104 research outputs found
Comprehensive analysis of synthetic learning applied to neonatal brain MRI segmentation
Brain segmentation from neonatal MRI images is a very challenging task due to
large changes in the shape of cerebral structures and variations in signal
intensities reflecting the gestational process. In this context, there is a
clear need for segmentation techniques that are robust to variations in image
contrast and to the spatial configuration of anatomical structures. In this
work, we evaluate the potential of synthetic learning, a contrast-independent
model trained using synthetic images generated from the ground truth labels of
very few subjects.We base our experiments on the dataset released by the
developmental Human Connectome Project, for which high-quality T1- and
T2-weighted images are available for more than 700 babies aged between 26 and
45 weeks post-conception. First, we confirm the impressive performance of a
standard Unet trained on a few T2-weighted volumes, but also confirm that such
models learn intensity-related features specific to the training domain. We
then evaluate the synthetic learning approach and confirm its robustness to
variations in image contrast by reporting the capacity of such a model to
segment both T1- and T2-weighted images from the same individuals. However, we
observe a clear influence of the age of the baby on the predictions. We improve
the performance of this model by enriching the synthetic training set with
realistic motion artifacts and over-segmentation of the white matter. Based on
extensive visual assessment, we argue that the better performance of the model
trained on real T2w data may be due to systematic errors in the ground truth.
We propose an original experiment combining two definitions of the ground truth
allowing us to show that learning from real data will reproduce any systematic
bias from the training set, while synthetic models can avoid this limitation.
Overall, our experiments confirm that synthetic learning is an effective
solution for segmenting neonatal brain MRI. Our adapted synthetic learning
approach combines key features that will be instrumental for large multi-site
studies and clinical applications
Motion robust acquisition and reconstruction of quantitative T2* maps in the developing brain
The goal of the research presented in this thesis was to develop methods for quantitative T2* mapping of the developing brain. Brain maturation in the early period of life involves complex structural and physiological changes caused by synaptogenesis, myelination and growth of cells. Molecular structures and biological processes give rise to varying levels of T2* relaxation time, which is an inherent contrast mechanism in magnetic resonance imaging. The knowledge of T2* relaxation times in the brain can thus help with evaluation of pathology by establishing its normative values in the key areas of the brain. T2* relaxation values are a valuable biomarker for myelin microstructure and iron concentration, as well as an important guide towards achievement of optimal fMRI contrast. However, fetal MR imaging is a significant step up from neonatal or adult MR imaging due to the complexity of the acquisition and reconstruction techniques that are required to provide high quality artifact-free images in the presence of maternal respiration and unpredictable fetal motion. The first contribution of this thesis, described in Chapter 4, presents a novel acquisition method for measurement of fetal brain T2* values. At the time of publication, this was the first study of fetal brain T2* values. Single shot multi-echo gradient echo EPI was proposed as a rapid method for measuring fetal T2* values by effectively freezing intra-slice motion. The study concluded that fetal T2* values are higher than those previously reported for pre-term neonates and decline with a consistent trend across gestational age. The data also suggested that longer than usual echo times or direct T2* measurement should be considered when performing fetal fMRI in order to reach optimal BOLD sensitivity. For the second contribution, described in Chapter 5, measurements were extended to a higher field strength of 3T and reported, for the first time, both for fetal and neonatal subjects at this field strength. The technical contribution of this work is a fully automatic segmentation framework that propagates brain tissue labels onto the acquired T2* maps without the need for manual intervention. The third contribution, described in Chapter 6, proposed a new method for performing 3D fetal brain reconstruction where the available data is sparse and is therefore limited in the use of current state of the art techniques for 3D brain reconstruction in the presence of motion. To enable a high resolution reconstruction, a generative adversarial network was trained to perform image to image translation between T2 weighted and T2* weighted data. Translated images could then be served as a prior for slice alignment and super resolution reconstruction of 3D brain image.Open Acces
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