52 research outputs found
Modeling Surfaces from Volume Data Using Nonparallel Contours
Magnetic resonance imaging: MRI) and computed tomography: CT) scanners have long been used to produce three-dimensional samplings of anatomy elements for use in medical visualization and analysis. From such datasets, physicians often need to construct surfaces representing anatomical shapes in order to conduct treatment, such as irradiating a tumor. Traditionally, this is done through a time-consuming and error-prone process in which an experienced scientist or physician marks a series of parallel contours that outline the structures of interest. Recent advances in surface reconstruction algorithms have led to methods for reconstructing surfaces from nonparallel contours that could greatly reduce the manual component of this process. Despite these technological advances, the segmentation process has remained unchanged.
This dissertation takes the first steps toward bridging the gap between the new surface reconstruction technologies and bringing those methods to use in clinical practice. We develop VolumeViewer, a novel interface for modeling surfaces from volume data by allowing the user to sketch contours on arbitrarily oriented cross-sections of the volume. We design the algorithms necessary to support nonparallel contouring, and we evaluate the system with medical professionals using actual patient data. In this way, we begin to understand how nonparallel contouring can aid the segmentation process and expose the challenges associated with a nonparallel contouring system in practice
Semiautomated 3D liver segmentation using computed tomography and magnetic resonance imaging
Le foie est un organe vital ayant une capacitĂ© de rĂ©gĂ©nĂ©ration exceptionnelle et un rĂŽle crucial dans le fonctionnement de lâorganisme. LâĂ©valuation du volume du foie est un outil important pouvant ĂȘtre utilisĂ© comme marqueur biologique de sĂ©vĂ©ritĂ© de maladies hĂ©patiques. La volumĂ©trie du foie est indiquĂ©e avant les hĂ©patectomies majeures, lâembolisation de la veine porte et la transplantation.
La méthode la plus répandue sur la base d'examens de tomodensitométrie (TDM) et d'imagerie par résonance magnétique (IRM) consiste à délimiter le contour du foie sur plusieurs coupes consécutives, un processus appelé la «segmentation».
Nous prĂ©sentons la conception et la stratĂ©gie de validation pour une mĂ©thode de segmentation semi-automatisĂ©e dĂ©veloppĂ©e Ă notre institution. Notre mĂ©thode reprĂ©sente une approche basĂ©e sur un modĂšle utilisant lâinterpolation variationnelle de forme ainsi que lâoptimisation de maillages de Laplace. La mĂ©thode a Ă©tĂ© conçue afin dâĂȘtre compatible avec la TDM ainsi que l' IRM.
Nous avons Ă©valuĂ© la rĂ©pĂ©tabilitĂ©, la fiabilitĂ© ainsi que lâefficacitĂ© de notre mĂ©thode semi-automatisĂ©e de segmentation avec deux Ă©tudes transversales conçues rĂ©trospectivement. Les rĂ©sultats de nos Ă©tudes de validation suggĂšrent que la mĂ©thode de segmentation confĂšre une fiabilitĂ© et rĂ©pĂ©tabilitĂ© comparables Ă la segmentation manuelle. De plus, cette mĂ©thode diminue de façon significative le temps dâinteraction, la rendant ainsi adaptĂ©e Ă la pratique clinique courante.
Dâautres Ă©tudes pourraient incorporer la volumĂ©trie afin de dĂ©terminer des marqueurs biologiques de maladie hĂ©patique basĂ©s sur le volume tels que la prĂ©sence de stĂ©atose, de fer, ou encore la mesure de fibrose par unitĂ© de volume.The liver is a vital abdominal organ known for its remarkable regenerative
capacity and fundamental role in organism viability. Assessment of liver volume is
an important tool which physicians use as a biomarker of disease severity. Liver
volumetry is clinically indicated prior to major hepatectomy, portal vein
embolization and transplantation.
The most popular method to determine liver volume from computed
tomography (CT) and magnetic resonance imaging (MRI) examinations involves
contouring the liver on consecutive imaging slices, a process called
âsegmentationâ. Segmentation can be performed either manually or in an
automated fashion.
We present the design concept and validation strategy for an innovative
semiautomated liver segmentation method developed at our institution. Our
method represents a model-based approach using variational shape interpolation
and Laplacian mesh optimization techniques. It is independent of training data,
requires limited user interactions and is robust to a variety of pathological cases.
Further, it was designed for compatibility with both CT and MRI examinations.
