Titlebook: Head and Neck Tumor Segmentation and Outcome Prediction; Third Challenge, HEC Vincent Andrearczyk,Valentin Oreiller,Adrien Depeu Conference

grudging

Finasteride

Canopy

,Automated Head and?Neck Tumor Segmentation from?3D PET/CT HECKTOR 2022 Challenge Report,t model checkpoint. 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 (..). It is implemented with Auto3DSeg (..).

多嘴多舌

Melanoma

0302-9743 urrent state-of-the-art methods for automatic head and neck tumor segmentation. In the context of this challenge, a dataset of 883 delineated? PET/CT images was made available for training.?.978-3-031-27419-0978-3-031-27420-6Series ISSN 0302-9743 Series E-ISSN 1611-3349

減至最低

,Overview of?the?HECKTOR Challenge at?MICCAI 2022: Automatic Head and?Neck Tumor Segmentation and?Ou a total of 883 cases consisting of FDG-PET/CT images and clinical information, split into 524 training and 359 test cases. The best methods obtained an aggregated Dice Similarity Coefficient (.) of 0.788 in Task 1, and a Concordance index (C-index) of 0.682 in Task 2.

ENACT

,A Coarse-to-Fine Ensembling Framework for?Head and?Neck Tumor and?Lymph Segmentation in?CT and?PET metastatic lymph nodes, where we proposed a ensembling refinement model. This framework is evaluated quantitatively with aggregated Dice Similarity Coefficient (DSC) of 0.77782 in the task 1 of the HECKTOR 2022 challenge[., .] as team SJTU426.

FLOUR

,A Fine-Tuned 3D U-Net for?Primary Tumor and?Affected Lymph Nodes Segmentation in?Fused Multimodal Int binary segmentation models are chosen, one for the primary tumor and one for the lymph nodes. During testing, majority voting is applied. Our results show promising performance on the training and validation cohorts, while moderate performance was observed in the test cohort.