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Brain tumors / / Harry S. Greenberg, William F. Chandler, Howard M. Sandler
| Brain tumors / / Harry S. Greenberg, William F. Chandler, Howard M. Sandler |
| Autore | Greenberg Harry <1946-> |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | New York ; , : Oxford University Press, , 2023 |
| Descrizione fisica | 1 online resource (367 p.) |
| Disciplina | 616.99281 |
| Collana |
Contemporary neurology series
Oxford scholarship online |
| Soggetto topico | Brain - Tumors |
| ISBN |
0-19-770583-9
1-280-83124-3 9786610831241 0-19-802965-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
CONTENTS; 1. BRAIN TUMOR CLASSIFICATION, GRADING, AND EPIDEMIOLOGY; 2. BRAIN TUMOR BIOLOGY; 3. TUMOR IMAGING AND RESPONSE; 4. SURGERY FOR BRAIN TUMORS; 5. RADIATION THERAPY FOR BRAIN TUMORS: CURRENT PRACTICE; 6. RADIATION THERAPY FOR BRAIN TUMORS: RECENT ADVANCES AND EXPERIMENTAL METHODS; 7. BRAIN TUMOR CHEMOTHERAPY AND IMMUNOTHERAPY; 8. MALIGNANT ASTROCYTOMA; 9. PILOCYTIC ASTROCYTOMA, LOW-GRADE ASTROCYTOMA, AND OTHER ""BENIGN"" NEUROEPITHELIAL NEOPLASMS; 10. OLIGODENDROGLIOMA AND OLIGO-ASTROCYTOMA; 11. POSTERIOR FOSSA TUMORS; 12. PRIMARY CENTRAL NERVOUS SYSTEM LYMPHOMA
13. PITUITARY AND PINEAL REGION TUMORS14. EXTRA-AXIAL BRAIN TUMORS; 15. BRAIN METASTASES; INDEX |
| Record Nr. | UNINA-9910972479803321 |
Greenberg Harry <1946->
|
||
| New York ; , : Oxford University Press, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Brain tumors [[electronic resource] /] / Harry S. Greenberg, William F. Chandler, Howard M. Sandler
| Brain tumors [[electronic resource] /] / Harry S. Greenberg, William F. Chandler, Howard M. Sandler |
| Autore | Greenberg Harry <1946-> |
| Pubbl/distr/stampa | New York, : Oxford University Press, 1999 |
| Descrizione fisica | 1 online resource (367 p.) |
| Disciplina | 616.99281 |
| Altri autori (Persone) |
ChandlerWilliam F
SandlerHoward M <1956-> (Howard Mark) |
| Collana | Contemporary neurology series |
| Soggetto topico |
Brain - Tumors
Oncology |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-83124-3
9786610831241 0-19-802965-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
CONTENTS; 1. BRAIN TUMOR CLASSIFICATION, GRADING, AND EPIDEMIOLOGY; 2. BRAIN TUMOR BIOLOGY; 3. TUMOR IMAGING AND RESPONSE; 4. SURGERY FOR BRAIN TUMORS; 5. RADIATION THERAPY FOR BRAIN TUMORS: CURRENT PRACTICE; 6. RADIATION THERAPY FOR BRAIN TUMORS: RECENT ADVANCES AND EXPERIMENTAL METHODS; 7. BRAIN TUMOR CHEMOTHERAPY AND IMMUNOTHERAPY; 8. MALIGNANT ASTROCYTOMA; 9. PILOCYTIC ASTROCYTOMA, LOW-GRADE ASTROCYTOMA, AND OTHER ""BENIGN"" NEUROEPITHELIAL NEOPLASMS; 10. OLIGODENDROGLIOMA AND OLIGO-ASTROCYTOMA; 11. POSTERIOR FOSSA TUMORS; 12. PRIMARY CENTRAL NERVOUS SYSTEM LYMPHOMA
13. PITUITARY AND PINEAL REGION TUMORS14. EXTRA-AXIAL BRAIN TUMORS; 15. BRAIN METASTASES; INDEX |
| Record Nr. | UNINA-9910454093203321 |
|
Greenberg Harry <1946->
|
||
| New York, : Oxford University Press, 1999 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Brain tumors [[electronic resource] /] / Harry S. Greenberg, William F. Chandler, Howard M. Sandler
| Brain tumors [[electronic resource] /] / Harry S. Greenberg, William F. Chandler, Howard M. Sandler |
| Autore | Greenberg Harry <1946-> |
| Pubbl/distr/stampa | New York, : Oxford University Press, 1999 |
| Descrizione fisica | 1 online resource (367 p.) |
| Disciplina | 616.99281 |
| Altri autori (Persone) |
ChandlerWilliam F
SandlerHoward M <1956-> (Howard Mark) |
| Collana | Contemporary neurology series |
| Soggetto topico |
Brain - Tumors
Oncology |
| ISBN |
0-19-770583-9
1-280-83124-3 9786610831241 0-19-802965-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
CONTENTS; 1. BRAIN TUMOR CLASSIFICATION, GRADING, AND EPIDEMIOLOGY; 2. BRAIN TUMOR BIOLOGY; 3. TUMOR IMAGING AND RESPONSE; 4. SURGERY FOR BRAIN TUMORS; 5. RADIATION THERAPY FOR BRAIN TUMORS: CURRENT PRACTICE; 6. RADIATION THERAPY FOR BRAIN TUMORS: RECENT ADVANCES AND EXPERIMENTAL METHODS; 7. BRAIN TUMOR CHEMOTHERAPY AND IMMUNOTHERAPY; 8. MALIGNANT ASTROCYTOMA; 9. PILOCYTIC ASTROCYTOMA, LOW-GRADE ASTROCYTOMA, AND OTHER ""BENIGN"" NEUROEPITHELIAL NEOPLASMS; 10. OLIGODENDROGLIOMA AND OLIGO-ASTROCYTOMA; 11. POSTERIOR FOSSA TUMORS; 12. PRIMARY CENTRAL NERVOUS SYSTEM LYMPHOMA
