This two volume-set LNCS 13769 and LNCS 14092 constitutes the refereed proceedings of the 8th International MICCAI Brainlesion Workshop, BrainLes 2022, as well as the Brain Tumor Segmentation (BraTS) Challenge, the Brain Tumor Sequence Registration (BraTS-Reg) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the Federated Tumor Segmentation (FeTS) Challenge. These were held jointly at the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2022, in September 2022. The 46 revised full papers presented in these volumes were selected form 65 submissions.The presented contributions describe the research of computational scientists and clinical researchers working on brain lesions - specifically glioma, multiple sclerosis, cerebral stroke, traumatic brain injuries, vestibular schwannoma, and white matter hyper-intensities of presumed vascular origin.
Reihe
Auflage
Sprache
Verlagsort
Verlagsgruppe
Springer International Publishing
Illustrationen
59
16 s/w Abbildungen, 59 farbige Abbildungen
XIX, 243 p. 75 illus., 59 illus. in color.
Dateigröße
ISBN-13
978-3-031-44153-0 (9783031441530)
DOI
10.1007/978-3-031-44153-0
Schweitzer Klassifikation
Applying Quadratic Penalty Method for Intensity-based Deformable Image Registration on BraTS-Reg Challenge 2022.- WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration Network.- Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients.- 3D Inception-Based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain Tumors.- Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction based on Semi-Supervised Contrastive Learning.- Koos Classification of Vestibular Schwannoma via Image Translation-Based Unsupervised Cross-Modality Domain Adaptation.- MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation.- An Unpaired Cross-modality Segmentation Framework Using Data Augmentation and Hybrid Convolutional Networks for Segmenting Vestibular Schwannoma and Cochlea.-Weakly Unsupervised Domain Adaptation for Vestibular Schwannoma Segmentation.- Multi-view Cross-Modality MR Image Translation for Vestibular Schwannoma and Cochlea Segmentation.- Enhancing Data Diversity for Self-training Based Unsupervised Cross-modality Vestibular Schwannoma and Cochlea Segmentation.- Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation.- A Local Score Strategy for Weight Aggregation in Federated Learning.- Ensemble Outperforms Single Models in Brain Tumor Segmentation.- FeTS Challenge 2022 Task 1: Implementing FedMGDA+ and a new partitioning.- Efficient Federated Tumor Segmentation via Parameter Distance Weighted Aggregation and Client Pruning.- Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation.- Robust Learning Protocol for Federated Tumor Segmentation Challenge.- Model Aggregation for Federated Learning Considering Non-IID andImbalanced Data Distribution.- FedPIDAvg: A PID controller inspired aggregation method for Federated Learning.- Federated Evaluation of nnU-Nets Enhanced with Domain Knowledge for Brain Tumor Segmentation.- Experimenting FedML and NVFLARE for Federated Tumor Segmentation Challenge.
