Head and neck tumor segmentation : Second Challenge, HECKTOR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, proceedings / / edited by Vincent Andrearczyk [and three others]

• Pubblicazione: Cham, Switzerland : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (339 pages)
• Disciplina: 616.0754
• Soggetto topico: Artificial intelligence - Medical applications; Diagnostic imaging - Data processing
• Persona (resp. second.): AndrearczykVincent
• Note generali: Includes index.
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images -- 1 Introduction: Research Context -- 2 Dataset -- 2.1 Mission of the Challenge -- 2.2 Challenge Dataset -- 3 Task 1: Segmentation -- 3.1 Methods: Reporting of Challenge Design -- 3.2 Results: Reporting of Segmentation Task Outcome -- 4 Tasks 2 and 3: Outcome Prediction -- 4.1 Methods: Reporting of Challenge Design -- 4.2 Results: Reporting of Challenge Outcome -- 5 Discussion: Putting the Results into Context -- 5.1 Outcomes and Findings -- 5.2 Limitations of the Challenge -- 6 Conclusions -- 1 Challenge Information -- 2 Image Acquisition Details -- References -- CCUT-Net: Pixel-Wise Global Context Channel Attention UT-Net for Head and Neck Tumor Segmentation -- 1 Introduction -- 2 Method -- 2.1 Data Preprocessing -- 2.2 Network Architecture -- 2.3 Transformer Block -- 2.4 Pixel-Wise Global Context Channel Attention -- 2.5 Squeeze and Excitation Normalization -- 2.6 Loss Function -- 3 Experiments -- 3.1 Dataset -- 3.2 Experiment Settings -- 3.3 Results -- 4 Conclusion -- References -- A Coarse-to-Fine Framework for Head and Neck Tumor Segmentation in CT and PET Images -- 1 Introduction -- 2 Method -- 2.1 Network Architecture -- 2.2 Coarse-to-Fine Framework -- 2.3 Training Details -- 3 Experiments -- 3.1 Dataset -- 3.2 Preprocessing -- 3.3 Implementation Detail -- 4 Results and Discussion -- References -- Automatic Segmentation of Head and Neck (H& -- N) Primary Tumors in PET and CT Images Using 3D-Inception-ResNet Model -- 1 Introduction -- 2 Material and Methods -- 2.1 Head and Neck Tumor 2021(HECKTOR2021) Dataset Descriptions -- 2.2 Proposed Method -- 2.3 Training and Optimization Parameters -- 3 Results and Discussion -- 3.1 Quantitative Results. ; 3.2 Qualitative Results -- 4 Conclusion and Future Work -- References -- The Head and Neck Tumor Segmentation in PET/CT Based on Multi-channel Attention Network -- 1 Introduction -- 2 Method -- 2.1 Data Preprocessing -- 2.2 MCA Network -- 2.3 Network Architecture -- 2.4 Loss Function -- 2.5 Ensembling -- 3 Results -- 4 Discussion and Conclusion -- References -- Multimodal Spatial Attention Network for Automatic Head and Neck Tumor Segmentation in FDG-PET and CT Images -- 1 Introduction -- 2 Method -- 2.1 Dataset -- 2.2 Data Preprocessing -- 2.3 Network Architecture -- 2.4 Training Scheme -- 3 Results -- 4 Discussion and Conclusions -- References -- PET Normalizations to Improve Deep Learning Auto-Segmentation of Head and Neck Tumors in 3D PET/CT -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data -- 2.2 PET-Clip and PET-Sin Normalization -- 2.3 Convolutional Neural Network (CNN) -- 2.4 Experiments -- 3 Results -- 4 Discussion and Conclusion -- References -- The Head and Neck Tumor Segmentation Based on 3D U-Net -- 1 Introduction -- 2 Data and Methods -- 2.1 Dataset -- 2.2 Data Preprocessing -- 2.3 Network Structure -- 2.4 Implementation Details -- 3 Results -- 4 Discussions -- 5 Conclusions -- References -- 3D U-Net Applied to Simple Attention Module for Head and Neck Tumor Segmentation in PET and CT Images -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 SimAM -- 2.3 Network Architecture -- 2.4 Data Preprocessing -- 2.5 Training Scheme -- 2.6 Ensembling -- 3 Result and Discussion -- 4 Conclusion -- References -- Skip-SCSE Multi-scale Attention and Co-learning Method for Oropharyngeal Tumor Segmentation on Multi-modal PET-CT Images -- 1 Introduction -- 2 Data and Methods -- 2.1 Dataset Description -- 2.2 Methods: An Overview -- 2.3 Co-learning Method with Multi-modal PET-CT -- 2.4 3D Skip-ScSE Multi-scale Attention Model. ; 3 Results -- 4 Discussion and Conclusion -- References -- Head and Neck Cancer Primary Tumor Auto Segmentation Using Model Ensembling of Deep Learning in PET/CT Images -- 1 Introduction -- 2 Methods -- 2.1 Imaging Data -- 2.2 Image Processing -- 2.3 Model Architecture -- 2.4 Model Implementation -- 2.5 Model Validation -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Priori and Posteriori Attention for Generalizing Head and Neck Tumors Segmentation -- 1 Introduction -- 2 Method -- 2.1 Attention Model -- 2.2 Construction of Unseen Domain -- 2.3 Model Ensemble -- 3 Experiments and Results -- 3.1 Data Preprocessing -- 3.2 Network Training -- 3.3 Experimental Results -- 4 Conclusion -- References -- Head and Neck Tumor Segmentation with Deeply-Supervised 3D UNet and Progression-Free