nils-herrmann/clean_scirepeval huggingface dataset:
Human-centric approaches to image understanding and retrieval

The amount of digital medical image data is increasing rapidly in terms of both quantity and heterogeneity. There exists a great need to format medical image archives so as to facilitate diagnostics and preventive medicine. To achieve this, in the past few decades great efforts have been made to investigate methods of applying content-based image retrieval (CBIR) techniques to retrieve images. However, several critical challenges remain. Recently, CBIR research has become intertwined with the fundamental problem of image understanding and it is recognized that computing solutions that bridge the “semantic gap” must capture higher-level domain knowledge of medical end users. We are investigating the incorporation of state-of-the-art visual categorization techniques into conventional CBIR approaches. Visual attention deployment strategies of medical experts serve as an objective measure to help us understand the perceptual and conceptual processes involved in identifying key visual features and selecting diagnostic regions of the images. Understanding these processes will inform and direct feature selection approaches on medical images, such as the dermatological images used in our study. We also explore systematic and effective information integration methods of image data and semantic descriptions with the long-term goals of building efficient human-centered multi-modal interactive CBIR systems.
Stochastic Framework for Symmetric Affine Matching between Point Sets

This paper presents a new approach to obtain symmetry in point matching problem. Here, symmetric matching means the essential property that the choices of source and target should not determine the eventual matching results. Most earlier approaches to achieve symmetric matching have been in deterministic fashions, where symmetry constraints are added into the matching cost functions to impose source-target symmetric property during the matching process. Nevertheless, these modified cost functions cannot generally converge to real ground truth, and further, the perfect source-target symmetry cannot be achieved. Given initial forward and backward matching matrices pair, computed from any reasonable matching strategies, our approach yields perfectly symmetric mapping matrices from a stochastic framework that simultaneously considers the errors underneath the initial matching matrices and the imperfectness of the symmetry constraint. An iterative generalized total least square (GTLS) strategy has been developed such that perfect source-target symmetry is imposed
Meshfree particle method

Many of the computer vision algorithms have been posed in various forms of differential equations, derived from minimization of specific energy functionals, and the finite element representation and computation have become the de facto numerical strategies for solving these problems. However, for cases where domain mappings between numerical iterations or image frames involve large geometrical shape changes, such as deformable models for object segmentation and nonrigid motion tracking, these strategies may exhibit considerable loss of accuracy when the mesh elements become extremely skewed or compressed. We present a new computational paradigm, the meshfree particle method, where the object representation and the numerical calculation are purely based on the nodal points and do not require the meshing of the analysis domain. This meshfree strategy can naturally handle large deformation and domain discontinuity issues and achieve desired numerical accuracy through adaptive node and polynomial shape function refinement. We discuss in detail the element-free Galerkin method, including the shape function construction using the moving least square approximation and the Galerkin weak form formulation, and we demonstrate its applications to deformable model based segmentation and mechanically motivated left ventricular motion analysis.
A reduced-rank square root filtering framework for noninvasive functional imaging of volumetric cardiac electrical activity

To noninvasively reconstruct transmembrane potential (TMP) dynamics throughout the 3D myocardium using body surface potential recordings, it is necessary to combine prior physiological models and patient's data with regard to their respective uncertainties. To fulfill model-data melding for this large-scale and high-dimensional system, data assimilation with proper computational reduction is needed for computational feasibility and efficiency. In this paper, we develop a reduced-rank square root TMP estimation algorithm, using dominant components of estimation uncertainties to guide a more efficient model-data coupling in the square root structure. The SVD-based reduced-rank error covariance is used to represent and track the dominant estimation errors, and unified into an integrated square root filtering framework. Phantom experiments demonstrate the ability of this framework to bring substantial computational reduction at slight expense of degraded estimation accuracy. It therefore improves the efficiency and applicability of the volumetric myocardial TMP imaging in practice.
State-Space Reconstruction of Pet Parametric Maps

