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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 | ||
| ||
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 | ||
| ||
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 | ||
| ||
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 | ||
| ||
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 | ||
| ||
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 | ||
| ||
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries [[electronic resource] ] : 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I / / edited by Alessandro Crimi, Spyridon Bakas
| Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries [[electronic resource] ] : 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I / / edited by Alessandro Crimi, Spyridon Bakas |
| Autore | Crimi Alessandro |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2022 |
| Descrizione fisica | 1 online resource (XXI, 489 p. 171 illus., 134 illus. in color.) |
| Disciplina | 006.37 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer vision
Artificial intelligence Computer engineering Computer networks Application software Computer Vision Artificial Intelligence Computer Engineering and Networks Computer and Information Systems Applications |
| Soggetto non controllato |
artificial intelligence
bioinformatics computer science computer systems computer vision education image analysis image processing image segmentation learning machine learning medical images neural networks pattern recognition segmentation methods software design software engineering software quality validation verification and validation |
| ISBN | 3-031-08999-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Supervoxel Merging towards Brain Tumor Segmentation -- Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI -- Modeling multi-annotator uncertainty as multi-class segmentation problem -- Modeling multi-annotator uncertainty as multi-class segmentation problem -- Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma -- Predicting isocitrate dehydrogenase mutation status in glioma using structural brain networks and graph neural networks -- Optimization of Deep Learning based Brain Extraction in MRI for Low Resource Environments. Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task -- Unet3D with Multiple Atrous Convolutions Attention Block for Brain Tumor Segmentation -- BRATS2021: exploring each sequence in multi-modal input for baseline U-net performance -- Automatic Brain Tumor Segmentation using Multi-scale Features and Attention Mechanism -- Simple and Fast Convolutional Neural Network applied to median cross sections for predicting the presence of MGMT promoter methylation in FLAIR MRI scans -- MSViT: Multi Scale Vision Transformer forBiomedical Image Segmentation -- Unsupervised Multimodal -- HarDNet-BTS: A Harmonic Shortcut Network for Brain Tumor Segmentation -- Multimodal Brain Tumor Segmentation Algorithm -- Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images -- Multi-plane UNet++ Ensemble for Glioblastoma Segmentation -- Multimodal Brain Tumor Segmentation using Modified UNet Architecture -- A video data based transfer learning approach for classification of MGMT status in brain tumor MR images -- Multimodal Brain Tumor Segmentation Using a 3D ResUNet in BraTS 2021 -- 3D MRI brain tumour segmentation with autoencoder regularization and Hausdorff distance loss function -- 3D CMM-Net with Deeper Encoder for Semantic Segmentation of Brain Tumors in BraTS2021 Challenge -- Cascaded training pipeline for 3D brain tumor segmentation -- nnU-Net with Region-based Training and Loss Ensembles for Brain Tumor Segmentation -- Brain Tumor Segmentation Using Attention Activated U-Net with Positive Mining -- Automatic segmentation of brain tumor using 3D convolutional neural networks -- Hierarchical and Global Modality Interaction for Brain Tumor Segmentation -- Ensemble Outperforms Single Models in Brain Tumor Segmentation -- Brain Tumor Segmentation using UNet-Context Encoding Network -- Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRI. |
| Record Nr. | UNISA-996483157303316 |
Crimi Alessandro
|
||
| Cham, : Springer Nature, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries : 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I / / edited by Alessandro Crimi, Spyridon Bakas
| Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries : 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I / / edited by Alessandro Crimi, Spyridon Bakas |
| Autore | Crimi Alessandro |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2022 |
| Descrizione fisica | 1 online resource (XXI, 489 p. 171 illus., 134 illus. in color.) |
| Disciplina |
006.37
616.8 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer vision
