overview of the proposed framework:
a) the traditional paradigm;
b) the foundation model paradigm;
c) the proposed mixture of modality experts (MoME) framework for constructing the foundation model.
Fig.2:Detailed analysis of the MoME result on seen datasets. a) A radar chart that compares the average Dice score of foundation models from the perspectives of different modalities and lesion types. b) t-SNE plots of latent spaces for nnU-Net and MoME, where each dot represents a brain image.
Please also cite this paper [link] if you are using MoME for your research!
@InProceedings{
author="Zhang, Xinru and Ou, Ni and Basaran, Berke Doga and Visentin, Marco and Gu, Renyang and Ouyang, Cheng and Liu, Yaou and Matthew, Paul M.
and Ye, Chuyang and Bai, Wenjia",
title="A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Experts",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024",
year="2024",
publisher="Springer International Publishing",
}
conda create --name MoME python=3.9.18
conda activate MoME
git clone https://github.com/ZhangxinruBIT/MoME.git
cd MoME/MoME_foundation
pip install -e .
Additionally, since we have made significant changes to the third-party library named dynamic_network_architectures, we did not take the original one.
Data Preprocessing
We perform affine registration, skull stripping, and brain imaging cropping, along with their respective annotations. But make sure you have downloaded the Advanced Normalization Tools (ANTs) before. The following command can be executed after you convert the dataset to the format required by nnU-NetV2.
cd MoME/Codes_prepro
python one-step.py -dataset_path -ss #If all the brain images in your dataset include the skull, please add the `-ss` flag. If none of the images include the skull, you can omit the flag. Do not mix brain images with and without skulls in the same directory, as their preprocessing steps are different.
Datasplit
Please set your own datasplit, for me we save the information in the datasplit.json. And then modify the do_split in nnUNetTrainer with the code like:
if self.fold == 'MoME':
tr_keys = DATA['train']['BraTS'] + DATA['train']['ATLAS'] +DATA['train']['OASIS']\
+DATA['train']['ISLES']+DATA['train']['WMH2017']+DATA['train']['MSSEG']
val_keys = DATA['val']['BraTS'] + DATA['val']['ATLAS'] +DATA['val']['OASIS']\
+DATA['val']['ISLES']+DATA['val']['WMH2017']+DATA['val']['MSSEG']
to better manage the data split.
For traning and inference, since we implement within the nnU-NetV2, the well-introduced usage can be follwed at nnU-Net. Please ensure that the dataset format meets the requirements expected by nnU-Net with our preprocessed data (Rename the directories to resemble imagesTr and labelsTr under Dataset_XXXX.).
Experiment planning and preprocessing
nnUNetv2_plan_and_preprocess -d DATASET_ID --verify_dataset_integrity
Training
Before utilizing the MoME training, make sure you have acquired pretrained modality experts trained with nnUNet using corresponding modality images. Our pretrained modality experts are available at Hugging Face Pretrained Experts.
You can adjust the path to align with the your own specialized modality nnUNets in nnUNetTrainer.initialize(from line 214 to 245). Alternatively, you can utilize our well-trained experts and configure the path accordingly.
nnUNet_train XXX 3d_fullres MoME
Inference
When performing inference, ensure you have the checkpoint list similar to MoME_CHECKPOINT.
nnUNetv2_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -d DATASET_NAME_OR_ID -f MoME -c 3d_fullres -chk checkpoint_best.pth
Additionally, the final MoME model has been released on Hugging Face at MoME_CHECKPOINT.