Confident head circumference measurement from ultrasound with real-time feedback for sonographers

Manual estimation of fetal Head Circumference (HC) from Ultrasound (US) is a key biometric for monitoring the healthy development of fetuses. Unfortunately, such measurements are subject to large inter-observer variability, resulting in low early-detection rates of fetal abnormalities. To address this issue, we propose a novel probabilistic Deep Learning approach for real-time automated estimation of fetal HC. This system feeds back statistics on measurement robustness to inform users how confident a deep neural network is in evaluating suitable views acquired during free-hand ultrasound examination. In real-time scenarios, this approach may be exploited to guide operators to scan planes that are as close as possible to the underlying distribution of training images, for the purpose of improving inter-operator consistency. We train on freehand ultrasound data from over 2000 subjects (2848 training/540 test) and show that our method is able to predict HC measurements within 1.81±1.65 mm deviation from the ground truth, with 50% of the test images fully contained within the predicted confidence margins, and an average of 1.82±1.78 mm deviation from the margin for the remaining cases that are not fully contained

Computer Assisted Learning in Obstetric Ultrasound

Ultrasound is a dynamic, real-time imaging modality that is widely used in clinical obstetrics. Simulation has been proposed as a training method, but how learners performance translates from the simulator to the clinic is poorly understood. Widely accepted, validated and objective measures of ultrasound competency have not been established for clinical practice. These are important because previous works have noted that some individuals do not achieve expert-like performance despite daily usage of obstetric ultrasound. Underlying foundation training in ultrasound was thought to be sub-optimal in these cases. Given the widespread use of ultrasound and the importance of accurately estimating the fetal weight for the management of high-risk pregnancies and the potential morbidity associated with iatrogenic prematurity or unrecognised growth restriction, reproducible skill minimising variability is of great importance. In this thesis, I will investigate two methods with the aim of improving training in obstetric ultrasound. The initial work will focus on quantifying operational performance. I collect data in the simulated and clinical environment to compare operator performance between novice and expert performance. In the later work I developed a mixed reality trainer to enhance trainee’s visualisation of how the ultrasound beam interacts with the anatomy being scanned. Mixed reality devices offer potential for trainees because they combine real-world items with items in the virtual world. In the training environment this allows for instructions, 3-dimensional visualisations or workflow instructions to be overlaid on physical models. The work is important because the techniques developed for the qualification of operator skill could be combined in future work with a training programme designed around educational theory to give trainee sonographers consistent feedback and instruction throughout their training

FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurements

In this paper, we propose an end-to-end multi-task neural network called FetalNet with an attention mechanism and stacked module for spatio-temporal fetal ultrasound scan video analysis. Fetal biometric measurement is a standard examination during pregnancy used for the fetus growth monitoring and estimation of gestational age and fetal weight. The main goal in fetal ultrasound scan video analysis is to find proper standard planes to measure the fetal head, abdomen and femur. Due to natural high speckle noise and shadows in ultrasound data, medical expertise and sonographic experience are required to find the appropriate acquisition plane and perform accurate measurements of the fetus. In addition, existing computer-aided methods for fetal US biometric measurement address only one single image frame without considering temporal features. To address these shortcomings, we propose an end-to-end multi-task neural network for spatio-temporal ultrasound scan video analysis to simultaneously localize, classify and measure the fetal body parts. We propose a new encoder-decoder segmentation architecture that incorporates a classification branch. Additionally, we employ an attention mechanism with a stacked module to learn salient maps to suppress irrelevant US regions and efficient scan plane localization. We trained on the fetal ultrasound video comes from routine examinations of 700 different patients. Our method called FetalNet outperforms existing state-of-the-art methods in both classification and segmentation in fetal ultrasound video recordings.Comment: Accepted to 28th International Conference on Neural Information Processing (ICONIP) 2021, Bali, Indonesia, 8-12 December, 202

Detecting hypo-plastic left heart syndrome in fetal ultrasound via disease-specific atlas maps

Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts. The level of skill required to diagnose such malformations from live ultrasound during examination is high and resources for screening are often limited. We present an interpretable, atlas-learning segmentation method for automatic diagnosis of Hypo-plastic Left Heart Syndrome (HLHS) from a single ‘4 Chamber Heart’ view image. We propose to extend the recently introduced Image-and-Spatial Transformer Networks (Atlas-ISTN) into a framework that enables sensitising atlas generation to disease. In this framework we can jointly learn image segmentation, registration, atlas construction and disease prediction while providing a maximum level of clinical interpretability compared to direct image classification methods. As a result our segmentation allows diagnoses competitive with expert-derived manual diagnosis and yields an AUC-ROC of 0.978 (1043 cases for training, 260 for validation and 325 for testing)

