nils-herrmann/clean_scirepeval huggingface dataset:
Automatic identification of internal carotid artery from 3DRA images

Geometric characteristics and arrangement of the cerebral vessels are assumed to be related to the development of vascular diseases. Identifying anatomical segments and bifurcations of the cerebral vasculature allows the comparison of these characteristics across and within subjects. In this paper, we focus on the automatic identification of internal carotid artery (ICA) from 3D rotational angiographic images. The steps of the proposed method are the following: Arterial vascular tree is first segmented and centerlines are computed. From a set of centerlines, vascular tree topology is constructed and its bifurcations geometrically characterized. Finally, ICA terminal bifurcation is detected, which enables ICA identification. To detect ICA terminal bifurcation, a support vector machine classifier is trained. We processed 82 images to obtain 274 feature vectors of bifurcations around the ICA. 10×5-fold cross-validation showed an average accuracy of 99.6%, with 99.5% specificity and 100% sensitivity. The two most discriminating bifurcation features were: radius ratio between the smaller branch and its parent vessel, and the long-axis component of the smaller branch vector.
Exploring Reliability for Automatic Identity Verification with Statistical Shape Models

One of the drawbacks of statistical shape models is their occasional failure to converge. Although visually this fact is usually easy to assess there is not, in general, an automatic way to detect the failure. In this work we introduce a generic reliability measure for statistical shape models. It is based on a probabilistic framework and uses information extracted by the model itself during the matching process. The proposed method was validated with two variants of Active Shape Models by working with facial images. The experimental results showed a high correlation between the segmentation accuracy and the estimated reliability.
Interventional Endocardial Motion Estimation from Electroanatomical Mapping Data: Application to Scar Characterization

Scar presence and its characteristics play a fundamental role in several cardiac pathologies. To accurately define the extent and location of the scar is essential for a successful ventricular tachycardia ablation procedure. Nowadays, a set of widely accepted electrical voltage thresholds applied to local electrograms recorded are used intraoperatively to locate the scar. Information about cardiac mechanics could be considered to characterize tissues with different viability properties. We propose a novel method to estimate endocardial motion from data obtained with an electroanatomical mapping system together with the endocardial geometry segmented from preoperative 3-D magnetic resonance images, using a statistical atlas constructed with bilinear models. The method was validated using synthetic data generated from ultrasound images of nine volunteers and was then applied to seven ventricular tachycardia patients. Maximum bipolar voltages, commonly used to intraoperatively locate scar tissue, were compared to endocardial wall displacement and strain for all the patients. The results show that the proposed method allows endocardial motion and strain estimation and that areas with low-voltage electrograms also present low strain values.
Improved Myocardial Motion Estimation Combining Tissue Doppler and B-Mode Echocardiographic Images

We propose a technique for myocardial motion estimation based on image registration using both B-mode echocardiographic images and tissue Doppler sequences acquired interleaved. The velocity field is modeled continuously using B-splines and the spatiotemporal transform is constrained to be diffeomorphic. Images before scan conversion are used to improve the accuracy of the estimation. The similarity measure includes a model of the speckle pattern distribution of B-mode images. It also penalizes the disagreement between tissue Doppler velocities and the estimated velocity field. Registration accuracy is evaluated and compared to other alternatives using a realistic synthetic dataset, obtaining mean displacement errors of about 1 mm. Finally, the method is demonstrated on data acquired from six volunteers, both at rest and during exercise. Robustness is tested against low image quality and fast heart rates during exercise. Results show that our method provides a robust motion estimate in these situations.
Complex wavelets for registration of tagged MRI sequences

Tagged magnetic resonance imaging (MRI) is currently the reference MR modality for myocardial motion and strain analysis. Mutual information (MI) based non rigid registration has proven to be an accurate method to retrieve cardiac deformation fields. However, this technique ignores high frequency information in tags. In a previous work this information was included by using feature vectors formed with wavelet coefficients and kNN graphs to estimate alphaMI. It was shown that cardiac motion estimation was feasible with these features. In this work, features were derived from complex wavelet transform (CWT), which is shift invariant and provides more high frequency subimages than conventional wavelets. Results show that lower errors are obtained with respect to the use of pixel intensity
Active shape model segmentation with optimal features

