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Biblioteca

MARC Lista (tabellare)
Brain tumors : current and emerging therapeutic strategies / / edited by Ana L. Abujamra
Brain tumors : current and emerging therapeutic strategies / / edited by Ana L. Abujamra
Pubbl/distr/stampa Rijeka, Croatia : , : InTech, , [2011]
Descrizione fisica 1 online resource (434 pages) : illustrations
Disciplina 616.99481
Soggetto topico Brain - Tumors
ISBN 953-51-6457-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti Brain tumors
Record Nr. UNINA-9910138259103321
Rijeka, Croatia : , : InTech, , [2011]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Brain tumors and spinal cord tumors
Brain tumors and spinal cord tumors
Pubbl/distr/stampa Bethesda, MD : , : Office of Communications and Public Liaison, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Department of Health and Human Services, , [2009]
Descrizione fisica 1 online resource (62 pages) : illustrations (some color)
Collana NIH publication
Hope through research
Soggetto topico Brain - Tumors
Spinal cord - Tumors
Tumors
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910699006803321
Bethesda, MD : , : Office of Communications and Public Liaison, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Department of Health and Human Services, , [2009]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Brain-cancer associated tumor marker genes expression pattern in humans / / Harun M. Said, Adrian Staab and Carsten Hagemann
Brain-cancer associated tumor marker genes expression pattern in humans / / Harun M. Said, Adrian Staab and Carsten Hagemann
Autore Said Harun M.
Pubbl/distr/stampa Hauppauge, New York : , : Nova Science, , [2011]
Descrizione fisica 1 online resource (68 pages) : illustrations (some color)
Disciplina 616.99/481
Collana Cancer etiology, diagnosis and treatments
Soggetto topico Brain - Tumors
Tumor markers
ISBN 1-61728-635-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Biochemistry of human brain tumor cells : metabolism in human brain tumor cells -- Potential role of glycolytic inhibitors in human brain cancer treatment -- Potential role of chemical inhibitors in human brain cancer treatment -- Glucose metabolism in human cancer cells -- Metabolic control analysis in human brain tumors -- Glycolytic regulation in human brain tumors : involvement of glucose availability in hypoxia induced gene expression -- Hypoxia induced HiF-1 gene regulation in human glioblastoma -- Hypoxia induced Ca9 expression in human brain tumor cells -- Role of VEGF in human brain tumors under hypoxic conditions -- Hypoxia induced OPN expression in human brain tumors cells -- Hypoxia induced Epo expression in human brain tumors cells -- Hypoxia induced NDRG1 expression in human brain tumor cells -- Regulation via other hypoxia gene regulators.
Record Nr. UNINA-9910162785503321
Said Harun M.  
Hauppauge, New York : , : Nova Science, , [2011]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Brainlesion . Part II : glioma, multiple sclerosis, stroke and traumatic brain injuries : 4th international workshop, BrainLes 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, revised selected papers. / / Alessandro Crimi and Spyridon Bakas, editors
Brainlesion . Part II : glioma, multiple sclerosis, stroke and traumatic brain injuries : 4th international workshop, BrainLes 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, revised selected papers. / / Alessandro Crimi and Spyridon Bakas, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer Nature Switzerland AG, , [2022]
Descrizione fisica 1 online resource (617 pages)
Disciplina 616.99481
Collana Lecture notes in computer science
Soggetto topico Brain - Wounds and injuries
Diagnostic imaging - Data processing
Brain - Tumors
ISBN 3-031-09002-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910584480403321
Cham, Switzerland : , : Springer Nature Switzerland AG, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Brainlesion . Part II : glioma, multiple sclerosis, stroke and traumatic brain injuries : 4th international workshop, BrainLes 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, revised selected papers. / / Alessandro Crimi and Spyridon Bakas, editors
Brainlesion . Part II : glioma, multiple sclerosis, stroke and traumatic brain injuries : 4th international workshop, BrainLes 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, revised selected papers. / / Alessandro Crimi and Spyridon Bakas, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer Nature Switzerland AG, , [2022]
Descrizione fisica 1 online resource (617 pages)
Disciplina 616.99481
Collana Lecture notes in computer science
Soggetto topico Brain - Wounds and injuries
Diagnostic imaging - Data processing
Brain - Tumors
ISBN 3-031-09002-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996483162803316
Cham, Switzerland : , : Springer Nature Switzerland AG, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries : 6th International Workshop, BrainLes 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, revised selected papers, part II / / Alessandro Crimi, Spyridon Bakas (editors)
Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries : 6th International Workshop, BrainLes 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, revised selected papers, part II / / Alessandro Crimi, Spyridon Bakas (editors)
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (539 pages)
Disciplina 616.99281
Collana Lecture Notes in Computer Science
Soggetto topico Brain - Tumors
Brain - Wounds and injuries
Cerebrovascular disease
ISBN 3-030-72087-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Brain Tumor Segmentation -- Lightweight U-Nets for Brain Tumor Segmentation -- 1 Introduction -- 2 Data -- 3 Methods -- 3.1 Data Pre-processing -- 3.2 Our Deep Network Architecture -- 4 Experimental Study -- 4.1 Setup -- 4.2 The Results -- 5 Conclusions -- References -- Efficient Brain Tumour Segmentation Using Co-registered Data and Ensembles of Specialised Learners -- 1 Introduction -- 2 Related Work -- 2.1 BraTS: Challenge and Data Set -- 2.2 Related Literature -- 3 Methodology -- 3.1 Data Pre-processing -- 3.2 Model Architecture -- 4 Empirical Evaluation -- 4.1 Results -- 4.2 Performance Comparison -- 4.3 Analysis -- 4.4 Impact and Implications of Loss Functions -- 4.5 Performance Evaluation -- 5 Discussion -- 6 Conclusions and Future Work -- References -- Efficient MRI Brain Tumor Segmentation Using Multi-resolution Encoder-Decoder Networks -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Segmentation -- 2.3 Survival Prediction -- 3 Results -- 3.1 Segmentation Task -- 3.2 Survival Prediction -- 4 Conclusion -- References -- Trialing U-Net Training Modifications for Segmenting Gliomas Using Open Source Deep Learning Framework -- 1 Introduction -- 2 Methods -- 2.1 Data -- 2.2 Model Architecture -- 2.3 Augmentation -- 2.4 Training -- 3 Experiments -- 3.1 Effects of Thresholding -- 3.2 Contours, Permutations, and Grouped Labels -- 3.3 Ensembles -- 3.4 Test Set -- 4 Results -- 4.1 Effects of Thresholding -- 4.2 Contours, Permutations, and Grouped Labels -- 4.3 Ensembles -- 4.4 Test Set Results -- 5 Discussion -- 6 Conclusion -- References -- HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation -- 1 Introduction -- 2 Proposed Method -- 3 Implementation Details -- 3.1 Dataset -- 3.2 Experiments -- 3.3 Evaluation Metrics -- 3.4 Results.
