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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Background and Objective: Biometric measurements of fetal head are importantindicators for maternal and fetal health monitoring during pregnancy. 3Dultrasound (US) has unique advantages over 2D scan in covering the whole fetalhead and may promote the diagnoses. However, automatically segmenting the wholefetal head in US volumes still pends as an emerging and unsolved problem. Thechallenges that automated solutions need to tackle include the poor imagequality, boundary ambiguity, long-span occlusion, and the appearancevariability across different fetal poses and gestational ages. In this paper,we propose the first fully-automated solution to segment the whole fetal headin US volumes.  Methods: The segmentation task is firstly formulated as an end-to-endvolumetric mapping under an encoder-decoder deep architecture. We then combinethe segmentor with a proposed hybrid attention scheme (HAS) to selectdiscriminative features and suppress the non-informative volumetric features ina composite and hierarchical way. With little computation overhead, HAS provesto be effective in addressing boundary ambiguity and deficiency. To enhance thespatial consistency in segmentation, we further organize multiple segmentors ina cascaded fashion to refine the results by revisiting context in theprediction of predecessors.  Results: Validated on a large dataset collected from 100 healthy volunteers,our method presents superior segmentation performance (DSC (Dice SimilarityCoefficient), 96.05%), remarkable agreements with experts. With another 156volumes collected from 52 volunteers, we ahieve high reproducibilities (meanstandard deviation 11.524 mL) against scan variations.  Conclusion: This is the first investigation about whole fetal headsegmentation in 3D US. Our method is promising to be a feasible solution inassisting the volumetric US-based prenatal studies.

Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes