nils-herrmann/clean_scirepeval huggingface dataset:
An Experimental Evaluation of Three Classifiers for Use in Self-Updating Face Recognition Systems

Previous studies have shown that the accuracy of Face Recognition Systems (FRSs) decreases with the time elapsed between enrollment and testing. The main reason for the decrease is the changes in appearance of the user due to factors such as ageing, beard growth, sun-tan etc. Self-update procedure, where the system learns the biometric characteristics of the user every time he/she interacts with it, can be used to automatically update the system. However, a commonly acknowledged problem is the corruption of biometric traits due to misclassification. In this article, we test FRS, based on three classification algorithms, on two challenging databases, GEFA and YT, with 14 279 and 31 951 images, respectively. Our results suggest that complex, state-of-the-art classifiers that make use of user-specific models, need not be the best choice for use in self updating systems. In other words, tolerance to corrupted training data decreases as the complexity of the classifier increases.
Automatic Quantitative Analysis of Myocardial Wall Motion and Thickening from Long-and Short-Axis Cine MRI Studies

This work presents a fully automatic method for regional myocardial contraction and motion assessment in cardiac cine MRI studies. The prerequisite of segmenting the left ventricle at all temporal phases of the study, is accomplished using a statistical model-based algorithm (3D-ASM). Subsequent functional analysis includes the assessment of LV global functional indexes like e.g. time-volume curves, ejection fraction, stroke volume and cardiac output, as well as regional function parameters, like segmental wall motion, thickening and dyssynchrony delays
Conical deformable model for myocardial segmentation in late-enhanced MRI

This paper presents a conical 3D deformable template for fully automatic and robust segmentation of late-enhanced MRI (LE-MRI) datasets. The proposed technique has several advantages over existing works. Firstly, it uses a thick-walled conical model that is suitable to derive fully automatic and reliable initialization by taking into account potential short-axis misalignments. Furthermore, it uses to its advantage the geometrical and appearance properties of the blood pool to decouple the endocardial and epicardial segmentations. The final epicardial result is obtained using thickness smoothness measures constrained on the initial robust segmentation of the endocardium. Detailed validation using 20 LE-MRI datasets and comparison to previous work demonstrates that the technique is reliable and promising for clinical assessment of LE-MRI data.
Model-based quantitation of 3-D magnetic resonance angiographic images

Quantification of the degree of stenosis or vessel dimensions are important for diagnosis of vascular diseases and planning vascular interventions. Although diagnosis from three-dimensional (3-D) magnetic resonance angiograms (MRA's) is mainly performed on two-dimensional (2-D) maximum intensity projections, automated quantification of vascular segments directly from the 3-D dataset is desirable to provide accurate and objective measurements of the 3-D anatomy. A model-based method for quantitative 3-D MRA is proposed. Linear vessel segments are modeled with a central vessel axis curve coupled to a vessel wall surface. A novel image feature to guide the deformation of the central vessel axis is introduced. Subsequently, concepts of deformable models are combined with knowledge of the physics of the acquisition technique to accurately segment the vessel wall and compute the vessel diameter and other geometrical properties. The method is illustrated and validated on a carotid bifurcation phantom, with ground truth and medical experts as comparisons. Also, results on 3-D time-of-flight (TOF) MRA images of the carotids are shown. The approach is a promising technique to assess several geometrical vascular parameters directly on the source 3-D images, providing an objective mechanism for stenosis grading.
Statistical Personalization of Ventricular Fiber Orientation Using Shape Predictors

This paper presents a predictive framework for the statistical personalization of ventricular fibers. To this end, the relationship between subject-specific geometry of the left (LV) and right ventricles (RV) and fiber orientation is learned statistically from a training sample of ex vivo diffusion tensor imaging datasets. More specifically, the axes in the shape space which correlate most with the myocardial fiber orientations are extracted and used for prediction in new subjects. With this approach and unlike existing fiber models, inter-subject variability is taken into account to generate latent shape predictors that are statistically optimal to estimate fiber orientation at each individual myocardial location. The proposed predictive model was applied to the task of personalizing fibers in 10 canine subjects. The results indicate that the ventricular shapes are good predictors of fiber orientation, with an improvement of 11.4% in accuracy over the average fiber model.
Comparison of steady-state and transient blood flow simulations of intracranial aneurysms