Artificial intelligence Computer engineering Computer networks Application software Computer Vision Artificial Intelligence Computer Engineering and Networks Computer and Information Systems Applications Tumors cerebrals Lesions cerebrals |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-031-08999-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Supervoxel Merging towards Brain Tumor Segmentation -- Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI -- Modeling multi-annotator uncertainty as multi-class segmentation problem -- Modeling multi-annotator uncertainty as multi-class segmentation problem -- Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma -- Predicting isocitrate dehydrogenase mutation status in glioma using structural brain networks and graph neural networks -- Optimization of Deep Learning based Brain Extraction in MRI for Low Resource Environments. Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task -- Unet3D with Multiple Atrous Convolutions Attention Block for Brain Tumor Segmentation -- BRATS2021: exploring each sequence in multi-modal input for baseline U-net performance -- Automatic Brain Tumor Segmentation using Multi-scale Features and Attention Mechanism -- Simple and Fast Convolutional Neural Network applied to median cross sections for predicting the presence of MGMT promoter methylation in FLAIR MRI scans -- MSViT: Multi Scale Vision Transformer forBiomedical Image Segmentation -- Unsupervised Multimodal -- HarDNet-BTS: A Harmonic Shortcut Network for Brain Tumor Segmentation -- Multimodal Brain Tumor Segmentation Algorithm -- Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images -- Multi-plane UNet++ Ensemble for Glioblastoma Segmentation -- Multimodal Brain Tumor Segmentation using Modified UNet Architecture -- A video data based transfer learning approach for classification of MGMT status in brain tumor MR images -- Multimodal Brain Tumor Segmentation Using a 3D ResUNet in BraTS 2021 -- 3D MRI brain tumour segmentation with autoencoder regularization and Hausdorff distance loss function -- 3D CMM-Net with Deeper Encoder for Semantic Segmentation of Brain Tumors in BraTS2021 Challenge -- Cascaded training pipeline for 3D brain tumor segmentation -- nnU-Net with Region-based Training and Loss Ensembles for Brain Tumor Segmentation -- Brain Tumor Segmentation Using Attention Activated U-Net with Positive Mining -- Automatic segmentation of brain tumor using 3D convolutional neural networks -- Hierarchical and Global Modality Interaction for Brain Tumor Segmentation -- Ensemble Outperforms Single Models in Brain Tumor Segmentation -- Brain Tumor Segmentation using UNet-Context Encoding Network -- Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRI. |
| Record Nr. | UNINA-9910585786203321 |
|
Crimi Alessandro
|
||
| Cham, : Springer Nature, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries [[electronic resource] ] : 8th International Workshop, BrainLes 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Revised Selected Papers / / edited by Spyridon Bakas, Alessandro Crimi, Ujjwal Baid, Sylwia Malec, Monika Pytlarz, Bhakti Baheti, Maximilian Zenk, Reuben Dorent
| Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries [[electronic resource] ] : 8th International Workshop, BrainLes 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Revised Selected Papers / / edited by Spyridon Bakas, Alessandro Crimi, Ujjwal Baid, Sylwia Malec, Monika Pytlarz, Bhakti Baheti, Maximilian Zenk, Reuben Dorent |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (294 pages) |
| Disciplina | 518.1 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer vision
Medical informatics Social sciences—Data processing Application software Education—Data processing Artificial intelligence Computer Vision Health Informatics Computer Application in Social and Behavioral Sciences Computer and Information Systems Applications Computers and Education Artificial Intelligence |
| ISBN | 3-031-33842-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Brainlesion -- Brain Tumor Segmentation (BraTS) Challenge -- Brain Tumor Sequence Registration (BraTS-Reg) Challenge -- Cross-Modality Domain Adaptation (CrossMoDA) Challenge -- Federated Tumor Segmentation (FeTS) Challenge. |
| Record Nr. | UNISA-996542666303316 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries : 8th International Workshop, BrainLes 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Revised Selected Papers / / edited by Spyridon Bakas, Alessandro Crimi, Ujjwal Baid, Sylwia Malec, Monika Pytlarz, Bhakti Baheti, Maximilian Zenk, Reuben Dorent
| Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries : 8th International Workshop, BrainLes 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Revised Selected Papers / / edited by Spyridon Bakas, Alessandro Crimi, Ujjwal Baid, Sylwia Malec, Monika Pytlarz, Bhakti Baheti, Maximilian Zenk, Reuben Dorent |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (294 pages) |
| Disciplina |
518.1
616.8 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer vision
Medical informatics Social sciences—Data processing Application software Education—Data processing Artificial intelligence Computer Vision Health Informatics Computer Application in Social and Behavioral Sciences Computer and Information Systems Applications Computers and Education Artificial Intelligence |
| ISBN | 3-031-33842-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Brainlesion -- Brain Tumor Segmentation (BraTS) Challenge -- Brain Tumor Sequence Registration (BraTS-Reg) Challenge -- Cross-Modality Domain Adaptation (CrossMoDA) Challenge -- Federated Tumor Segmentation (FeTS) Challenge. |
| Record Nr. | UNINA-9910734849303321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