4 Conclusion -- References -- H2NF-Net for Brain Tumor Segmentation Using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task -- 1 Introduction -- 2 Dataset -- 3 Method -- 3.1 Single HNF-Net -- 3.2 Cascaded HNF-Net -- 4 Experiments and Results -- 4.1 Implementation Details -- 4.2 Results on the BraTS 2020 Challenge Dataset -- 5 Conclusion -- References -- 2D Dense-UNet: A Clinically Valid Approach to Automated Glioma Segmentation -- 1 Introduction -- 2 Methods -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Attention U-Net with Dimension-Hybridized Fast Data Density Functional Theory for Automatic Brain Tumor Image Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Normalization and Augmentation -- 2.2 Feature Pre-extraction Using Fast Data Density Functional Theory -- 2.3 Encoder, Decoder, and Attention Block -- 2.4 Optimization -- 3 Results -- 4 Conclusion -- References -- MVP U-Net: Multi-View Pointwise U-Net for Brain Tumor Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Preprocessing -- 2.2 Network Architecture -- 2.3 Loss -- 2.4 Optimization -- 3 Experiments and Results -- 4 Conclusion -- References -- Glioma Segmentation with 3D U-Net Backed with Energy-Based Post-Processing -- 1 Introduction -- 1.1 Related Work -- 2 Data -- 3 Methods -- 3.1 Preprocessing -- 3.2 Model -- 3.3 Loss Function -- 3.4 Training -- 3.5 Post Processing -- 4 Performance Evaluation -- 5 Conclusion -- References -- nnU-Net for Brain Tumor Segmentation -- 1 Introduction -- 2 Method -- 2.1 Rankings Should Be Used for Model Selection -- 2.2 nnU-Net Baseline -- 2.3 BraTS-Specific Optimizations -- 2.4 Further nnU-Net Modifications -- 3 Results -- 3.1 Aggregated Scores -- 3.2 Internal BraTS-Like Ranking -- 3.3 Qualitative Results -- 3.4 Test Set Results -- 4 Discussion -- References.
A Deep Random Forest Approach for Multimodal Brain Tumor Segmentation -- 1 Introduction -- 2 Proposed Architecture -- 3 Experimental Results -- 3.1 Dataset -- 3.2 Preprocessing -- 3.3 Feature Generation -- 3.4 Implementation Details -- 3.5 Performance -- 4 Conclusion -- References -- Brain Tumor Segmentation and Associated Uncertainty Evaluation Using Multi-sequences MRI Mixture Data Preprocessing -- 1 Introduction -- 2 Datasets Description -- 2.1 BraTS Dataset -- 2.2 Siberian Brain Tumor Dataset -- 3 Methods -- 4 Results -- 5 Conclusions -- References -- A Deep Supervision CNN Network for Brain Tumor Segmentation -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Main Network -- 3.2 Deep Supervision Method -- 3.3 Loss Functions -- 4 Experiments -- 4.1 Pre-processing -- 4.2 Post-processing -- 4.3 Training Details -- 5 Results -- 6 Conclusion -- References -- Multi-threshold Attention U-Net (MTAU) Based Model for Multimodal Brain Tumor Segmentation in MRI Scans -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Model Architecture -- 3 Figures of Merit -- 3.1 Dice Coefficient (DSC) -- 3.2 Sensitivity (SN) -- 3.3 Specificity (SP) -- 3.4 Hausdorff Distance (h) -- 4 Results and Discussions -- 5 Conclusions -- References -- Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Deep Layer Aggregation -- 2.2 Loss Function -- 3 Experimental Setup -- 3.1 Data -- 3.2 Pre-processing and Data Augmentation -- 3.3 Settings and Model Training -- 3.4 Post-processing -- 4 Results -- 5 Conclusion -- References -- Glioma Segmentation Using Ensemble of 2D/3D U-Nets and Survival Prediction Using Multiple Features Fusion -- 1 Introduction -- 2 Proposed Methodology -- 2.1 Segmentation Task -- 2.2 Survival Prediction Task -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 4 Results.
5 Conclusion -- References -- Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for Brain Tumor Segmentation: BraTS 2020 Challenge -- 1 Introduction -- 2 Method: Varying the Three Main Ingredients of the Optimization of Deep Neural Networks -- 2.1 Changing the Per-Sample Loss Function: The Generalized Wasserstein Dice Loss ch18fidon2017generalised -- 2.2 Changing the Optimization Problem: Distributionally Robust Optimization ch18fidon2020sgd -- 2.3 Changing the Optimizer: Ranger ch18liu2019variance,ch18zhang2019lookahead -- 2.4 Deep Neural Networks Ensembling -- 3 Experiments and Results -- 3.1 Data and Implementation Details -- 3.2 Models Description -- 3.3 Mean Segmentation Performance -- 3.4 Robustness Performance -- 4 Conclusion -- References -- 3D Semantic Segmentation of Brain Tumor for Overall Survival Prediction -- 1 Introduction -- 1.1 Literature Review: BraTS 2019 -- 2 Dataset -- 3 Proposed Method -- 3.1 Task 1: Tumor Segmentation -- 3.2 Task 2: Overall Survival Prediction -- 4 Implementation Details -- 4.1 Pre-processing -- 4.2 Training -- 4.3 Post-processing -- 5 Results -- 5.1 Segmentation -- 5.2 OS Prediction -- 6 Conclusion -- References -- Segmentation, Survival Prediction, and Uncertainty Estimation of Gliomas from Multimodal 3D MRI Using Selective Kernel Networks -- 1 Introduction -- 2 Methods -- 2.1 Preprocessing -- 2.2 Network Architecture -- 2.3 Loss Function -- 2.4 Optimization -- 2.5 Inference -- 2.6 Postprocessing -- 2.7 Overall Survival Prediction -- 2.8 Uncertainty Estimation -- 3 Results -- 3.1 Validation Set -- 3.2 Testing Set -- 4 Discussion and Conclusion -- References -- 3D Brain Tumor Segmentation and Survival Prediction Using Ensembles of Convolutional Neural Networks -- 1 Introduction -- 2 Method -- 2.1 Dataset -- 2.2 Preprocessing and Post-processing -- 2.3 Segmentation Pipeline.
