nils-herrmann/clean_scirepeval huggingface dataset:
Integration of Multi-Plane Tissue Doppler and B-Mode Echocardiographic Images for Left Ventricular Motion Estimation

Although modern ultrasound acquisition systems allow recording of 3D echocardiographic images, tracking anatomical structures from them is still challenging. In addition, since these images are typically created from information obtained across several cardiac cycles, it is not yet possible to acquire high-quality 3D images from patients presenting varying heart rhythms. In this paper, we propose a method to estimate the motion field from multi-plane echocardiographic images of the left ventricle, which are acquired simultaneously during a single cardiac cycle. The method integrates tri-plane B-mode and tissue Doppler images acquired at different rotation angles around the long axis of the left ventricle. It uses a diffeomorphic continuous spatio-temporal transformation model with a spherical data representation for a better interpolation in the circumferential direction. This framework allows exploiting the spatial relation among the acquired planes. In addition, higher temporal resolution of the transformation in the beam direction is achieved by uncoupling the estimation of the different components of the velocity field. The method was validated using a realistic synthetic dataset including healthy and ischemic cases, obtaining errors of 0.14 ± 0.09 mm for displacements, 0.96 ± 1.03% for longitudinal strain and 3.94 ± 4.38% for radial strain estimation. In addition, the method was also demonstrated on a healthy volunteer and two patients with ischemia.
@neurIST - Towards a System Architecture for Advanced Disease Management through Integration of Heterogeneous Data, Computing, and Complex Processing Services

This paper presents the system architecture of the @neurIST project, which aims at supporting the research and treatment of cerebral aneurysms by bringing together heterogeneous data, computing and complex processing services. The architecture is generic enough to adapt it to the treatment of other diseases beyond cerebral aneurysms. The paper describes the generic requirements of the system and presents the architecture, applications and middleware technologies used to realise the system and highlights the innovations in @neurIST.
Reliability Estimation for 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 recognize, there is no automatic way to detect it. In this paper, 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 in the context facial image analysis. Experimental results on more than 3700 facial images showed a high degree of correlation between the segmentation accuracy and the estimated reliability metric.
Fast 3D centerline computation for tubular structures by front collapsing and fast marching

In this work, we propose a fast approach to compute centerlines from 3D tubular domains. This technique is based on the distance transform (DT) from the object boundaries, whose implementation together with an efficient technique to detect the points where the DT collapse, allow to obtain an initial centerline, very close to the final solution. This initial centerline and the DT computed, are used in a second step to obtain a connected, one voxel thick centerline, with a fast marching approach. The method proposed here is accurate, computationally efficient, fully automatic, is able to account for loops, and also obtains the bifurcation and end points automatically.
Three-Dimensional Deconvolution of Wide Field Microscopy with Sparse Priors: Application to Zebrafish Imagery

Zebra fish, as a popular experimental model organism, has been frequently used in biomedical research. For observing, analysing and recording labelled transparent features in zebra fish images, it is often efficient and convenient to adopt the fluorescence microscopy. However, the acquired z-stack images are always blurred, which makes deblurring/deconvolution critical for further image analysis. In this paper, we propose a Bayesian Maximum a-Posteriori (MAP) method with the sparse image priors to solve three-dimensional (3D) deconvolution problem for Wide Field (WF) fluorescence microscopy images from zebra fish embryos. The novel sparse image priors include a global Hyper-Laplacian model and a local smooth region mask. These two kinds of prior are deployed for preserving sharp edges and suppressing ringing artifacts, respectively. Both synthetic and real WF fluorescent zebra fish embryo data are used for evaluation. Experimental results demonstrate the potential applicability of the proposed method for 3D fluorescence microscopy images, compared with state-of-the-art 3D deconvolution algorithms.
Automatic Aneurysm Neck Detection Using Surface Voronoi Diagrams

A new automatic approach for saccular intracranial aneurysm isolation is proposed in this work. Due to the inter- and intra-observer variability in manual delineation of the aneurysm neck, a definition based on a minimum cost path around the aneurysm sac is proposed that copes with this variability and is able to make consistent measurements along different data sets, as well as to automate and speedup the analysis of cerebral aneurysms. The method is based on the computation of a minimal path along a scalar field obtained on the vessel surface, to find the aneurysm neck in a robust and fast manner. The computation of the scalar field on the surface is obtained using a fast marching approach with a speed function based on the exponential of the distance from the centerline bifurcation between the aneurysm dome and the parent vessels. In order to assure a correct topology of the aneurysm sac, the neck computation is constrained to a region defined by a surface Voronoi diagram obtained from the branches of the vessel centerline. We validate this method comparing our results in 26 real cases with manual aneurysm isolation obtained using a cut-plane, and also with results obtained using manual delineations from three different observers by comparing typical morphological measures.
Reproducibility of image-based computational hemodynamics in intracranial aneurysms: Comparison of CTA AND 3DRA

