Titlebook: Brainlesion:Glioma, Multiple Sclerosis, Strokeand Traumatic Brain Injuries; 8th International Wo Spyridon Bakas,Alessandro Crimi,Reuben Dor

Washington

书目名称Brainlesion:Glioma, Multiple Sclerosis, Strokeand Traumatic Brain Injuries影响因子(影响力)




书目名称Brainlesion:Glioma, Multiple Sclerosis, Strokeand Traumatic Brain Injuries影响因子(影响力)学科排名




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书目名称Brainlesion:Glioma, Multiple Sclerosis, Strokeand Traumatic Brain Injuries读者反馈学科排名

Harridan

WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration Networkf segmentation masks of the fixed and moving volumes. These masks are then used to attend to the input volume, which are then provided as inputs to a registration network in the second step. The registration network computes the deformation field to perform the alignment between the fixed and the mo

凹室

Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patientse appearance changes. This paper describes our contribution to the registration of the longitudinal brain MRI task of the Brain Tumor Sequence Registration Challenge 2022 (BraTS-Reg 2022). We developed an enhanced unsupervised learning-based method that extends our previously developed registration

Isolate

3D Inception-Based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain Tumoences between pre-operative and follow-up scans of the same patient diagnosed with an adult brain diffuse high-grade glioma and intends to address the challenging task of registering longitudinal data with major tissue appearance changes. In this work, we proposed a two-stage cascaded network based

胆汁

apiary

Koos Classification of Vestibular Schwannoma via Image Translation-Based Unsupervised Cross-Modalityctures. The Koos classification captures many of the characteristics of treatment decisions and is often used to determine treatment plans. Although both contrast-enhanced T1 (ceT1) scanning and high-resolution T2 (hrT2) scanning can be used for Koos Classification, hrT2 scanning is gaining interest

amygdala

MS-MT: Multi-scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular sing cross-modality segmentation performance by distilling knowledge from a label-rich source domain to a target domain without labels. In this work, we propose a multi-scale self-ensembling based UDA framework for automatic segmentation of two key brain structures . Vestibular Schwannoma (VS) and C

shrill

LAVA

Weakly Unsupervised Domain Adaptation for Vestibular Schwannoma Segmentationare contrast-enhanced T1 (ceT1), with a growing interest in high-resolution T2 images (hrT2) to replace ceT1, which involves the use of a contrast agent. As hrT2 images are currently scarce, it is less likely to train robust machine learning models to segment VS or other brain structures. In this wo

玉米棒子

Multi-view Cross-Modality MR Image Translation for Vestibular Schwannoma and Cochlea Segmentation MR imaging for unsupervised vestibular schwannoma and cochlea segmentation. We adopt two image translation models in parallel that use a pixel-level consistent constraint and a patch-level contrastive constraint, respectively. Thereby, we can augment pseudo-hr. images reflecting different perspecti