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).

Requirements

• r1
• r2
• r3
• r4

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:

python hecktor/src/data/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:

python hecktor/model/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

TODO

Results

Paper

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

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