Hemodynamics play an important role in the pathogenesis of intracranial aneurysms and are believed to provide valuable information to predict aneurysmal rupture. Using image-based vascular models and computational fluid dynamics (CFD) techniques, the inter-aneurysmal hemodynamics can be studied in depth. In this paper, the effect of using different image-modalities is evaluated by investigating 4 middle cerebral arteries bifurcation aneurysms imaged with three-dimensional rotational angiography (3DRA) and computed tomographic angiography (CTA). The presented visualizations show that the main flow characteristics are preserved. However, there are large discrepancies in quantitative measurements.
The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)

A new multimodal biometric database designed and acquired within the framework of the European BioSecure Network of Excellence is presented. It is comprised of more than 600 individuals acquired simultaneously in three scenarios: 1 over the Internet, 2 in an office environment with desktop PC, and 3 in indoor/outdoor environments with mobile portable hardware. The three scenarios include a common part of audio/video data. Also, signature and fingerprint data have been acquired both with desktop PC and mobile portable hardware. Additionally, hand and iris data were acquired in the second scenario using desktop PC. Acquisition has been conducted by 11 European institutions. Additional features of the BioSecure Multimodal Database (BMDB) are: two acquisition sessions, several sensors in certain modalities, balanced gender and age distributions, multimodal realistic scenarios with simple and quick tasks per modality, cross-European diversity, availability of demographic data, and compatibility with other multimodal databases. The novel acquisition conditions of the BMDB allow us to perform new challenging research and evaluation of either monomodal or multimodal biometric systems, as in the recent BioSecure Multimodal Evaluation campaign. A description of this campaign including baseline results of individual modalities from the new database is also given. The database is expected to be available for research purposes through the BioSecure Association during 2008.
Segmentation and Quantification for Angle-Closure Glaucoma Assessment in Anterior Segment OCT

Angle-closure glaucoma is a major cause of irreversible visual impairment and can be identified by measuring the anterior chamber angle (ACA) of the eye. The ACA can be viewed clearly through anterior segment optical coherence tomography (AS-OCT), but the imaging characteristics and the shapes and locations of major ocular structures can vary significantly among different AS-OCT modalities, thus complicating image analysis. To address this problem, we propose a data-driven approach for automatic AS-OCT structure segmentation, measurement, and screening. Our technique first estimates initial markers in the eye through label transfer from a hand-labeled exemplar data set, whose images are collected over different patients and AS-OCT modalities. These initial markers are then refined by using a graph-based smoothing method that is guided by AS-OCT structural information. These markers facilitate segmentation of major clinical structures, which are used to recover standard clinical parameters. These parameters can be used not only to support clinicians in making anatomical assessments, but also to serve as features for detecting anterior angle closure in automatic glaucoma screening algorithms. Experiments on Visante AS-OCT and Cirrus high-definition-OCT data sets demonstrate the effectiveness of our approach.
Deep learning for automatic cell detection in wide-field microscopy zebrafish images

The zebrafish has become a popular experimental model organism for biomedical research. In this paper, a unique framework is proposed for automatically detecting Tyrosine Hydroxylase-containing (TH-labeled) cells in larval zebrafish brain z-stack images recorded through the wide-field microscope. In this framework, a supervised max-pooling Convolutional Neural Network (CNN) is trained to detect cell pixels in regions that are preselected by a Support Vector Machine (SVM) classifier. The results show that the proposed deep-learned method outperforms hand-crafted techniques and demonstrate its potential for automatic cell detection in wide-field microscopy z-stack zebrafish images.
Efficient pipeline for image-based patient-specific analysis of cerebral aneurysm hemodynamics: technique and sensitivity

