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Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes by Yang, Xin; Wang, Xu; Wang, Yi; Dou, Haoran; Li, Shengli; Wen, Huaxuan; Lin, Yi; Heng, Pheng-Ann; Ni, Dong is a scholarly article available to read on EtoBox.

What is Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes about?

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 f

Author

Yang, Xin; Wang, Xu; Wang, Yi; Dou, Haoran; Li, Shengli; Wen, Huaxuan; Lin, Yi; Heng, Pheng-Ann; Ni, Dong

Published

2020

Language

EN