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Showing 1 - 2 of
2 matches in All Departments
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Head and Neck Tumor Segmentation and Outcome Prediction - Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings (Paperback, 1st ed. 2022)
Vincent Andrearczyk, Valentin Oreiller, Mathieu Hatt, Adrien Depeursinge
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R2,371
Discovery Miles 23 710
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Ships in 10 - 15 working days
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This book constitutes the Second 3D Head and Neck Tumor
Segmentation in PET/CT Challenge, HECKTOR 2021, which was held in
conjunction with the 24th International Conference on Medical Image
Computing and Computer-Assisted Intervention, MICCAI 2021. The
challenge took place virtually on September 27, 2021, due to the
COVID-19 pandemic.The 29 contributions presented, as well as an
overview paper, were carefully reviewed and selected form numerous
submissions. This challenge aims to evaluate and compare the
current state-of-the-art methods for automatic head and neck tumor
segmentation. In the context of this challenge, a dataset of 325
delineated PET/CT images was made available for training.
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Head and Neck Tumor Segmentation - First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings (Paperback, 1st ed. 2021)
Vincent Andrearczyk, Valentin Oreiller, Adrien Depeursinge
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R1,580
Discovery Miles 15 800
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Ships in 10 - 15 working days
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This book constitutes the First 3D Head and Neck Tumor Segmentation
in PET/CT Challenge, HECKTOR 2020, which was held in conjunction
with the 23rd International Conference on Medical Image Computing
and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in
October 2020. The challenge took place virtually due to the
COVID-19 pandemic.The 2 full and 8 short papers presented together
with an overview paper in this volume were carefully reviewed and
selected form numerous submissions. This challenge aims to evaluate
and compare the current state-of-the-art methods for automatic head
and neck tumor segmentation. In the context of this challenge, a
dataset of 204 delineated PET/CT images was made available for
training as well as 53 PET/CT images for testing. Various deep
learning methods were developed by the participants with excellent
results.
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