Towards automated semantic segmentation in prenatal volumetric ultrasound

Xin Yang, Lequan Yu, Shengli Li, Huaxuan Wen, Dandan Luo, Cheng Bian, Jing Qin, Dong Ni, Pheng Ann Heng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

35 Citations (Scopus)

Abstract

Volumetric ultrasound is rapidly emerging as a viable imaging modality for routine prenatal examinations. Biometrics obtained from the volumetric segmentation shed light on the reformation of precise maternal and fetal health monitoring. However, the poor image quality, low contrast, boundary ambiguity, and complex anatomy shapes conspire toward a great lack of efficient tools for the segmentation. It makes 3-D ultrasound difficult to interpret and hinders the widespread of 3-D ultrasound in obstetrics. In this paper, we are looking at the problem of semantic segmentation in prenatal ultrasound volumes. Our contribution is threefold: 1) we propose the first and fully automatic framework to simultaneously segment multiple anatomical structures with intensive clinical interest, including fetus, gestational sac, and placenta, which remains a rarely studied and arduous challenge; 2) we propose a composite architecture for dense labeling, in which a customized 3-D fully convolutional network explores spatial intensity concurrency for initial labeling, while a multi-directional recurrent neural network (RNN) encodes spatial sequentiality to combat boundary ambiguity for significant refinement; and 3) we introduce a hierarchical deep supervision mechanism to boost the information flow within RNN and fit the latent sequence hierarchy in fine scales, and further improve the segmentation results. Extensively verified on in-house large data sets, our method illustrates a superior segmentation performance, decent agreements with expert measurements and high reproducibilities against scanning variations, and thus is promising in advancing the prenatal ultrasound examinations.

Original languageEnglish
Article number8418398
Pages (from-to)180-193
Number of pages14
JournalIEEE Transactions on Medical Imaging
Volume38
Issue number1
DOIs
Publication statusPublished - Jan 2019

Keywords

  • fully convolutional networks
  • Prenatal examination
  • recurrent neural networks
  • semantic segmentation
  • volumetric ultrasound

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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