Volumetric (3D) imaging reduces inter‐ and intraobserver variation of fetal biometry measurements

Chan, Lin Wai; Fung, Tung; Leung, Tak Yeung; Sahota, Daljit Singh; Lau, Tze Kin

doi:10.1002/uog.6321

Cited by 38 publications

(41 citation statements)

References 10 publications

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“…Similar findings have been reported before, demonstrating the potential use of 3D ultrasound in training of sonographers, in assessing their skills, and in improving basic obstetric biometric measurement [12][13][14]. Most modern obstetric ultrasound scanners are now equipped with 3D capability.…”

Section: Discussionsupporting

confidence: 81%

Use of three-dimensional (3D) sonography to assess the true midsagittal plane of fetal spine

Yang

Chen

Leung

et al. 2010

The Journal of Maternal-Fetal & Neonatal Medicine

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Section: Discussionsupporting

confidence: 81%

Use of three-dimensional (3D) sonography to assess the true midsagittal plane of fetal spine

Yang

Chen

Leung

et al. 2010

The Journal of Maternal-Fetal & Neonatal Medicine

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“…Three dimensional US (3D-US) offers volumetric assessment of the placenta, which provides increased accuracy of placental growth compared to standard two-dimensional measures of length or thickness, while also minimizing errors of measurement (44). Early studies of placental volume using 3D-US in the first trimester have been shown to predict preeclampsia and fetal growth restriction.…”

Section: The Role Of Ultrasound In Placental Evaluationmentioning

confidence: 99%

“…These quantitative MRI studies are providing important insights into the rate and progression of placental growth in the healthy and high-risk pregnancy in utero. As discussed earlier, 3D volumetric imaging has demonstrated more accurate measurement of fetal biometry (44) and shown that biometric measures such as placental length alone, are insufficient clinical markers of fetal growth (57, 58). Moreover, 3D-MR imaging of the placenta provides more reliable measures of growth compared to 3D US (59, 60).…”

Section: Role Of Magnetic Resonance Imaging In Assessing Placental Hementioning

confidence: 99%

Advanced MR imaging of the placenta: Exploring the in utero placenta–brain connection

Andescavage

Plessis

Limperopoulos

2015

Seminars in Perinatology

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The placenta is a vital organ necessary for the healthy neurodevelopment of the fetus. Despite the known associations between placental dysfunction and neurologic impairment, there is a paucity of tools available to reliably assess in vivo placental health and function. Existing clinical tools for placental assessment remain insensitive in predicting and assessing placental well-being. Advanced MRI techniques hold significant promise for the dynamic, non-invasive, real-time assessment of placental health and identification of early placental-based disorders. In this review, we summarize the available clinical tools for placental assessment including ultrasound, Doppler, and conventional MRI. We then explore the emerging role of advanced placental MR imaging techniques for supporting the developing fetus, appraise the strengths and limitations of quantitative MRI in identifying early markers of placental dysfunction for improved pregnancy monitoring and fetal outcomes.

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“…The advantage of such data is in visualization of planes in which 2-D acquisition is not possible, surface rendering of anatomies, and postexam data processing and review [2]. The 3-D acquisition decreases the examination time [4], [5] and reduces inter-and intra-observer variations of biometric measurements [3], especially for less experienced sonographers. Similar to 2-D exams, the major bottleneck remains the navigation to the standardized planes [6], in which measurements are performed.…”

Section: Automatic Detection and Measurement Of Structures In Fetal Hmentioning

confidence: 99%

“…The idea of importance sampling is to introduce a proposal distribution which includes the support of . In order for the samples to be proper [41], the weights are defined as (3) Since the current states do not depend on observations from other objects then (4) Note, that was left out of the first term since the states in the sequence do not depend on it. The states are computed as (5) Substituting (4) and (5) into (3), we have (6) (7) (8) In this paper, we adopt the transition prior as the proposal distribution.…”

Section: Sequential Sampling For Multi-object Detectionmentioning

confidence: 99%

Automatic Detection and Measurement of Structures in Fetal Head Ultrasound Volumes Using Sequential Estimation and Integrated Detection Network (IDN)

Sofka

Zhang

Good

et al. 2014

IEEE Trans. Med. Imaging

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Abstract-Routine ultrasound exam in the second and third trimesters of pregnancy involves manually measuring fetal head and brain structures in 2-D scans. The procedure requires a sonographer to find the standardized visualization planes with a probe and manually place measurement calipers on the structures of interest. The process is tedious, time consuming, and introduces user variability into the measurements. This paper proposes an automatic fetal head and brain (AFHB) system for automatically measuring anatomical structures from 3-D ultrasound volumes. The system searches the 3-D volume in a hierarchy of resolutions and by focusing on regions that are likely to be the measured anatomy. The output is a standardized visualization of the plane with correct orientation and centering as well as the biometric measurement of the anatomy. The system is based on a novel framework for detecting multiple structures in 3-D volumes. Since a joint model is difficult to obtain in most practical situations, the structures are detected in a sequence, one-by-one. The detection relies on Sequential Estimation techniques, frequently applied to visual tracking. The interdependence of structure poses and strong prior information embedded in our domain yields faster and more accurate results than detecting the objects individually. The posterior distribution of the structure pose is approximated at each step by sequential Monte Carlo. The samples are propagated within the sequence across multiple structures and hierarchical levels. The probabilistic model helps solve many challenges present in the ultrasound images of the fetus such as speckle noise, signal drop-out, shadows caused by bones, and appearance variations caused by the differences in the fetus gestational age. This is possible by discriminative learning on an extensive database of scans comprising more than two thousand volumes and more than thirteen thousand annotations. The average difference between ground truth and automatic measurements is below 2 mm with a running time of 6.9 s (GPU) or 14.7 s (CPU). The accuracy of the AFHB system is within inter-user variability and the running time is fast, which meets the requirements for clinical use.Index Terms-Fetal brain measurements, fetal head measurements, fetal utrasound, object detection, sequential sampling, three-dimensional (3-D) ultrasound.

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Volumetric (3D) imaging reduces inter‐ and intraobserver variation of fetal biometry measurements

Abstract: Objective To compare the inter-and intraobserver variation of fetal biometric measurements utilizing twodimensional (2D) and three-dimensional (3D) ultrasound imaging. Methods

Cited by 38 publications

References 10 publications

Use of three-dimensional (3D) sonography to assess the true midsagittal plane of fetal spine

Use of three-dimensional (3D) sonography to assess the true midsagittal plane of fetal spine

Advanced MR imaging of the placenta: Exploring the in utero placenta–brain connection

Automatic Detection and Measurement of Structures in Fetal Head Ultrasound Volumes Using Sequential Estimation and Integrated Detection Network (IDN)

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