The primary goal of dynamic positron emission tomography (PET) is to quantify the physiological and biological processes through tracer kinetics analysis. However, the process is difficult and complicated because of the compromising imaging data quality, i.e. either longer scans with good counting statistics but poor temporal resolution, or noisy shorter scans with good temporal resolution. In this paper, we explore the usage of state space principles for physiological parameter estimation in dynamic PET imaging. The system equation is constructed from particular tracer kinetic models, with the number and relationship between tissue compartments dictated by the physiological and biochemical properties of the process under study. And the observation equation on measurement data is formed based on the specific types of imaging or image-derived data. Once the Poisson distributed PET data are converted to Gaussian ones through the Anscombe transformation, an extended Kalman filter is adopted to estimate the tracer kinetics parameters from the system and observations. More appropriate estimation strategies which better take care of the PET statistics are also under development. The framework is tested on simulated digital phantom data, and the results are of sufficient accuracy and robustness.
Joint estimation of cardiac kinematics and material parameters from noisy imaging data and uncertain mechanical model

There have been many efforts using image analysis algorithms to study cardiac kinematics, or using biomechanics strategies to study myocardial material properties. In this paper, we propose a novel stochastic mechanics strategy and an extended Kalman filter (EKF) computational framework to estimate the cardiac kinematics functions and material model parameters simultaneously, given a particular a priori myocardial material model with uncertain parameters and a posteriori noisy imaging/imaging-derived data. We address the issues concerning the data-dependent uncertainty of the constraining mechanical models (and their parameters), which are needed in the ill-posed problems. Because of the periodic nature of the cardiac dynamics, we conclude experimentally that it is possible to adopt this physical-model based optimal estimation approach to achieve converged estimates. Results from canine MR phase contrast images and linear elastic model are presented.
Representing Variability and Transmural Differences in a Model of Human Heart Failure

During heart failure (HF) at the cellular level, the electrophysiological properties of single myocytes get remodeled, which can trigger the occurrence of ventricular arrhythmias that could be manifested in many forms such as early afterdepolarizations (EADs) and alternans (ALTs). In this paper, based on experimentally observed human HF data, specific ionic and exchanger current strengths are modified from a recently developed human ventricular cell model: the O'Hara-Virág-Varró-Rudy (OVVR) model. A new transmural HF-OVVR model is developed that incorporates HF changes and variability of the observed remodeling. This new heterogeneous HF-OVVR model is able to replicate many of the failing action potential (AP) properties and the dynamics of both [Ca2+]i and [Na+]i in accordance with experimental data. Moreover, it is able to generate EADs for different cell types and exhibits ALTs at modest pacing rate for transmural cell types. We have assessed the HF-OVVR model through the examination of the AP duration and the major ionic currents' rate dependence in single myocytes. The evaluation of the model comes from utilizing the steady-state (S-S) and S1-S2 restitution curves and from probing the accommodation of the HF-OVVR model to an abrupt change in cycle length. In addition, we have investigated the effect of chosen currents on the AP properties, such as blocking the slow sodium current to shorten the AP duration and suppress the EADs, and have found good agreement with experimental observations. This study should help elucidate arrhythmogenic mechanisms at the cellular level and predict unseen properties under HF conditions. In addition, this AP cell model might be useful for modeling and simulating HF at the tissue and organ levels.
Spatiotemporal Strategies for Joint Segmentation and Motion Tracking From Cardiac Image Sequences

Although accurate and robust estimations of the deforming cardiac geometry and kinematics from cine tomographic medical image sequences remain a technical challenge, they have significant clinical value. Traditionally, boundary or volumetric segmentation and motion estimation problems are considered as two sequential steps, even though the order of these processes can be different. In this paper, we present an integrated, spatiotemporal strategy for the simultaneous joint recovery of these two ill-posed problems. We use a mesh-free Galerkin formulation as the representation and computation platform, and adopt iterative procedures to solve the governing equations. Specifically, for each nodal point, the external driving forces are individually constructed through the integration of data-driven edginess measures, prior spatial distributions of myocardial tissues, temporal coherence of image-derived salient features, imaging/image-derived Eulerian velocity information, and cyclic motion model of myocardial behavior. The proposed strategy is accurate and very promising application results are shown from synthetic data, magnetic resonance (MR) phase contrast, tagging image sequences, and gradient echo cine MR image sequences.
Sampled-Data Filtering Framework for Cardiac Motion Recovery: Optimal Estimation of Continuous Dynamics From Discrete Measurements