2.4 Survival Prediction Pipeline -- 2.5 Training -- 2.6 Uncertainty Estimation -- 3 Experiments and Results -- 3.1 Segmentation Performance -- 3.2 Survival Prediction -- 3.3 Evaluation of Uncertainty Measures in Segmentation -- 4 Discussion -- 5 Conclusions -- References -- Brain Tumour Segmentation Using Probabilistic U-Net -- 1 Introduction -- 2 Data -- 2.1 Preprocessing -- 3 Architecture -- 3.1 Probabilistic UNet -- 3.2 Attention -- 4 Training Details -- 5 Visualization and Analysis -- 5.1 Probabilistic UNet -- 5.2 Attention Maps -- 6 Results -- 7 Discussion and Future Scope -- 8 Conclusion -- References -- Segmenting Brain Tumors from MRI Using Cascaded 3D U-Nets -- 1 Introduction -- 2 Data -- 3 Methods -- 3.1 Data Standardization -- 3.2 Our U-Net-Based Architecture -- 3.3 Post-processing -- 3.4 Regularization Strategies -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Training Process -- 4.3 The Results -- 5 Conclusion -- References -- A Deep Supervised U-Attention Net for Pixel-Wise Brain Tumor Segmentation -- 1 Introduction -- 2 Method -- 2.1 Network Architecture -- 2.2 Evaluation Metrics -- 2.3 Loss Function -- 3 Experiments -- 3.1 Dataset Description -- 3.2 Data Pre-processing -- 3.3 Data Augmentation -- 3.4 Label Distribution -- 3.5 Training Procedure -- 4 Results -- 5 Conclusion -- References -- A Two-Stage Atrous Convolution Neural Network for Brain Tumor Segmentation and Survival Prediction -- 1 Introduction -- 2 Data -- 3 The Segmentation Algorithm -- 3.1 Brief Description of the Model -- 3.2 Details of the First Stage -- 3.3 Details of the Second Stage -- 3.4 Preprocessing -- 3.5 Training Details -- 3.6 Inference -- 3.7 Results -- 4 Overall Survival Prediction -- 4.1 Feature Extraction -- 4.2 Results -- 5 Conclusion -- References -- TwoPath U-Net for Automatic Brain Tumor Segmentation from Multimodal MRI Data -- 1 Introduction.
2 Methods.
Record Nr. UNINA-9910484791403321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries : 6th International Workshop, BrainLes 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, revised selected papers, part II / / Alessandro Crimi, Spyridon Bakas (editors)
Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries : 6th International Workshop, BrainLes 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, revised selected papers, part II / / Alessandro Crimi, Spyridon Bakas (editors)
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (539 pages)
Disciplina 616.99281
Collana Lecture Notes in Computer Science
Soggetto topico Brain - Tumors
Brain - Wounds and injuries
Cerebrovascular disease
ISBN 3-030-72087-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Brain Tumor Segmentation -- Lightweight U-Nets for Brain Tumor Segmentation -- 1 Introduction -- 2 Data -- 3 Methods -- 3.1 Data Pre-processing -- 3.2 Our Deep Network Architecture -- 4 Experimental Study -- 4.1 Setup -- 4.2 The Results -- 5 Conclusions -- References -- Efficient Brain Tumour Segmentation Using Co-registered Data and Ensembles of Specialised Learners -- 1 Introduction -- 2 Related Work -- 2.1 BraTS: Challenge and Data Set -- 2.2 Related Literature -- 3 Methodology -- 3.1 Data Pre-processing -- 3.2 Model Architecture -- 4 Empirical Evaluation -- 4.1 Results -- 4.2 Performance Comparison -- 4.3 Analysis -- 4.4 Impact and Implications of Loss Functions -- 4.5 Performance Evaluation -- 5 Discussion -- 6 Conclusions and Future Work -- References -- Efficient MRI Brain Tumor Segmentation Using Multi-resolution Encoder-Decoder Networks -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Segmentation -- 2.3 Survival Prediction -- 3 Results -- 3.1 Segmentation Task -- 3.2 Survival Prediction -- 4 Conclusion -- References -- Trialing U-Net Training Modifications for Segmenting Gliomas Using Open Source Deep Learning Framework -- 1 Introduction -- 2 Methods -- 2.1 Data -- 2.2 Model Architecture -- 2.3 Augmentation -- 2.4 Training -- 3 Experiments -- 3.1 Effects of Thresholding -- 3.2 Contours, Permutations, and Grouped Labels -- 3.3 Ensembles -- 3.4 Test Set -- 4 Results -- 4.1 Effects of Thresholding -- 4.2 Contours, Permutations, and Grouped Labels -- 4.3 Ensembles -- 4.4 Test Set Results -- 5 Discussion -- 6 Conclusion -- References -- HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation -- 1 Introduction -- 2 Proposed Method -- 3 Implementation Details -- 3.1 Dataset -- 3.2 Experiments -- 3.3 Evaluation Metrics -- 3.4 Results.
