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

Source: [Automated head and neck tumor segmentation from 3D PET/CT](<https://www.sota2.com/research/paper/68fa6d711e209131cea39e74>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:34:23.044Z

Authors: Andriy Myronenko, Md Mahfuzur Rahman Siddiquee, Dong Yang, Yufan He, Daguang Xu

Published: 2022-09-22T06:24:09.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2209.10809>)

[PDF](<https://arxiv.org/pdf/2209.10809.pdf>)