Hemodynamics play an important role in the pathogenesis of intracranial aneurysms and patient-specific computational hemodynamic simulations could provide valuable information to clinicians. Transient simulations that capture the pulsatility of blood flow are commonly used for research purposes. However, steady-state simulations might provide enough information at a lower computational cost, which could help facilitate the introduction of hemodynamic simulations into clinical practice. In this study, we compared steady-state simulations to transient simulations for two aneurysms. The effect of a change in flow rate waveform was investigated and virtual treatment techniques were employed to compare post-treatment flow reduction predictions. We found that the difference in the time-averaged wall shear stress on the aneurysm was less than 5% and the distribution of wall shear stress was qualitatively assessed to be very similar.
Towards negotiable SLA-based QoS support for biomedical data services

Researchers in data intensive domains, like the Virtual Physiological Human initiative (VPH-I), are commonly overwhelmed with the vast and increasing amount of data available. Advanced studies in biomedicine and other domains often require a considerable amount of effort to achieve data access to a critical mass of relevant data to analyze the problem at hand. We aim to improve this situation and propose a novel application of Quality of Service (QoS) mechanisms for data services. This enables scientists to obtain exactly the data they require, rather than being spoilt for choice which data source might comprise suitable data. The proposed QoS support includes a negotiation model based on service level agreements (SLAs), which in turn comprises data-related service level objectives (SLOs) to express the required guarantees about the quantity or quality of data. Moreover a corresponding QoS management model is presented which resolves the complex process of the SLA generation within data access and data mediation services. The benefits of this approach are materialized in the context of the @neurIST data environment and an initial experimental evaluation demonstrates promising performance improvements in a real world scenario.
Simulation and Synthesis in Medical Imaging

This editorial introduces the Special Issue on Simulation and Synthesis in Medical Imaging. In this editorial, we define so-far ambiguous terms of simulation and synthesis in medical imaging. We also briefly discuss the synergistic importance of mechanistic (hypothesis-driven) and phenomenological (data-driven) models of medical image generation. Finally, we introduce the twelve papers published in this issue covering both mechanistic (5) and phenomenological (7) medical image generation. This rich selection of papers covers applications in cardiology, retinopathy, histopathology, neurosciences, and oncology. It also covers all mainstream diagnostic medical imaging modalities. We conclude the editorial with a personal view on the field and highlight some existing challenges and future research opportunities.
A Virtual Coiling Technique for Image-Based Aneurysm Models by Dynamic Path Planning

Computational algorithms modeling the insertion of endovascular devices, such as coil or stents, have gained an increasing interest in recent years. This scientific enthusiasm is due to the potential impact that these techniques have to support clinicians by understanding the intravascular hemodynamics and predicting treatment outcomes. In this work, a virtual coiling technique for treating image-based aneurysm models is proposed. A dynamic path planning was used to mimic the structure and distribution of coils inside aneurysm cavities, and to reach high packing densities, which is desirable by clinicians when treating with coils. Several tests were done to evaluate the performance on idealized and image-based aneurysm models. The proposed technique was validated using clinical information of real coiled aneurysms. The virtual coiling technique reproduces the macroscopic behavior of inserted coils and properly captures the densities, shapes and coil distributions inside aneurysm cavities. A practical application was performed by assessing the local hemodynamic after coiling using computational fluid dynamics (CFD). Wall shear stress and intra-aneurysmal velocities were reduced after coiling. Additionally, CFD simulations show that coils decrease the amount of contrast entering the aneurysm and increase its residence time.
Endocardial motion estimation from electro-anatomical data

An accurate scar characterization plays an essential role for the treatment of ventricular tachycardia in patients with ischemic cardiomyopathy. Cardiac ablation based on electrical information is a widely used procedure to fix ventricular tachycardia. However, intra-operative scar characterization may be improved using mechanical information together with the electrical data. We propose in this paper a method to reconstruct the endocardial motion intra-operatively by exploiting both information from an electro-anatomical mapping system and the heart anatomy segmented from any pre-operative 3D imaging modality, using a bilinear statistical atlas to approximate motion in the areas where no information is provided. We applied the proposed methodology to four patients with ischemic cardiomyopathy. The results showed abnormal strain curves in presence of scar, which was defined using contrast-enhanced magnetic resonance images.
Spatial normalization of cardiac Diffusion Tensor Imaging for modeling the muscular structure of the myocardium

To build a model of the myocardium, a first step is the spatial normalization of data to a reference framework. Diffusion Tensor Imaging (DTI) provides information about tissue orientation, that is, fiber structures. Therefore, proper registration algorithms must be defined to deal with this image modality. In this paper, we propose a registration framework for cardiac DTI that takes into account the special features of DTI when applied to visualization of the myocardium. We propose a similarity measure adapted to the tensorial nature of the images, as well as an appropriate framework for averaging the DTI data sets. Results shown the advantages of the proposed methodology over other related approaches.
Automatic Construction of 3D-ASM Intensity Models by Simulating Image Acquisition: Application to Myocardial Gated SPECT Studies