4 Conclusion -- References -- H2NF-Net for Brain Tumor Segmentation Using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task -- 1 Introduction -- 2 Dataset -- 3 Method -- 3.1 Single HNF-Net -- 3.2 Cascaded HNF-Net -- 4 Experiments and Results -- 4.1 Implementation Details -- 4.2 Results on the BraTS 2020 Challenge Dataset -- 5 Conclusion -- References -- 2D Dense-UNet: A Clinically Valid Approach to Automated Glioma Segmentation -- 1 Introduction -- 2 Methods -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Attention U-Net with Dimension-Hybridized Fast Data Density Functional Theory for Automatic Brain Tumor Image Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Normalization and Augmentation -- 2.2 Feature Pre-extraction Using Fast Data Density Functional Theory -- 2.3 Encoder, Decoder, and Attention Block -- 2.4 Optimization -- 3 Results -- 4 Conclusion -- References -- MVP U-Net: Multi-View Pointwise U-Net for Brain Tumor Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Preprocessing -- 2.2 Network Architecture -- 2.3 Loss -- 2.4 Optimization -- 3 Experiments and Results -- 4 Conclusion -- References -- Glioma Segmentation with 3D U-Net Backed with Energy-Based Post-Processing -- 1 Introduction -- 1.1 Related Work -- 2 Data -- 3 Methods -- 3.1 Preprocessing -- 3.2 Model -- 3.3 Loss Function -- 3.4 Training -- 3.5 Post Processing -- 4 Performance Evaluation -- 5 Conclusion -- References -- nnU-Net for Brain Tumor Segmentation -- 1 Introduction -- 2 Method -- 2.1 Rankings Should Be Used for Model Selection -- 2.2 nnU-Net Baseline -- 2.3 BraTS-Specific Optimizations -- 2.4 Further nnU-Net Modifications -- 3 Results -- 3.1 Aggregated Scores -- 3.2 Internal BraTS-Like Ranking -- 3.3 Qualitative Results -- 3.4 Test Set Results -- 4 Discussion -- References.
A Deep Random Forest Approach for Multimodal Brain Tumor Segmentation -- 1 Introduction -- 2 Proposed Architecture -- 3 Experimental Results -- 3.1 Dataset -- 3.2 Preprocessing -- 3.3 Feature Generation -- 3.4 Implementation Details -- 3.5 Performance -- 4 Conclusion -- References -- Brain Tumor Segmentation and Associated Uncertainty Evaluation Using Multi-sequences MRI Mixture Data Preprocessing -- 1 Introduction -- 2 Datasets Description -- 2.1 BraTS Dataset -- 2.2 Siberian Brain Tumor Dataset -- 3 Methods -- 4 Results -- 5 Conclusions -- References -- A Deep Supervision CNN Network for Brain Tumor Segmentation -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Main Network -- 3.2 Deep Supervision Method -- 3.3 Loss Functions -- 4 Experiments -- 4.1 Pre-processing -- 4.2 Post-processing -- 4.3 Training Details -- 5 Results -- 6 Conclusion -- References -- Multi-threshold Attention U-Net (MTAU) Based Model for Multimodal Brain Tumor Segmentation in MRI Scans -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Model Architecture -- 3 Figures of Merit -- 3.1 Dice Coefficient (DSC) -- 3.2 Sensitivity (SN) -- 3.3 Specificity (SP) -- 3.4 Hausdorff Distance (h) -- 4 Results and Discussions -- 5 Conclusions -- References -- Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Deep Layer Aggregation -- 2.2 Loss Function -- 3 Experimental Setup -- 3.1 Data -- 3.2 Pre-processing and Data Augmentation -- 3.3 Settings and Model Training -- 3.4 Post-processing -- 4 Results -- 5 Conclusion -- References -- Glioma Segmentation Using Ensemble of 2D/3D U-Nets and Survival Prediction Using Multiple Features Fusion -- 1 Introduction -- 2 Proposed Methodology -- 2.1 Segmentation Task -- 2.2 Survival Prediction Task -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 4 Results.
5 Conclusion -- References -- Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for Brain Tumor Segmentation: BraTS 2020 Challenge -- 1 Introduction -- 2 Method: Varying the Three Main Ingredients of the Optimization of Deep Neural Networks -- 2.1 Changing the Per-Sample Loss Function: The Generalized Wasserstein Dice Loss ch18fidon2017generalised -- 2.2 Changing the Optimization Problem: Distributionally Robust Optimization ch18fidon2020sgd -- 2.3 Changing the Optimizer: Ranger ch18liu2019variance,ch18zhang2019lookahead -- 2.4 Deep Neural Networks Ensembling -- 3 Experiments and Results -- 3.1 Data and Implementation Details -- 3.2 Models Description -- 3.3 Mean Segmentation Performance -- 3.4 Robustness Performance -- 4 Conclusion -- References -- 3D Semantic Segmentation of Brain Tumor for Overall Survival Prediction -- 1 Introduction -- 1.1 Literature Review: BraTS 2019 -- 2 Dataset -- 3 Proposed Method -- 3.1 Task 1: Tumor Segmentation -- 3.2 Task 2: Overall Survival Prediction -- 4 Implementation Details -- 4.1 Pre-processing -- 4.2 Training -- 4.3 Post-processing -- 5 Results -- 5.1 Segmentation -- 5.2 OS Prediction -- 6 Conclusion -- References -- Segmentation, Survival Prediction, and Uncertainty Estimation of Gliomas from Multimodal 3D MRI Using Selective Kernel Networks -- 1 Introduction -- 2 Methods -- 2.1 Preprocessing -- 2.2 Network Architecture -- 2.3 Loss Function -- 2.4 Optimization -- 2.5 Inference -- 2.6 Postprocessing -- 2.7 Overall Survival Prediction -- 2.8 Uncertainty Estimation -- 3 Results -- 3.1 Validation Set -- 3.2 Testing Set -- 4 Discussion and Conclusion -- References -- 3D Brain Tumor Segmentation and Survival Prediction Using Ensembles of Convolutional Neural Networks -- 1 Introduction -- 2 Method -- 2.1 Dataset -- 2.2 Preprocessing and Post-processing -- 2.3 Segmentation Pipeline.
