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200 00$aBrainlesion$hPart I $eglioma, multiple sclerosis, stroke and traumatic brain injuries : 6th International Workshop, BrainLes 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, revised selected papers /$fAlessandro Crimi, Spyridon Bakas (editors)
210  1$aCham, Switzerland :$cSpringer,$d[2021]
210  4$d©2021
215   $a1 online resource (544 pages)
225 1 $aLecture Notes in Computer Science ;$vv.12658
311   $a3-030-72083-7 
327   $aIntro -- 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.
327   $a2 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.
327   $a2 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.
327   $a3 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.
327   $a3.2 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.
327   $a2.2 Segmentation and Normalization.
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