Symbiotic deep learning for medical image analysis with applications in real-time diagnosis for fetal ultrasound screening

The last hundred years have seen a monumental rise in the power and capability of machines to perform intelligent tasks in the stead of previously human operators. This rise is not expected to slow down any time soon and what this means for society and humanity as a whole remains to be seen. The overwhelming notion is that with the right goals in mind, the growing influence of machines on our every day tasks will enable humanity to give more attention to the truly groundbreaking challenges that we all face together. This will usher in a new age of human machine collaboration in which humans and machines may work side by side to achieve greater heights for all of humanity. Intelligent systems are useful in isolation, but the true benefits of intelligent systems come to the fore in complex systems where the interaction between humans and machines can be made seamless, and it is this goal of symbiosis between human and machine that may democratise complex knowledge, which motivates this thesis. In the recent past, datadriven methods have come to the fore and now represent the state-of-the-art in many different fields. Alongside the shift from rule-based towards data-driven methods we have also seen a shift in how humans interact with these technologies. Human computer interaction is changing in response to data-driven methods and new techniques must be developed to enable the same symbiosis between man and machine for data-driven methods as for previous formula-driven technology. We address five key challenges which need to be overcome for data-driven human-in-the-loop computing to reach maturity. These are (1) the ’Categorisation Challenge’ where we examine existing work and form a taxonomy of the different methods being utilised for data-driven human-in-the-loop computing; (2) the ’Confidence Challenge’, where data-driven methods must communicate interpretable beliefs in how confident their predictions are; (3) the ’Complexity Challenge’ where the aim of reasoned communication becomes increasingly important as the complexity of tasks and methods to solve also increases; (4) the ’Classification Challenge’ in which we look at how complex methods can be separated in order to provide greater reasoning in complex classification tasks; and finally (5) the ’Curation Challenge’ where we challenge the assumptions around bottleneck creation for the development of supervised learning methods.Open Acces

Sonography data science

Fetal sonography remains a highly specialised skill in spite of its necessity and importance. Because of differences in fetal and maternal anatomy, and human pyschomotor skills, there is an intra- and inter-sonographer variability amoungst expert sonographers. By understanding their similarities and differences, we want to build more interpretive models to assist a sonographer who is less experienced in scanning. This thesis’s contributions to the field of fetal sonography can be grouped into two themes. First I have used data visualisation and machine learning methods to show that a sonographer’s search strategy is anatomical (plane) dependent. Second, I show that a sonographer’s style and human skill of scanning is not easily disentangled. We first examine task-specific spatio-temporal gaze behaviour through the use of data visualisation, where a task is defined as a specific anatomical plane the sonographer is searching for. The qualitative analysis is performed at both a population and individual level, where we show that the task being performed determines the sonographer’s gaze behaviour. In our population-level analysis, we use unsupervised methods to identify meaningful gaze patterns and visualise task-level differences. In our individual-level analysis, we use a deep learning model to provide context to the eye-tracking data with respect to the ultrasound image. We then use an event-based visualisation to understand differences between gaze patterns of sonographers performing the same task. In some instances, sonographers adopt a different search strategy which is seen in the misclassified instances of an eye-tracking task classification model. Our task classification model supports the qualitative behaviour seen in our population-level analysis, where task-specific gaze behaviour is quantitatively distinct. We also investigate the use of time-based skill definitions and their appropriateness in fetal ultrasound sonography; a time-based skill definition uses years of clinical experience as an indicator of skill. The developed task-agnostic skill classification model differentiates gaze behaviour between sonographers in training and fully qualified sonographers. The preliminary results also show that fetal sonography scanning remains an operator-dependent skill, where the notion of human skill and individual scanning stylistic differences cannot be easily disentangled. Our work demonstrates how and where sonographers look at whilst scanning, which can be used as a stepping stone for building style-agnostic skill models

