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 II

Alessandro Crimi, Spyridon Bakas (Herausgeber)

Buch | Softcover

XXIII, 601 Seiten

2022 | 1st ed. 2022
Springer International Publishing (Verlag)
978-3-031-09001-1 (ISBN)

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This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually.

BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentation.- Optimized U-Net for Brain Tumor Segmentation.- MS UNet: Multi-Scale 3D UNet for Brain Tumor Segmentation.- Evaluating Scale Attention Network for Automatic Brain Tumor Segmentation with Large Multi-parametric MRI Database.- Orthogonal-Nets: A large ensemble of 2D neural networks for 3D Brain Tumor Segmentation.- Feature Learning by Attention and Ensemble with 3D U-Net to Glioma Tumor Segmentation.- MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks.- Brain Tumor Segmentation with Patch-based 3D Attention UNet from Multi-parametric MRI.- Dice Focal Loss with ResNet-like Encoder-Decoder architecture in 3D Brain Tumor Segmentation.- HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging.- Disparity Autoencoders for Multi-class Brain Tumor Segmentation.- Disparity Autoencoders for Multi-class Brain Tumor Segmentation.- Disparity Autoencoders for Multi-class BrainTumor Segmentation.- Brain Tumor Segmentation in Multi-parametric Magnetic Resonance Imaging using Model Ensembling and Super-resolution.- Quality-aware Model Ensemble for Brain Tumor Segmentation.- Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs.- An Ensemble Approach to Automatic Brain Tumor Segmentation.- Extending nn-UNet for brain tumor segmentation.- Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challenge.- Coupling nnU-Nets with Expert Knowledge for Accurate Brain Tumor Segmentation from MRI.- Deep Learning based Ensemble Approach for 3D MRI Brain Tumor Segmentation.- Prediction of MGMT Methylation Status of Glioblastoma using Radiomics and Latent Space Shape Features.- bining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation.- Automatic Brain Tumor Segmentation with a Bridge-Unet deeply supervised enhanced with downsampling pooling combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and EvoNorm.

Erscheinungsdatum	21.07.2022
Reihe/Serie	Lecture Notes in Computer Science
Zusatzinfo	XXIII, 601 p. 225 illus., 195 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	949 g
Themenwelt	Informatik ► Grafik / Design ► Digitale Bildverarbeitung
Themenwelt	Sozialwissenschaften
Schlagworte	Applications • Artificial Intelligence • Bioinformatics • Computer Science • Computer systems • computer vision • conference proceedings • Deep learning • Education • Image Analysis • Image Processing • Image Segmentation • Informatics • learning • machine learning • Medical Images • Neural networks • Research • segmentation methods • Software Design • Software engineering • Software Quality • Validation • Verification and Validation
ISBN-10	3-031-09001-2 / 3031090012
ISBN-13	978-3-031-09001-1 / 9783031090011
Zustand	Neuware