Sylvia Rueda

Universidade Estadual de Campinas (UNICAMP)

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J. Alison Noble

University of Oxford

Aris T. Papageorghiou

John Radcliffe Hospital

Caroline L. Knight

King's College London

Jayaram K. Udupa

University of Pennsylvania

M. Ahmed

University of Memphis

Li Bai

University of Nottingham

Mohammad Yaqub

Mohamed bin Zayed University of Artificial Intelligence

Kate M. Edwards

University of Sydney

Peter Sullivan

University of Oxford

Kenneth McCormick

University of Alabama at Birmingham

Cooperative Institutions

University of Oxford

John Radcliffe Hospital

National Institute on Population Medical Genetics

Universidade Estadual de Campinas (UNICAMP)

Centro de Educación Médica e Investigaciones Clínicas Norberto Quirno

Consejo Nacional de Investigaciones Científicas y Técnicas

Hospital de Clínicas da Unicamp

National Taiwan University of Science and Technology

Imperial College London

University of Pennsylvania

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Local Scale for Boundary Shape Description: Application in Locating Landmarks Automatically

Proceedings - Brazilian Symposium on Computer Graphics and Image Processing/Proceedings (2007)

Sylvia Rueda Ja Udupa Li Bai

A good shape descriptor is necessary for automatically identifying landmarks on boundaries. Our method of boundary shape description is based on the notion of c- scale, which is a new local scale concept, defined at each boundary element. From this representation we can extract special points of interest such as convex and concave corners, straight lines, circular segments, and inflection points. The results show that this method gives a complete description of shape and allows the automatic positioning of mathematical landmarks, which agree with our intuitive ideas of where landmarks may be defined. This method is applicable to spaces of any dimensionality, although we have focused in this paper on 2D shapes.

Representation

Inflection point

10.1109/sibgra.2007.4368199

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OP17.04: Fetal arm fat: development throughout pregnancy in selectively screened optimally healthy patients

Ultrasound in Obstetrics and Gynecology (2012)

Caroline L. Knight Sylvia Rueda J. Alison Noble Aris T. Papageorghiou

To describe the normal development of fetal arm fat in an optimally healthy patient population, and establish a normal range for this population to enable comparison with other patient groups. In this prospective study, fetal arm fat was measured using serial ultrasound scans at 4–6 weekly intervals during pregnancy (16–40 weeks' gestation). The women underwent careful screening to select only those who were optimally healthy at booking, and those with confirmed pregnancy dating from first trimester scan. Cross sectional fetal arm fat area was measured twice at mid-humeral level, with mean area plotted against gestational age in days. Intra and inter observer variability were assessed using Bland-Altman plots. 91 women were scanned 1–6 times each. 161 mean segmentations were plotted against gestational age in days (see graph). Fetal arm fat increased approximately 10-fold from 16 to 40 weeks of pregnancy. The fetal arm fat area values have a wider distribution in the third trimester compared with the second trimester, consistent with variation in nutrition and growth. Intraobserver variability was 3.7% (95% CI ± 15.0%), interobserver variability was 6.1% (95% CI ± 14.4%). These data add to the small body of published work about fetal arm fat. Fetal arm fat area can be assessed by ultrasound from 16 weeks of gestation until term, and increases markedly throughout the second half of pregnancy. It is a reproducible measurement which may be useful as a marker of fetal body composition. Supporting information can be found in the online version of this abstract. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Reference range

10.1002/uog.11550

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Citations (1)

Shape modeling via local curvature scale

Pattern Recognition Letters (2009)

Sylvia Rueda Jayaram K. Udupa Li Bai

10.1016/j.patrec.2009.09.007

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Citations (15)

P08.16: Systematic thickness analysis of the adipose tissue around the fetal arm across gestation using 2D ultrasound images

Ultrasound in Obstetrics and Gynecology (2013)