An active shape model segmentation scheme is presented that is steered by optimal local features, contrary to normalized first order derivative profiles, as in the original formulation [Cootes and Taylor, 1995, 1999, and 2001]. A nonlinear kNN-classifier is used, instead of the linear Mahalanobis distance, to find optimal displacements for landmarks. For each of the landmarks that describe the shape, at each resolution level taken into account during the segmentation optimization procedure, a distinct set of optimal features is determined. The selection of features is automatic, using the training images and sequential feature forward and backward selection. The new approach is tested on synthetic data and in four medical segmentation tasks: segmenting the right and left lung fields in a database of 230 chest radiographs, and segmenting the cerebellum and corpus callosum in a database of 90 slices from MRI brain images. In all cases, the new method produces significantly better results in terms of an overlap error measure (p<0.001 using a paired T-test) than the original active shape model scheme.
Automated Detection of Regional Wall Motion Abnormalities Based on a Statistical Model Applied to Multislice Short-Axis Cardiac MR Images

In this paper, a statistical shape analysis method for myocardial contraction is presented that was built to detect and locate regional wall motion abnormalities (RWMA). For each slice level (base, middle, and apex), 44 short-axis magnetic resonance images were selected from healthy volunteers to train a statistical model of normal myocardial contraction using independent component analysis (ICA). A classification algorithm was constructed from the ICA components to automatically detect and localize abnormally contracting regions of the myocardium. The algorithm was validated on 45 patients suffering from ischemic heart disease. Two validations were performed; one with visual wall motion scores (VWMS) and the other with wall thickening (WT) used as references. Accuracy of the ICA-based method on each slice level was 69.93% (base), 89.63% (middle), and 72.78% (apex) when WT was used as reference, and 63.70% (base), 67.41% (middle), and 66.67% (apex) when VWMS was used as reference. From this we conclude that the proposed method is a promising diagnostic support tool to assist clinicians in reducing the subjectivity in VWMS.
Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images Using Weakly-Supervised Joint Convolutional Sparse Coding

Magnetic Resonance Imaging (MRI) offers high-resolution in vivo imaging and rich functional and anatomical multimodality tissue contrast. In practice, however, there are challenges associated with considerations of scanning costs, patient comfort, and scanning time that constrain how much data can be acquired in clinical or research studies. In this paper, we explore the possibility of generating high-resolution and multimodal images from low-resolution single-modality imagery. We propose the weakly-supervised joint convolutional sparse coding to simultaneously solve the problems of super-resolution (SR) and cross-modality image synthesis. The learning process requires only a few registered multimodal image pairs as the training set. Additionally, the quality of the joint dictionary learning can be improved using a larger set of unpaired images. To combine unpaired data from different image resolutions/modalities, a hetero-domain image alignment term is proposed. Local image neighborhoods are naturally preserved by operating on the whole image domain (as opposed to image patches) and using joint convolutional sparse coding. The paired images are enhanced in the joint learning process with unpaired data and an additional maximum mean discrepancy term, which minimizes the dissimilarity between their feature distributions. Experiments show that the proposed method outperforms state-of-the-art techniques on both SR reconstruction and simultaneous SR and cross-modality synthesis.
Effect of Statistically Derived Fiber Models on the Estimation of Cardiac Electrical Activation

Myocardial fiber orientation plays a critical role in the electrical activation and subsequent contraction of the heart. To increase the clinical potential of electrophysiological (EP) simulation for the study of cardiac phenomena and the planning of interventions, accurate personalization of the fibers is a necessary yet challenging task. Due to the difficulties associated with the in vivo imaging of cardiac fiber structure, researchers have developed alternative techniques to personalize fibers. Thus far, cardiac simulation was performed mainly based on rule-based fiber models. More recently, there has been a significant interest in data-driven and statistically derived fiber models. In particular, our predictive method in  allows us to estimate the unknown subject-specific fiber orientation based on the more easily available shape information. The aim of this work is to estimate the effect of using such statistical predictive models for the estimation of cardiac electrical activation times and patterns. To this end, we perform EP simulations based on a database of ten canine ex vivo diffusion tensor imaging (DTI) datasets that include normal and failing cases. To assess the strength of the fiber models under varying conditions, we consider both sinus rhythm and biventricular pacing simulations. The results show that 1) the statistically derived fibers improve the estimation of the local activation times by an average of 53.7% over traditional rule-based models, and that 2) the obtained electrical activations are consistently similar to those of the DTI-based fibers.
A High-Resolution Atlas and Statistical Model of the Human Heart From Multislice CT

