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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 / Vincent Andrearczyk, Valentin Oreiller, Adrien Depeursinge (eds.).

By: Contributor(s): Material type: TextTextSeries: Lecture notes in computer science ; 12603. | LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics.Publisher: Cham, Switzerland : Springer, [2021]Description: 1 online resource (x, 109 pages) : illustrations (some color)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783030671945
  • 3030671941
Other title:
  • HECKTOR 2020
  • MICCAI 2020
Subject(s): Genre/Form: Additional physical formats: Printed edition:: No titleDDC classification:
  • 616.07/54 23
LOC classification:
  • RC78.7.D53
Online resources:
Contents:
Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT -- Two-stage approach for segmenting gross tumor volume in head and neck cancer with CT and PET imaging -- The Head and Neck Tumor Segmentation Using nnU-Net with Spatial and Channel 'Squeeze & Excitation' Blocks -- Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images -- Automatic Head and Neck Tumor Segmentation in PET/CT with Scale Attention Network -- Iteratively Refine the Segmentation of Head and Neck Tumor in FDG-PET and CT images -- Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET images -- Oropharyngeal Tumour Segmentation using Ensemble 3D PET-CT Fusion Networks for the HECKTOR Challenge -- Patch-based 3D UNet for Head and Neck Tumor Segmentation with an Ensemble of Conventional and Dilated Convolutions -- Tumor Segmentation in Patients with Head and Neck Cancers using Deep Learning based-on Multi-modality PET/CT Images -- GAN-based Bi-modal Segmentation using Mumford-Shah Loss: Application to Head and Neck Tumors in PET-CT Images.
Summary: 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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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.

Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT -- Two-stage approach for segmenting gross tumor volume in head and neck cancer with CT and PET imaging -- The Head and Neck Tumor Segmentation Using nnU-Net with Spatial and Channel 'Squeeze & Excitation' Blocks -- Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images -- Automatic Head and Neck Tumor Segmentation in PET/CT with Scale Attention Network -- Iteratively Refine the Segmentation of Head and Neck Tumor in FDG-PET and CT images -- Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET images -- Oropharyngeal Tumour Segmentation using Ensemble 3D PET-CT Fusion Networks for the HECKTOR Challenge -- Patch-based 3D UNet for Head and Neck Tumor Segmentation with an Ensemble of Conventional and Dilated Convolutions -- Tumor Segmentation in Patients with Head and Neck Cancers using Deep Learning based-on Multi-modality PET/CT Images -- GAN-based Bi-modal Segmentation using Mumford-Shah Loss: Application to Head and Neck Tumors in PET-CT Images.

Includes bibliographical references and index.

Online resource; title from PDF title page (SpringerLink, viewed March 3, 2021).

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