Hemodynamic factors are thought to be implicated in the progression and rupture of intracranial aneurysms. Current efforts aim to study the possible associations of hemodynamic characteristics such as complexity and stability of intra-aneurysmal flow patterns, size and location of the region of flow impingement with the clinical history of aneurysmal rupture. However, there are no reliable methods for measuring blood flow patterns in vivo. In this paper, an efficient methodology for patient-specific modeling and characterization of the hemodynamics in cerebral aneurysms from medical images is described. A sensitivity analysis of the hemodynamic characteristics with respect to variations of several variables over the expected physiologic range of conditions is also presented. This sensitivity analysis shows that although changes in the velocity fields can be observed, the characterization of the intra-aneurysmal flow patterns is not altered when the mean input flow, the flow division, the viscosity model, or mesh resolution are changed. It was also found that the variable that has the greater impact on the computed flow fields is the geometry of the vascular structures. We conclude that with the proposed modeling pipeline clinical studies involving large numbers cerebral aneurysms are feasible.
Efficient reconstruction of cardiac LV surfaces using a 3D sparse ASM

Cardiac magnetic resonance imaging (MRI) has demonstrated to be the most accurate and reproducible tool for assessment of the cardiovascular system. Traditional quantification methods require acquisition of densely sampled data sets, resulting in increased analysis time. This paper investigates whether comparable quantification accuracy can be achieved with a combination of sparsely sampled data and using an active shape model (ASM) of the left ventricle
Two-dimensional PCA: a new approach to appearance-based face representation and recognition

In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices, and its eigenvectors are derived for image feature extraction. To test 2DPCA and evaluate its performance, a series of experiments were performed on three face image databases: ORL, AR, and Yale face databases. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2DPCA than PCA.
A non-parametric framework for no-reference image quality assessment

In most state-of-the-art non-distortion-specific no-reference image quality assessment (NDS NR-IQA) methods, the image quality is predicted by training a regression model based on examples of distorted images and their corresponding human subjective scores. However, one drawback of these approaches is the fact that they require a training phase of the regression parameters. In this paper, a non-parametric framework for NDS NR-IQA task is presented where no training is necessary. A nearest-neighbour (NN) classifier is first employed to determine the distortion class of the test image. Once the distortion class is identified, the quality assessment prediction is then performed through &-NN regression that utilizes the differential mean opinion score (DMOS) value associated with the labelled patches within the identified class. The proposed algorithm is simple but effective. Experimental results on the LIVE IQA database show that our algorithm achieves high correlation to human perceptual measures of image quality as well as provides competitive performance to previous NDS NR-IQA algorithms.
Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling

A novel method is introduced for the generation of landmarks for three-dimensional (3-D) shapes and the construction of the corresponding 3-D statistical shape models. Automatic landmarking of a set of manual segmentations from a class of shapes is achieved by 1) construction of an atlas of the class, 2) automatic extraction of the landmarks from the atlas, and 3) subsequent propagation of these landmarks to each example shape via a volumetric nonrigid registration technique using multiresolution B-spline deformations. This approach presents some advantages over previously published methods: it can treat multiple-part structures and requires less restrictive assumptions on the structure's topology. In this paper, we address the problem of building a 3-D statistical shape model of the left and right ventricle of the heart from 3-D magnetic resonance images. The average accuracy in landmark propagation is shown to be below 2.2 mm. This application demonstrates the robustness and accuracy of the method in the presence of large shape variability and multiple objects.
Hip fracture discrimination using 3D reconstructions from Dual-energy X-ray Absorptiometry

Due to the aging of our society, osteoporosis and the subsequent fractures is becoming an increasingly important problem. Currently, Dual-energy X-ray Absortiometry (DXA) derived areal Bone Mineral Density (BMD) is the gold standard for diagnosing osteoporosis, but is limited to a two-dimensional analysis. In this work, we propose a method to discriminate between healthy and fracture patients from DXA images by incorporating a 3D reconstruction method. A statistical model of the 3D shape and BMD distribution was registered onto the DXA scans of fracture and non-fracture patients. The resulting values for the scale and the first model parameter, together with the femoral neck areal BMD, was processed by Linear Discriminant Analysis and the discriminating accuracy was evaluated by generating the Receiver Operating Characteristic curve. The Area Under the Curve using the proposed method was approximately 0.080 and 0.063 higher than using only areal BMD, indicating an improved power of discrimination.
@neurIST: Infrastructure for Advanced Disease Management Through Integration of Heterogeneous Data, Computing, and Complex Processing Services