Atlases and statistical models play important roles in the personalization and simulation of cardiac physiology. For the study of the heart, however, the construction of comprehensive atlases and spatio-temporal models is faced with a number of challenges, in particular the need to handle large and highly variable image datasets, the multi-region nature of the heart, and the presence of complex as well as small cardiovascular structures. In this paper, we present a detailed atlas and spatio-temporal statistical model of the human heart based on a large population of 3D+time multi-slice computed tomography sequences, and the framework for its construction. It uses spatial normalization based on nonrigid image registration to synthesize a population mean image and establish the spatial relationships between the mean and the subjects in the population. Temporal image registration is then applied to resolve each subject-specific cardiac motion and the resulting transformations are used to warp a surface mesh representation of the atlas to fit the images of the remaining cardiac phases in each subject. Subsequently, we demonstrate the construction of a spatio-temporal statistical model of shape such that the inter-subject and dynamic sources of variation are suitably separated. The framework is applied to a 3D+time data set of 138 subjects. The data is drawn from a variety of pathologies, which benefits its generalization to new subjects and physiological studies. The obtained level of detail and the extendability of the atlas present an advantage over most cardiac models published previously.
Statistical Shape Modeling of the Left Ventricle: Myocardial Infarct Classification Challenge

Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes the left ventricular remodeling after MI is an open research question. A better descriptor of remodeling is expected to provide a more accurate evaluation of disease status in MI patients. We therefore designed a challenge to test shape characterization in MI given a set of three-dimensional left ventricular surface points. The training set comprised 100 MI patients, and 100 asymptomatic volunteers (AV). The challenge was initiated in 2015 at the Statistical Atlases and Computational Models of the Heart workshop, in conjunction with the MICCAI conference. The training set with labels was provided to participants, who were asked to submit the likelihood of MI from a different (validation) set of 200 cases (100 AV and 100 MI). Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were used as the outcome measures. The goals of this challenge were to 1) establish a common dataset for evaluating statistical shape modeling algorithms in MI, and 2) test whether statistical shape modeling provides additional information characterizing MI patients over standard clinical measures. Eleven groups with a wide variety of classification and feature extraction approaches participated in this challenge. All methods achieved excellent classification results with accuracy ranges from 0.83 to 0.98. The areas under the receiver operating characteristic curves were all above 0.90. Four methods showed significantly higher performance than standard clinical measures. The dataset and software for evaluation are available from the Cardiac Atlas Project website.11 http://www.cardiacatlas.org.
Branching medial models for cardiac shape representation

The cm-rep (continuous medial representation) is a powerful shape representation method that models a 3D object by describing its medial axis (skeleton) and boundary as continuous parametric manifolds. It provides parametrization of the entire interior of the object, which can be used for combined statistical analysis of shape and appearance. This paper extends the cm-rep to more complex shapes with multi-figures, i.e., shapes whose skeletons have branches. Along the branching curves, the equality constraints enforced by the medial geometry are implemented as soft penalties in the deformable model. The remaining small violations are corrected by local adjustments. As a proof of concept, the branching continuous medial representation is applied to a 2-chamber heart model data set consisting of 428 cardiac shapes from 90 subjects. The results show that our model can capture the heart shape accurately.
A registration-based approach to quantify flow-mediated dilation (FMD) of the brachial artery in ultrasound image sequences

Flow-mediated dilation (FMD) offers a mechanism to characterize endothelial function and, therefore, may play a role in the diagnosis of cardiovascular diseases. Computerized analysis techniques are very desirable to give accuracy and objectivity to the measurements. Virtually all methods proposed up to now to measure FMD rely on accurate edge detection of the arterial wall, and they are not always robust in the presence of poor image quality or image artifacts. A novel method for automatic dilation assessment based on a global image analysis strategy is presented. We model interframe arterial dilation as a superposition of a rigid motion and a scaling factor perpendicular to the artery. Rigid motion can be interpreted as a global compensation for patient and probe movements, an aspect that has not been sufficiently studied before. The scaling factor explains arterial dilation. The ultrasound sequence is analyzed in two phases using image registration to recover both transformation models. Temporal continuity in the registration parameters along the sequence is enforced with a Kalman filter since the dilation process is known to be a gradual physiological phenomenon. Comparing automated and gold standard measurements (average of manual measurements) we found a negligible bias (0.05%FMD) and a small standard deviation (SD) of the differences (1.05%FMD). These values are comparable with those obtained from manual measurements (bias=0.23%FMD, SD/sub intra-obs/=1.13%FMD, SD/sub inter-obs/=1.20%FMD). The proposed method offers also better reproducibility (CV=0.40%) than the manual measurements (CV=1.04%).
Segmentation of bone tumor in MR perfusion images using neural networks and multiscale pharmacokinetic features

The decrease in the volume of viable tumor is an indicator for the effect preoperative chemotherapy has on bone tumors. We develop an approach for segmenting dynamic perfusion MR-images into viable tumor, nonviable tumor and healthy tissue. Two cascaded feedforward neural networks are trained to perform the pixel-based segmentation. As features, we use the parameters obtained from a pharmacokinetic model of the tissue perfusion (parametric images). Additional multiscale features that incorporate contextual information are included. Experiments indicate that multiscale blurred versions of the parametric images together with a multiscale formulation of the local image entropy are the most discriminative features.
In-vitro verification of CFD simulations for predicting flow in a stented aneurysm model

In this work, under fully controlled flow conditions, we perform an in vitro verification of computational fluid dynamic (CFD) simulations against the experimental data of a stented aneurysm phantom. The stent model is simplified to have different sizes and its influence on the hemodynamic parameters is demonstrated. For CFD validation, quantitative measures are defined for CFD by virtual angiography. The results show general good correspondence in the parent vessel and the impingement site inside the aneurysm. Compared to the experimentally acquired angiograms, slight delay of the bolus arrival time is detected in the aneurysm sac in the virtual angiograms. This is correlated with over estimated stent function in the CFD model.
Morphological Characterization of Intracranial Aneurysms Using 3-D Moment Invariants

Rupture of intracranial saccular aneurysms is the most common cause of spontaneous subarachnoid hemorrhage, which has significant morbidity and mortality. Although there is still controversy regarding the decision on which unruptured aneurysms should be treated, this is based primarily on their size. Nonetheless, many large lesions do not rupture whereas some small ones do. It is commonly accepted that hemodynamical factors are important to better understand the natural history of cerebral aneurysms. However, it might not always be practical to carry out a detailed computational analysis of such factors if a prompt assessment is required. Since shape is likely to be dependent on the balance between hemodynamic forces and the aneurysmal surrounding environment, an appropriate morphological 3-D characterization is likely to provide a practical surrogate to quickly evaluate the risk of rupture. In this paper, an efficient and novel methodology for 3-D shape characterization of cerebral aneurysms is described. The aneurysms are isolated by taking into account a portion of their adjacent vessels. Two methods to characterize the morphology of the aneurysms models using moment invariants have been considered: geometrical moment invariants (GMI) and Zernike moment invariants (ZMI). The results have been validated in a database containing 53 patients with a total of 31 ruptured aneurysms and 24 unruptured aneurysms. It has been found that ZMI indices are more robust than GMI, and seem to provide a reliable way to discriminate between ruptured and unruptured aneurysms. Correct rupture prediction rates of sime80% were achieved in contrast to 66% that is found when the aspect ratio index is considered.
Integration of different cardiac electrophysiological models into a single simulation pipeline

Clinical translation of computational models of the heart has been hampered by the absence of complete and rigorous technical and clinical validation, as well as benchmarking of the developed tools. To address this issue, a dataset containing the cardiac anatomy and fibre orientations from magnetic resonance images (MRI), as well as epicardial transmembrane potentials from optical mapping acquired on ex-vivo porcine hearts, have previously been made available to the community. Image processing techniques were developed to integrate MRI images with electrical information. Different models were tested and compared with the integrated data1, including: i) a new methodology to customize and regularize heart shape and myocardial fibre orientation to predict activation waves in these personalized meshes with generic conduction parameters; ii) a statistical model-constrained framework to produce maximum a posteriori (MAP) estimation of the 3D distribution of transmembrane potential; iii) a personalized simplified reaction-diffusion 3D electrophysiological model; iv) a personalized fast conduction Purkinje system with a simple Eikonal-based electrophysiological model.
KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition

This paper examines the theory of kernel Fisher discriminant analysis (KFD) in a Hilbert space and develops a two-phase KFD framework, i.e., kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). This framework provides novel insights into the nature of KFD. Based on this framework, the authors propose a complete kernel Fisher discriminant analysis (CKFD) algorithm. CKFD can be used to carry out discriminant analysis in "double discriminant subspaces." The fact that, it can make full use of two kinds of discriminant information, regular and irregular, makes CKFD a more powerful discriminator. The proposed algorithm was tested and evaluated using the FERET face database and the CENPARMI handwritten numeral database. The experimental results show that CKFD outperforms other KFD algorithms.
Quantification improvement in EIT reconstruction algorithms by incorporating a priori geometrical information

In this paper we develop a method that introduces a priori geometrical information about a conductivity distribution in EIT reconstruction algorithms in order to improve their quantification ability. We propose correction expressions for two reconstruction algorithms: backprojection algorithm and exponential algorithm. There is no theoretical evidence of the uniqueness of the solution to the inverse boundary problem which EIT deals with. We focused our attention to the problem of a circular centred uniform perturbation in a uniform background. It is easily seen that for cosine profiles there are different combinations of conductivity change and perturbation radius that yield the same voltage change. A similar result is obtained for adjacent injection profiles. This supports the fact that the kind of correction we propose is necessary not only as a consequence of the behaviour of the reconstruction algorithm but also because of the nature of the problem.