Active shape models bear a great promise for model-based medical image analysis. Their practical use, though, is undermined due to the need to train such models on large image databases. Automatic building of point distribution models (PDMs) has been successfully addressed and a number of autolandmarking techniques are currently available. However, the need for strategies to automatically build intensity models around each landmark has been largely overlooked in the literature. This work demonstrates the potential of creating intensity models automatically by simulating image generation. We show that it is possible to reuse a 3D PDM built from computed tomography (CT) to segment gated single photon emission computed tomography (gSPECT) studies. Training is performed on a realistic virtual population where image acquisition and formation have been modeled using the SIMIND Monte Carlo simulator and ASPIRE image reconstruction software, respectively. The dataset comprised 208 digital phantoms (4D-NCAT) and 20 clinical studies. The evaluation is accomplished by comparing point-to-surface and volume errors against a proper gold standard. Results show that gSPECT studies can be successfully segmented by models trained under this scheme with subvoxel accuracy. The accuracy in estimated LV function parameters, such as end diastolic volume, end systolic volume, and ejection fraction, ranged from 90.0% to 94.5% for the virtual population and from 87.0% to 89.5% for the clinical population.
Pre to Intraoperative Data Fusion Framework for Multimodal Characterization of Myocardial Scar Tissue

Merging multimodal information about myocardial scar tissue can help electrophysiologists to find the most appropriate target during catheter ablation of ventricular arrhythmias. A framework is presented to analyze and combine information from delayed enhancement magnetic resonance imaging (DE-MRI) and electro-anatomical mapping data. Using this information, electrical, mechanical, and image-based characterization of the myocardium are performed. The presented framework allows the left ventricle to be segmented by DE-MRI and the scar to be characterized prior to the intervention based on image information. It allows the electro-anatomical maps obtained during the intervention from a navigation system to be merged together with the anatomy and scar information extracted from DE-MRI. It also allows for the estimation of endocardial motion and deformation to assess cardiac mechanics. Therefore, electrical, mechanical, and image-based characterization of the myocardium can be performed. The feasibility of this approach was demonstrated on three patients with ventricular tachycardia associated to ischemic cardiomyopathy by integrating images from DE-MRI and electro-anatomical maps data in a common framework for intraoperative myocardial tissue characterization. The proposed framework has the potential to guide and monitor delivery of radio frequency ablation of ventricular tachycardia. It is also helpful for research purposes, facilitating the study of the relationship between electrical and mechanical properties of the tissue, as well as with tissue viability from DE-MRI.
Bilinear point distribution models for heart motion analysis

This paper presents a cardiac motion modeling method that separates motion from anatomical patient specific cardiac features by using bilinear models on segmented heart volumes. This factoring in a quasi-independent anatomy-motion transform significantly reduces the dimensionality of the data to analyze: instead of having to check all the points on the volumetric mesh, we analyze a reduced number of parameters corresponding to the shape variation induced by motion. Bilinear models are first learnt in a large population of subjects, and then applied individually to each subject to derive a patient specific model. This provides information about individual specific motion parameters, facilitating the detection of individuals that are far from the mean cardiac motion pattern and motion interpolation.
Morphodynamic Analysis of Cerebral Aneurysm Pulsation From Time-Resolved Rotational Angiography

This paper presents a technique to estimate and model patient-specific pulsatility of cerebral aneurysms over one cardiac cycle, using 3D rotational X-ray angiography (3DRA) acquisitions. Aneurysm pulsation is modeled as a time varying B-spline tensor field representing the deformation applied to a reference volume image, thus producing the instantaneous morphology at each time point in the cardiac cycle. The estimated deformation is obtained by matching multiple simulated projections of the deforming volume to their corresponding original projections. A weighting scheme is introduced to account for the relevance of each original projection for the selected time point. The wide coverage of the projections, together with the weighting scheme, ensures motion consistency in all directions. The technique has been tested on digital and physical phantoms that are realistic and clinically relevant in terms of geometry, pulsation and imaging conditions. Results from digital phantom experiments demonstrate that the proposed technique is able to recover subvoxel pulsation with an error lower than 10% of the maximum pulsation in most cases. The experiments with the physical phantom allowed demonstrating the feasibility of pulsation estimation as well as identifying different pulsation regions under clinical conditions.
Lip reading for robust speech recognition on embedded devices