13. PITUITARY AND PINEAL REGION TUMORS14. EXTRA-AXIAL BRAIN TUMORS; 15. BRAIN METASTASES; INDEX |
| Record Nr. | UNINA-9910782473503321 |
|
Greenberg Harry <1946->
|
||
| New York, : Oxford University Press, 1999 | ||
| 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: 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 I / / edited by Alessandro Crimi, Spyridon Bakas
| 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 I / / edited by Alessandro Crimi, Spyridon Bakas |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (544 pages) |
| Disciplina | 616.99281 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Computer vision
Machine learning Pattern recognition systems Bioinformatics Computer Vision Machine Learning Automated Pattern Recognition Computational and Systems Biology |
| ISBN | 3-030-72084-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Papers -- Glioma Diagnosis and Classification: Illuminating the Gold Standard -- Multiple Sclerosis Lesion Segmentation - A Survey of Supervised CNN-Based Methods -- Computational Diagnostics of GBM Tumors in the Era of Radiomics and Radiogenomics -- Brain Lesion Image Analysis -- Automatic Segmentation of Non-Tumor Tissues in Glioma MR Brain Images Using Deformable Registration with Partial Convolutional Networks -- Convolutional neural network with asymmetric encoding and decoding structure for brain vessel segmentation on computed tomographic angiography -- Volume Preserving Brain Lesion Segmentation -- Microstructural modulations in the hippocampus allow to characterizing relapsing-remitting versus primary progressive multiple sclerosis -- Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology -- Multivariate analysis is sufficient for lesion-behaviour mapping -- Label-Efficient Multi-Task Segmentation using Contrastive Learning -- Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation -- MMSSD: Multi-scale and Multi-level Single Shot Detector for Brain Metastases Detection -- Unsupervised 3D Brain Anomaly Detection -- Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRI Tejas Sudharshan Mathai, Yi Wang, Nathan Cross -- Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression -- Bayesian Skip Net: Building on Prior Information for the Prediction and Segmentation of Stroke Lesions -- Brain Tumor Segmentation -- Brain Tumor Segmentation Using Dual-Path Attention U-net in 3D MRI Images -- Multimodal Brain Image Analysis and Survival Prediction -- Using Neuromorphic Attention-based Neural Networks -- Context Aware 3D UNet for Brain Tumor Segmentation -- Modality-Pairing Learning for Brain Tumor Segmentation -- Transfer Learning for Brain Tumor Segmentation -- Efficient embedding network for 3D brain tumor segmentation -- Segmentation of the multimodal brain tumor images used Res-U-Net -- Vox2Vox: 3D-GAN for Brain Tumour Segmentation -- Automatic Brain Tumor Segmentation with Scale Attention Network -- Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction -- Overall Survival Prediction for Glioblastoma on Pre-Treatment MRI Using Robust Radiomics and Priors -- Glioma segmentation using encoder-decoder network and survival prediction based on cox analysis -- Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution -- Brain tumour segmentation using a triplanar ensemble of U-Nets on MR images -- MRI brain tumor segmentation using a 2D-3D U-Net ensemble -- Multimodal Brain Tumor Segmentation and Survival Prediction Using a 3D Self-Ensemble ResUNet -- MRI Brain Tumor Segmentation and Uncertainty Estimation using 3D-UNet architectures -- Utility of Brain Parcellation in Enhancing Brain Tumor Segmentation and Survival Prediction -- Uncertainty-driven refinement of tumor core segmentation using 3D-to-2D networks with label uncertainty -- Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation -- MultiATTUNet: Brain Tumor Segmentation and Survival Multitasking -- A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation -- Ensemble of Two Dimensional Networks for Bain Tumor Segmentation -- Cascaded Coarse-to-Fine Neural Network for Brain Tumor Segmentation -- Low-Rank Convolutional Networks for Brain Tumor Segmentation -- Brain tumour segmentation using cascaded 3D densely-connected U-net -- Segmentation then Prediction: A Multi-task Solution to Brain Tumor Segmentation and Survival Prediction -- Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network -- Self-training for Brain Tumour Segmentation with Uncertainty Estimation and Biophysics-Guided Survival Prediction. |
| Record Nr. | UNINA-9910484791303321 |
| Cham : , : Springer International Publishing : , : Imprint : 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 / / edited by Alessandro Crimi, Spyridon Bakas
| 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 / / edited by Alessandro Crimi, Spyridon Bakas |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (539 pages) |
| Disciplina | 616.99281 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Computer vision
Machine learning Pattern recognition systems Bioinformatics Computer Vision Machine Learning Automated Pattern Recognition Computational and Systems Biology |
| ISBN | 3-030-72087-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Brain Tumor Segmentation -- Lightweight U-Nets for Brain Tumor Segmentation -- Efficient Brain Tumour Segmentation using Co-registered Data and Ensembles of Specialised Learners -- Efficient MRI Brain Tumor Segmentation using Multi-Resolution Encoder-Decoder Networks -- Trialing U-Net Training Modifications for Segmenting Gliomas Using Open Source Deep Learning Framework -- HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation -- H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task -- 2D Dense-UNet: A Clinically Valid Approach to Automated Glioma Segmentation -- Attention U-Net with Dimension-hybridized Fast Data Density Functional Theory for Automatic Brain Tumor Image Segmentation -- MVP U-Net: Multi-View Pointwise U-Net for Brain Tumor Segmentation -- Glioma Segmentation with 3D U-Net Backed with Energy- Based Post- Processing -- nnU-Net for Brain Tumor Segmentation -- A Deep Random Forest Approach forMultimodal Brain Tumor Segmentation -- Brain tumor segmentation and associated uncertainty evaluation using Multi-sequences MRI Mixture Data Preprocessing -- A Deep supervision CNN network for Brain tumor Segmentation -- Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans -- Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation -- Glioma Segmentation Using Ensemble of 2D/3D U-Nets and Survival Prediction Using Multiple Features Fusion -- Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challenge -- 3D Semantic Segmentation of Brain Tumor for Overall Survival Prediction -- Segmentation, Survival Prediction, and Uncertainty Estimation of Gliomas from Multimodal 3D MRI using Selective Kernel Networks -- 3D brain tumor segmentation and survival prediction using ensembles of Convolutional Neural Networks -- Brain Tumour Segmentation using Probabilistic U-Net -- Segmenting Brain Tumors from MRI Using Cascaded 3D U-Nets -- A Deep Supervised U-Attention Net for Pixel-wise Brain Tumor Segmentation -- A two stage atrous convolution neural network for brain tumor segmentation -- TwoPath U-Net for Automatic Brain Tumor Segmentation from Multimodal MRI data -- Brain Tumor Segmentation and Survival Prediction using Automatic Hardmining in 3D CNN Architecture -- Some New Tricks for Deep Glioma Segmentation -- PieceNet: A Redundant UNet Ensemble -- Cerberus: A Multi-headed Network for BrainTumor Segmentation -- An Automatic Overall Survival Time Prediction System for Glioma Brain Tumor Patients based on Volumetric and Shape Features -- Squeeze-and-Excitation Normalization for Brain Tumor Segmentation -- Modified MobileNet for Patient Survival Prediction -- Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation -- Brain Tumor Segmentation and Survival Prediction Using Patch Based Modified U-Net -- DR-Unet104 for Multimodal MRI brain tumor segmentation -- Glioma Sub-region Segmentation on Multi-parameter MRI with Label Dropout -- Variational-Autoencoder Regularized 3D MultiResUNet for the BraTS 2020 Brain Tumor Segmentation -- Learning Dynamic Convolutions for Multi-Modal 3D MRI Brain Tumor Segmentation -- Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification -- Automatic Glioma Grading Based on Two-stage Networks by Integrating Pathology and MRI Images -- Brain Tumor Classification Based on MRI Images and Noise Reduced Pathology Images -- Multimodal brain tumor classification -- A Hybrid Convolutional Neural Network Based-Method for Brain Tumor Classification Using mMRI and WSI -- CNN-based Fully Automatic Glioma Classification with Multi-modal Medical Images -- Glioma Classification Using Multimodal Radiology and Histology Data. |
| Record Nr. | UNINA-9910484791403321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries [[electronic resource] ] : 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part II / / edited by Alessandro Crimi, Spyridon Bakas
| Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries [[electronic resource] ] : 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part II / / edited by Alessandro Crimi, Spyridon Bakas |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (411 pages) : illustrations |
| Disciplina | 616.99281 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Machine learning Application software Pattern recognition Computers Image Processing and Computer Vision Machine Learning Computer Applications Pattern Recognition Computing Milieux |
| ISBN | 3-030-46643-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Brain Lesion Image Analysis -- Brain Tumor Image Segmentation -- Combined MRI and Pathology Brain Tumor Classification -- Tools Allowing Clinical Translation of Image Computing Algorithms. |
| Record Nr. | UNISA-996418316703316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries [[electronic resource] ] : 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part I / / edited by Alessandro Crimi, Spyridon Bakas
| Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries [[electronic resource] ] : 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part I / / edited by Alessandro Crimi, Spyridon Bakas |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (xvi, 400 pages) |
| Disciplina | 616.99281 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Machine learning Application software Education—Data processing Pattern recognition Image Processing and Computer Vision Machine Learning Computer Applications Computers and Education Pattern Recognition |
| ISBN | 3-030-46640-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Brain Lesion Image Analysis -- Brain Tumor Image Segmentation -- Combined MRI and Pathology Brain Tumor Classification -- Tools Allowing Clinical Translation of Image Computing Algorithms. |
| Record Nr. | UNISA-996418311403316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries : 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part II / / edited by Alessandro Crimi, Spyridon Bakas
| Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries : 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part II / / edited by Alessandro Crimi, Spyridon Bakas |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (411 pages) : illustrations |
| Disciplina | 616.99281 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Computer vision
Machine learning Application software Pattern recognition systems Computers Computer Vision Machine Learning Computer and Information Systems Applications Automated Pattern Recognition Computing Milieux |
| ISBN | 3-030-46643-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Brain Lesion Image Analysis -- Brain Tumor Image Segmentation -- Combined MRI and Pathology Brain Tumor Classification -- Tools Allowing Clinical Translation of Image Computing Algorithms. |
| Record Nr. | UNINA-9910409665203321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
