# Automated head and neck tumor segmentation from 3D PET/CT

arXiv: https://arxiv.org/abs/2209.10809 · HTML: https://mlsanity.com/paper/2209.10809/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 4 references that are in the map.
- First posted on arXiv 22 Sep 2022.

## Builds on (most cited first, 3 of 3)

- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018)
- [Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images](https://mlsanity.com/paper/2201.04138/) (2022)

## Built on by (most cited first, 9 of 9)

- [The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI](https://mlsanity.com/paper/2405.18368/) (2024)
- [Comprehensive framework for evaluation of deep neural networks in detection and quantification of lymphoma from PET/CT images: clinical insights, pitfalls, and observer agreement analyses](https://mlsanity.com/paper/2311.09614/) (2023)
- [EDUE: Expert Disagreement-Guided One-Pass Uncertainty Estimation for Medical Image Segmentation](https://mlsanity.com/paper/2403.16594/) (2024)
- [AutoPET Challenge: Tumour Synthesis for Data Augmentation](https://mlsanity.com/paper/2409.08068/) (2024)
- [ENSAM: an efficient foundation model for interactive segmentation of 3D medical images](https://mlsanity.com/paper/2509.15874/) (2025)
- [Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancer](https://mlsanity.com/paper/2409.06605/) (2024)
- [Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided Radiotherapy](https://mlsanity.com/paper/2411.14752/) (2024)
- [Comprehensive Evaluation of Quantitative Measurements from Automated Deep Segmentations of PSMA PET/CT Images](https://mlsanity.com/paper/2504.16237/) (2025)
- [AMO-ENE: Attention-based Multi-Omics Fusion Model for Outcome Prediction in Extra Nodal Extension and HPV-associated Oropharyngeal Cancer](https://mlsanity.com/paper/2604.09280/) (2026)

## Abstract (arXiv)

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.