Pulse Coupled Neural Networks for the Segmentation of Magnetic Resonance Brain Images

This research develops an automated method for segmenting Magnetic Resonance (MR) brain images based on Pulse Coupled Neural Networks (PCNN). MR brain image segmentation has proven difficult, primarily due to scanning artifacts such as interscan and intrascan intensity inhomogeneities. The method developed and presented here uses a PCNN to both filter and segment MR brain images. The technique begins by preprocessing images with a PCNN filter to reduce scanning artifacts. Images are then contrast enhanced via histogram equalization. Finally, a PCNN is used to segment the images to arrive at the final result. Modifications to the original PCNN model are made that drastically improve performance while greatly reducing memory requirements. These modifications make it possible to extend the method to filter and segment three dimensionally. Volumes represented as series of images are segmented using this new method. This new three dimensional segmentation technique can be used to obtain a better segmentation of a single image or of an entire volume. Results indicate that the PCNN shows promise as an image analysis tool

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

High-Performance Cloud Computing: A View of Scientific Applications

Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a Service Level Agreement (SLA), which ensure the desired Quality of Service (QoS). Aneka, an enterprise Cloud computing solution, harnesses the power of compute resources by relying on private and public Clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the Cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.Comment: 13 pages, 9 figures, conference pape

Mapping Topographic Structure in White Matter Pathways with Level Set Trees

Fiber tractography on diffusion imaging data offers rich potential for describing white matter pathways in the human brain, but characterizing the spatial organization in these large and complex data sets remains a challenge. We show that level set trees---which provide a concise representation of the hierarchical mode structure of probability density functions---offer a statistically-principled framework for visualizing and analyzing topography in fiber streamlines. Using diffusion spectrum imaging data collected on neurologically healthy controls (N=30), we mapped white matter pathways from the cortex into the striatum using a deterministic tractography algorithm that estimates fiber bundles as dimensionless streamlines. Level set trees were used for interactive exploration of patterns in the endpoint distributions of the mapped fiber tracks and an efficient segmentation of the tracks that has empirical accuracy comparable to standard nonparametric clustering methods. We show that level set trees can also be generalized to model pseudo-density functions in order to analyze a broader array of data types, including entire fiber streamlines. Finally, resampling methods show the reliability of the level set tree as a descriptive measure of topographic structure, illustrating its potential as a statistical descriptor in brain imaging analysis. These results highlight the broad applicability of level set trees for visualizing and analyzing high-dimensional data like fiber tractography output

Magnetic Resonance Image segmentation using Pulse Coupled Neural Networks

The Pulse Couple Neural Network (PCNN) was developed by Eckhorn to model the observed synchronization of neural assemblies in the visual cortex of small mammals such as a cat. In this dissertation, three novel PCNN based automatic segmentation algorithms were developed to segment Magnetic Resonance Imaging (MRI) data: (a) PCNN image \u27signature\u27 based single region cropping; (b) PCNN - Kittler Illingworth minimum error thresholding and (c) PCNN -Gaussian Mixture Model - Expectation Maximization (GMM-EM) based multiple material segmentation. Among other control tests, the proposed algorithms were tested on three T2 weighted acquisition configurations comprising a total of 42 rat brain volumes, 20 T1 weighted MR human brain volumes from Harvard\u27s Internet Brain Segmentation Repository and 5 human MR breast volumes. The results were compared against manually segmented gold standards, Brain Extraction Tool (BET) V2.1 results, published results and single threshold methods. The Jaccard similarity index was used for numerical evaluation of the proposed algorithms. Our quantitative results demonstrate conclusively that PCNN based multiple material segmentation strategies can approach a human eye\u27s intensity delineation capability in grayscale image segmentation tasks

Model-based segmentation and registration of multimodal medical images

Ph.DDOCTOR OF PHILOSOPH

Brain semantic segmentation: a deep learning approach in human and Rat MRI studies

Dissertação de mestrado em Biomedical Engineering Dissertation (área de especialização em Field of Medical informatics)Magnetic Resonance Imaging (MRI) provides information about anatomy and pathology. This type of technique is the most popular used for the study of rat and human brain. Classifying voxels according to the presence of relevant anatomic features is an important step in the pre-processing of the data. A precise delineation and automatic segmentation of the brain structures is required in preclinical rodent imaging field and can substitute the manual segmentation where time consuming or human-error problems can occur. Current solutions are based on traditional segmentation algorithms that raise accuracy issues and generally need human intervention during or after the segmentation process. In the humans’ field, most of the tools created in DL (deep learning) are used in tumour or lesion segmentation. Brain segmentation tissues are not as explored as oncology problems and lesions complications. In the rats’ field, there are no segmentation studies in DL. It was decided to use a DL approach in Rats to solve some of the old techniques’ problems. This dissertation will present an approach on semantic segmentation of white matter and gray matter in Human’s images, evaluate the algorithm’s performance with outliers. It will also present an FCN (fully convolutional network) solution for on semantic segmentation using rat’s and human’s MRI of anatomical features. A two-dimensional convolution (slice-by-slice) approach and a three-dimensional (volume) convolutions approach were evaluated. At the end, the results found, using FCN U-NET in rats’ MRI for a 2D convolutions approach, DSC were 94.65 % for WM, 91.03% for GM and 76.89 % for cerebrospinal fluid. Using the 3D convolutions approach, the results using DSC found are 93.81 % for WM, 89.69 % for GM and 74.68 % for cerebrospinal fluid. The results using humans’ MRI using DSC were 91.59% for WM and 84,58% for GM.Imagens de ressonância magnética providenciam informação acerca da anatomia humana e possíveis patologias existentes. Este é o tipo de técnica mais popular entre os estudos na área da neurociência, tanto em humanos como em roedores. A classificação de voxéis de acordo com a presença de informação anatómica relevante é um importante passo no pré-processamento de dados na comunidade científica na área da neurociência. Uma delineação precisa das várias estruturas do cérebro humano ou roedor é uma das requisições para a maioria dos estudos clínicos de imagens de ressonância magnética. A segmentação automática através de inteligência artificial pode vir a substituir ferramentas ou algoritmos semiautomáticos já existentes ou substituir também a segmentação manual que se trata de um processo muito demorado que está ligado a erro-humano. O avanço tecnológico provocou um estudo mais aprofundado no Deep Learning (DL) a partir de 2012, provando que estas técnicas de inteligência artificial estão a revelar-se melhores do que o que já existe na área médica. Dos estudos com ressonância magnética em humanos, a maioria das ferramentas criadas que utilizam DL são usadas na segmentação de tumores ou lesões cerebrais. A segmentação de tecidos cerebrais não está tão explorada como problemas oncológicos ou lesões cerebrais. Dos estudos com ressonância magnética em roedores, não existem ferramentas que utilizam as técnicas de DL. Tendo em conta que as técnicas de segmentação que já existem ainda têm muitas complicações e erros, foi decidido tentar uma abordagem de DL em Roedores, também. Esta dissertação irá apresentar uma abordagem de segmentação semântica de massa branca e massa cinzenta utilizando técnicas de DL em humanos. Irá também verificar a capacidade de generalização com casos de pacientes idosos. Irá ser apresentado uma técnica de DL nas imagens de ressonância magnética em roedores para a segmentação semântica de massa branca, massa cinzenta e líquido cérebroespinal. No final irá ser comparado as técnicas entre as duas espécies e também entre a utilização de convoluções com duas dimensões e de convoluções com três dimensões nos roedores. No final, os resultados encontrados utilizando uma FCN em Ratazanas numa abordagem 2D, os valores de DSC foram 94,65 % para massa branca, 91.03% para massa cinzenta e 76.89 % para o líquido cérebroespinal. Na abordagem 3D ,os valores de DSC encontrados foram 93.81 % % para massa branca, 89.69 % para massa cinzenta e 74.68 % para o líquido cérebroespinal. Os resultados utilizando as imagens humanas, foram 91.59% para massa branca e 84,58% para massa cinzenta.This work is part of the SIGMA project with the reference FCT-ANR/NEU-OSD/0258/2012, co-financed by the French public funding agency ANR (Agence Nationale pour la Recherche, APP Blanc International II 2012), the Portuguese FCT (Fundação para a Ciência e Tecnologia) and the Portuguese North Regional Operational Program (ON.2 – O Novo Norte) under the National Strategic Reference Framework (QREN), through the European Regional Development Fund (FEDER) as well as the Projecto Estratégico co-funded by FCT (PEst-C/SAU/LA0026-/2013) and the European Regional Development Fund COMPETE (FCOMP-01-0124-FEDER-037298).This work was also supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT within the Project Scope: UID/CEC/00319/2013