2.4 Survival Prediction Pipeline -- 2.5 Training -- 2.6 Uncertainty Estimation -- 3 Experiments and Results -- 3.1 Segmentation Performance -- 3.2 Survival Prediction -- 3.3 Evaluation of Uncertainty Measures in Segmentation -- 4 Discussion -- 5 Conclusions -- References -- Brain Tumour Segmentation Using Probabilistic U-Net -- 1 Introduction -- 2 Data -- 2.1 Preprocessing -- 3 Architecture -- 3.1 Probabilistic UNet -- 3.2 Attention -- 4 Training Details -- 5 Visualization and Analysis -- 5.1 Probabilistic UNet -- 5.2 Attention Maps -- 6 Results -- 7 Discussion and Future Scope -- 8 Conclusion -- References -- Segmenting Brain Tumors from MRI Using Cascaded 3D U-Nets -- 1 Introduction -- 2 Data -- 3 Methods -- 3.1 Data Standardization -- 3.2 Our U-Net-Based Architecture -- 3.3 Post-processing -- 3.4 Regularization Strategies -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Training Process -- 4.3 The Results -- 5 Conclusion -- References -- A Deep Supervised U-Attention Net for Pixel-Wise Brain Tumor Segmentation -- 1 Introduction -- 2 Method -- 2.1 Network Architecture -- 2.2 Evaluation Metrics -- 2.3 Loss Function -- 3 Experiments -- 3.1 Dataset Description -- 3.2 Data Pre-processing -- 3.3 Data Augmentation -- 3.4 Label Distribution -- 3.5 Training Procedure -- 4 Results -- 5 Conclusion -- References -- A Two-Stage Atrous Convolution Neural Network for Brain Tumor Segmentation and Survival Prediction -- 1 Introduction -- 2 Data -- 3 The Segmentation Algorithm -- 3.1 Brief Description of the Model -- 3.2 Details of the First Stage -- 3.3 Details of the Second Stage -- 3.4 Preprocessing -- 3.5 Training Details -- 3.6 Inference -- 3.7 Results -- 4 Overall Survival Prediction -- 4.1 Feature Extraction -- 4.2 Results -- 5 Conclusion -- References -- TwoPath U-Net for Automatic Brain Tumor Segmentation from Multimodal MRI Data -- 1 Introduction.
2 Methods.
Record Nr. UNISA-996464433103316
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Brainlesion . Part I : glioma, multiple sclerosis, stroke and traumatic brain injuries : 6th International Workshop, BrainLes 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, revised selected papers / / Alessandro Crimi, Spyridon Bakas (editors)
Brainlesion . Part I : glioma, multiple sclerosis, stroke and traumatic brain injuries : 6th International Workshop, BrainLes 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, revised selected papers / / Alessandro Crimi, Spyridon Bakas (editors)
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (544 pages)
Disciplina 616.99281
Collana Lecture Notes in Computer Science
Soggetto topico Brain - Tumors
ISBN 3-030-72084-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Invited Papers -- Glioma Diagnosis and Classification: Illuminating the Gold Standard -- 1 Features of Infiltrating Gliomas -- 1.1 Introduction -- 1.2 The Importance of IDH Mutations in Infiltrating Gliomas -- 1.3 Diagnostic Format -- 1.4 Refining Diagnoses and Improving Patient Care -- References -- Multiple Sclerosis Lesion Segmentation - A Survey of Supervised CNN-Based Methods -- 1 Introduction -- 2 Review of Methods -- 2.1 Data Pre-processing -- 2.2 Data Representation -- 2.3 Data Preparation -- 2.4 Network Architecture -- 2.5 Multiple Modalities, Timepoints, Views and Scales -- 2.6 Loss Functions and Regularization -- 2.7 Implementation -- 2.8 Prediction and Post-processing -- 2.9 Transfer Learning and Domain Adaptation -- 2.10 Methods for Subtypes of MS Lesions -- 3 Comparison of Experiments and Results -- 3.1 Datasets -- 3.2 Evaluation Metrics -- 3.3 Results -- 4 Conclusion -- References -- Computational Diagnostics of GBM Tumors in the Era of Radiomics and Radiogenomics -- 1 Introduction -- 2 Patient Prognosis -- 3 Intratumor Heterogeneity and Tumor Recurrence -- 4 Radiogenomics -- 5 Current Challenges and Future Directions -- 6 Conclusion -- References -- Brain Lesion Image Analysis -- Automatic Segmentation of Non-tumor Tissues in Glioma MR Brain Images Using Deformable Registration with Partial Convolutional Networks -- 1 Introduction -- 2 Method -- 2.1 Tumor Segmentation Network -- 2.2 Tumor Region Recovery Network -- 3 Results -- 3.1 Data and Experimental Setting -- 3.2 Evaluation of Image Recovery -- 3.3 Evaluation of Image Registration -- 4 Conclusion -- References -- Convolutional Neural Network with Asymmetric Encoding and Decoding Structure for Brain Vessel Segmentation on Computed Tomographic Angiography -- 1 Introduction.
2 Data Acquisition and Preprocessing -- 3 Method -- 3.1 Asymmetric Encoding and Decoding-Based Convolutional Neural Net -- 3.2 Centerline Loss Construction -- 3.3 Network Training -- 4 Results -- 5 Conclusions -- References -- Volume Preserving Brain Lesion Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Background: Existing Inequality Volume Constraints -- 2.2 Volume Constraints for Fully Supervised Settings -- 2.3 Implementation Details -- 3 Results -- 3.1 Dataset -- 3.2 Evaluation -- 4 Discussion -- 5 Conclusion -- References -- Microstructural Modulations in the Hippocampus Allow to Characterizing Relapsing-Remitting Versus Primary Progressive Multiple Sclerosis -- 1 Introduction -- 2 Materials and Methods -- 2.1 Study Participants and MRI Acquisition -- 2.2 Signal Modelling and Microstructural Indices -- 2.3 Image Preprocessing -- 2.4 Features Extraction -- 2.5 Statistical Analysis -- 3 Results -- 4 Discussion -- 5 Conclusions -- References -- Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology -- 1 Introduction -- 2 Methods -- 2.1 Global Perception Inference -- 2.2 Context-Aware Patch Swapping -- 2.3 Feature-to-Image Translator -- 2.4 Quasi-Symmetry Constraint -- 3 Experiments and Results -- 4 Conclusion -- References -- Multivariate Analysis is Sufficient for Lesion-Behaviour Mapping -- 1 Introduction -- 2 Multivariate Methods Considered -- 3 Causal Analysis of the Problem -- 4 Experiments -- 5 Discussion -- 5.1 Outlook -- 5.2 Future Work -- References -- Label-Efficient Multi-task Segmentation Using Contrastive Learning -- 1 Introduction -- 2 Methods -- 2.1 Encoder-Decoder Network with Regularization Branches -- 3 Experiments and Results -- 4 Discussion and Conclusion -- References -- Spatio-Temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation -- 1 Introduction.
