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

Heterogeneous neural networks and the leader2 algorithm

This paper is the final document written to gather the impressions and conclusions which we have come to during the development of this master thesis. In this research project you will find the description of a new kind of artificial neural network, Heterogeneous Neural Network 2 (HNN2), which can be seen as a general abstraction of the Radial Basis Function network. The model of neuron used is an improved version of the one presented by Belanche [1] and the neural network is initialized using a clustering algorithm, Leader2, developed at [2]. We will explain the way we have followed to get this artificial neural network that works allways with understandable information, uses the concept of similarity and allows users to improve the algorithm results taking advantage of expert information. The basic Heterogeneous Neural Network (HNN) is also known as Similarity Neural Network (SNN), by the importance of the similarity measures inside this method. The basic idea is that a combination of similarity functions, comparing variables independently, is more capable of catching better the singularity of an heterogeneous data set than other methods which require previous data transformation. Each variable has its own characteristics, which is information that can be used by the expert that knows it to choose its most suitable similarity function, taking advantage of all the information he has. If this is done for each variable, we will be working probably with a similarity measure that understands better the data. Missing values are also a relevant characteristic of heterogeneous data, so we have to learn to deal with them. All these ideas are applied to HNN and Leader2, joint to several improvements performed to the neural network, like regularization or Alternate Optimization, in order to fit better the data but avoiding overfitting. This is why we have called it Heterogeneous Neural Network 2 (HNN2). This document is divided in several chapters. Initially, we will give an in-depth description of the problem which we want to solve. In the second chapter, State of the art, you will get a wide perspective of how was the field in which this project has been developed before we started. Then, there is a description of the used methodology, where you can find the main decisions and the development itself, followed by the explanation of the experimental settings done to test the HNN2. Their results are commented and evaluated in the next chapter, and next some conclusions are inferred. Finally, you will find the references used in the research and several annexes with additional relevant information. But in first term, before starting the description of the problem and in the way of making the reading easier, it is necessary to provide you some vocabulary to know exactly the meaning we have given to several key words. Next, in the same terms, you will find the most used symbols with their description