Quantitative and noninvasive estimation of cardiac kinematics has significant physiological and clinical implications. In this paper, a sampled-data filtering framework is presented for the recovery of cardiac motion and deformation functions from periodic medical image sequences. Cardiac dynamics is a continuously evolving physical/physiological process, whereas the imaging data can provide only sampled measurements at discrete time instants. Given such a hybrid paradigm, stochastic multiframe filtering frameworks are constructed to couple the continuous dynamics with the discrete measurements, and to coordinately deal with the parameter uncertainty of the biomechanical constraining model and the noisy nature of the imaging data. The state estimates are predicted according to the continuous-time biomechanically constructed state equation between observation time points, and then updated with the new imaging-derived measurements at discrete time instants, yielding physically more meaningful and more accurate estimation results. Both continuous-discrete Kalman filter and sampled-data Hinfin filter are applied for motion recovery. While Kalman filter is the optimal estimator under Gaussian noises, the Hinfin scheme can give robust estimation results when the types and levels of model uncertainties and data disturbances are not available a priori. The strategies are validated through synthetic data experiments to illustrate their advantages and on canine MR phase contrast images and human MR tagging data to show their clinical potential.
Dynamic structural-image-guided noninvasive volumetric cardiac electrophysiological mapping

Body surface potential (BSP) has been used as the single data source in inverse electrocardiography (IECG). The lack of volumetric spatial resolutions in BSP, however, hinders the noninvasive mapping of volumetric cardiac transmembrane potentials (TMPs). Tomographic image sequence, which contains temporally sparse but spatially dense cardiac kinematic measures, becomes ideal dynamic complements to BSPs through cardiac electromechanical (EM) coupling. In this paper, we present a model-constrained Bayesian framework to integrate tomographic image sequence and BSP maps (BSPM) for volumetric cardiac TMP mapping. A priori physiological knowledge is incorporated via stochastic modeling of the cardiac electrophysiological system with unknown systematic errors. With this system as a platform for data integration, adaptive data assimilation is used to estimate patient specific TMPs from BSPMs under the guidance of tomographic images. In this way, complementary images are integrated in accord with their respective merits and limitations. Phantom and real data experiments exhibit notable improvements and practicability of the presented framework.
Noninvasive Computational Imaging of Cardiac Electrophysiology for 3-D Infarct

Myocardial infarction (MI) creates electrophysiologically altered substrates that are responsible for ventricular ar rhythmias, such as tachycardia and fibrillation. The presence, size, location, and composition of infarct scar bear significant prognostic and therapeutic implications for individual subjects. We have developed a statistical physiological model-constrained framework that uses noninvasive body-surface-potential data and tomographic images to estimate subject-specific transmembrane potential (TMP) dynamics inside the 3-D myocardium. In this paper, we adapt this framework for the purpose of noninvasive imaging, detection, and quantification of 3-D scar mass for postMI patients: the framework requires no prior knowledge of MI and converges to final subject-specific TMP estimates after several passes of estimation with intermediate feedback; based on the primary features of the estimated spatiotemporal TMP dynamics, we provide 3-D imaging of scar tissue and quantitative evaluation of scar location and extent. Phantom experiments were performed on a computational model of realistic heart-torso geometry, considering 87 transmural infarct scars of different sizes and locations inside the myocardium, and 12 compact infarct scars (extent between 10% and 30%) at different transmural depths. Real data experiments were carried out on BSP and magnetic resonance imaging (MRI) data from four postMI patients, validated by gold standards and existing results. This framework shows unique advantage of noninvasive, quantitative, computational imaging of subject-specific TMP dynamics and infarct mass of the 3-D myocardium, with the potential to reflect details in the spatial structure and tissue composition/heterogeneity of 3-D infarct scar.
A model-based integrated approach to track myocardial deformation using displacement and velocity constraints

Accurate estimation of heart wall dense field motion and deformation could help to better understand the physiological processes associated with ischemic heart diseases, and to provide significant improvement in patient treatment. We present a new method of estimating left ventricular deformation which integrates instantaneous velocity information obtained within the mid-wall region with shape information found on the boundaries of the left ventricle. Velocity information is obtained from phase contrast magnetic resonance images, and boundary information is obtained from shape-based motion tracking of the endo- and cardial boundaries. The integration takes place within a continuum biomechanical heart model which is embedded in a finite element framework. We also employ a feedback mechanism to improve tracking accuracy. The integration of the two disparate but complementary sources overcomes some of the limitations of previous work in the field which concentrates on motion estimation from a single image-derived source.<<ETX>>
Simultaneous recovery of left ventricular shape and motion using meshfree particle framework