In this article a complete audio-visual speech recognition system suitable for embedded devices is presented. As visual feature extraction algorithms active shape models (ASM) and discrete cosine transformation (DCT) have been investigated and discussed for an embedded implementation. The audio-visual information integration has also been designed by taking into account device limitations. It is well known that the use of visual cues improves the recognition results especially in scenarios with high level of acoustical noise. We wanted to compare the performance of lip reading and the conventional noise reduction systems in these degraded scenarios, as well as the combination of both kinds of solutions. Important improvements are obtained especially for nonstationary background noise like voice interference, car acceleration or indicator clicks. For this kind of noise lip reading outperforms the results obtained with conventional noise reduction technologies.
Cross-Modality Image Synthesis via Weakly Coupled and Geometry Co-Regularized Joint Dictionary Learning

Multi-modality medical imaging is increasingly used for comprehensive assessment of complex diseases in either diagnostic examinations or as part of medical research trials. Different imaging modalities provide complementary information about living tissues. However, multi-modal examinations are not always possible due to adversary factors, such as patient discomfort, increased cost, prolonged scanning time, and scanner unavailability. In additionally, in large imaging studies, incomplete records are not uncommon owing to image artifacts, data corruption or data loss, which compromise the potential of multi-modal acquisitions. In this paper, we propose a weakly coupled and geometry co-regularized joint dictionary learning method to address the problem of cross-modality synthesis while considering the fact that collecting the large amounts of training data is often impractical. Our learning stage requires only a few registered multi-modality image pairs as training data. To employ both paired images and a large set of unpaired data, a cross-modality image matching criterion is proposed. Then, we propose a unified model by integrating such a criterion into the joint dictionary learning and the observed common feature space for associating cross-modality data for the purpose of synthesis. Furthermore, two regularization terms are added to construct robust sparse representations. Our experimental results demonstrate superior performance of the proposed model over state-of-the-art methods.
Archetype-based semantic mediation: Incremental provisioning of data services

Modern organizations need to exploit the information stored in heterogeneous and interrelated data sources, but often have no means to integrate them in a principled fashion. This general database research challenge is particularly relevant in distributed e-Science. Specifically, biomed-ical research generates a vast amount of heterogeneous data, which exceeds the current technological capacity to exploit it efficiently. Typically, service-oriented architectures are used in this context to define a unified view over all sources to be integrated. This unified schema needs to be mapped onto the underlying data sources, often including also semantic annotations. This approach suffers from high complexity and setup costs. In this paper we propose a novel application of semantic and mediation technologies, which leads to an incremental and on-demand definition of data mediation services. The so-called archetypes provide the context and semantics needed to setup such services, which significantly simplify their definition.
Three-dimensional modeling for functional analysis of cardiac images, a review

Three-dimensional (3-D) imaging of the heart is a rapidly developing area of research in medical imaging. Advances in hardware and methods for fast spatio-temporal cardiac imaging are extending the frontiers of clinical diagnosis and research on cardiovascular diseases. In the last few years, many approaches have been proposed to analyze images and extract parameters of cardiac shape and function from a variety of cardiac imaging modalities. In particular, techniques based on spatio-temporal geometric models have received considerable attention. This paper surveys the literature of two decades of research on cardiac modeling. The contribution of the paper is three-fold: (1) to serve as a tutorial of the field for both clinicians and technologists, (2) to provide an extensive account of modeling techniques in a comprehensive and systematic manner, and (3) to critically review these approaches in terms of their performance and degree of clinical evaluation with respect to the final goal of cardiac functional analysis. From this review it is concluded that whereas 3-D model-based approaches have the capability to improve the diagnostic value of cardiac images, issues as robustness, 3-D interaction, computational complexity and clinical validation still require significant attention.
MULTIVIEW REGISTRATION OF CARDIAC TAGGING MRI IMAGES

This paper introduces a new method based on k-nearest neighbors graphs (KNNG) for bringing into alignment multiple views of the same scene acquired at two different time points. This framework is applied to cardiac motion estimation from tagging MRI sequences. Features acquired in each view are collected in a high dimensional feature space and an efficient estimator of alpha-joint entropy (alphaJE) is used for selecting the optimal alignment. In order to register 4D datasets, an analytical expression of the alphaJE estimator was derived, enabling a fast implementation of gradient based optimization. The technique was tested in a set of six sequences and the results compared with respect to manual measurements made at tag crossing points, obtaining good accuracy and low processing times compared to published state of the art methods