FUSQA: Fetal Ultrasound Segmentation Quality Assessment

Deep learning models have been effective for various fetal ultrasound segmentation tasks. However, generalization to new unseen data has raised questions about their effectiveness for clinical adoption. Normally, a transition to new unseen data requires time-consuming and costly quality assurance processes to validate the segmentation performance post-transition. Segmentation quality assessment efforts have focused on natural images, where the problem has been typically formulated as a dice score regression task. In this paper, we propose a simplified Fetal Ultrasound Segmentation Quality Assessment (FUSQA) model to tackle the segmentation quality assessment when no masks exist to compare with. We formulate the segmentation quality assessment process as an automated classification task to distinguish between good and poor-quality segmentation masks for more accurate gestational age estimation. We validate the performance of our proposed approach on two datasets we collect from two hospitals using different ultrasound machines. We compare different architectures, with our best-performing architecture achieving over 90% classification accuracy on distinguishing between good and poor-quality segmentation masks from an unseen dataset. Additionally, there was only a 1.45-day difference between the gestational age reported by doctors and estimated based on CRL measurements using well-segmented masks. On the other hand, this difference increased and reached up to 7.73 days when we calculated CRL from the poorly segmented masks. As a result, AI-based approaches can potentially aid fetal ultrasound segmentation quality assessment and might detect poor segmentation in real-time screening in the future.Comment: 13 pages, 3 figures, 3 table

Clinical impact of the methodological quality of fetal doppler standards in the management of fetal growth restriction

