Automated head and neck tumor segmentation from 3D PET/CT
- URL: http://arxiv.org/abs/2209.10809v1
- Date: Thu, 22 Sep 2022 06:24:09 GMT
- Title: Automated head and neck tumor segmentation from 3D PET/CT
- Authors: Andriy Myronenko, Md Mahfuzur Rahman Siddiquee, Dong Yang, Yufan He,
Daguang Xu
- Abstract summary: Head and neck tumor segmentation challenge (HECKTOR) 2022 offers a platform to compare solutions to segmentation of tumors and lymph nodes from 3D CT and PET images.
We re-sample all images to a common resolution, crop around head and neck region, and train SegResNet semantic segmentation network from MONAI.
Our solution achieves the 1st place on the HECKTOR22 challenge leaderboard with an aggregated dice score of 0.78802.
- Score: 9.814838162752112
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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