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[autoPET III Winning Solution] MICCAI 2024 Challenge Submission: Team LesionTracer

🏆 This repository provides the code for running our approach to the AutoPET III Challenge (Team LesionTracer) which was awared the first place in the model centric category. When using this repository, please cite our paper:

From FDG to PSMA: A Hitchhiker's Guide to Multitracer, Multicenter Lesion Segmentation in PET/CT Imaging

    arXiv

Authors:
Maximilian Rokuss, Balint Kovacs, Yannick Kirchhoff, Shuhan Xiao, Constantin Ulrich, Klaus H. Maier-Hein and Fabian Isensee

Author Affiliations:
Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg
Faculty of Mathematics and Computer Science, Heidelberg University

Overview

Our model builds on nnU-Net with a ResEncL architecture preset as a strong baseline. We then introduce several improvements:

  • We adjusted the plans file using a patch size of [192,192,192] such that the model has a high contextual understanding. You can find the the plans here (remember to change the "dataset_name" field to your dataset name).
  • The model is pretrained on a diverse collection of medical imaging datasets to establish a strong anatomical understanding, which we then fine-tune on the autoPET III challange dataset. The pretrained checkpoint we use for initializing the model before training is availabe here (Dataset619_nativemultistem). Note, that this is not the final model checkpoint.
  • The model is trained using misalignment data augmentation as well as omitting the smoothing term in the dice loss calcuation.
  • We use a dual-headed architecture for organ and lesion segmentation which improves performance as well as speeds up convergence, especially in cases without lesions.

You can download the final checkpoint here! We updated the trainer class on Oct 29, 2024. Please download again in case you had troubles with the old checkpoint.

Getting started

Installation

We recommend to create a new conda environment and then run:

git clone https://github.com/MIC-DKFZ/autopet-3-submission.git
cd autopet-3-submission
pip install -e .

Preprocessing

Download the autoPET dataset and use the standard nnUNet preprocessing pipeline. You can adjust the number of processes for faster processing. You can freely chose your dataset number which we quote as DATASET_ID_LESIONS.

nnUNetv2_plan_and_preprocess -d DATASET_ID_LESIONS -c 3d_fullres -np 20 -npfp 20

Extract organs from CT image

In order to train on the organ segmentation as a secondary objective we use TotalSegmentator to predict 10 anatomical structures in part relevant to PET tracer uptake: spleen, kidneys, liver, urinary bladder, lungs, brain, heart, stomach, prostate, and glands in the head region (parotid glands, submandibular glands).

You can either:

  1. Use our predicted organ masks which we made availabe in this repo, or

  2. Follow these instructions for your own dataset: This one is a bit time-consuming to redo, so hang with me here. First, install TotalSegmentator using pip install TotalSegmentator. We did it in a separate environment. Then put this script into your nnUNet_raw dataset directory for autoPETIII. Running this script can take a long time for the whole dataset (several days on our machines) since the TotalSegmentator inference is not optimized to handle several cases simultaneously.

    python predict_and_extract_organs.py

When this step is done, copy the raw nnUNet dataset such that you have a new dataset which is identical. The original dataset containing lesions annotations should have a different DATASET_ID_ORGANS than the original DATASET_ID_LESIONS. E.g. "Dataset200_autoPET3_lesions" and "Dataset201_autoPET3_organs". Then exchange the content of the labelsTr folder with the provided organ labels or in case you ran the above script (TotalSegmentator inference) use the labels from labelsTr_organs. Now run the preprocessing again for the new dataset. Important: do not use the --verify_dataset_integrity flag.

Lastly, to combine the datasets run

nnUNetv2_merge_lesion_and_organ_dataset -l DATASET_ID_LESIONS -o DATASET_ID_ORGANS

Now you are good to go to start a training. Use the dataset with DATASET_ID_LESIONS for any further steps. If everything runs smoothly you could discard the dataset folder with DATASET_ID_ORGANS.

If you want to do the last step step manually head over to the preprocessed folder. You should have two folders now with the different preprocessed datasets, containing lesion or organ segmentations. Navigate to the dataset containing the organ labels and then into the folder nnUNetPlans_3d_fullres. Either unpack the .npz files using a script or start a default nnUNet training on the organ dataset such that they are automatically unpacked to .npy files. Now we are almost there. Search for all files ending with _seg.npy and rename them to have the ending _seg_org.npy. Finally copy these files into the nnUNetPlans_3d_fullres folder of the preprocessed dataset containing the lesion segmentations. That's it - easy right?

Training

Training the model can be simply achieved by downloading the pretrained (not the final) checkpoint (Dataset619_nativemultistem) and running:

nnUNetv2_train DATASET_ID_LESIONS 3d_fullres 0 -tr autoPET3_Trainer -p nnUNetResEncUNetLPlansMultiTalent -pretrained_weights /path/to/pretrained/weights/fold_all/checkpoint_final.pth

We train a five fold cross-validation for our final submission.

Inference

After training your own model or downloading our final checkpoint here you can use the standard nnUNet inference, for more information see here. MODEL_FOLDER refers to the folder containing all 5 folds. Remember that the files in the input folder have to be named according to the nnUNet format, i.e. the CT files end with _0000.nii.gz and the PET images with _0001.nii.gz.

nnUNetv2_predict_from_modelfolder -i INPUT_FOLDER -o OUTPUT_FOLDER -m MODEL_FOLDER

Happy coding! 🚀

Citation

@article{rokuss2024fdgpsmahitchhikersguide,
      title={From FDG to PSMA: A Hitchhiker's Guide to Multitracer, Multicenter Lesion Segmentation in PET/CT Imaging}, 
      author={Maximilian Rokuss and Balint Kovacs and Yannick Kirchhoff and Shuhan Xiao and Constantin Ulrich and Klaus H. Maier-Hein and Fabian Isensee},
      journal={ArXiv},
      year={2024},
      publisher={arXiv},
      eprint={2409.09478},
      archivePrefix={arXiv},
      url={https://arxiv.org/abs/2409.09478}, 
}

Acknowledgements

nnU-Net is developed and maintained by the Applied Computer Vision Lab (ACVL) of Helmholtz Imaging and the Division of Medical Image Computing at the German Cancer Research Center (DKFZ).