21,127 research outputs found
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Silver Standard Masks for Data Augmentation Applied to Deep-Learning-Based Skull-Stripping
The bottleneck of convolutional neural networks (CNN) for medical imaging is
the number of annotated data required for training. Manual segmentation is
considered to be the "gold-standard". However, medical imaging datasets with
expert manual segmentation are scarce as this step is time-consuming and
expensive. We propose in this work the use of what we refer to as silver
standard masks for data augmentation in deep-learning-based skull-stripping
also known as brain extraction. We generated the silver standard masks using
the consensus algorithm Simultaneous Truth and Performance Level Estimation
(STAPLE). We evaluated CNN models generated by the silver and gold standard
masks. Then, we validated the silver standard masks for CNNs training in one
dataset, and showed its generalization to two other datasets. Our results
indicated that models generated with silver standard masks are comparable to
models generated with gold standard masks and have better generalizability.
Moreover, our results also indicate that silver standard masks could be used to
augment the input dataset at training stage, reducing the need for manual
segmentation at this step
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