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Automated organ localisation in fetal Magnetic Resonance Imaging

Fetal Magnetic Resonance Imaging (MRI) provides an invaluable diagnostic tool complementary to ultrasound due to its high resolution and tissue contrast. In order to accommodate fetal and maternal motion, MR images of the fetus are typically acquired as stacks of two-dimensional (2D) slices that freeze in-plane motion, but may form an inconsistent three-dimensional (3D) volume. Motion correction methods, which reconstruct a high-resolution 3D volume from such motion corrupted stacks of 2D slices, have revolutionised fetal MRI, enabling detailed studies of the fetal brain development. However, such motion correction and reconstruction procedures require a substantial amount of manual data preprocessing in order to isolate fetal tissues from the rest of the image. Beside the presence of motion artefacts, the main challenges when automating the processing of fetal MRI are the unpredictable position and orientation of the fetus, as well as the variability in anatomy due to fetal development. This thesis presents novel methods based on machine learning and prior knowledge of fetal development to localise automatically organs in fetal MRI in order to automate the preprocessing step of motion correction. This localisation can also be used to initialise a segmentation, or orient images based on the fetal anatomy to facilitate clinical examination. The fetal brain is first localised independently of the orientation of the fetus, and then used as an anchor point to steer features used in the subsequent localisation of the heart, lungs and liver. The localisation results are used to segment fetal tissues in each 2D slice and this segmentation can be further refined throughout the motion correction procedure. The proposed method to segment the fetal brain is shown to perform as well as a manual preprocessing. Preliminary results on a similar application to the motion correction of the fetal thorax are also presented.

Identiferoai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:679688
Date January 2015
CreatorsKeraudren, Kevin
ContributorsRueckert, Daniel
PublisherImperial College London
Source SetsEthos UK
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation
Sourcehttp://hdl.handle.net/10044/1/29432

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