Description |
1 online resource (x, 109 pages) : illustrations (some color) |
Series |
Lecture notes in computer science ; 12603 |
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LNCS sublibrary, SL 6, Image processing, computer vision, pattern recognition, and graphics |
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Lecture notes in computer science ; 12603.
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LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics.
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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 |
Bibliography |
Includes bibliographical references and index |
Notes |
Online resource; title from PDF title page (SpringerLink, viewed March 3, 2021) |
Subject |
Diagnostic imaging -- Data processing -- Congresses
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Artificial intelligence -- Medical applications -- Congresses
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Cancer -- Treatment -- Technological innovations -- Congresses
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Optical data processing.
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Bioinformatics.
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Machine learning.
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Software engineering.
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Computational Biology
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Machine Learning
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Artificial intelligence -- Medical applications
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Bioinformatics
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Cancer -- Treatment -- Technological innovations
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Diagnostic imaging -- Data processing
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Machine learning
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Optical data processing
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Software engineering
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Genre/Form |
proceedings (reports)
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Conference papers and proceedings
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Conference papers and proceedings.
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Actes de congrès.
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Form |
Electronic book
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Author |
Andrearczyk, Vincent, editor
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Oreiller, Valentin, editor
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Depeursinge, Adrien, editor
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International Conference on Medical Image Computing and Computer-Assisted Intervention (23rd : 2020 : Online), jointly held conference.
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ISBN |
9783030671945 |
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3030671941 |
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