A machine learning approach to statistical shape models with applications to medical image analysis

Statistical shape models have become an indispensable tool for image analysis. The use of shape models is especially popular in computer vision and medical image analysis, where they were incorporated as a prior into a wide range of different algorithms. In spite of their big success, the study of statistical shape models has not received much attention in recent years. Shape models are often seen as an isolated technique, which merely consists of applying Principal Component Analysis to a set of example data sets. In this thesis we revisit statistical shape models and discuss their construction and applications from the perspective of machine learning and kernel methods. The shapes that belong to an object class are modeled as a Gaussian Process whose parameters are estimated from example data. This formulation puts statistical shape models in a much wider context and makes the powerful inference tools from learning theory applicable to shape modeling. Furthermore, the formulation is continuous and thus helps to avoid discretization issues, which often arise with discrete models. An important step in building statistical shape models is to establish surface correspondence. We discuss an approach which is based on kernel methods. This formulation allows us to integrate the statistical shape model as an additional prior. It thus unifies the methods of registration and shape model fitting. Using Gaussian Process regression we can integrate shape constraints in our model. These constraints can be used to enforce landmark matching in the fitting or correspondence problem. The same technique also leads directly to a new solution for shape reconstruction from partial data. In addition to experiments on synthetic 2D data sets, we show the applicability of our methods on real 3D medical data of the human head. In particular, we build a 3D model of the human skull, and present its applications for the planning of cranio-facial surgeries

Finite element surface registration incorporating curvature, volume preservation, and statistical model information

We present a novel method for nonrigid registration of 3D surfaces and images. The method can be used to register surfaces by means of their distance images, or to register medical images directly. It is formulated as a minimization problem of a sum of several terms representing the desired properties of a registration result: smoothness, volume preservation, matching of the surface, its curvature, and possible other feature images, as well as consistency with previous registration results of similar objects, represented by a statistical deformation model. While most of these concepts are already known, we present a coherent continuous formulation of these constraints, including the statistical deformation model. This continuous formulation renders the registration method independent of its discretization. The finite element discretization we present is, while independent of the registration functional, the second main contribution of this paper. The local discontinuous Galerkin method has not previously been used in image registration, and it provides an efficient and general framework to discretize each of the terms of our functional. Computational efficiency and modest memory consumption are achieved thanks to parallelization and locally adaptive mesh refinement. This allows for the first time the use of otherwise prohibitively large 3D statistical deformation models

Morphable Face Models - An Open Framework

In this paper, we present a novel open-source pipeline for face registration based on Gaussian processes as well as an application to face image analysis. Non-rigid registration of faces is significant for many applications in computer vision, such as the construction of 3D Morphable face models (3DMMs). Gaussian Process Morphable Models (GPMMs) unify a variety of non-rigid deformation models with B-splines and PCA models as examples. GPMM separate problem specific requirements from the registration algorithm by incorporating domain-specific adaptions as a prior model. The novelties of this paper are the following: (i) We present a strategy and modeling technique for face registration that considers symmetry, multi-scale and spatially-varying details. The registration is applied to neutral faces and facial expressions. (ii) We release an open-source software framework for registration and model-building, demonstrated on the publicly available BU3D-FE database. The released pipeline also contains an implementation of an Analysis-by-Synthesis model adaption of 2D face images, tested on the Multi-PIE and LFW database. This enables the community to reproduce, evaluate and compare the individual steps of registration to model-building and 3D/2D model fitting. (iii) Along with the framework release, we publish a new version of the Basel Face Model (BFM-2017) with an improved age distribution and an additional facial expression model

Error-Controlled Model Approximation for Gaussian Process Morphable Models

Gaussian Process Morphable Models (GPMMs) unify a variety of non-rigid deformation models for surface and image registration. Deformation models, such as B-splines, radial basis functions, and PCA models are defined as a probability distribution using a Gaussian process. The method depends heavily on the low-rank approximation of the Gaussian process, which is mandatory to obtain a parametric representation of the model. In this article, we propose the use of the pivoted Cholesky decomposition for this task, which has the following advantages: (1) Compared to the current state of the art used in GPMMs, it provides a fully controllable approximation error. The algorithm greedily computes new basis functions until the user-defined approximation accuracy is reached. (2) Unlike the currently used approach, this method can be used in a black-box-like scenario, whereas the method automatically chooses the amount of basis functions for a given model and accuracy. (3) We propose the Newton basis as an alternative basis for GPMMs. The proposed basis does not need an SVD computation and can be iteratively refined. We show that the proposed basis functions achieve competitive registration results while providing the mentioned advantages for its computation

Probabilistic Joint Face-Skull Modelling for Facial Reconstruction

We present a novel method for co-registration of two independent statistical shape models. We solve the problem of aligning a face model to a skull model with stochastic optimization based on Markov Chain Monte Carlo (MCMC). We create a probabilistic joint face-skull model and show how to obtain a distribution of plausible face shapes given a skull shape. Due to environmental and genetic factors, there exists a distribution of possible face shapes arising from the same skull. We pose facial reconstruction as a conditional distribution of plausible face shapes given a skull shape. Because it is very difficult to obtain the distribution directly from MRI or CT data, we create a dataset of artificial face-skull pairs. To do this, we propose to combine three data sources of independent origin to model the joint face-skull distribution: a face shape model, a skull shape model and tissue depth marker information. For a given skull, we compute the posterior distribution of faces matching the tissue depth distribution with Metropolis-Hastings. We estimate the joint faceskull distribution from samples of the posterior. To find faces matching to an unknown skull, we estimate the probability of the face under the joint faceskull model. To our knowledge, we are the first to provide a whole distribution of plausible faces arising from a skull instead of only a single reconstruction. We show how the face-skull model can be used to rank a face dataset and on average successfully identify the correct match in top 30%. The face ranking even works when obtaining the face shapes from 2D images. We furthermore show how the face-skull model can be useful to estimate the skull position in an MR-image

