English

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

Image and Video Processing 2022-09-23 v1 Computer Vision and Pattern Recognition
Authors: Andriy Myronenko , Md Mahfuzur Rahman Siddiquee , Dong Yang , Yufan He , Daguang Xu
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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.

Cite

@article{arxiv.2209.10809,
  title  = {Automated head and neck tumor segmentation from 3D PET/CT},
  author = {Andriy Myronenko and Md Mahfuzur Rahman Siddiquee and Dong Yang and Yufan He and Daguang Xu},
  journal= {arXiv preprint arXiv:2209.10809},
  year   = {2022}
}

Comments

HECKTOR22 segmentation challenge. MICCAI 2022. arXiv admin note: text overlap with arXiv:2209.09546

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