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

Posterior shape models

We present a method to compute the conditional distribution of a statistical shape model given partial data. The result is a "posterior shape model", which is again a statistical shape model of the same form as the original model. This allows its direct use in the variety of algorithms that include prior knowledge about the variability of a class of shapes with a statistical shape model. Posterior shape models then provide a statistically sound yet easy method to integrate partial data into these algorithms. Usually, shape models represent a complete organ, for instance in our experiments the femur bone, modeled by a multivariate normal distribution. But because in many application certain parts of the shape are known a priori, it is of great interest to model the posterior distribution of the whole shape given the known parts. These could be isolated landmark points or larger portions of the shape, like the healthy part of a pathological or damaged organ. However, because for most shape models the dimensionality of the data is much higher than the number of examples, the normal distribution is singular, and the conditional distribution not readily available. In this paper, we present two main contributions: First, we show how the posterior model can be efficiently computed as a statistical shape model in standard form and used in any shape model algorithm. We complement this paper with a freely available implementation of our algorithms. Second, we show that most common approaches put forth in the literature to overcome this are equivalent to probabilistic principal component analysis (PPCA), and Gaussian Process regression. To illustrate the use of posterior shape models, we apply them on two problems from medical image analysis: model-based image segmentation incorporating prior knowledge from landmarks, and the prediction of anatomically correct knee shapes for trochlear dysplasia patients, which constitutes a novel medical application. Our experiments confirm that the use of conditional shape models for image segmentation improves the overall segmentation accuracy and robustness

Gaussian Process Morphable Models

Statistical shape models (SSMs) represent a class of shapes as a normal distribution of point variations, whose parameters are estimated from example shapes. Principal component analysis (PCA) is applied to obtain a low-dimensional representation of the shape variation in terms of the leading principal components. In this paper, we propose a generalization of SSMs, called Gaussian Process Morphable Models (GPMMs). We model the shape variations with a Gaussian process, which we represent using the leading components of its Karhunen-Loeve expansion. To compute the expansion, we make use of an approximation scheme based on the Nystrom method. The resulting model can be seen as a continuous analogon of an SSM. However, while for SSMs the shape variation is restricted to the span of the example data, with GPMMs we can define the shape variation using any Gaussian process. For example, we can build shape models that correspond to classical spline models, and thus do not require any example data. Furthermore, Gaussian processes make it possible to combine different models. For example, an SSM can be extended with a spline model, to obtain a model that incorporates learned shape characteristics, but is flexible enough to explain shapes that cannot be represented by the SSM. We introduce a simple algorithm for fitting a GPMM to a surface or image. This results in a non-rigid registration approach, whose regularization properties are defined by a GPMM. We show how we can obtain different registration schemes,including methods for multi-scale, spatially-varying or hybrid registration, by constructing an appropriate GPMM. As our approach strictly separates modelling from the fitting process, this is all achieved without changes to the fitting algorithm. We show the applicability and versatility of GPMMs on a clinical use case, where the goal is the model-based segmentation of 3D forearm images

Treatment Perspectives in Crohn’s Disease

Background: Crohn’s disease (CD) is a chronic immune-mediated disorder of the gastrointestinal tract. The pathophysiological understanding of this disease is limited and no curative therapy is available so far. Therefore, most patients require long-lasting or even life-long immunosuppressive therapies for the suppression of symptoms to improve quality of life and reduction of long-term risks. However, in a relevant subgroup of patients, these therapeutic goals cannot be sufficiently attained. Summary: Clinically established therapies in active CD comprise corticosteroids and immunosuppressants such as azathioprine. After the introduction of anti-TNFα (Tumor necrosis factor alpha) antibodies, other biologicals (e.g., vedolizumab and ustekinumab) have also been approved. New drugs in the pipeline like filgotinib, upadacitinib, risankizumab or rifaximin could improve the therapy of CD in the near future. Thus, an individualized therapy management, based on optimal selection of therapeutic agents will become more important. Additionally, the local application of mesenchymal stem cells might be helpful in the management of fistulas. Key Messages: The targeted biological therapeutic agents (anti-TNFα antibodies, vedolizumab, ustekinumab) are well established for therapy in CD. There are several new substances in the pipeline with promising results in phase II trials (filgotinib, rifaximin, risankizumab, upadacitinib). The upcoming extension of the therapeutic arsenal will require methods for an optimized selection of substances, thus enabling a more individualized therapy

