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      Scale-aware Auto-context-guided Fetal US Segmentation with Structured Random Forests

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          Accurate measurement of fetal biometrics in ultrasound at different trimesters is essential in assisting clinicians to conduct pregnancy diagnosis. However, the accuracy of manual segmentation for measurement is highly user-dependent. Here, we design a general framework for automatically segmenting fetal anatomical structures in two-dimensional (2D) ultrasound (US) images and thus make objective biometric measurements available. We first introduce structured random forests (SRFs) as the core discriminative predictor to recognize the region of fetal anatomical structures with a primary classification map. The patch-wise joint labeling presented by SRFs has inherent advantages in identifying an ambiguous/fuzzy boundary and reconstructing incomplete anatomical boundary in US. Then, to get a more accurate and smooth classification map, a scale-aware auto-context model is injected to enhance the contour details of the classification map from various visual levels. Final segmentation can be obtained from the converged classification map with thresholding. Our framework is validated on two important biometric measurements, which are fetal head circumference (HC) and abdominal circumference (AC). The final results illustrate that our proposed method outperforms state-of-the-art methods in terms of segmentation accuracy.

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          Most cited references 26

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          Intra- and interobserver variability in fetal ultrasound measurements.

           I Sarris,  Penelopi Ioannou,   (2012)
          To assess intra- and interobserver variability of fetal biometry measurements throughout pregnancy. A total of 175 scans (of 140 fetuses) were prospectively performed at 14-41 weeks of gestation ensuring an even distribution throughout gestation. From among three experienced sonographers, a pair of observers independently acquired a duplicate set of seven standard measurements for each fetus. Differences between and within observers were expressed in measurement units (mm), as a percentage of fetal dimensions and as gestational age-specific Z-scores. For all comparisons, Bland-Altman plots were used to quantify limits of agreement. When using measurement units (mm) to express differences, both intra- and interobserver variability increased with gestational age. However, when measurement of variability took into account the increasing fetal size and was expressed as a percentage or Z-score, it remained constant throughout gestation. When expressed as a percentage or Z-score, the 95% limits of agreement for intraobserver difference for head circumference (HC) were ± 3.0% or 0.67; they were ± 5.3% or 0.90 and ± 6.6% or 0.94 for abdominal circumference (AC) and femur length (FL), respectively. The corresponding values for interobserver differences were ± 4.9% or 0.99 for HC, ± 8.8% or 1.35 for AC and ± 11.1% or 1.43 for FL. Although intra- and interobserver variability increases with advancing gestation when expressed in millimeters, both are constant as a percentage of the fetal dimensions or when reported as a Z-score. Thus, measurement variability should be considered when interpreting fetal growth rates. Copyright © 2012 ISUOG. Published by John Wiley & Sons, Ltd.
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            Detection and Measurement of Fetal Anatomies from Ultrasound Images using a Constrained Probabilistic Boosting Tree

            We propose a novel method for the automatic detection and measurement of fetal anatomical structures in ultrasound images. This problem offers a myriad of challenges, including: difficulty of modeling the appearance variations of the visual object of interest, robustness to speckle noise and signal dropout, and large search space of the detection procedure. Previous solutions typically rely on the explicit encoding of prior knowledge and formulation of the problem as a perceptual grouping task solved through clustering or variational approaches. These methods are constrained by the validity of the underlying assumptions and usually are not enough to capture the complex appearances of fetal anatomies. We propose a novel system for fast automatic detection and measurement of fetal anatomies that directly exploits a large database of expert annotated fetal anatomical structures in ultrasound images. Our method learns automatically to distinguish between the appearance of the object of interest and background by training a constrained probabilistic boosting tree classifier. This system is able to produce the automatic segmentation of several fetal anatomies using the same basic detection algorithm. We show results on fully automatic measurement of biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femur length (FL), humerus length (HL), and crown rump length (CRL). Notice that our approach is the first in the literature to deal with the HL and CRL measurements. Extensive experiments (with clinical validation) show that our system is, on average, close to the accuracy of experts in terms of segmentation and obstetric measurements. Finally, this system runs under half second on a standard dual-core PC computer.
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              Evaluation and Comparison of Current Fetal Ultrasound Image Segmentation Methods for Biometric Measurements: A Grand Challenge

              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.

                Author and article information

                BIO Integration
                Compuscript (Ireland )
                01 December 2020
                29 September 2020
                : 1
                : 3
                : 118-129
                1National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China
                2Department of Electronic Engineering, the Chinese University of Hong Kong, Hong Kong, China
                Author notes
                Correspondence to: Dong Ni and Li Liu, E-mail: nidong@ 123456szu.edu.cn ; liliu@ 123456cuhk.edu.hk
                Copyright © 2020 The Authors

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/). See https://bio-integration.org/copyright-and-permissions/

                Self URI (journal-page): https://bio-integration.org/
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