TY  - GEN
AB  - 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.
AU  - Andrearczyk, Vincent,
AU  - Oreiller, Valentin,
AU  - Depeursinge, Adrien,
CN  - RC78.7.D53
DO  - 10.1007/978-3-030-67194-5
DO  - doi
ID  - 1434201
KW  - Diagnostic imaging
KW  - Artificial intelligence
KW  - Cancer
KW  - Optical data processing.
KW  - Bioinformatics.
KW  - Machine learning.
KW  - Software engineering.
KW  - Imagerie pour le diagnostic
KW  - Intelligence artificielle en médecine
KW  - Traitement optique de l'information.
KW  - Bio-informatique.
KW  - Apprentissage automatique.
KW  - Génie logiciel.
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-67194-5
N2  - 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.
SN  - 9783030671945
SN  - 3030671941
T1  - Head and neck tumor segmentation :First Challenge, HECKTOR 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, proceedings /
TI  - Head and neck tumor segmentation :First Challenge, HECKTOR 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, proceedings /
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-67194-5
VL  - 12603
ER  -