Literature DB >> 27810260

Automatic Detection of Standard Sagittal Plane in the First Trimester of Pregnancy Using 3-D Ultrasound Data.

Siqing Nie1, Jinhua Yu2, Ping Chen3, Yuanyuan Wang1, Jian Qiu Zhang1.   

Abstract

Fetal nuchal translucency (NT) thickness is one of the most important parameters in prenatal screening. Locating the mid-sagittal plane is one of the key points to measure NT. In this paper, an automatic method for the sagittal plane detection using 3-D ultrasound data is proposed. To avoid unnecessary massive searching and the corresponding huge computation load, a model is proposed to turn the sagittal plane detection problem into a symmetry plane and axis searching problem. The deep belief network (DBN) and a modified circle detection method provide prior knowledge for the searching. The experiments show that in most cases, the result plane has small distance error and angle error at the same time-88.6% of the result planes have a distance error less than 4 mm and 71.0% have angle error less than 20°. Copyright Â
© 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  3-D ultrasound data; Deep belief network; Sagittal plane detection; Symmetry detection

Mesh:

Year:  2016        PMID: 27810260     DOI: 10.1016/j.ultrasmedbio.2016.08.034

Source DB:  PubMed          Journal:  Ultrasound Med Biol        ISSN: 0301-5629            Impact factor:   2.998


  4 in total

Review 1.  Artificial Intelligence in Prenatal Ultrasound Diagnosis.

Authors:  Fujiao He; Yaqin Wang; Yun Xiu; Yixin Zhang; Lizhu Chen
Journal:  Front Med (Lausanne)       Date:  2021-12-16

2.  Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester.

Authors:  Mark C Walker; Inbal Willner; Olivier X Miguel; Malia S Q Murphy; Darine El-Chaâr; Felipe Moretti; Alysha L J Dingwall Harvey; Ruth Rennicks White; Katherine A Muldoon; André M Carrington; Steven Hawken; Richard I Aviv
Journal:  PLoS One       Date:  2022-06-22       Impact factor: 3.752

3.  Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations.

Authors:  Igbe Tobore; Jingzhen Li; Liu Yuhang; Yousef Al-Handarish; Abhishek Kandwal; Zedong Nie; Lei Wang
Journal:  JMIR Mhealth Uhealth       Date:  2019-08-02       Impact factor: 4.773

4.  Automated Recognition of Ultrasound Cardiac Views Based on Deep Learning with Graph Constraint.

Authors:  Yanhua Gao; Yuan Zhu; Bo Liu; Yue Hu; Gang Yu; Youmin Guo
Journal:  Diagnostics (Basel)       Date:  2021-06-29
  4 in total

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