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Head and Neck Tumor Segmentation and Outcome Prediction: Third Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings 1st ed. 2023 [Mīkstie vāki]

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  • Formāts: Paperback / softback, 257 pages, height x width: 235x155 mm, weight: 421 g, 67 Illustrations, color; 8 Illustrations, black and white; XI, 257 p. 75 illus., 67 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 13626
  • Izdošanas datums: 19-Mar-2023
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031274199
  • ISBN-13: 9783031274190
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Head and Neck Tumor Segmentation and Outcome Prediction: Third Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings 1st ed. 2023Palielināt
  • Formāts: Paperback / softback, 257 pages, height x width: 235x155 mm, weight: 421 g, 67 Illustrations, color; 8 Illustrations, black and white; XI, 257 p. 75 illus., 67 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 13626
  • Izdošanas datums: 19-Mar-2023
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031274199
  • ISBN-13: 9783031274190
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783031274190.html
Keywords:
This book constitutes the Third 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, which was held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, on September 22, 2022.

The 22 contributions presented, as well as an overview paper, were carefully reviewed and selected from 24 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 883 delineated  PET/CT images was made available for training. 

Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and
Neck Tumor Segmentation and Outcome Prediction in PET/CT 1.- Automated head
and neck tumor segmentation from 3D PET/CTHECKTOR 2022 challenge report.- A
Coarse-to-Fine Ensembling Framework for Head and Neck Tumorand Lymph
Segmentation in CT and PET Images.- A General Web-based Platform for
Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT
Images.- Octree Boundary Transfiner: Effcient Transformers for
Tumor Segmentation Refinement.- Head and Neck Primary Tumor and Lymph Node
Auto-Segmentationfor PET/CT Scans.- Fusion-based Automated Segmentation in
Head and Neck Cancer via Advance Deep Learning Techniques.- Stacking Feature
Maps of Multi-Scaled Medical Images in U-Net for 3DHead and Neck Tumor
Segmentation.- A fine-tuned 3D U-net for primary tumor and affected lymph
nodessegmentationin fused multimodal images of oropharyngeal cancer.- A U-Net
convolutional neural network with multiclass Dice loss for automated
segmentation of tumors and lymph nodes from head and neck cancer PET/CT
images.- Multi-Scale Fusion Methodologies for Head and Neck Tumor
Segmentation.- Swin UNETR for tumor and lymph node delineation of
multicentre oropharyngeal cancer patients with PET/CT imaging.- Simplicity is
All You Need: Out-of-the-Box nnUNet followed by Binary-Weighted Radiomic
Model for Segmentation and Outcome Prediction in Head and Neck
PET/CT.- Radiomics-enhanced Deep Multi-task Learning for Outcome
Prediction in Head and Neck Cancer.- Recurrence-free Survival Prediction
under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and
Neck Cancers.- Joint nnU-Net and Radiomics Approaches for Segmentation
and Prognosis of Head and Neck Cancers with PET/CT images.- LC at HECKTOR
2022: The Effect and Importance of Training Data when Analyzing Cases of Head
and Neck Tumors using Machine Learning.- Towards Tumour Graph Learning for
Survival Prediction in Head NeckCancer Patients.- Combining nnUNet and AutoML
for Automatic Head and Neck Tumor Segmentation and Recurrence-Free Survival
Prediction in PET/CT Images.- Head and neck cancer localization with Retina
Unet for automated segmentation and time-to-event prognosis from PET/CT
images.- HNT-AI: An Automatic Segmentation Framework for Head and
Neck Primary Tumors and Lymph Nodes in FDG-PET/CT images.- Head and Neck
Tumor Segmentation with 3D UNet and Survival Prediction with Multiple
Instance Neural Network.- Deep Learning and Machine Learning Techniques for
Automated PET/CT Segmentation and Survival Prediction in Head and Neck
Cancer.- Deep learning and radiomics based PET/CT image feature
extractionfrom auto segmented tumor volumes for recurrence-free
survival prediction in oropharyngeal cancer patients.