Sylvia Rueda Caroline L. Knight Aris T. Papageorghiou J. Alison Noble

To prove that single thickness measurements of the fetal arm adipose tissue layer, acquired in a mid-humeral transverse section on 2D ultrasound images across a range of gestational ages, are inconsistent and unreliable as a biomarker. Previous work used single thickness measurements, taken at a predefined location, as an indicator of fat. However, these were not measured at corresponding locations between different arms, since the image acquisition varied between fetuses. Area (volume) measurements would be more accurate to estimate arm fat. We aim at investigating the magnitude of the error of that clinical approximation by systematically measuring the thickness at each point around the arm. Fetal arm adipose tissue thickness was automatically measured at each point of the arm boundary, from manually delineated images, on 65 cross-sectional ultrasound images at the mid-humeral level from optimally healthy fetuses between 21 and 36 weeks of gestation. Fetal arm adipose tissue thickness showed high variability around the arm, proving that a single measurement is not suitable to characterize fetal arm adipose tissue across different fetuses. Correlations across gestational age were obtained for minimum (R2 = 0.61), maximum (R2 = 0.71), and mean (R2 = 0.82) thicknesses. The correlations between minimum (R2 = 0.74), maximum (R2 = 0.81), and median (R2 = 0.94) thicknesses with respect to the overall adipose tissue areas were calculated, the latter showing the strongest correlation. Results demonstrate that one single thickness measurement is not appropriate for a good characterization of the fetal arm adipose tissue due to the high thickness variability existing around the arm.

10.1002/uog.13025

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Difference of Gaussians revolved along elliptical paths for ultrasound fetal head segmentation

Computerized Medical Imaging and Graphics (2014)

Alessandro Foi Matteo Maggioni Antonietta Pepe Sylvia Rueda J. Alison Noble

Fetal head

Ellipse

3D ultrasound

10.1016/j.compmedimag.2014.09.006

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Citations (27)

P27.07: Assessing fetal fractional arm adipose tissue volume using 3D ultrasound: the importance of measuring perpendicular to the humerus

Ultrasound in Obstetrics and Gynecology (2012)

M. Ahmed Sylvia Rueda Caroline L. Knight Aris T. Papageorghiou J. Alison Noble

To assess whether fractional arm adipose tissue volume varies when cross-sectional slices are extracted perpendicular to the humerus, rather than parallel to the ultrasound acquisition plane. In this study, fractional arm adipose tissue volume was measured in 20 healthy fetuses using 3D ultrasound scans between 30 and 39 weeks of gestation. In 10 cases, the humerus was acquired horizontally (0.2°–2.7°) and in 10 cases it was angled (3.0°–24.7°). For each scan, the humerus length was measured and five equidistant cross-sectional slices were extracted along the 50% mid-shaft of the humerus. Two slice extraction methods were used: parallel to the acquisition plane and perpendicular to the humerus. The cross-sectional adipose tissue area was then manually traced and the overall fractional adipose tissue volume was calculated after interpolation between cross-sections for each method. Fractional adipose tissue volumes obtained via both slice extraction methods were compared using Bland-Altman plots. Perpendicular slice extraction resulted in smaller fractional adipose tissue volumes in 70% of scans. The mean difference in fractional adipose tissue volume was much larger for the more angled scans (0.51 cm3) than for the near horizontal scans (0.17 cm3). The most angled humerus (24.7°) also resulted in the largest difference in adipose tissue areas (2.42 cm3). In most cases, parallel slice extraction to the acquisition plane significantly overestimates cross-sectional adipose tissue areas and hence fractional adipose tissue volumes. The larger the humerus angle, the greater the difference between both methods. Therefore, perpendicular slice extraction is a preferential method for more accurate volumetric measurements. Supporting information can be found in the online version of this abstract. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

10.1002/uog.12138

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P13.20: Fetal limb fat and lean volume reference ranges from an optimally healthy population

Ultrasound in Obstetrics and Gynecology (2014)