The increasing volume of data describing human disease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the @neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system's architecture is generic enough that it could be adapted to the treatment of other diseases. Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers clinicians the tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medical researchers gain access to a critical mass of aneurysm related data due to the system's ability to federate distributed information sources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access and work on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand for performing computationally intensive simulations for treatment planning and research.
Reconstructing the 3D Shape and Bone Mineral Density Distribution of the Proximal Femur From Dual-Energy X-Ray Absorptiometry

The accurate diagnosis of osteoporosis has gained increasing importance due to the aging of our society. Areal bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA) is an established criterion in the diagnosis of osteoporosis. This measure, however, is limited by its two-dimensionality. This work presents a method to reconstruct both the 3D bone shape and 3D BMD distribution of the proximal femur from a single DXA image used in clinical routine. A statistical model of the combined shape and BMD distribution is presented, together with a method for its construction from a set of quantitative computed tomography (QCT) scans. A reconstruction is acquired in an intensity based 3D-2D registration process whereby an instance of the model is found that maximizes the similarity between its projection and the DXA image. Reconstruction experiments were performed on the DXA images of 30 subjects, with a model constructed from a database of QCT scans of 85 subjects. The accuracy was evaluated by comparing the reconstructions with the same subject QCT scans. The method presented here can potentially improve the diagnosis of osteoporosis and fracture risk assessment from the low radiation dose and low cost DXA devices currently used in clinical routine.
Blind image quality assessment via a two-stage non-parametric framework

In this paper, a no-reference image quality assessment (NR-IQA) algorithm based on a two-stage non-parametric framework is presented. At the first stage, the type of distortion affecting the test image patches is first identified via a nearest-neighbor (NN) based classifier. Utilizing the differential mean opinion score (DMOS) values associated with the labelled patches within the identified distortion class, the quality of each test patch is then predicted using k-NN regression. The predicted scores are then pooled together to obtain the quality score of the test image. The proposed algorithm is simple yet effective. No training phase is required and the algorithm also offers prediction of a local region's quality which is not available in most of the previous NR-IQA methods. Experimental results on the standard LIVE IQA database indicate that the proposed algorithm correlates highly with human perceptual measures and deliver competitive performance to state-of-the-art NR-IQA algorithms.
Nonparametric Quality Assessment of Natural Images

In this article, the authors explore an alternative way to perform no-reference image quality assessment (NR-IQA). Following a feature extraction stage in which spatial domain statistics are utilized as features, a two-stage nonparametric NR-IQA framework is proposed. This approach requires no training phase, and it enables prediction of the image distortion type as well as local regions' quality, which is not available in most current algorithms. Experimental results on IQA databases show that the proposed framework achieves high correlation to human perception of image quality and delivers competitive performance to state-of-the-art NR-IQA algorithms.
A Framework for Weighted Fusion of Multiple Statistical Models of Shape and Appearance

This paper presents a framework for weighted fusion of several active shape and active appearance models. The approach is based on the eigenspace fusion method proposed by Hall et al., which has been extended to fuse more than two weighted eigenspaces using unbiased mean and covariance matrix estimates. To evaluate the performance of fusion, a comparative assessment on segmentation precision as well as facial verification tests are performed using the AR, EQUINOX, and XM2VTS databases. Based on the results, it is concluded that the fusion is useful when the model needs to be updated online or when the original observations are absent
Mixture of Probabilistic Principal Component Analyzers for Shapes from Point Sets

Inferring a probability density function (pdf) for shape from a population of point sets is a challenging problem. The lack of point-to-point correspondences and the non-linearity of the shape spaces undermine the linear models. Methods based on manifolds model the shape variations naturally, however, statistics are often limited to a single geodesic mean and an arbitrary number of variation modes. We relax the manifold assumption and consider a piece-wise linear form, implementing a mixture of distinctive shape classes. The pdf for point sets is defined hierarchically, modeling a mixture of Probabilistic Principal Component Analyzers (PPCA) in higher dimension. A Variational Bayesian approach is designed for unsupervised learning of the posteriors of point set labels, local variation modes, and point correspondences. By maximizing the model evidence, the numbers of clusters, modes of variations, and points on the mean models are automatically selected. Using the predictive distribution, we project a test shape to the spaces spanned by the local PPCA's. The method is applied to point sets from: i) synthetic data, ii) healthy versus pathological heart morphologies, and iii) lumbar vertebrae. The proposed method selects models with expected numbers of clusters and variation modes, achieving lower generalization-specificity errors compared to state-of-the-art.