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.

Format:

Book

Author/Creator:

Crimi, Alessandro., Editor.

Contributor:

Crimi, Alessandro, Editor.

Bakas, Spyridon, Editor.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 12962

Language:

English

Subjects (All):

Computer vision.

Artificial intelligence.

Computer engineering.

Computer networks.

Application software.

Computer Vision.

Artificial Intelligence.

Computer Engineering and Networks.

Computer and Information Systems Applications.

Local Subjects:

Computer Vision.

Artificial Intelligence.

Computer Engineering and Networks.

Computer and Information Systems Applications.

Physical Description:

1 online resource (XXI, 489 p. 171 illus., 134 illus. in color.)

Edition:

1st ed. 2022.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2022.

Language Note:

English

Summary:

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.

Contents:

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

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.

ISBN:

3-031-08999-5

OCLC:

1354205775