We have shown that meshfree particle methods possess better flexibility, efficiency, and accuracy in solving domain evolution problems in medical image analysis with complex geometries and discontinuities. In this paper, we present a unified cardiac image analysis framework which simultaneously recovers the shape and motion of the left ventricle, including the endo-, epi-, and mid-wall myocardium. Both segmentation and volumetric motion/deformation results are achieved at the same time through analyzing the spatiotemporal evolution of a set of sampling myocardial nodes bounded by endo- and epicardial boundaries. An meshfree Galerkin formulation provides the representation and computation platform for our effort, and iterative procedures are used to solve the governing equations. Specifically, the external driving forces are individually constructed for each nodal point through the integration of the data-driven edginess measures, the prior spatial distributions of the myocardial tissues, the temporal coherence of the image-derived salient features, the imaging/image-derived Eulerian velocity information, and the cyclic motion model of the myocardial behavior. Robustness and accuracy of the strategy with very promising application results from canine magnetic resonance phase contrast and tagging image sequences are demonstrated.
A Meshfree Method for Solving Cardiac Electrical Propagation

We present a novel numerical scheme to accurately and efficiently simulate the spatiotemporal electrical propagation for three dimensional heart model. A meshfree particle representation of myocardial volume is first developed, upon which the electrical propagation can be obtained using the element-free Galerkin (EFG) method for the FitzHugh-Nagumo model. This method is based on a sufficient amount of sampling nodes of the three-dimensional myocardial volume, but without the needs to construct the often expensive and complicated mesh structure between these nodes. Compared to the traditional finite element method, this new approach provides a more efficient numerical method to model the effects of the myocardial geometrical complexity and material inhomogeneity/anisotropicness. Experiments on synthetic and real heart geometries with uniform and nonuniform diffuse materials are presented. Related implementation issues are also discussed
Meshfree framework for image-derived modelling

Disorder of electrical propagation pattern might lead to serious heart attacks, so a variety of efforts have been developed to identify pathological patterns. Among those works, the most promising attempt is 3D model-based imaging of cardiac electrical activities. However, current models are computationally expensive and often too complicated to be adopted into clinical data. In this paper, we propose a meshfree framework, which can build a computational model from the image-derived geometry straightforwardly, without burden mesh generation. This image-derived framework opens great possibilities, including the ability to be directly integrated into our previous cardiac information recovery framework or explore cardiac electrical activities with clinical data. Experiments have been conducted on synthetic data to show the ability of the framework, and on real human data to show its practical potential.
Tracking tagged MR images with energy-minimizing deformable grids

A framework for automatically tracking tag lines from a sequence of magnetic resonance (MR) images is described. MR tagging is used to create a spatial pattern of varying magnetization so that objects which may otherwise have small intensity variation are textured. The algorithm presented here uses a dynamic programming solution to a variational integral for energy minimization of curves, where the behavior of the curves is controlled by image features and by geometric smoothness.<<ETX>>
Noninvasive volumetric imaging of cardiac electrophysiology

Volumetric details of cardiac electrophysiology, such as transmembrane potential dynamics and tissue excitability of the myocardium, are of fundamental importance for understanding normal and pathological cardiac mechanisms, and for aiding the diagnosis and treatment of cardiac arrhythmia. Noninvasive observations, however, are made on body surface as an integration-projection of the volumetric phenomena inside patient's heart. We present a physiological-model-constrained statistical framework where prior knowledge of general myocardial electrical activity is used to guide the reconstruction of patient-specific volumetric cardiac electrophysiological details from body surface potential data. Sequential data assimilation with proper computational reduction is developed to estimate transmembrane potential and myocardial excitability inside the heart, which are then utilized to depict arrhythmogenic substrates. Effectiveness and validity of the framework is demonstrated through its application to evaluate the location and extent of myocardial infract using real patient data.
Robust recovery of volumetric cardiac motion with physically constrained H/sup /spl infin// filtering

We present a robust methodology for volumetric heart motion recovery from medical image sequences, where mechanically motivated spatial constraints of myocardium is combined with the temporal filtering of the H/sup /spl infin// strategy. Cardiac system dynamics is transformed into state space representation, and the heart kinematics are then estimated using the min-max criterion. Unlike the popular Kalman filtering paradigm which assumes known Gaussian statistics of the process and measurement noises, our effort more realistically assumes no prior knowledge on the noise statistics but only noise energy levels, and it employs a minimum worst-case error estimation philosophy. Experiments with two types of magnetic resonance images, the tagging and the phase contrast data, are conducted with physiologically sensible results.