2 Methodology -- 2.1 Baseline Longitudinal Network -- 2.2 Multitask Learning with Deformable Registration -- 3 Experiment Setup -- 3.1 Datasets and Preprocessing -- 3.2 Implementation Details -- 3.3 Evaluation Metrics -- 3.4 Method Comparisons -- 4 Results and Discussion -- 5 Conclusion -- References -- MMSSD: Multi-scale and Multi-level Single Shot Detector for Brain Metastases Detection -- 1 Introduction -- 2 Methodology -- 2.1 Multi-scale Feature Maps for BM Detection -- 2.2 Multi-level Feature Fusion Modules -- 3 Experiments and Results -- 3.1 Dataset and Data Processing -- 3.2 Training -- 3.3 Experiment Results -- 3.4 Discussion -- 4 Conclusion -- References -- Unsupervised 3D Brain Anomaly Detection -- 1 Introduction -- 2 Methods -- 3 Results and Discussion -- 4 Conclusion -- References -- Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRI -- 1 Introduction -- 2 Methods -- 3 Experiments -- 4 Results and Discussion -- 5 Conclusion and Future Work -- References -- Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression -- 1 Introduction -- 2 Methods -- 2.1 Data -- 2.2 Pre-processing -- 2.3 Network Topology -- 2.4 Experimental Design -- 2.5 Evaluation Metric -- 3 Results -- 4 Discussion -- References -- Bayesian Skip Net: Building on Prior Information for the Prediction and Segmentation of Stroke Lesions -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data -- 2.2 Data Pre-processing -- 2.3 Network Architecture -- 2.4 Experimental Setup -- 3 Results -- 4 Discussion and Conclusion -- References -- Brain Tumor Segmentation -- Brain Tumor Segmentation Using Dual-Path Attention U-Net in 3D MRI Images -- 1 Introduction -- 2 Method -- 2.1 Backbone Architecture for Segmentation -- 2.2 Dual Residual Block -- 2.3 Dual-Path Attention (DPA) Block for Tumor Segmentation -- 2.4 Loss Function.
3 Experiments -- 3.1 Datasets Detail -- 3.2 Preprocessing Methods -- 3.3 Evaluating Metrics -- 3.4 Experimental Results -- 4 Discussion -- 5 Conclusion -- References -- Multimodal Brain Image Analysis and Survival Prediction Using Neuromorphic Attention-Based Neural Networks -- 1 Introduction -- 2 Methods -- 2.1 Neuromorphic Neural Network Inspired by Visual Cortex -- 2.2 Neuromorphic Pre-processing for Convolutional Neural Networks with Neuromorphic Attention-Based Learner -- 2.3 Feature Selection for Survival Days Prediction -- 3 Result and Discussion -- 3.1 Tumor Segmentation -- 3.2 Overall Survival Prediction -- 4 Conclusion -- References -- Context Aware 3D UNet for Brain Tumor Segmentation -- 1 Introduction -- 2 Proposed Method -- 2.1 Dense Blocks -- 2.2 Residual-Inception Blocks -- 3 Experimental Results -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Qualitative Analysis -- 3.4 Quantitative Analysis -- 4 Discussion and Conclusion -- References -- Brain Tumor Segmentation Network Using Attention-Based Fusion and Spatial Relationship Constraint -- 1 Introduction -- 2 Method -- 2.1 Multi-modal Tumor Segmentation Network -- 2.2 Spatial-Channel Fusion Block (SCFB) -- 2.3 Spatial Relationship Constraint -- 3 Experiment -- 3.1 Dataset -- 3.2 Implementations -- 3.3 Results -- 4 Conclusion -- References -- Modality-Pairing Learning for Brain Tumor Segmentation -- 1 Introduction -- 2 Methods -- 3 Experiments -- 3.1 Dataset -- 3.2 Experimental Settings -- 3.3 Evaluation Metrics -- 3.4 Experimental Results -- 4 Discussion and Conclusion -- References -- Transfer Learning for Brain Tumor Segmentation -- 1 Introduction -- 2 Method -- 2.1 Extending the AlbuNet Architecture -- 2.2 Loss Function -- 2.3 Choice of Hyperparameters -- 2.4 Preprocessing and Data Augmentation -- 2.5 Prediction -- 3 Evaluation -- 3.1 Extended AlbuNet with and Without Pretraining.
3.2 Clinical Dataset of the Syrian-Lebanese Hospital -- 3.3 Final Results -- 4 Conclusion -- References -- Efficient Embedding Network for 3D Brain Tumor Segmentation -- 1 Introduction -- 2 Method -- 2.1 Data Pre-processing -- 2.2 Encoder Branch -- 2.3 Decoder Branch -- 2.4 Loss -- 2.5 Training -- 3 Results -- 4 Conclusion -- References -- Segmentation of the Multimodal Brain Tumor Images Used Res-U-Net -- 1 Introduction -- 2 Method -- 2.1 Pre-processing -- 2.2 Architecture -- 3 Experiments -- 3.1 Dataset -- 3.2 Setup -- 3.3 Evaluation -- 3.4 Result -- 4 Conclusion -- References -- Vox2Vox: 3D-GAN for Brain Tumour Segmentation -- 1 Introduction -- 1.1 Related Works -- 2 Method -- 2.1 Data -- 2.2 Image Pre-processing and Augmentation -- 2.3 Model Architecture -- 2.4 Losses -- 2.5 Optimization and Regularization -- 2.6 Model Ensembling and Post-processing -- 3 Results -- 4 Conclusions -- References -- Automatic Brain Tumor Segmentation with Scale Attention Network -- 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 -- 4 Results -- 5 Summary -- References -- Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction -- 1 Introduction -- 2 Brain Tumor Segmentation -- 2.1 Background -- 2.2 Spherical Coordinates Transformation -- 2.3 Post-processing -- 2.4 Segmentation Results -- 3 Prediction of Patient OS -- 3.1 LesionEncoder Framework -- 3.2 Tweedie Regressor -- 3.3 Performance Evaluation -- 3.4 OS Prediction Results -- 4 Discussion -- 5 Conclusion -- References -- Overall Survival Prediction for Glioblastoma on Pre-treatment MRI Using Robust Radiomics and Priors -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data.