Caroline Knight M. Ahmed Kate M. Edwards V. Donadono David Parry

To assess fetal arm and thigh compartments using 3D ultrasound and produce reference ranges for fat and lean volumes in each limb. Fat and lean volumes have not been published, although 2D areas and 3D limb volumes (whole and fractional) have been assessed. We hypothesized that 3D assessment of limb fat may provide a better reflection of fetal nutritional status in utero. Axial mid-humeral and mid-femoral volumes from 16-41 weeks' gestation were acquired in optimally healthy pregnant women from Oxford, UK (INTERGROWTH-21st study). A specifically-designed MATLAB tool (based on Lee et al's 50% fractional limb volume) enabled fat volume assessment. Lean volume (muscle and bone) was subtracted from limb volume to give fat volume. 3rd, 50th and 97th centiles were calculated using polynomial regression. Scans were obtained from 231 women. Fat and lean volumes could be measured in 78% overall: 556/667 arm volumes and 518/709 leg volumes. Of those not able to be measured, more than half were due to non-protocol scans or early gestation; the remainder due to indistinct fat borders due to shadowing, often in later gestations. 50th centile values were calculated from 16 to 41 weeks: arm fat 0.4 - 21.1cm3, arm lean 0.3 - 12.1cm3; thigh fat 0.5 - 38.8cm3, thigh lean 0.6 - 33.8cm3. 3rd and 97th centiles were also calculated. Supporting information can be found in the online version of this abstract Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Lean tissue

10.1002/uog.14250

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Citations (1)

nD statistical shape model building via recursive boundary subdivision

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2009)

Sylvia Rueda Jayaram K. Udupa

Landmark based statistical object modeling techniques, such as Active Shape Modeling, have proven useful in medical image analysis. Identification of the same homologous set of points in a training set of object shapes is the most crucial step in ASM, which has encountered challenges, the most crucial among these being (C1) defining and characterizing landmarks; (C2) ensuring homology; (C3) generalizing to n > 2 dimensions; (C4) achieving practical computations. In this paper, we propose a novel global-to-local strategy that attempts to address C3 and C4 directly and works in Rⁿ. The 3D version of it attempts to address C1 and C2 indirectly by starting from three initial corresponding points determined in all training shapes via a method α, and subsequently by subdividing the shapes into connected boundary segments by a plane determined by these points. A shape analysis method β is applied on each segment to determine a landmark on the segment. This point introduces more triplets of points, the planes defined by which are used to further subdivide the boundary segments. This recursive boundary subdivision (RBS) process continues simultaneously on all training shapes, maintaining synchrony of the level of recursion, and thereby keeping correspondence among generated points automatically by the correspondence of the homologous shape segments in all training shapes. The process terminates when no subdividing planes are left to be considered that indicate (as per method β) that a point can continue to be selected on the associated segment. Several examples of α and β are provided as well as some preliminary results on 3D shapes.

Subdivision surface

10.1117/12.812454

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

Medical Image Analysis (2015)

Sylvia Rueda Caroline L. Knight Aris T. Papageorghiou J. Alison Noble

Medical ultrasound (US) image segmentation and quantification can be challenging due to signal dropouts, missing boundaries, and presence of speckle, which gives images of similar objects quite different appearance. Typically, purely intensity-based methods do not lead to a good segmentation of the structures of interest. Prior work has shown that local phase and feature asymmetry, derived from the monogenic signal, extract structural information from US images. This paper proposes a new US segmentation approach based on the fuzzy connectedness framework. The approach uses local phase and feature asymmetry to define a novel affinity function, which drives the segmentation algorithm, incorporates a shape-based object completion step, and regularises the result by mean curvature flow. To appreciate the accuracy and robustness of the methodology across clinical data of varying appearance and quality, a novel entropy-based quantitative image quality assessment of the different regions of interest is introduced. The new method is applied to 81 US images of the fetal arm acquired at multiple gestational ages, as a means to define a new automated image-based biomarker of fetal nutrition. Quantitative and qualitative evaluation shows that the segmentation method is comparable to manual delineations and robust across image qualities that are typical of clinical practice.

Robustness

Feature (linguistics)

Segmentation-based object categorization

10.1016/j.media.2015.07.002

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Citations (29)

Evaluation and Comparison of Current Fetal Ultrasound Image Segmentation Methods for Biometric Measurements: A Grand Challenge

IEEE Transactions on Medical Imaging (2013)

Sylvia Rueda Sana Fathima Caroline L. Knight Mohammad Yaqub Aris T. Papageorghiou

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.

Fetal head

Ground truth

3D ultrasound

10.1109/tmi.2013.2276943

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Citations (178)