Liver segmentation using automatically defined patient specific B-Spline surface models

This paper presents a novel liver segmentation algorithm. This is a model-driven approach; however, unlike previous techniques which use a statistical model obtained from a training set, we initialize patient-specific models directly from their own pre-segmentation. As a result, the non-trivial problems such as landmark correspondences, model registration etc. can be avoided. Moreover, by dividing the liver region into three sub-regions, we convert the problem of building one complex shape model into constructing three much simpler models, which can be fitted independently, greatly improving the computation efficiency. A robust graph-based narrow band optimal surface fitting scheme is also presented. The proposed approach is evaluated on 35 CT images. Compared to contemporary approaches, our approach has no training requirement and requires significantly less processing time, with an RMS error of 2.440.53mm against manual segmentation

Doctor of Philosophy

dissertationImage segmentation entails the partitioning of an image domain, usually two or three dimensions, so that each partition or segment has some meaning that is relevant to the application at hand. Accurate image segmentation is a crucial challenge in many disciplines, including medicine, computer vision, and geology. In some applications, heterogeneous pixel intensities; noisy, ill-defined, or diffusive boundaries; and irregular shapes with high variability can make it challenging to meet accuracy requirements. Various segmentation approaches tackle such challenges by casting the segmentation problem as an energy-minimization problem, and solving it using efficient optimization algorithms. These approaches are broadly classified as either region-based or edge (surface)-based depending on the features on which they operate. The focus of this dissertation is on the development of a surface-based energy model, the design of efficient formulations of optimization frameworks to incorporate such energy, and the solution of the energy-minimization problem using graph cuts. This dissertation utilizes a set of four papers whose motivation is the efficient extraction of the left atrium wall from the late gadolinium enhancement magnetic resonance imaging (LGE-MRI) image volume. This dissertation utilizes these energy formulations for other applications, including contact lens segmentation in the optical coherence tomography (OCT) data and the extraction of geologic features in seismic data. Chapters 2 through 5 (papers 1 through 4) explore building a surface-based image segmentation model by progressively adding components to improve its accuracy and robustness. The first paper defines a parametric search space and its discrete formulation in the form of a multilayer three-dimensional mesh model within which the segmentation takes place. It includes a generative intensity model, and we optimize using a graph formulation of the surface net problem. The second paper proposes a Bayesian framework with a Markov random field (MRF) prior that gives rise to another class of surface nets, which provides better segmentation with smooth boundaries. The third paper presents a maximum a posteriori (MAP)-based surface estimation framework that relies on a generative image model by incorporating global shape priors, in addition to the MRF, within the Bayesian formulation. Thus, the resulting surface not only depends on the learned model of shapes,but also accommodates the test data irregularities through smooth deviations from these priors. Further, the paper proposes a new shape parameter estimation scheme, in closed form, for segmentation as a part of the optimization process. Finally, the fourth paper (under review at the time of this document) presents an extensive analysis of the MAP framework and presents improved mesh generation and generative intensity models. It also performs a thorough analysis of the segmentation results that demonstrates the effectiveness of the proposed method qualitatively, quantitatively, and clinically. Chapter 6, consisting of unpublished work, demonstrates the application of an MRF-based Bayesian framework to segment coupled surfaces of contact lenses in optical coherence tomography images. This chapter also shows an application related to the extraction of geological structures in seismic volumes. Due to the large sizes of seismic volume datasets, we also present fast, approximate surface-based energy minimization strategies that achieve better speed-ups and memory consumption

Automatic detection of drusen associated with age-related macular degeneration in optical coherence tomography: a graph-based approach

