1 research outputs found
Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes
Background and Objective: Biometric measurements of fetal head are important
indicators for maternal and fetal health monitoring during pregnancy. 3D
ultrasound (US) has unique advantages over 2D scan in covering the whole fetal
head and may promote the diagnoses. However, automatically segmenting the whole
fetal head in US volumes still pends as an emerging and unsolved problem. The
challenges that automated solutions need to tackle include the poor image
quality, boundary ambiguity, long-span occlusion, and the appearance
variability across different fetal poses and gestational ages. In this paper,
we propose the first fully-automated solution to segment the whole fetal head
in US volumes.
Methods: The segmentation task is firstly formulated as an end-to-end
volumetric mapping under an encoder-decoder deep architecture. We then combine
the segmentor with a proposed hybrid attention scheme (HAS) to select
discriminative features and suppress the non-informative volumetric features in
a composite and hierarchical way. With little computation overhead, HAS proves
to be effective in addressing boundary ambiguity and deficiency. To enhance the
spatial consistency in segmentation, we further organize multiple segmentors in
a cascaded fashion to refine the results by revisiting context in the
prediction of predecessors.
Results: Validated on a large dataset collected from 100 healthy volunteers,
our method presents superior segmentation performance (DSC (Dice Similarity
Coefficient), 96.05%), remarkable agreements with experts. With another 156
volumes collected from 52 volunteers, we ahieve high reproducibilities (mean
standard deviation 11.524 mL) against scan variations.
Conclusion: This is the first investigation about whole fetal head
segmentation in 3D US. Our method is promising to be a feasible solution in
assisting the volumetric US-based prenatal studies.Comment: Accepted by Computer Methods and Programs in Biomedicin