Survival Prediction with Linear Model -- 1 Introduction -- 2 Methods -- 2.1 Image Pre-processing -- 2.2 Patch Extraction -- 2.3 Network Structure and Training -- 2.4 Prediction -- 2.5 Image Post-processing -- 2.6 Survival Prediction -- 3 Results -- 3.1 Head and Neck Tumor Segmentation -- 3.2 Survival Prediction -- 4 Discussion and Conclusions -- References -- Deep Learning Based GTV Delineation and Progression Free Survival Risk Score Prediction for Head and Neck Cancer Patients -- 1 Introduction -- 2 Materials and Methods -- 2.1 Preprocessing -- 2.2 Segmentation -- 2.3 Survival Prediction -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Multi-task Deep Learning for Joint Tumor Segmentation and Outcome Prediction in Head and Neck Cancer -- 1 Introduction -- 2 Materials and Methods -- 2.1 Patients -- 2.2 Multi-task Deep Model -- 2.3 Loss Function -- 2.4 Data Preprocessing -- 2.5 Training Procedure -- 2.6 Ensemble -- 2.7 Evaluation Metrics -- 3 Results and Discussion -- References. ; PET/CT Head and Neck Tumor Segmentation and Progression Free Survival Prediction Using Deep and Machine Learning Techniques -- 1 Introduction -- 2 Task 1: Tumor Segmentation -- 2.1 Materials and Methods -- 2.2 Results -- 3 Task 2: Progression Free Survival (PFS) -- 3.1 Materials and Methods -- 3.2 Results -- 4 Conclusions -- References -- Automatic Head and Neck Tumor Segmentation and Progression Free Survival Analysis on PET/CT Images -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Overall Network Structure -- 3.2 Encoding Pathway -- 3.3 Decoding Pathway -- 3.4 Scale Attention Block -- 3.5 Implementation -- 3.6 PFS Prediction -- 4 Results -- 5 Summary -- References -- Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival Using a Full-Scale UNet with Attention -- 1 Introduction -- 2 Methods and Data -- 2.1 Data -- 2.2 Models Description -- 2.3 Evaluation Metrics -- 3 Results -- 3.1 Segmentation Task -- 3.2 Survival Task -- 4 Conclusion -- References -- Advanced Automatic Segmentation of Tumors and Survival Prediction in Head and Neck Cancer -- 1 Introduction -- 2 Material and Method -- 2.1 Data Collection -- 2.2 Analysis Procedure -- 3 Results -- 3.1 Segmentation Results -- 3.2 Survival Prediction Results -- 4 Discussion -- 5 Conclusion -- 6 Code Availability -- References -- Fusion-Based Head and Neck Tumor Segmentation and Survival Prediction Using Robust Deep Learning Techniques and Advanced Hybrid Machine Learning Systems -- 1 Introduction -- 2 Material and Methods -- 2.1 Dataset, PET/CT Acquisition -- 2.2 Survival Prediction -- 3 Results -- 3.1 Segmentation Task -- 3.2 Progression Free Survival Prediction Task -- 4 Conclusion -- 5 Code Availability -- References -- Head and Neck Primary Tumor Segmentation Using Deep Neural Networks and Adaptive Ensembling -- 1 Introduction -- 2 Materials and Methods. ; 2.1 Dataset Description -- 2.2 Data Preprocessing -- 2.3 Task 1. Modified NnUNET Model -- 2.4 Tasks 2 & -- 3. Survival Prediction -- 3 Results -- 3.1 Task 1 Segmentation Results -- 3.2 Tasks 2 & -- 3 Progression Prediction Results -- 4 Discussion -- 4.1 Task 1 -- 4.2 Task 2 & -- 3 -- 5 Conclusion -- References -- Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks -- 1 Introduction -- 2 Material and Methods -- 2.1 Dataset -- 2.2 Proposed Methods -- 3 Results -- 3.1 Segmentation Results -- 3.2 Survival Analysis -- 4 Discussion -- 5 Conclusion -- References -- Dual-Path Connected CNN for Tumor Segmentation of Combined PET-CT Images and Application to Survival Risk Prediction -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Tumor Segmentation -- 2.3 Survival Analysis -- 2.4 Experimental Settings -- 3 Results -- 3.1 Task 1 - Segmentation -- 3.2 Task 2 - Survival Prediction -- 4 Discussion -- 5 Conclusion -- References -- Deep Supervoxel Segmentation for Survival Analysis in Head and Neck Cancer Patients -- 1 Introduction -- 2 Methods -- 2.1 Deep Supervoxel Segmentation -- 2.2 Patient Risk Score Regression -- 3 Experimental Setup -- 3.1 Data Splitting -- 3.2 Preprocessing -- 3.3 Training -- 3.4 Inference -- 4 Results -- 4.1 Segmentation Results -- 4.2 Patient Risk Score Results -- 5 Discussion and Conclusion -- References -- A Hybrid Radiomics Approach to Modeling Progression-Free Survival in Head and Neck Cancers -- 1 Introduction -- 2 Preliminary Considerations -- 2.1 Dataset Description -- 2.2 Cross-Validation Strategy -- 2.3 Evaluation of the Model -- 3 Methods -- 3.1 Feature Engineering -- 3.2 Modeling Approach -- 4 Results for Task 3 -- 4.1 Feature Selection -- 4.2 Submitted Models -- 5 Results for Task 2 -- 6 Discussion -- References. ; An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data.
• Titolo autorizzato: Head and neck tumor segmentation
• ISBN: 3-030-98253-X
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996464453703316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Computer Science