English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Automatic methods to segment the vestibular schwannoma (VS) tumors and thecochlea from magnetic resonance imaging (MRI) are critical to VS treatmentplanning. Although supervised methods have achieved satisfactory performance inVS segmentation, they require full annotations by experts, which is laboriousand time-consuming. In this work, we aim to tackle the VS and cochleasegmentation problem in an unsupervised domain adaptation setting. Our proposedmethod leverages both the image-level domain alignment to minimize the domaindivergence and semi-supervised training to further boost the performance.Furthermore, we propose to fuse the labels predicted from multiple models vianoisy label correction. In the MICCAI 2021 crossMoDA challenge, our results onthe final evaluation leaderboard showed that our proposed method has achievedpromising segmentation performance with mean dice score of 79.9% and 82.5% andASSD of 1.29 mm and 0.18 mm for VS tumor and cochlea, respectively. The cochleaASSD achieved by our method has outperformed all other competing methods aswell as the supervised nnU-Net.

Unsupervised Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation via Semi-supervised Learning and Label Fusion