Authors:
Maximilian Rokuss, Michael Baumgartner, Yannick Kirchhoff, Klaus H. Maier-Hein*, Fabian Isensee*
*: equal contribution
Author Affiliations:
Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg
Helmholtz Imaging
Introduction
This document describes our submission to the AortaSeg24 Challenge. Our model is essentially a nnU-Net ResEnc L with modified data augmentation. We disable left/right mirroring and use the heavy data augmentation DA5 Trainer. Training was performed on an A100 40GB GPU.
Experiment Planning and Preprocessing
After converting the data into the nnUNet format (either keep and just rename the .mha files or convert them to .nii.gz), you can run the preprocessing:
nnUNetv2_plan_and_preprocess -d 610 -c 3d_fullres -pl nnUNetPlannerResEncL -np 16
Training
We train our model using:
nnUNetv2_train 610 3d_fullres all -p nnUNetResEncUNetLPlans -tr nnUNetTrainer_onlyMirror01_DA5
Models are trained from scratch. We train one model using all the images and a five fold cross validation ensemble for the submission.
We recommend to increase the number of processes used for data augmentation. Otherwise you can run into CPU bottlenecks.
Use export nnUNet_n_proc_DA=32 or higher (if your system permits!).
Inference
For inference you can use the default nnUNet inference functionalities. Specifically, once the training is finished, run:
nnUNetv2_predict_from_modelfolder -i INPUT_FOLDER -o OUTPUT_FOLDER -m MODEL_FOLDER -f all
for the single model trained on all the data and
nnUNetv2_predict_from_modelfolder -i INPUT_FOLDER -o OUTPUT_FOLDER -m MODEL_FOLDER
for the five fold ensemble.