We evaluated the repeatability, agreement and efficiency of our
semiautomated method in two retrospective cross-sectional studies. The results of
our validation studies suggest that semiautomated liver segmentation can provide
strong agreement and repeatability when compared to manual segmentation.
Further, segmentation automation significantly shortens interaction time, thus
making it suitable for daily clinical practice.
Future studies may incorporate liver volumetry to determine volume-averaged
biomarkers of liver disease, such as such as fat, iron or fibrosis measurements per
unit volume. Segmental volumetry could also be assessed based on
subsegmentation of vascular anatomy
Recommended from our members
Neuromantic : from semi-manual to semi-automatic reconstruction of neuron morphology
The ability to create accurate geometric models of neuronal morphology is
important for understanding the role of shape in information processing.
Despite a significant amount of research on automating neuron
reconstructions from image stacks obtained via microscopy, in practice
most data are still collected manually. This paper describes Neuromantic,
an open source system for three dimensional digital tracing of neurites.
Neuromantic reconstructions are comparable in quality to those of
existing commercial and freeware systems while balancing speed and
accuracy of manual reconstruction. The combination of semi-automatic
tracing, intuitive editing, and ability of visualizing large image stacks on
standard computing platforms provides a versatile tool that can help
address the reconstructions availability bottleneck. Practical
considerations for reducing the computational time and space
requirements of the extended algorithm are also discussed
Variational methods and its applications to computer vision
Many computer vision applications such as image segmentation can be formulated in a ''variational'' way as energy minimization problems. Unfortunately, the computational task of minimizing these energies is usually difficult as it generally involves non convex functions in a space with thousands of dimensions and often the associated combinatorial problems are NP-hard to solve. Furthermore, they are ill-posed inverse problems and therefore are extremely sensitive to perturbations (e.g. noise). For this reason in order to compute a physically reliable approximation from given noisy data, it is necessary to incorporate into the mathematical model appropriate regularizations that require complex computations.
The main aim of this work is to describe variational segmentation methods that are particularly effective for curvilinear structures. Due to their complex geometry, classical regularization techniques cannot be adopted because they lead to the loss of most of low contrasted details. In contrast, the proposed method not only better preserves curvilinear structures, but also reconnects some parts that may have been disconnected by noise. Moreover, it can be easily extensible to graphs and successfully applied to different types of data such as medical imagery (i.e. vessels, hearth coronaries etc), material samples (i.e. concrete) and satellite signals (i.e. streets, rivers etc.). In particular, we will show results and performances about an implementation targeting new generation of High Performance Computing (HPC) architectures where different types of coprocessors cooperate. The involved dataset consists of approximately 200 images of cracks, captured in three different tunnels by a robotic machine designed for the European ROBO-SPECT project.Open Acces
Interactive feature detection in volumetric data
Im Rahmen dieser Dissertation wurden drei Techniken fĂŒr die interaktive Merkmalsdetektion in Volumendaten entwickelt. Das erste Verfahren auf Basis des LH-Transferfunktionsraumes ermöglicht es dem Benutzer, Objekt-OberflĂ€chen in einem Volumendatensatz durch direktes Markieren im gerenderten Bild zu identifizieren, wobei keine Interaktion im Datenraum des Volumens benötigt wird. Zweitens wird ein formbasiertes Klassifikationsverfahren vorgestellt, das ausgehend von einer groben Vorsegmentierung den Volumendatensatz in eine Menge von kleineren Regionen zerlegt, deren Form anschlieĂend mit eigens entwickelten Klassifikatoren bestimmt wird. Drittens wird ein interaktives Volumen-Segmentierungsverfahren auf Basis des Random Walker-Algorithmus beschrieben, das speziell auf die Verringerung von Fehlklassifizierungen in der resultierenden Segmentierung abzielt. This dissertation presents three volumetric feature detection approaches
that focus on an efficient interplay between user and system. The first
technique exploits the LH transfer function space in order to enable
the user to classify boundaries by directly marking them in the volume
rendering image, without requiring interaction in the data domain.
Second, we propose a shape-based feature detection approach that blurs
the border between fast but limited classification and powerful but
laborious segmentation techniques. Third, we present a guided
probabilistic volume segmentation workflow that focuses on the
minimization of uncertainty in the resulting segmentation. In an
iterative process, the system continuously assesses uncertainty of an
intermediate random walker-based segmentation in order to detect regions
with high ambiguity, to which the userâs attention is directed to
support the correction of potential segmentation errors
Contributions of Continuous Max-Flow Theory to Medical Image Processing
Discrete graph cuts and continuous max-flow theory have created a paradigm shift in many areas of medical image processing. As previous methods limited themselves to analytically solvable optimization problems or guaranteed only local optimizability to increasingly complex and non-convex functionals, current methods based now rely on describing an optimization problem in a series of general yet simple functionals with a global, but non-analytic, solution algorithms. This has been increasingly spurred on by the availability of these general-purpose algorithms in an open-source context. Thus, graph-cuts and max-flow have changed every aspect of medical image processing from reconstruction to enhancement to segmentation and registration.
