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AI-enabled Imaging, Emerging Imaging

Predicting premature birth from MRI using deep learning

Project ID: 2021_033

1st Supervisor: Jana Hutter, King’s College London
2nd Supervisor: Emma Robinson, King’s College London
Additional Supervisor: Lisa Story, King’s College London
Clinical Champions: Mary Rutherford, King’s College London

Aim of the PhD Project:

  • Deep Generative Modelling for prediction of gestation and weight at birth from fetal MRI and birth metrics
  • Emphasis on the use of model interpretation to improve understanding of elements involved in preterm birth and late fetal growth restriction
  • Link prediction algorithm and acquisition to inform future protocol developments and thus facilitate clinical transition

Project Description / Background:

Human pregnancy and birth are among the most fascinating processes in life but also carry significant risks for both mother and unborn child. Consequences from preterm birth and fetal growth restriction are severe and lifelong [1].

Human development occurs largely hidden: clinically available Ultrasound screening only catch glimpses into the womb. Advanced knowledge of the appropriate gestation when the birth should start, the size of the fetus, as well as the type of delivery most likely to occur, would allow for a step change in adequate preparation and monitoring of all but especially high-risk pregnancies.

Recent advances in fetal MRI techniques [2,3] have successfully been able to assess the fetal organs and placenta both structurally as well as functionally. These show altered T2* values in  pregnancies with fetal growth restriction and reduced lung volume in pregnancies threatened by preterm labour. Dynamic information such as the frequency of fetal motion and lung breathing exercises is an indicator for preterm birth. Fetal MRI scans contain a vast amount of information and typically cover the uterus in multiple planes. However, only subsets of the data acquired are currently analysed, e.g. only the brain structure or placental attachment. This is in parts due to the lack of appropriate analysis tools able to benefit from the full extent of obtained information.

However, regardless of scan indication the questions of when the baby will be born and how much they will weigh is of high relevance for clinical planning of all births. A prediction for these questions would allow to optimize antenatal care and time point of delivery.

Recent deep learning (DL) [4] techniques provide two important opportunities: They allow to seek patterns from complex image data sets without making simplifying assumptions, and at the same time can be tuned to return features from the images which are salient to answering these questions. Thus, the goal of this project is to use generative Deep Learning to develop PLATYPUS (Prediction of Length of gestation, Approximate weight and TYpe of delivery from Pregnancy whole-Uterus MRI Scanning: a tool for asking fundamental questions about each pregnancy, mode and time of delivery. A significant emphasis will be to leverage recent developments in model interpretability to inform improved acquisition strategies which will continuously map development in the last weeks before birth.

The extensive database available from large scale fetal projects at King’s provides the well characterized training data which is critical for deep learning.

Expected background: Familiarity with imaging techniques and basic programming skills are essential. Dedicated Deep Learning knowledge is not required but will be acquired during this project.

References:

  1. Moster, Lie, Markestad “Long-Term Medical and Social Consequences of Preterm Birth”, NEJM 2008, 359:262-273
  2. StoryHutter, Zhang, Shennan, Rutherford “The use of antenatal fetal MRI in the assessment of patients at high risk of preterm birth”, EJOG 2018:222:134-141
  3. Hutter, Slator, …Story, Rutherford, Hajnal “Multi‐modal functional MRI to explore placental function over gestation”, MRM 2019
  4. Clough et al. “Global and Local Interpretability for Cardiac MRI Classification” arXiv:2019:1906.06188.

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