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{ "item_title" : "Head and Neck Tumor Segmentation", "item_author" : [" Vincent Andrearczyk", "Valentin Oreiller", "Adrien Depeursinge "], "item_description" : "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.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/3/03/067/193/3030671933_b.jpg", "price_data" : { "retail_price" : "54.99", "online_price" : "54.99", "our_price" : "54.99", "club_price" : "54.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Head and Neck Tumor Segmentation|Vincent Andrearczyk

Head and Neck Tumor Segmentation : First Challenge, Hecktor 2020, Held in Conjunction with Miccai 2020, Lima, Peru, October 4, 2020, Proceedings

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Overview

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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Details

  • ISBN-13: 9783030671938
  • ISBN-10: 3030671933
  • Publisher: Springer
  • Publish Date: January 2021
  • Dimensions: 9.21 x 6.14 x 0.25 inches
  • Shipping Weight: 0.39 pounds
  • Page Count: 109

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