AortaSeg24

documentation/competitions/AortaSeg24.md

@@ -0,0 +1,46 @@
+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](https://aortaseg24.grand-challenge.org/). 
+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](../../nnunetv2/training/nnUNetTrainer/variants/data_augmentation/nnUNetTrainerDA5.py). Training was performed on an A100 40GB GPU.
+
+# Experiment Planning and Preprocessing
+After converting the data into the [nnUNet format](../../../nnUNet/documentation/dataset_format.md) (either keep and just rename the .mha files or convert them to .nii.gz), you can run the preprocessing:
+
+```bash
+nnUNetv2_plan_and_preprocess -d 610 -c 3d_fullres -pl nnUNetPlannerResEncL -np 16
+```
+
+# Training
+We train our model using:
+
+```bash
+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](../../../nnUNet/documentation/how_to_use_nnunet.md). Specifically, once the training is finished, run:
+
+```bash
+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 
+
+```bash
+nnUNetv2_predict_from_modelfolder -i INPUT_FOLDER -o OUTPUT_FOLDER -m MODEL_FOLDER
+```
+
+for the five fold ensemble.

nnunetv2/dataset_conversion/Dataset119_ToothFairy2_All.py

@@ -11,7 +11,7 @@
 
 
 def mapping_DS119() -> Dict[int, int]:
-    """Remove all NA Classes and make Class IDs continious"""
+    """Remove all NA Classes and make Class IDs continuous"""
     mapping = {}
     mapping.update({i: i for i in range(1, 19)})  # [1-10]->[1-10] | [11-18]->[11-18]
     mapping.update({i: i - 2 for i in range(21, 29)})  # [21-28]->[19-26]

nnunetv2/training/nnUNetTrainer/variants/competitions/aortaseg24.py

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+from nnunetv2.training.nnUNetTrainer.variants.data_augmentation.nnUNetTrainerNoMirroring import nnUNetTrainer_onlyMirror01
+from nnunetv2.training.nnUNetTrainer.variants.data_augmentation.nnUNetTrainerDA5 import nnUNetTrainerDA5
+
+class nnUNetTrainer_onlyMirror01_DA5(nnUNetTrainer_onlyMirror01, nnUNetTrainerDA5):
+    pass