Efficient computation of low-rank Gaussian process models for surface and image registration

Gaussian Process Morphable Models (GPMMs) are a unifying approach to non-rigid surface and image registration, where a deformation prior is defined using a Gaussian process. By a simple exchange of the covariance function we can formulate a wide variety of different deformation priors, such as spline-based models, free-form deformations or statistical shape and deformation models. How well the method works in practical applications depends crucially on how well a low-rank approximation of the Gaussian process can be computed. In this article we propose the use of the pivoted Cholesky decomposition for this task. This method makes it possible to efficiently compute a low-rank approximation for very large point sets, such as given by 3D meshes or 3D image grids, with a rigorously controlled approximation error. Compared to the current state of the art, which is based on the Nystro ̈m method, the approximation error is controllable and can be specified by a user-defined threshold. Further we propose a computationally more efficient and greedy alternative to currently used Karhunen-Loève expansion. This makes it possible to compute more accurate model approximations at the same computational costs. Detailed experiments from the registration of high quality human face scans and medical CT images containing the forearm with Ulna and Radius demonstrate the efficiency of the method and the computational advantages over the Nyström method

Automated, 3-D and Sub-Micron Accurate Ablation-Volume Determination by Inverse Molding and X-Ray Computed Tomography.

Ablation of materials in combination with element-specific analysis of the matter released is a widely used method to accurately determine a material's chemical composition. Among other methods, repetitive ablation using femto-second pulsed laser systems provides excellent spatial resolution through its incremental removal of nanometer thick layers. The method can be combined with high-resolution mass spectrometry, for example, laser ablation ionization mass spectrometry, to simultaneously analyze chemically the material released. With increasing depth of the volume ablated, however, secondary effects start to play an important role and the ablation geometry deviates substantially from the desired cylindrical shape. Consequently, primarily conical but sometimes even more complex, rather than cylindrical, craters are created. Their dimensions need to be analyzed to enable a direct correlation with the element-specific analytical signals. Here, a post-ablation analysis method is presented that combines generic polydimethylsiloxane-based molding of craters with the volumetric reconstruction of the crater's inverse using X-ray computed tomography. Automated analysis yields the full, sub-micron accurate anatomy of the craters, thereby a scalable and generic method to better understand the fundamentals underlying ablation processes applicable to a wide range of materials. Furthermore, it may serve toward a more accurate determination of heterogeneous material's composition for a variety of applications without requiring time- and labor-intensive analyses of individual craters

Labour of Love: An Open Access Manifesto for Freedom, Integrity, and Creativity in the Humanities and Interpretive Social Sciences

We are a group of scholar-publishers based in the humanities and social sciences who are questioning the fairness and scientific tenability of a system of scholarly communication dominated by large commercial publishers. With this Manifesto we wish to repoliticise Open Access to challenge existing rapacious practices in academic publishing—namely, often invisible and unremunerated labour, toxic hierarchies of academic prestige, and a bureaucratic ethos that stifles experimentation—and to bear witness to the indifference they are predicated upon. We mobilise an extended notion of research output, which encompasses the work of building and maintaining the systems, processes, and relations of production that make scholarship possible. We believe that the humanities and social sciences are too often disengaged from the public and material afterlives of their scholarship. We worry that our fields are sleepwalking into a new phase of control and capitalisation, to include continued corporate extraction of value and transparency requirements designed by managers, entrepreneurs, and politicians. We fervently believe that OA can be a powerful tool to advance the ends of civil society and social movements. But opening up the products of our scholarship without questioning how this is done, who stands to profit from it, what model of scholarship is being normalised, and who stands to be silenced by this process may come at a particularly high cost for scholars in the humanities and social sciences

Un atto d’amore: Manifesto Open Access per la libertà, l’integrità e la creatività nelle scienze umane e nelle scienze sociali interpretative

Labour of Love: An Open Access Manifesto for Freedom, Integrity, and Creativity in the Humanities and Interpretive Social Sciences, is the result of an LSE Research Infrastructure and Investment–funded workshop entitled Academic Freedom, Academic Integrity and Open Access in the Social Sciences, organised by Andrea E. Pia and held at the London School of Economics on September 9, 2019.Un atto d’amore: Manifesto Open Access per la libertà, l’integrità e la creatività nelle scienze umane e nelle scienze sociali interpretative, è il risultato di un workshop finanziato da LSE Research Infrastructure and Investment Funds dal titolo Academic Freedom, Academic Integrity and Open Access in the Social Sciences, organizzato da Andrea E. Pia e tenuto presso la London School of Economics il 9 settembre 2019

Un acto de amor. Un Manifiesto de Acceso Abierto por la libertad, la integridad y la creatividad en las humanidades y las ciencias sociales interpretativas

Labour of Love. An Open Access Manifesto for Freedom, Integrity, and Creativity in the Humanities and Interpretive Social Sciences, is the result of an LSE Research Infrastructure and Investment–funded workshop entitled Academic Freedom, Academic Integrity and Open Access in the Social Sciences, organised by Andrea E. Pia and held at the London School of Economics on September 9, 2019.Un acto de amor. Un Manifiesto de Acceso Abierto por la Libertad, la Integridad y la Creatividad en las Humanidades y las Ciencias Sociales Interpretativas, es el resultado de un taller financiado por la Infraestructura de Investigación y la Inversión de la LSE, titulado Academic Freedom, Academic Integrity and Open Access in the Social Sciences, organizado por Andrea E. Pia y celebrado en la London School of Economics el 9 de septiembre de 2019