To wax philosophical, continuous max-flow theory in particular has the potential to bring a high degree of mathematical elegance to the field, bridging the conceptual gap between the discrete and continuous domains in which we describe different imaging problems, properties and processes. In Chapter 1, we use the notion of infinitely dense and infinitely densely connected graphs to transfer between the discrete and continuous domains, which has a certain sense of mathematical pedantry to it, but the resulting variational energy equations have a sense of elegance and charm. As any application of the principle of duality, the variational equations have an enigmatic side that can only be decoded with time and patience.
The goal of this thesis is to show the contributions of max-flow theory through image enhancement and segmentation, increasing incorporation of topological considerations and increasing the role played by user knowledge and interactivity. These methods will be rigorously grounded in calculus of variations, guaranteeing fuzzy optimality and providing multiple solution approaches to addressing each individual problem
Interactive Brain Tumor Segmentation with Inclusion Constraints
This thesis proposes an improved interactive brain tumor segmentation method based on graph cuts, which is an efficient global optimization framework for image segmentation, and star shape, which is a general segmentation shape prior with minimal user assistance. Our improvements lie in volume ballooning, compactness measure and inclusion constraints.
Volume ballooning is incorporated to help to balloon segmentation for situations where the foreground and background have similar appearance models and changing relative weight between appearance model and smoothness term cannot help to achieve an accurate segmentation. We search different ballooning parameters for different slices since an appropriate ballooning force may vary between slices.
As the evaluation for goodness of segmentation in parameter searching, two new compactness measures are introduced, ellipse fitting and convexity deviation. Ellipse fitting is a measure of compactness based on the deviation from an ellipse of best fit, which prefers segmentation with an ellipse shape. And convexity deviation is a more strict measure for preferring convex segmentation. It uses the number of convexity violation pixels as the measure for compactness.
Inclusion constraints is added between slices to avoid side slice segmentation larger than the middle slice problem. The inclusion constraints consist of mask inclusion, which is implemented by an unary term in graph cuts, and pairwise inclusion, which is implemented by a pairwise term. Margin is allowed in inclusion so that the inclusion region is enlarged.
With all these improvements, the final result is promising. The best performance for our dataset is 88% compared to the previous system that achieved 87%
A machine learning approach to the unsupervised segmentation of mitochondria in subcellular electron microscopy data
Recent advances in cellular and subcellular microscopy demonstrated its potential towards unravelling the mechanisms of various diseases at the molecular level. The biggest challenge in both human- and computer-based visual analysis of micrographs is the variety of nanostructures and mitochondrial morphologies. The state-of-the-art is, however, dominated by supervised manual data annotation and early attempts to automate the segmentation process were based on supervised machine learning techniques which require large datasets for training. Given a minimal number of training sequences or none at all, unsupervised machine learning formulations, such as spectral dimensionality reduction, are known to be superior in detecting salient image structures.
This thesis presents three major contributions developed around the spectral clustering framework which is proven to capture perceptual organization features. Firstly, we approach the problem of mitochondria localization. We propose a novel grouping method for the extracted line segments which describes the normal mitochondrial morphology. Experimental findings show that the clusters obtained successfully model the inner mitochondrial membrane folding and therefore can be used as markers for the subsequent segmentation approaches. Secondly, we developed an unsupervised mitochondria segmentation framework. This method follows the evolutional ability of human vision to extrapolate salient membrane structures in a micrograph. Furthermore, we designed robust non-parametric similarity models according to Gestaltic laws of visual segregation. Experiments demonstrate that such models automatically adapt to the statistical structure of the biological domain and return optimal performance in pixel classification tasks under the wide variety of distributional assumptions. The last major contribution addresses the computational complexity of spectral clustering. Here, we introduced a new anticorrelation-based spectral clustering formulation with the objective to improve both: speed and quality of segmentation. The experimental findings showed the applicability of our dimensionality reduction algorithm to very large scale problems as well as asymmetric, dense and non-Euclidean datasets
- âŠ