Esta tesis, que lleva por título “Clinical impact of the methodological quality of fetal Doppler standards in the management of fetal growth Restriction”, es un trabajo realizado en la Universidad de Zaragoza con colaboración de la Universidad de Oxford por lo que opta a la mención internacional. Además, está elaborada según la normativa de la Universidad de Zaragoza como tesis por compendio de publicaciones, con 4 artículos publicados en revistas de elevado factor de impacto.El crecimiento intrauterino restringido (CIR) es una de las enfermedades con mayor repercusión médica, social y económica en obstetricia. Estos fetos pueden interrumpir su crecimiento como consecuencia de una insuficiencia placentaria, apareciendo alteraciones en el Doppler fetal, lo que conlleva un riesgo elevado de resultado perinatal adverso. Para que una herramienta como el Doppler fetal sea fiable, los valores obtenidos deben ser adecuados y reproducibles, la medición del Doppler debe estar estandarizada y así, maximizaremos su potencial en la evaluación del CIR en la práctica clínica. Con este objetivo se desarrolló la primera de las publicaciones que propone un sistema de puntuación objetiva para evaluar imágenes Doppler de la arteria cerebral media (ACM), demostrando que los controles de calidad de imágenes son esenciales, así como el uso de sistemas objetivos que hagan que las imágenes sean reproducibles.Por otro lado, la secuencia de progresión del Doppler fetal ha sido descrita claramente y hay evidencia de que los cambios cualitativos en el Doppler de la arterial umbilical (AU), como la presencia, ausencia o inversión del flujo diastólico, indican un mayor riesgo de muerte fetal. Sin embargo, la asociación entre los cambios semi-cuantitativos en el Doppler de AU y ACM (medidos con el índice de pulsatilidad) y los resultados perinatales y a largo plazo no se han establecido claramente. Como consecuencia, se han publicado multitud de valores de referencia del índice de pulsatilidad del Doppler fetal. Esta falta de evidencia podría explicarse, al menos parcialmente, por la calidad metodología utilizada para establecer estos valores, lo que podría tener importantes implicaciones para la práctica clínica. Con esta hipótesis, en el segundo trabajo se realizó una revisión sistemática de todos los estudios publicados con el objetivo de crear curvas de referencia para la AU, ACM e índice cerebroplacentario (ICP). Tras utilizar una lista de verificación ya validada y evaluar 38 estudios, se llegó a la conclusión de que todos los estudios en los que se basan los valores de referencia que se usan en la práctica clínica tienen numerosos sesgos metodológicos, haciendo que las diferencias entre los valores sean importantes. Además, en el tercer estudio, se comparó todos estos valores demostrando su gran variabilidad y se realizó una simulación clínica en la que se observó que el manejo de un feto con crecimiento intrauterino restringido puede variar en dependencia del valor de referencia que se elija, decidiendo finalizar la gestación o no y produciendo en algunos casos prematuridad iatrogénica y en otros, aumento del riesgo de muerte fetal intrauterina.Finalmente, como solución a todos los problemas planteados y a la falta de estudios de alta calidad metodológica en los que basar nuestras actuaciones médicas, se propone el estudio FETHUS, un estudio de cohortes, longitudinal, multicéntrico, internacional y prospectivo, con el objetivo de crear unos valores de referencia basados en un estudio con alta calidad metodológica que sirvan de referencia universal para el Doppler fetal, unificando así el manejo del feto con crecimiento intrauterino restringido.1. Alfirevic Z, Stampalija T, Dowswell T. Fetal and umbilical Doppler ultrasound in high-risk pregnancies. Vol. 2017, Cochrane Database of Systematic Reviews. John Wiley and Sons Ltd; 2017. 2. Alfirevic Z, Stampalija T, Medley N. Fetal and umbilical Doppler ultrasound in normal pregnancy. Vol. 2015, Cochrane Database of Systematic Reviews. John Wiley and Sons Ltd; 2015. 3. Fetal Growth Restriction. Practice Bulletin No. 134. American College of Obstetricians & Gyncologists. Obstet Gynecol. 2013;4. Gordijn SJ, Beune IM, Thilaganathan B, Papageorghiou A, Baschat AA, Baker PN, et al. Consensus definition of fetal growth restriction: a Delphi procedure. Ultrasound Obstet Gynecol [Internet]. 2016 Sep [cited 2019 Aug 3];48(3):333–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/269096645. RCOG Green-top Guideline, 2nd Edition J 2014. Investigation and Management of the Small for Gestational Age Fetus. R Coll Obstet Gynaecol (RCOG) [Internet]. Available from: http://www.rcog.org.uk/files/rcog-corp/6. Conde-Agudelo A, Villar J, Kennedy SH, Papageorghiou AT. Predictive accuracy of cerebroplacental ratio for adverse perinatal and neurodevelopmental outcomes in suspected fetal growth restriction: systematic review and meta-analysis. Ultrasound Obstet Gynecol. 2018; 7. Oros D, Figueras F, Cruz-Martinez R, Padilla N, Meler E, Hernandez-Andrade E, et al. Middle versus anterior cerebral artery Doppler for the prediction of perinatal outcome and neonatal neurobehavior in term small-for-gestational-age fetuses with normal umbilical artery Doppler. Ultrasound Obstet Gynecol [Internet]. 2010 Apr [cited 2019 Aug 22];35(4):456–61. Available from: http://www.ncbi.nlm.nih.gov/pubmed/201781158. DeVore GR. The importance of the cerebroplacental ratio in the evaluation of fetal well-being in SGA and AGA fetuses. Vol. 213, American Journal of Obstetrics and Gynecology. Mosby Inc.; 2015. p. 5–15. 9. Arduini D, Rizzo G. Normal values of Pulsatility Index from fetal vessels: a cross-sectional study on 1556 healthy fetuses. J Perinat Med [Internet]. 1990 [cited 2019 Aug 3];18(3):165–72. Available from: http://www.ncbi.nlm.nih.gov/pubmed/220086210. Morales-Roselló J, Diaz García-Donato J. Study of fetal femoral and umbilical artery blood flow by Doppler ultrasound throughout pregnancy.11. Figueras F, Gardosi J. Intrauterine growth restriction: New concepts in antenatal surveillance, diagnosis, and management. Vol. 204, American Journal of Obstetrics and Gynecology. Mosby Inc.; 2011. p. 288–300.12. Royston P, Wright EM. How to construct “normal ranges” for fetal variables. Ultrasound Obstet Gynecol. 1998;11(1):30–8.13. Ruiz-Martinez S, Volpe G, Vannuccini S, Cavallaro A, Impey L, Ioannou C. An objective scoring method to evaluate image quality of middle cerebral artery Doppler. J Matern Fetal Neonatal Med [Internet]. 2018 Jun 27 [cited 2019 Sep 12];1–181. Available from: http://www.ncbi.nlm.nih.gov/pubmed/2995015614. Salomon LJ, Bernard JP, Duyme M, Buvat I, Ville Y. The impact of choice of reference charts and equations on the assessment of fetal biometry. Ultrasound Obstet Gynecol. 2005 Jun;25(6):559–65.<br /

A regression framework to head-circumference delineation from US fetal images

Background and Objectives: Measuring head-circumference (HC) length from ultrasound (US) images is a crucial clinical task to assess fetus growth. To lower intra- and inter-operator variability in HC length measuring, several computer-assisted solutions have been proposed in the years. Recently, a large number of deep-learning approaches is addressing the problem of HC delineation through the segmentation of the whole fetal head via convolutional neural networks (CNNs). Since the task is a edge-delineation problem, we propose a different strategy based on regression CNNs. Methods: The proposed framework consists of a region-proposal CNN for head localization and centering, and a regression CNN for accurately delineate the HC. The first CNN is trained exploiting transfer learning, while we propose a training strategy for the regression CNN based on distance fields. Results: The framework was tested on the HC18 Challenge dataset, which consists of 999 training and 335 testing images. A mean absolute difference of 1.90 ( ± 1.76) mm and a Dice similarity coefficient of 97.75 ( ± 1.32) % were achieved, overcoming approaches in the literature. Conclusions: The experimental results showed the effectiveness of the proposed framework, proving its potential in supporting clinicians during the clinical practice

Optimisation of gestational age estimates in low-income settings

Accurate estimates of gestational age are fundamental to the provision of obstetric care, helping to facilitate appropriate antenatal care schedules and the identification and management of high-risk pregnancies. At a population level, accurate estimates of gestational age are required for the global reporting of obstetric and neonatal outcomes, for example, the rates of pre-term birth, and are a key component of strategies to reduce neonatal morbidity and mortality. Early pregnancy ultrasound is considered the most accurate way to determine gestational age and is undertaken as part of routine care in high-income settings. However, despite the recommendation from the World Health Organisation that all women receive at least one ultrasound prior to 24 weeks’ gestation, this remains unavailable to the majority of women in low-income settings. Instead, gestational age is derived from the last menstrual period or by measurement of the symphysis fundal height, methods known to be considerably less accurate. There are a number of barriers to the widespread provision of ultrasound as part of routine care in low- and middle- income settings, not least the lack of trained practitioners. Although effective, the length and complexity of many previous training programmes has been prohibitive, with practitioners struggling to secure cover for their clinical duties in order to provide or attend training. Furthermore, few initiatives have explored the widespread implementation of these programmes and how they may be sustained within pre-existing healthcare structures. Ultrasound determination of gestational age relies on the assumption that the size of the fetus is consistent with its age and is therefore best performed prior to 14 weeks’ gestation, when natural variation in fetal size is least apparent. Unfortunately, the majority of women in low- and middle- income countries do not seek antenatal care until later and would therefore require dating by different biometric parameters. In high-income settings the gold standard would be a combination of measurements, however there are concerns about the time investment required to develop such skills. The work in this Thesis explores the development of a novel strategy to optimise estimates of gestational age in Malawi, through the development and implementation of a bespoke education package to teach midwives how to date pregnancies using ultrasound measurement of the fetal femur length. A systematic review investigated the previous initiatives that had been undertaken to train practitioners in low- and middle- income countries to determine gestational age using ultrasound, finding major inconsistences in the current provision of ultrasound training and highlighting the need for a more consistent and robust approach. Less than half of the programmes met international recommendations for the delivery of safe and sustainable training, and many had not considered how ultrasound may be integrated into clinical practice thereafter. The evidence synthesised went on to inform the development of a new programme, where it was hypothesised that ultrasound-naive midwives could be taught to date pregnancies using fetal femur length. Pilot work helped to shape and refine the programme, which was delivered by local teams across six sites in Malawi in 2021. All but one midwife completed the course, with all demonstrating significant increases in their knowledge, confidence, and practical skills, achieving the criteria specified for competency within the specified two weeks. Skills were sustained at a 3-month follow up, and of the images submitted for remote image review, over 87% were deemed acceptable. These results suggest that femur length is a sufficiently simple measurement to be taught effectively over a short timescale, making it a potentially viable option for the upscale of ultrasound to date pregnancies in this setting. A mixed methods study, run by the wider collaborative group, evaluated the implementation of ultrasound into routine services, however the work in this Thesis focused more specifically on the provision of the programme itself. Outcomes were reported in the context of an implementation framework, providing valuable insight into factors influencing the longterm sustainability of such endeavours. It is clear this is an important area for ongoing research. In conclusion, this Thesis proposes that measurement of fetal femur length should be considered a potential option for the determination of gestational age in low- and middle- income settings. Not only is it considerably more accurate than the current standard of care, but midwives with no prior experience of ultrasound can be trained to perform these measurements, confidently and competently, after just two weeks of training, a substantially shorter training duration than many previous initiatives. Although many implementation challenges persist, this programme provides a potentially more sustainable means by which to provide a greater number of women more accurate estimates of gestational age