1st Place Solution for the HECKTOR challenge

The official implementation of the winning solution for the MICCAI 2020 HEad and neCK TumOR segmentation challenge (HECKTOR).

Main requirements

• PyTorch 1.6.0 (cuda 10.2)
• SimpleITK 1.2.4 (ITK 4.13)
• nibabel 3.1.1
• skimage 0.17.2

Dataset

Train and test images are available through the competition website. The concise description of the dataset is present in notebooks/make_dataset.ipynb.

Data preprocessing

The data preprocessing consists of:

• Resampling the pair of PET & CT images for each patient to a common reference space.
• Extracting the region of interest (bounding box) of the size of 144x144x144 voxels.
• Saving the transformed images in NIfTI format.

To prepare the dataset in an interactive manner, one can use notebooks/make_dataset.ipynb, that gives an explanation about each step. Alternatively, the fully automated data preprocessing can be performed by running src/data/make_dataset.py. All required parameters must be provided as a single config file in the YAML data format:

cd hecktor/src/data/
python make_dataset.py -p hecktor/config/make_dataset.yaml

Use /config/make_dataset.yaml to specify all required parameters.

Training

For training the model from scratch, one can use notebooks/model_train.ipynb setting all parameters right in the notebook. Otherwise, with all parameters written in the config file, one needs to run hecktor/model/train.py from its current directory:

cd hecktor/model/
python train.py -p hecktor/config/model_train.yaml

All parameters are described in hecktor/config/model_train.yaml that should be used as a template to build your own config file.

Inference

For inference, run the script hecktor/model/predict.py with parameters defined in the config file hecktor/config/model_predict.yaml:

cd hecktor/model/
python predict.py -p hecktor/config/model_predict.yaml

Model weights

To reproduce results presented in the paper on different train / validation folds, one must download and save pretrained weights in the folder hecktor/model/weights/. Weights of a single model are stored in files named {split}_best_model_weights.pt. IDs of the patients in the train / validation folds for each data split are stored in train_val_{split}.pkl files located in the folder hecktor/src/data/splits/.

In order to download weights of all pretrained model (eight models in total) built on the different train / validation, use the following link.

Example

Paper

If you use this code in you research, please cite the following paper (arXiv):

Iantsen A., Visvikis D., Hatt M. (2021) Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images. In: Andrearczyk V., Oreiller V., Depeursinge A. (eds) Head and Neck Tumor Segmentation. HECKTOR 2020. Lecture Notes in Computer Science, vol 12603. Springer, Cham. https://doi.org/10.1007/978-3-030-67194-5_4