Automated head and neck tumor segmentation from 3D PET/CT
This work addresses automated tumor segmentation for medical imaging in head and neck cancer, but it is incremental as it applies existing methods to a specific challenge.
The authors tackled the problem of segmenting head and neck tumors from 3D PET/CT images in the HECKTOR 2022 challenge, achieving first place with an aggregated dice score of 0.78802.
Head and neck tumor segmentation challenge (HECKTOR) 2022 offers a platform for researchers to compare their solutions to segmentation of tumors and lymph nodes from 3D CT and PET images. In this work, we describe our solution to HECKTOR 2022 segmentation task. We re-sample all images to a common resolution, crop around head and neck region, and train SegResNet semantic segmentation network from MONAI. We use 5-fold cross validation to select best model checkpoints. The final submission is an ensemble of 15 models from 3 runs. Our solution (team name NVAUTO) achieves the 1st place on the HECKTOR22 challenge leaderboard with an aggregated dice score of 0.78802.