Literature DB >> 26341043

3-D Ultrasound Segmentation of the Placenta Using the Random Walker Algorithm: Reliability and Agreement.

Gordon N Stevenson1, Sally L Collins2, Jane Ding3, Lawrence Impey4, J Alison Noble5.   

Abstract

Volumetric segmentation of the placenta using 3-D ultrasound is currently performed clinically to investigate correlation between organ volume and fetal outcome or pathology. Previously, interpolative or semi-automatic contour-based methodologies were used to provide volumetric results. We describe the validation of an original random walker (RW)-based algorithm against manual segmentation and an existing semi-automated method, virtual organ computer-aided analysis (VOCAL), using initialization time, inter- and intra-observer variability of volumetric measurements and quantification accuracy (with respect to manual segmentation) as metrics of success. Both semi-automatic methods require initialization. Therefore, the first experiment compared initialization times. Initialization was timed by one observer using 20 subjects. This revealed significant differences (p < 0.001) in time taken to initialize the VOCAL method compared with the RW method. In the second experiment, 10 subjects were used to analyze intra-/inter-observer variability between two observers. Bland-Altman plots were used to analyze variability combined with intra- and inter-observer variability measured by intra-class correlation coefficients, which were reported for all three methods. Intra-class correlation coefficient values for intra-observer variability were higher for the RW method than for VOCAL, and both were similar to manual segmentation. Inter-observer variability was 0.94 (0.88, 0.97), 0.91 (0.81, 0.95) and 0.80 (0.61, 0.90) for manual, RW and VOCAL, respectively. Finally, a third observer with no prior ultrasound experience was introduced and volumetric differences from manual segmentation were reported. Dice similarity coefficients for observers 1, 2 and 3 were respectively 0.84 ± 0.12, 0.94 ± 0.08 and 0.84 ± 0.11, and the mean was 0.87 ± 0.13. The RW algorithm was found to provide results concordant with those for manual segmentation and to outperform VOCAL in aspects of observer reliability. The training of an additional untrained observer was investigated, and results revealed that with the appropriate initialization protocol, results for observers with varying levels of experience were concordant. We found that with appropriate training, the RW method can be used for fast, repeatable 3-D measurement of placental volume.
Copyright © 2015 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Keywords:  3-D ultrasound; Agreement; Intra-class correlation coefficient; Placenta; Random walker; Repeatability; Virtual organ computer-aided analysis (VOCAL); Volume

Mesh:

Year:  2015        PMID: 26341043     DOI: 10.1016/j.ultrasmedbio.2015.07.021

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


  9 in total

1.  Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning.

Authors:  Pádraig Looney; Gordon N Stevenson; Kypros H Nicolaides; Walter Plasencia; Malid Molloholli; Stavros Natsis; Sally L Collins
Journal:  JCI Insight       Date:  2018-06-07

2.  User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP.

Authors:  Paul A Yushkevich; Artem Pashchinskiy; Ipek Oguz; Suyash Mohan; J Eric Schmitt; Joel M Stein; Dženan Zukić; Jared Vicory; Matthew McCormick; Natalie Yushkevich; Nadav Schwartz; Yang Gao; Guido Gerig
Journal:  Neuroinformatics       Date:  2019-01

3.  Segmentation of the placenta and its vascular tree in Doppler ultrasound for fetal surgery planning.

Authors:  Enric Perera-Bel; Mario Ceresa; Jordina Torrents-Barrena; Narcís Masoller; Brenda Valenzuela-Alcaraz; Eduard Gratacós; Elisenda Eixarch; Miguel A González Ballester
Journal:  Int J Comput Assist Radiol Surg       Date:  2020-09-19       Impact factor: 2.924

4.  Minimally interactive placenta segmentation from three-dimensional ultrasound images.

Authors:  Ipek Oguz; Natalie Yushkevich; Alison Pouch; Baris U Oguz; Jiancong Wang; Shobhana Parameshwaran; James Gee; Paul A Yushkevich; Nadav Schwartz
Journal:  J Med Imaging (Bellingham)       Date:  2020-02-22

5.  Applying spatial-temporal image correlation to the fetal kidney: Repeatability of 3D segmentation and volumetric impedance indices.

Authors:  Bonita Gu; Gordon N Stevenson; Ana Ferreira; Sudeshni Pathirana; Jennifer Sanderson; Amanda Henry; Jennifer Alphonse; Alec W Welsh
Journal:  Australas J Ultrasound Med       Date:  2018-05-11

6.  Fully Automated Placental Volume Quantification From 3D Ultrasound for Prediction of Small-for-Gestational-Age Infants.

Authors:  Nadav Schwartz; Ipek Oguz; Jiancong Wang; Alison Pouch; Natalie Yushkevich; Shobhana Parameshwaran; James Gee; Paul Yushkevich; Baris Oguz
Journal:  J Ultrasound Med       Date:  2021-09-23       Impact factor: 2.754

7.  Fully Automated 3-D Ultrasound Segmentation of the Placenta, Amniotic Fluid, and Fetus for Early Pregnancy Assessment.

Authors:  Padraig Looney; Yi Yin; Sally L Collins; Kypros H Nicolaides; Walter Plasencia; Malid Molloholli; Stavros Natsis; Gordon N Stevenson
Journal:  IEEE Trans Ultrason Ferroelectr Freq Control       Date:  2021-05-25       Impact factor: 3.267

8.  3D fractional moving blood volume (3D-FMBV) demonstrates decreased first trimester placental vascularity in pre-eclampsia but not the term, small for gestation age baby.

Authors:  Sally L Collins; Alec W Welsh; Lawrence Impey; J Alison Noble; Gordon N Stevenson
Journal:  PLoS One       Date:  2017-06-01       Impact factor: 3.240

9.  APPLAUSE: Automatic Prediction of PLAcental health via U-net Segmentation and statistical Evaluation.

Authors:  Maximilian Pietsch; Alison Ho; Alessia Bardanzellu; Aya Mutaz Ahmad Zeidan; Lucy C Chappell; Joseph V Hajnal; Mary Rutherford; Jana Hutter
Journal:  Med Image Anal       Date:  2021-06-23       Impact factor: 8.545

  9 in total

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