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

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arxiv 2209.10809 v1 pith:4SDE26SL submitted 2022-09-22 eess.IV cs.CV

Andriy Myronenko , Md Mahfuzur Rahman Siddiquee , Dong Yang , Yufan He , Daguang Xu This is my paper · ORCID
classification eess.IVcs.CV
keywords segmentationheadneckchallengehecktorimagessolutiontumor
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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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  1. Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided Radiotherapy

    eess.IV 2024-11 conditional novelty 4.0 of 10

    A challenge report showing a MedNeXt small model winning Task 1 (DSC 0.8254) and an nnUNet ensemble placing 8th in Task 2 (0.7005) for MRI-guided radiotherapy tumor segmentation.

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