Literature DB >> 23934664

Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge.

Sylvia Rueda, Sana Fathima, Caroline L Knight, Mohammad Yaqub, Aris T Papageorghiou, Bahbibi Rahmatullah, Alessandro Foi, Matteo Maggioni, Antonietta Pepe, Jussi Tohka, Richard V Stebbing, John E McManigle, Anca Ciurte, Xavier Bresson, Meritxell Bach Cuadra, Changming Sun, Gennady V Ponomarev, Mikhail S Gelfand, Marat D Kazanov, Ching-Wei Wang, Hsiang-Chou Chen, Chun-Wei Peng, Chu-Mei Hung, J Alison Noble.   

Abstract

This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to compare and evaluate current fetal ultrasound image segmentation methods. It consisted of automatically segmenting fetal anatomical structures to measure standard obstetric biometric parameters, from 2D fetal ultrasound images taken on fetuses at different gestational ages (21 weeks, 28 weeks, and 33 weeks) and with varying image quality to reflect data encountered in real clinical environments. Four independent sub-challenges were proposed, according to the objects of interest measured in clinical practice: abdomen, head, femur, and whole fetus. Five teams participated in the head sub-challenge and two teams in the femur sub-challenge, including one team who tackled both. Nobody attempted the abdomen and whole fetus sub-challenges. The challenge goals were two-fold and the participants were asked to submit the segmentation results as well as the measurements derived from the segmented objects. Extensive quantitative (region-based, distance-based, and Bland-Altman measurements) and qualitative evaluation was performed to compare the results from a representative selection of current methods submitted to the challenge. Several experts (three for the head sub-challenge and two for the femur sub-challenge), with different degrees of expertise, manually delineated the objects of interest to define the ground truth used within the evaluation framework. For the head sub-challenge, several groups produced results that could be potentially used in clinical settings, with comparable performance to manual delineations. The femur sub-challenge had inferior performance to the head sub-challenge due to the fact that it is a harder segmentation problem and that the techniques presented relied more on the femur's appearance.

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Year:  2013        PMID: 23934664     DOI: 10.1109/TMI.2013.2276943

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  17 in total

1.  Automatic image quality assessment and measurement of fetal head in two-dimensional ultrasound image.

Authors:  Lei Zhang; Nicholas J Dudley; Tryphon Lambrou; Nigel Allinson; Xujiong Ye
Journal:  J Med Imaging (Bellingham)       Date:  2017-04-17

2.  Toward point-of-care ultrasound estimation of fetal gestational age from the trans-cerebellar diameter using CNN-based ultrasound image analysis.

Authors:  Mohammad A Maraci; Mohammad Yaqub; Rachel Craik; Sridevi Beriwal; Alice Self; Peter von Dadelszen; Aris Papageorghiou; J Alison Noble
Journal:  J Med Imaging (Bellingham)       Date:  2020-01-13

Review 3.  Multi-atlas segmentation of biomedical images: A survey.

Authors:  Juan Eugenio Iglesias; Mert R Sabuncu
Journal:  Med Image Anal       Date:  2015-07-06       Impact factor: 8.545

4.  Methods for 2-D and 3-D Endobronchial Ultrasound Image Segmentation.

Authors:  Xiaonan Zang; Rebecca Bascom; Christopher Gilbert; Jennifer Toth; William Higgins
Journal:  IEEE Trans Biomed Eng       Date:  2015-10-26       Impact factor: 4.538

5.  Fetal Ultrasound Image Segmentation for Automatic Head Circumference Biometry Using Deeply Supervised Attention-Gated V-Net.

Authors:  Yan Zeng; Po-Hsiang Tsui; Weiwei Wu; Zhuhuang Zhou; Shuicai Wu
Journal:  J Digit Imaging       Date:  2021-01-22       Impact factor: 4.056

6.  Feature-based fuzzy connectedness segmentation of ultrasound images with an object completion step.

Authors:  Sylvia Rueda; Caroline L Knight; Aris T Papageorghiou; J Alison Noble
Journal:  Med Image Anal       Date:  2015-07-17       Impact factor: 8.545

Review 7.  Automated Techniques for the Interpretation of Fetal Abnormalities: A Review.

Authors:  Vidhi Rawat; Alok Jain; Vibhakar Shrimali
Journal:  Appl Bionics Biomech       Date:  2018-06-10       Impact factor: 1.781

8.  Automated measurement of fetal head circumference using 2D ultrasound images.

Authors:  Thomas L A van den Heuvel; Dagmar de Bruijn; Chris L de Korte; Bram van Ginneken
Journal:  PLoS One       Date:  2018-08-23       Impact factor: 3.240

9.  Deep Learning strategies for Ultrasound in Pregnancy.

Authors:  Pedro H B Diniz; Yi Yin; Sally Collins
Journal:  Eur Med J Reprod Health       Date:  2020-08-25

10.  Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.

Authors:  Anca Ciurte; Xavier Bresson; Olivier Cuisenaire; Nawal Houhou; Sergiu Nedevschi; Jean-Philippe Thiran; Meritxell Bach Cuadra
Journal:  PLoS One       Date:  2014-07-10       Impact factor: 3.240

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