2.2 Segmentation and Normalization.
Record Nr. UNISA-996464446503316
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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Brainlesion . Part I : glioma, multiple sclerosis, stroke and traumatic brain injuries : 6th International Workshop, BrainLes 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, revised selected papers / / Alessandro Crimi, Spyridon Bakas (editors)
Brainlesion . Part I : glioma, multiple sclerosis, stroke and traumatic brain injuries : 6th International Workshop, BrainLes 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, revised selected papers / / Alessandro Crimi, Spyridon Bakas (editors)
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (544 pages)
Disciplina 616.99281
Collana Lecture Notes in Computer Science
Soggetto topico Brain - Tumors
ISBN 3-030-72084-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Invited Papers -- Glioma Diagnosis and Classification: Illuminating the Gold Standard -- 1 Features of Infiltrating Gliomas -- 1.1 Introduction -- 1.2 The Importance of IDH Mutations in Infiltrating Gliomas -- 1.3 Diagnostic Format -- 1.4 Refining Diagnoses and Improving Patient Care -- References -- Multiple Sclerosis Lesion Segmentation - A Survey of Supervised CNN-Based Methods -- 1 Introduction -- 2 Review of Methods -- 2.1 Data Pre-processing -- 2.2 Data Representation -- 2.3 Data Preparation -- 2.4 Network Architecture -- 2.5 Multiple Modalities, Timepoints, Views and Scales -- 2.6 Loss Functions and Regularization -- 2.7 Implementation -- 2.8 Prediction and Post-processing -- 2.9 Transfer Learning and Domain Adaptation -- 2.10 Methods for Subtypes of MS Lesions -- 3 Comparison of Experiments and Results -- 3.1 Datasets -- 3.2 Evaluation Metrics -- 3.3 Results -- 4 Conclusion -- References -- Computational Diagnostics of GBM Tumors in the Era of Radiomics and Radiogenomics -- 1 Introduction -- 2 Patient Prognosis -- 3 Intratumor Heterogeneity and Tumor Recurrence -- 4 Radiogenomics -- 5 Current Challenges and Future Directions -- 6 Conclusion -- References -- Brain Lesion Image Analysis -- Automatic Segmentation of Non-tumor Tissues in Glioma MR Brain Images Using Deformable Registration with Partial Convolutional Networks -- 1 Introduction -- 2 Method -- 2.1 Tumor Segmentation Network -- 2.2 Tumor Region Recovery Network -- 3 Results -- 3.1 Data and Experimental Setting -- 3.2 Evaluation of Image Recovery -- 3.3 Evaluation of Image Registration -- 4 Conclusion -- References -- Convolutional Neural Network with Asymmetric Encoding and Decoding Structure for Brain Vessel Segmentation on Computed Tomographic Angiography -- 1 Introduction.
2 Data Acquisition and Preprocessing -- 3 Method -- 3.1 Asymmetric Encoding and Decoding-Based Convolutional Neural Net -- 3.2 Centerline Loss Construction -- 3.3 Network Training -- 4 Results -- 5 Conclusions -- References -- Volume Preserving Brain Lesion Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Background: Existing Inequality Volume Constraints -- 2.2 Volume Constraints for Fully Supervised Settings -- 2.3 Implementation Details -- 3 Results -- 3.1 Dataset -- 3.2 Evaluation -- 4 Discussion -- 5 Conclusion -- References -- Microstructural Modulations in the Hippocampus Allow to Characterizing Relapsing-Remitting Versus Primary Progressive Multiple Sclerosis -- 1 Introduction -- 2 Materials and Methods -- 2.1 Study Participants and MRI Acquisition -- 2.2 Signal Modelling and Microstructural Indices -- 2.3 Image Preprocessing -- 2.4 Features Extraction -- 2.5 Statistical Analysis -- 3 Results -- 4 Discussion -- 5 Conclusions -- References -- Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology -- 1 Introduction -- 2 Methods -- 2.1 Global Perception Inference -- 2.2 Context-Aware Patch Swapping -- 2.3 Feature-to-Image Translator -- 2.4 Quasi-Symmetry Constraint -- 3 Experiments and Results -- 4 Conclusion -- References -- Multivariate Analysis is Sufficient for Lesion-Behaviour Mapping -- 1 Introduction -- 2 Multivariate Methods Considered -- 3 Causal Analysis of the Problem -- 4 Experiments -- 5 Discussion -- 5.1 Outlook -- 5.2 Future Work -- References -- Label-Efficient Multi-task Segmentation Using Contrastive Learning -- 1 Introduction -- 2 Methods -- 2.1 Encoder-Decoder Network with Regularization Branches -- 3 Experiments and Results -- 4 Discussion and Conclusion -- References -- Spatio-Temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation -- 1 Introduction.