Tese de Doutoramento em Líderes para Indústrias TecnológicasThe age-related macular degeneration (AMD) starts to manifest itself with the appearance of drusen. Progressively, the drusen increase in size and in number without causing alterations to vision. Nonetheless, their quantification is important because it correlates with the evolution of the disease to an advanced stage, which could lead to the loss of central vision. Manual quantification of drusen is impractical, since it is time-consuming and it requires specialized knowledge. Therefore, this work proposes a method for quantifying drusen automatically In this work, it is proposed a method for segmenting boundaries limiting drusen and another method for locating them through classification. The segmentation method is based on a multiple surface framework that is adapted for segmenting the limiting boundaries of drusen: the inner boundary of the retinal pigment epithelium + drusen complex (IRPEDC) and the Bruch’s membrane (BM). Several segmentation methods have been considerably successful in segmenting layers of healthy retinas in optical coherence tomography (OCT) images. These methods were successful because they incorporate prior information and regularization. However, these factors have the side-effect of hindering the segmentation in regions of altered morphology that often occur in diseased retinas. The proposed segmentation method takes into account the presence of lesion related with AMD, i.e., drusen and geographic atrophies (GAs). For that, it is proposed a segmentation scheme that excludes prior information and regularization that is only valid for healthy regions. Even with this segmentation scheme, the prior information and regularization can still cause the oversmoothing of some drusen. To address this problem, it is also proposed the integration of local shape priors in the form of a sparse high order potentials (SHOPs) into the multiple surface framework. Drusen are commonly detected by thresholding the distance among the boundaries that limit drusen. This approach misses drusen or portions of drusen with a height below the threshold. To improve the detection of drusen, Dufour et al. [1] proposed a classification method that detects drusen using textural information. In this work, the method of Dufour et al. [1] is extended by adding new features and performing multi-label classification, which allow the individual detection of drusen when these occur in clusters. Furthermore, local information is incorporated into the classification by combining the classifier with a hidden Markov model (HMM). Both the segmentation and detections methods were evaluated in a database of patients with intermediate AMD. The results suggest that both methods frequently perform better than some methods present in the literature. Furthermore, the results of these two methods form drusen delimitations that are closer to expert delimitations than two methods of the literature.A degenerescência macular relacionada com a idade (DMRI) começa a manifestar-se com o aparecimento de drusas. Progressivamente, as drusas aumentam em tamanho e em número sem causar alterações à visão. Porém, a sua quantificação é importante porque está correlacionada com a evolução da doença para um estado avançado, levar à perda de visão central. A quantificação manual de drusas é impraticável, já que é demorada e requer conhecimento especializado. Por isso, neste trabalho é proposto um método para segmentar drusas automaticamente. Neste trabalho, é proposto um método para segmentar as fronteiras que limitam as drusas e outro método para as localizar através de classificação. O método de segmentação é baseado numa ”framework” de múltiplas superfícies que é adaptada para segmentar as fronteiras que limitam as drusas: a fronteira interior do epitélio pigmentar + complexo de drusas e a membrana de Bruch. Vários métodos de segmentação foram consideravelmente bem-sucedidos a segmentar camadas de retinas saudáveis em imagens de tomografia de coerência ótica. Estes métodos foram bem-sucedidos porque incorporaram informação prévia e regularização. Contudo, estes fatores têm como efeito secundário dificultar a segmentação em regiões onde a morfologia da retina está alterada devido a doenças. O método de segmentação proposto toma em consideração a presença de lesões relacionadas com DMRI, .i.e., drusas e atrofia geográficas. Para isso, é proposto um esquema de segmentação que exclui informação prévia e regularização que são válidas apenas em regiões saudáveis da retina. Mesmo com este esquema de segmentação, a informação prévia e a regularização podem causar a suavização excessiva de algumas drusas. Para tentar resolver este problema, também é proposta a integração de informação prévia local sob a forma de potenciais esparsos de ordem elevada na ”framework” multi-superfície. As drusas são usalmente detetadas por ”thresholding” da distância entre as fronteiras que limitam as drusas. Esta abordagem falha drusas ou porções de drusas abaixo do ”threshold”. Para melhorar a deteção de drusas, Dufour et al. [1] propuseram um método de classificação que deteta drusas usando informação de texturas. Neste trabalho, o método de Dufour et al. [1] é estendido, adicionando novas características e realizando uma classificação com múltiplas classes, o que permite a deteção individual de drusas em aglomerados. Além disso, é incorporada informação local na classificação, combinando o classificador com um modelo oculto de Markov. Ambos os métodos de segmentação e deteção foram avaliados numa base de dados de pacientes com DMRI intermédia. Os resultados sugerem que ambos os métodos obtêm frequentemente melhores resultados que alguns métodos descritos na literatura. Para além disso, os resultados destes dois métodos formam delimitações de drusas que estão mais próximas das delimitações dos especialistas que dois métodos da literatura.This work was supported by FCT with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 – Programa Operacional Competitividade e Internacionalização (POCI) with the reference project POCI-01-0145-FEDER-006941. Furthermore, the Portuguese funding institution Fundação Calouste Gulbenkian has conceded me a Ph.D. grant for this work. For that, I wish to acknowledge this institution. Additionally, I want to thank one of its members, Teresa Burnay, for all her assistance with issues related with the grant, for believing that my work was worth supporting and for encouraging me to apply for the grant

Higher-Order Regularization in Computer Vision

At the core of many computer vision models lies the minimization of an objective function consisting of a sum of functions with few arguments. The order of the objective function is defined as the highest number of arguments of any summand. To reduce ambiguity and noise in the solution, regularization terms are included into the objective function, enforcing different properties of the solution. The most commonly used regularization is penalization of boundary length, which requires a second-order objective function. Most of this thesis is devoted to introducing higher-order regularization terms and presenting efficient minimization schemes. One of the topics of the thesis covers a reformulation of a large class of discrete functions into an equivalent form. The reformulation is shown, both in theory and practical experiments, to be advantageous for higher-order regularization models based on curvature and second-order derivatives. Another topic is the parametric max-flow problem. An analysis is given, showing its inherent limitations for large-scale problems which are common in computer vision. The thesis also introduces a segmentation approach for finding thin and elongated structures in 3D volumes. Using a line-graph formulation, it is shown how to efficiently regularize with respect to higher-order differential geometric properties such as curvature and torsion. Furthermore, an efficient optimization approach for a multi-region model is presented which, in addition to standard regularization, is able to enforce geometric constraints such as inclusion or exclusion of different regions. The final part of the thesis deals with dense stereo estimation. A new regularization model is introduced, penalizing the second-order derivatives of a depth or disparity map. Compared to previous second-order approaches to dense stereo estimation, the new regularization model is shown to be more easily optimized

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure