nils-herrmann/clean_scirepeval huggingface dataset:
Computational Anatomy Atlas of the Heart

A computational anatomy atlas of the heart has been built for the main purposes of patient-specific functional analysis and electromechanics simulations. The atlas is composed of an underlying statistical shape model, based on a point distribution model (PDM), which was constructed from a training set comprising 100 asymptomatic and pathologic subjects. A key advantage of the proposed building procedure is the needless of manual delineations. Another one is the use of multi-slice computed tomography (MSCT) images, which have an excellent spatial resolution. Both, inter-subject and temporal variability were captured with the inclusion of all available time frames of each 4-D (3-D + time) image. Embedded in the shape model, a number of functional structures and properties have been incorporated to the atlas, such as myocardial fiber orientation, specialized electrical conduction pathways in the ventricles and atria, a set of anatomical landmarks, and the coronary venous and arterial trees. While numerous potential improvements are planed for the future, the atlas is already workable for FEM-based simulations of ventricular electrophysiology and cardiac functional analysis.
An Algorithm for the Segmentation of Highly Abnormal Hearts Using a Generic Statistical Shape Model

Statistical shape models (SSMs) have been widely employed in cardiac image segmentation. However, in conditions that induce severe shape abnormality and remodeling, such as in the case of pulmonary hypertension (PH) or hypertrophic cardiomyopathy (HCM), a single SSM is rarely capable of capturing the anatomical variability in the extremes of the distribution. This work presents a new algorithm for the segmentation of severely abnormal hearts. The algorithm is highly flexible, as it does not require a priori knowledge of the involved pathology or any specific parameter tuning to be applied to the cardiac image under analysis. The fundamental idea is to approximate the gross effect of the abnormality with a virtual remodeling transformation between the patient-specific geometry and the average shape of the reference model (e.g., average normal morphology). To define this mapping, a set of landmark points are automatically identified during boundary point search, by estimating the reliability of the candidate points. With the obtained transformation, the feature points extracted from the patient image volume are then projected onto the space of the reference SSM, where the model is used to effectively constrain and guide the segmentation process. The extracted shape in the reference space is finally propagated back to the original image of the abnormal heart to obtain the final segmentation. Detailed validation with patients diagnosed with PH and HCM shows the robustness and flexibility of the technique for the segmentation of highly abnormal hearts of different pathologies.
3D reconstruction of both shape and Bone Mineral Density distribution of the femur from DXA images

The diagnosis of osteoporosis and the prevention of femur fractures is a major challenge for our society. However, the diagnosis performed in clinical routine from Dual Energy X-ray Absorptiometry (DXA) images is limited. This paper proposes a 3D reconstruction method of both the shape and the Bone Mineral Density (BMD) distribution of the proximal femur from routinely used DXA images. The reconstruction accuracy that can be obtained from single-view and multi-view DXA devices was assessed. This evaluation, from 20 bone specimens and simulated DXA images, highlighted a mean shape accuracy of 1.3mm and a BMD accuracy of 4.4% from a single-view DXA image. A multi-view configuration with 2 views (frontal-sagittal) appeared as a good compromise (mean shape accuracy of 0.9mm and BMD accuracy of 3.2%). We are currently using this method for in vivo clinical studies in order to improve the diagnosis of osteoporosis and the prevention of femur fractures.
Anatomical Labeling of the Circle of Willis Using Maximum A Posteriori Probability Estimation

Anatomical labeling of the cerebral arteries forming the Circle of Willis (CoW) enables inter-subject comparison, which is required for geometric characterization and discovering risk factors associated with cerebrovascular pathologies. We present a method for automated anatomical labeling of the CoW by detecting its main bifurcations. The CoW is modeled as rooted attributed relational graph, with bifurcations as its vertices, whose attributes are characterized as points on a Riemannian manifold. The method is first trained on a set of pre-labeled examples, where it learns the variability of local bifurcation features as well as the variability in the topology. Then, the labeling of the target vasculature is obtained as maximum a posteriori probability (MAP) estimate where the likelihood of labeling individual bifurcations is regularized by the prior structural knowledge of the graph they span. The method was evaluated by cross-validation on 50 subjects, imaged with magnetic resonance angiography, and showed a mean detection accuracy of 95%. In addition, besides providing the MAP, the method can rank the labelings. The proposed method naturally handles anatomical structural variability and is demonstrated to be suitable for labeling arterial segments of the CoW.
Construction of a Computational Anatomical Model of the Peripheral Cardiac Conduction System

A methodology is presented here for automatic construction of a ventricular model of the cardiac conduction system (CCS), which is currently a missing block in many multiscale cardiac electromechanic models. It includes the His bundle, left bundle branches, and the peripheral CCS. The algorithm is fundamentally an enhancement of a rule-based method known as the Lindenmayer systems (L-systems). The generative procedure has been divided into three consecutive independent stages, which subsequently build the CCS from proximal to distal sections. Each stage is governed by a set of user parameters together with anatomical and physiological constrains to direct the generation process and adhere to the structural observations derived from histology studies. Several parameters are defined using statistical distributions to introduce stochastic variability in the models. The CCS built with this approach can generate electrical activation sequences with physiological characteristics.
Automated intracranial aneurysm isolation and quantification

Until today, geometrical descriptors of intracranial aneurysms are largely used for diagnosis and treatment selection. Nevertheless, relatively little work has been devoted to automatize these measurements. In this work we propose a methodology for the automated isolation and quantification from vascular segmentation. The proposed methodology is based on skeleton topology analysis, geometrical analysis and deformable models to isolate, automatically, the aneurysm dome as well as its geometrical characteristics. The accuracy of this methodology when compared to manually isolated aneurysms is evaluated in 10 patient-specific vascular geometries. Good correspondence is observed between manual and automated results.
Flexible modeling for anatomically-based cardiac conduction system construction

We present a method to automatically deploy the peripheral section of the cardiac conduction system in ventricles. The method encodes anatomical information thorough rules that ensure that Purkinje network structures generated are realistic and comparable to those observed in ex-vivo studies. The core methodology is based in non-deterministic production rules that are parameterized by means of statistical functions. Input parameters allow the construction of a great diversity of Purkinje structures that could be incorporated in fine element ventricular models to perform electrophysiology simulations. Resulting Purkinje trees show good geometrical approximations of Purkinje core network and bundles when compared to histological diagrams and do not require user interaction. Simulations carried out with these models result in activation sequences remarkably similar to micro-electrode electrical mapping studies.
3D reconstruction of coronary arteries from rotational X-ray angiography

This paper presents a 3D reconstruction method of coronary arteries from multiple projections, that works efficiently in two steps: first, a 2D processing is performed to obtain and classify the vessels branches on the projections, estimating also the radii. Then, a novel 3D reconstruction step is performed from pair of projections, finding the best intersections between ray bundles corresponding to the same branches to extract the 3D vessel trajectories, that is robust to motion. Topological constraints are included implicitly imposing vessel connectivity and tubular construction. The preliminary results presented here from a digital phantom and from real data, show good accuracy and performance of the method.
Characterization and Modeling of the Peripheral Cardiac Conduction System

The development of biophysical models of the heart has the potential to get insights in the patho-physiology of the heart, which requires to accurately modeling anatomy and function. The electrical activation sequence of the ventricles depends strongly on the cardiac conduction system (CCS). Its morphology and function cannot be observed in vivo, and therefore data available come from histological studies. We present a review on data available of the peripheral CCS including new experiments. In order to build a realistic model of the CCS we designed a procedure to extract morphological characteristics of the CCS from stained calf tissue samples. A CCS model personalized with our measurements has been built using L-systems. The effect of key unknown parameters of the model in the electrical activation of the left ventricle has been analyzed. The CCS models generated share the main characteristics of observed stained Purkinje networks. The timing of the simulated electrical activation sequences were in the physiological range for CCS models that included enough density of PMJs. These results show that this approach is a potential methodology for collecting knowledge-domain data and build improved CCS models of the heart automatically.
Femoral strength prediction using a 3D reconstruction method from Dual-energy X-ray Absorptiometry

Femoral strength is estimated in clinical routine from 2D Dual-energy X-rays Absorptiometry (DXA). In this study, a new pipeline for femoral strength prediction from DXA images is presented, using a 3D reconstruction method of the shape and the Bone Mineral Density (BMD) distribution and a regression analysis based on partial least squares. A database of 90 proximal femoral cadaveric specimens, that were previously imaged and tested to measure their fracture load, was used to develop and to validate the method. The proposed pipeline resulted in a correlation coefficient of 0.85 between predicted and measured fracture load, while a regression using 2D BMD measurements from DXA resulted in a correlation coefficient of 0.77. With an improved femoral strength prediction from DXA images, this method opens interesting translational perspectives in clinics for a better diagnosis of osteoporosis and fracture risk prediction.
Three-dimensional model-based stenosis quantification of the carotid arteries from contrast-enhanced MR angiography

A model-based technique for quantitative analysis of three-dimensional magnetic resonance angiography (MRA) is presented. The model consists of a deformable B-spline representation of the central vessel axis and the vessel wall. An efficient interaction mechanism to initialize the models in a three-dimensional setting is devised. Two novel image features are introduced in order to deform the central vessel axis and the vessel wall model respectively. A segmental optimization scheme for deforming the B-spline vessel wall model is introduced. Finally, the paper presents results on clinical contrast enhanced (CE) MRA of 19 carotid arteries. Percentages of stenosis obtained with the authors' technique are compared to those of two experts using caliper measurements on Maximum Intensity Projection (MIP) CE MRA, and Digital Subtraction Angiography (DSA) as standard of reference.
Ventricularwall thickness analysis in acute myocardial infarction and hypertrophic cardiomyopathy

Ventricular wall thickness and thickening are important biomarkers that are informative about myocardial function, especially for patients suffering from acute myocardial infarction (AMI) and hypertrophic cardiomyopathy (HCM). In this paper, the medial modeling is applied to produce thickness and thickening maps on skeleton meshes, on which surface-based statistical analysis can be directly performed. As an example, the statistics derived from healthy group is used to identify regions with abnormal thickness or thickening in a new patient. Cluster-based analysis is also performed to study the prevalent patterns of thickness and thickening change for AMI and HCM patients in the dataset.
3D mesh based wall thickness measurement: Identification of left ventricular hypertrophy phenotypes

Left ventricular hypertrophy (LVH) is a complex cardiac condition mainly identified by the thickening of the myocardial wall. Although most of the contemporary cardiac imaging modalities provide high resolution 3D images, the wall thickness (WT) is still measured within the acquired planes. This way of measurement may introduce an error as cardiac wall is not necessarily orthogonal to the plane. In this study we analyze how different approaches to measure WT can affect an automatic identification of hypertrophy. The compared approaches are: WT measured along surface normal and the one provided by a medial surface. For both approaches we evaluated their ability to identify LVH phenotypes by testing with two classifiers: Transductive Confidence Machine-k Nearest Neighbor (TCM-kNN) and Linear Discriminant Analysis (LDA). Fifty three subjects were included in this study: 18 patients with hypertrophic cardiomyopathy (HCM), 13 patients with hypertensive heart disease (HDD) and 22 sedentary subjects (CG). Medial surface based approach allowed obtaining higher classification accuracy in HDD patients, while normal based approach allowed for higher classification accuracy in HCM patients.
Analysis of the helix and transverse angles of the muscle fibers in the myocardium based on Diffusion Tensor Imaging

Realistic models of the muscle fibers in the myocardium improve the understanding and simulation of the bio-mechanical behavior of the heart. Since Diffusion Tensor Imaging (DTI) allows to visualize the fiber structures in the tissues, this modality can be used to build fiber models. In this paper, we propose an automatic method for the analysis of the helix and transverse angles between the fibers and the myocardial wall. It computes automatically the theoretical value of these angles (according to a mathematical model described in the literature) at each voxel of the image as well as the real value based on the DTI acquisition. In addition, new parameters of the mathematical model can be estimated based on our approach to personalize the model for specific data-sets.
ESTIMATION OF INDEPENDENT NON-LINEAR DEFORMATION MODES FOR ANALYSIS OF CRANIOFACIAL MALFORMATIONS IN CROUZON MICE

Crouzon syndrome is a genetic disease resulting in premature fusion of cranial sutures and synchondroses causing craniosynostosis. A decade ago the Crouzon gene was discovered, and recently the first mouse model of the syndrome was generated. In this study, a set of micro CT scannings of the heads of wild-type (normal) mice and Crouzon mice were investigated. We present for what we believe is the first time, a statistical deformation model based on independent component analysis (ICA). A set of deformation parameters for each mouse was calculated using a B-spline-based non-rigid registration. From the parameters controlling the deformations for each subject, the statistical model was estimated. ICA is demonstrated to provide localized deformation components, many of which give a clear separation between Crouzon and wild-type mice. This is a clear improvement of a previous principal component-based model, which only provided one global deformation component describing the disease. The ICA components allow interpretation of each deformation feature to be carried out independently of other features, and provides a basis for linking the observed craniofacial malformations to the fusing of sutures. ICA revealed an interesting new finding, not previously reported in the literature, namely asymmetries in the head in Crouzon mice. This phenomenon is probably caused by asymmetric closure of craniofacial sutures
Active Shape Models with Invariant Optimal Features: Application to Facial Analysis

This work is framed in the field of statistical face analysis. In particular, the problem of accurate segmentation of prominent features of the face in frontal view images is addressed. We propose a method that generalizes linear active shape models (ASMs)l which have already been used for this task. The technique is built upon the development of a nonlinear intensity model, incorporating a reduced set of differential invariant features as local image descriptors. These features are invariant to rigid transformations, and a subset of them is chosen by sequential feature selection for each landmark and resolution level. The new approach overcomes the unimodality and Gaussianity assumptions of classical ASMs regarding the distribution of the intensity values across the training set. Our methodology has demonstrated a significant improvement in segmentation precision as compared to the linear ASM and optimal features ASM (a nonlinear extension of the pioneer algorithm) in the tests performed on AR, XM2VTS, and EQUINOX databases.
A non-linear gray-level appearance model improves active shape model segmentation