2 Methodology -- 2.1 Baseline Longitudinal Network -- 2.2 Multitask Learning with Deformable Registration -- 3 Experiment Setup -- 3.1 Datasets and Preprocessing -- 3.2 Implementation Details -- 3.3 Evaluation Metrics -- 3.4 Method Comparisons -- 4 Results and Discussion -- 5 Conclusion -- References -- MMSSD: Multi-scale and Multi-level Single Shot Detector for Brain Metastases Detection -- 1 Introduction -- 2 Methodology -- 2.1 Multi-scale Feature Maps for BM Detection -- 2.2 Multi-level Feature Fusion Modules -- 3 Experiments and Results -- 3.1 Dataset and Data Processing -- 3.2 Training -- 3.3 Experiment Results -- 3.4 Discussion -- 4 Conclusion -- References -- Unsupervised 3D Brain Anomaly Detection -- 1 Introduction -- 2 Methods -- 3 Results and Discussion -- 4 Conclusion -- References -- Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRI -- 1 Introduction -- 2 Methods -- 3 Experiments -- 4 Results and Discussion -- 5 Conclusion and Future Work -- References -- Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression -- 1 Introduction -- 2 Methods -- 2.1 Data -- 2.2 Pre-processing -- 2.3 Network Topology -- 2.4 Experimental Design -- 2.5 Evaluation Metric -- 3 Results -- 4 Discussion -- References -- Bayesian Skip Net: Building on Prior Information for the Prediction and Segmentation of Stroke Lesions -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data -- 2.2 Data Pre-processing -- 2.3 Network Architecture -- 2.4 Experimental Setup -- 3 Results -- 4 Discussion and Conclusion -- References -- Brain Tumor Segmentation -- Brain Tumor Segmentation Using Dual-Path Attention U-Net in 3D MRI Images -- 1 Introduction -- 2 Method -- 2.1 Backbone Architecture for Segmentation -- 2.2 Dual Residual Block -- 2.3 Dual-Path Attention (DPA) Block for Tumor Segmentation -- 2.4 Loss Function.
3 Experiments -- 3.1 Datasets Detail -- 3.2 Preprocessing Methods -- 3.3 Evaluating Metrics -- 3.4 Experimental Results -- 4 Discussion -- 5 Conclusion -- References -- Multimodal Brain Image Analysis and Survival Prediction Using Neuromorphic Attention-Based Neural Networks -- 1 Introduction -- 2 Methods -- 2.1 Neuromorphic Neural Network Inspired by Visual Cortex -- 2.2 Neuromorphic Pre-processing for Convolutional Neural Networks with Neuromorphic Attention-Based Learner -- 2.3 Feature Selection for Survival Days Prediction -- 3 Result and Discussion -- 3.1 Tumor Segmentation -- 3.2 Overall Survival Prediction -- 4 Conclusion -- References -- Context Aware 3D UNet for Brain Tumor Segmentation -- 1 Introduction -- 2 Proposed Method -- 2.1 Dense Blocks -- 2.2 Residual-Inception Blocks -- 3 Experimental Results -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Qualitative Analysis -- 3.4 Quantitative Analysis -- 4 Discussion and Conclusion -- References -- Brain Tumor Segmentation Network Using Attention-Based Fusion and Spatial Relationship Constraint -- 1 Introduction -- 2 Method -- 2.1 Multi-modal Tumor Segmentation Network -- 2.2 Spatial-Channel Fusion Block (SCFB) -- 2.3 Spatial Relationship Constraint -- 3 Experiment -- 3.1 Dataset -- 3.2 Implementations -- 3.3 Results -- 4 Conclusion -- References -- Modality-Pairing Learning for Brain Tumor Segmentation -- 1 Introduction -- 2 Methods -- 3 Experiments -- 3.1 Dataset -- 3.2 Experimental Settings -- 3.3 Evaluation Metrics -- 3.4 Experimental Results -- 4 Discussion and Conclusion -- References -- Transfer Learning for Brain Tumor Segmentation -- 1 Introduction -- 2 Method -- 2.1 Extending the AlbuNet Architecture -- 2.2 Loss Function -- 2.3 Choice of Hyperparameters -- 2.4 Preprocessing and Data Augmentation -- 2.5 Prediction -- 3 Evaluation -- 3.1 Extended AlbuNet with and Without Pretraining.
3.2 Clinical Dataset of the Syrian-Lebanese Hospital -- 3.3 Final Results -- 4 Conclusion -- References -- Efficient Embedding Network for 3D Brain Tumor Segmentation -- 1 Introduction -- 2 Method -- 2.1 Data Pre-processing -- 2.2 Encoder Branch -- 2.3 Decoder Branch -- 2.4 Loss -- 2.5 Training -- 3 Results -- 4 Conclusion -- References -- Segmentation of the Multimodal Brain Tumor Images Used Res-U-Net -- 1 Introduction -- 2 Method -- 2.1 Pre-processing -- 2.2 Architecture -- 3 Experiments -- 3.1 Dataset -- 3.2 Setup -- 3.3 Evaluation -- 3.4 Result -- 4 Conclusion -- References -- Vox2Vox: 3D-GAN for Brain Tumour Segmentation -- 1 Introduction -- 1.1 Related Works -- 2 Method -- 2.1 Data -- 2.2 Image Pre-processing and Augmentation -- 2.3 Model Architecture -- 2.4 Losses -- 2.5 Optimization and Regularization -- 2.6 Model Ensembling and Post-processing -- 3 Results -- 4 Conclusions -- References -- Automatic Brain Tumor Segmentation with Scale Attention Network -- 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 -- 4 Results -- 5 Summary -- References -- Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction -- 1 Introduction -- 2 Brain Tumor Segmentation -- 2.1 Background -- 2.2 Spherical Coordinates Transformation -- 2.3 Post-processing -- 2.4 Segmentation Results -- 3 Prediction of Patient OS -- 3.1 LesionEncoder Framework -- 3.2 Tweedie Regressor -- 3.3 Performance Evaluation -- 3.4 OS Prediction Results -- 4 Discussion -- 5 Conclusion -- References -- Overall Survival Prediction for Glioblastoma on Pre-treatment MRI Using Robust Radiomics and Priors -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data.
2.2 Segmentation and Normalization.
Record Nr. UNINA-9910484791303321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Clinical Management and Evolving Novel Therapeutic Strategies for Patients with Brain Tumors / / edited by Terry Lichtor
Clinical Management and Evolving Novel Therapeutic Strategies for Patients with Brain Tumors / / edited by Terry Lichtor
Pubbl/distr/stampa Croatia : , : IntechOpen, , 2013
Descrizione fisica 1 online resource (654 pages) : illustrations
Disciplina 616.99481
Soggetto topico Brain - Tumors
ISBN 953-51-7123-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910317745603321
Croatia : , : IntechOpen, , 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui