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200 00$aBrainlesion $eglioma, multiple sclerosis, stroke and traumatic brain injuries : 6th International Workshop, BrainLes 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, revised selected papers, part II /$fAlessandro Crimi, Spyridon Bakas (editors)
210  1$aCham, Switzerland :$cSpringer,$d[2021]
210  4$d©2021
215   $a1 online resource (539 pages)
225 1 $aLecture Notes in Computer Science ;$v12659
311   $a3-030-72086-1 
327   $aIntro -- 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.
327   $a4 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.
327   $aA 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.
327   $a5 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.
327   $a2.4 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.
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