Active Shape Models (ASMs), a knowledge-based segmentation algorithm developed by Cootes and Taylor  In this work, we explore a new way to model the gray-level appearance of the objects, using a k-nearest-neighbors (kNN) classifier and a set of selected features for each location and resolution of the Active Shape Model. The construction of the kNN classifier and the selection of features from training images is fully automatic. We compare our approach with the standard ASMs on synthetic data and in four medical segmentation tasks. In all cases, the new method produces significantly better results (p<0.001).
Bilinear Models for Spatio-Temporal Point Distribution Analysis: Application to Extrapolation of Whole Heart Cardiac Dynamics

In this work we introduce the usage of bilinear models as a means of factorising the shape variation induced by subject variability and the contraction of the human heart. We show that it is feasible to reconstruct the shape of the heart at a certain point in the cardiac cycle if we are given a small number of shapes representing the same heart at different points in the same cycle, using the bilinear model. Depending on pathology and the ratios between healthy and pathological hearts in the training set, RMS reconstruction errors measured between 1.39 and 16.58 millimetres, with a median of 6.79 and 90th percentile of 9.95 millimetres.
A theoretical analysis of noise in electrical impedance tomographic images

This paper theoretically analyses the propagation of measurement noise in Electrical Impedance Tomography (EIT) and its influence in image noise. The authors compare the behavior of three backprojection algorithms in front of noise. They present close-form approximated expressions for noise power in EIT. EIT image noise is argued to be gaussian distributed but with spatially variant statistics. There is a linear relation between measurement noise reduction and reduction in image noise. The study was validated with numerical simulation.
Accurate Segmentation of Vertebral Bodies and Processes Using Statistical Shape Decomposition and Conditional Models

Detailed segmentation of the vertebrae is an important pre-requisite in various applications of image-based spine assessment, surgery and biomechanical modeling. In particular, accurate segmentation of the processes is required for image-guided interventions, for example for optimal placement of bone grafts between the transverse processes. Furthermore, the geometry of the processes is now required in musculoskeletal models due to their interaction with the muscles and ligaments. In this paper, we present a new method for detailed segmentation of both the vertebral bodies and processes based on statistical shape decomposition and conditional models. The proposed technique is specifically developed with the aim to handle the complex geometry of the processes and the large variability between individuals. The key technical novelty in this work is the introduction of a part-based statistical decomposition of the vertebrae, such that the complexity of the subparts is effectively reduced, and model specificity is increased. Subsequently, in order to maintain the statistical and anatomic coherence of the ensemble, conditional models are used to model the statistical inter-relationships between the different subparts. For shape reconstruction and segmentation, a robust model fitting procedure is used to exclude improbable inter-part relationships in the estimation of the shape parameters. Segmentation results based on a dataset of 30 healthy CT scans and a dataset of 10 pathological scans show a point-to-surface error improvement of 20% and 17% respectively, and the potential of the proposed technique for detailed vertebral modeling.
Morphological descriptors as rupture indicators in middle cerebral artery aneurysms

The rupture of intracranial aneurysms is associated to significant morbidity and mortality rates. Although the mechanisms triggering this event are still unclear, morphology is among the factors considered by interventional neuroradiologists to decide treatment. The aim of this work is to explore the potential of morphological descriptors as rupture risk predictors in middle cerebral artery aneurysms (MCA) and to provide the subset showing the best predictive capabilities. The set of evaluated descriptors include basic shape descriptors related to the aneurysm size, and most sophisticated ones such as the Zernike Moment Invariants. The population analyzed included 71 patients harboring 86 MCA aneurysms (64 unruptured vs. 22 ruptured). An existing image-based processing pipeline was used to extract such descriptors from Three-Dimensional Rotational Angiography (3DRA) images routinely acquired during standard clinical practice. Univariate and multivariate statistical analyses have shown that among the evaluated descriptors, Zernike moment invariants computed on the aneurysm and a small portion of the surrounding vessels, together with the non-sphericity index, provide the best predictive capabilities of aneurysm rupture.
AngioLab: Integrated technology for patient-specific management of intracranial aneurysms

AngioLab is a software tool developed within the GIMIAS framework and is part of a more ambitious pipeline for the integrated management of cerebral aneurysms. AngioLab currently includes three plug-ins: angio segmentation, angio morphology and stenting, as well as supports advanced rendering techniques for the visualization of virtual angiographies. In December 2009, 23 clinicians completed an evaluation questionnaire about AngioLab. This activity was part of a teaching course held during the 2nd European Society for Minimally Invasive Neurovascular Treatment (ESMINT) Teaching Course held at the Universitat Pompeu Fabra, Barcelona, Spain. The Automated Morphological Analysis (angio morphology plug-in) and the Endovascular Treatment Planning (stenting plug-in) were evaluated. In general, the results provided by these tools were considered as relevant and as an emerging need in their clinical field.