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

Treatment Perspectives in Crohn’s Disease

Background: Crohn’s disease (CD) is a chronic immune-mediated disorder of the gastrointestinal tract. The pathophysiological understanding of this disease is limited and no curative therapy is available so far. Therefore, most patients require long-lasting or even life-long immunosuppressive therapies for the suppression of symptoms to improve quality of life and reduction of long-term risks. However, in a relevant subgroup of patients, these therapeutic goals cannot be sufficiently attained. Summary: Clinically established therapies in active CD comprise corticosteroids and immunosuppressants such as azathioprine. After the introduction of anti-TNFα (Tumor necrosis factor alpha) antibodies, other biologicals (e.g., vedolizumab and ustekinumab) have also been approved. New drugs in the pipeline like filgotinib, upadacitinib, risankizumab or rifaximin could improve the therapy of CD in the near future. Thus, an individualized therapy management, based on optimal selection of therapeutic agents will become more important. Additionally, the local application of mesenchymal stem cells might be helpful in the management of fistulas. Key Messages: The targeted biological therapeutic agents (anti-TNFα antibodies, vedolizumab, ustekinumab) are well established for therapy in CD. There are several new substances in the pipeline with promising results in phase II trials (filgotinib, rifaximin, risankizumab, upadacitinib). The upcoming extension of the therapeutic arsenal will require methods for an optimized selection of substances, thus enabling a more individualized therapy

Emerging oral targeted therapies in inflammatory bowel diseases: opportunities and challenges

To improve quality of life and prevent long-term risks in patients with inflammatory bowel diseases (IBDs: Crohn’s disease, ulcerative colitis), it is essential to suppress inflammatory activity adequately. However, corticosteroids are only suitable for therapy of acute flares and the evidence for positive effects of immunosuppressive substances like azathioprine or 6-mercapropurine is mainly limited to maintenance of remission. In addition, only subgroups of patients benefit from biologicals targeting tumour necrosis factor α or α4β7 integrins. In summary, until now the disease activity is not sufficiently controlled in a relevant fraction of the patients with IBD. Thus, there is an urge for the development of new substances in the therapy of ulcerative colitis and Crohn’s disease. Fortunately, new oral and parenteral substances are in the pipeline. This review will focus on oral substances, which have already passed phase II studies successfully at this stage. In this article, we summarize data regarding AJM300, phosphatidylcholine (LT-02), mongersen, ozanimod, filgotinib and tofacitinib. AJM300 and ozanimod were tested in patients with ulcerative colitis and target lymphocyte trafficking through inhibition of the α subunit of integrin, respectively binding to the sphingosine-1-phosphate receptor (subtypes 1 and 5) on lymphocytes. Mongersen was utilized in patients with Crohn’s disease and accelerates the degradation of SMAD7 mRNA, which consequently strengthens the mainly anti-inflammatory signalling pathway of transforming growth factor β1. Various Janus kinase (JAK) inhibitors were developed, which inhibit the intracellular signalling pathway of cytokines. For example, the JAK1 blocker filgotinib was tested in Crohn’s disease, whereas the JAK1/3 inhibitor tofacitinib was tested in clinical trials for both Crohn’s disease and ulcerative colitis. A different therapeutic approach is the substitution of phosphatidylcholine (LT-02), which might recover the colonic mucus. Taken together, clinical trials with these new agents have opened avenues for further clinical studies and it can be expected that at least some of these agents will be finally approved for clinical therapy

Quality of Life and Independent Factors Associated with Poor Digestive Function after Ivor Lewis Esophagectomy

Transthoracic esophagectomy results in a radical change in foregut anatomy with multiple consequences for digestive physiology. The aim of this study was to identify factors associated with poor functional outcomes by assessing multiple dimensions of digestive performance and health-related quality of life (HRQL). Patients with cancer-free survival after Ivor Lewis esophagectomy were included. Four functional syndromes (dysphagia, gastroesophageal reflux disease (GERD), delayed gastric conduit emptying (DGCE), and dumping syndrome (DS)) and HRQL were assessed using specifically designed questionnaires. Patient outcomes were compared with healthy controls. Independent factors associated with poor digestive performance were identified through multivariable analysis. Sixty-five postoperative patients and 50 healthy volunteers participated in this study. Compared with controls, patients had worse outcomes for dysphagia, GERD, DS, and HRQL, but not for DGCE. A multivariate analysis showed a significant correlation of reduced digestive performance with ASA score, squamous cell carcinoma, open or hybrid surgical approach, and (neo)adjuvant therapy. In contrast, no individual patient factor was found to be associated with dumping syndrome. Digestive function and HRQL are substantially impaired after Ivor Lewis esophagectomy for cancer. Comorbid patients undergoing multimodal treatment and open access surgery for squamous cell carcinoma have the highest risk of poor functional outcome

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