nils-herrmann/clean_scirepeval huggingface dataset:
Left-Atrial Segmentation From 3-D Ultrasound Using B-Spline Explicit Active Surfaces With Scale Uncoupling

Segmentation of the left atrium (LA) of the heart allows quantification of LA volume dynamics which can give insight into cardiac function. However, very little attention has been given to LA segmentation from three-dimensional (3-D) ultrasound (US), most efforts being focused on the segmentation of the left ventricle (LV). The B-spline explicit active surfaces (BEAS) framework has been shown to be a very robust and efficient methodology to perform LV segmentation. In this study, we propose an extension of the BEAS framework, introducing B-splines with uncoupled scaling. This formulation improves the shape support for less regular and more variable structures, by giving independent control over smoothness and number of control points. Semiautomatic segmentation of the LA endocardium using this framework was tested in a setup requiring little user input, on 20 volumetric sequences of echocardiographic data from healthy subjects. The segmentation results were evaluated against manual reference delineations of the LA. Relevant LA morphological and functional parameters were derived from the segmented surfaces, in order to assess the performance of the proposed method on its clinical usage. The results showed that the modified BEAS framework is capable of accurate semiautomatic LA segmentation in 3-D transthoracic US, providing reliable quantification of the LA morphology and function.
A Fourier-based formalism for 3D ultrafast imaging with diverging waves

Ultrafast imaging based on diverging wave is an active area of research in ultrasound sectorial acquisition because of its capacity of reaching high frame rate. The current state-of-the-art technique in 3D sectorial ultrafast imaging is based on diverging wave transmission and a reconstruction strategy using delay and sum (DAS). Recently, we have introduced a Fourier-based technique for the reconstruction of 2D images for sectorial acquisition. In this study, we extend our previous formalism to 3D acquisition. We demonstrate that the flexibility of the proposed formalism allows a direct use of compounding in order to improve image quality from a set of diverging waves with different virtual source positions. Simulation results show that the proposed approach provides competitive results in terms of image quality compared to the state-of-the-art DAS technique, but with a much lower computational complexity.
Multiview myocardial segmentation in echocardiographic images using a piecewise parametric shape prior

The segmentation of the myocardium in echocardiographic images is an important task for the diagnosis of heart diseases. This step is made difficult due to the inherent problems of echographic images (e.g. low contrast, speckle noise). In this article, we propose a method to segment the whole myocardium (endocardium and epicardium) from 2D echographic scans. This is achieved using a level-set model constrained by a new shape formulation that allows to model both contours. The novelty of this work also lays in the fact that our framework allows to segment the myocardium in any view. The method is evaluated on a set of clinical images. The obtained results are compared to expert segmentations and to the results provided by the recent approach described in .
Improvements of scale fourier linear combiner for impedance cardiography analysis

In this paper, we present two additions to the SFLC algorithm in order to improve the beat by beat estimation of fiducial points (B and X) and the maximum value of the derivative in impedance cardiography signal: addition of a reference input and scanning each beat more than once. On data synthesized from real ICG which exhibits beat by beat changes and baseline variations, we used statistical measures to show that the modified algorithms give accurate beat by beat estimation conversely to the original version of SFLC. We also give results on a real ICG using phono cardiography as a reference. In this case too, results show the pertinence of the proposed improvements.
Dynamical modelling and state estimation of anaerobic wastewater treatment plants

A mass balance based model has been derived to represent the dynamical behaviour of two anaerobic digester (one pilot-scale, one industrial-scale). Its parameters have been calibrated for both reactors on the basis of experimental data and following a predefined design of experiments. This mass balance model constitutes the basis for software sensor design that takes advantage of the partial information on the process dynamics and uses on-line gas and alkalinity measurement. It computes the concentration of inorganic carbon and volatile fatty acids (VFA) in the digester. Simulations show the theoretical efficiency of this software sensor for realistic variations of the organic loading rate. The predictions of this software sensor when it is applied to experimental data are very close to the actual off-line measurements. The sensor predicts the accumulation of VFA, and thus very early detects a destabilization of the digester due to an overloading. The presented methodology demonstrates the usefulness of advanced monitoring techniques for a better understanding of the internal working of the process.
Sub-sampled Doppler ultrasound reconstruction using block sparse Bayesian learning

Duplex Doppler is an echographic mode that allows visualizing at the same time a B-mode image and the blood flow, for which a strategy for alternating velocity and B-mode emissions is required. In a recent study  we have shown that compressed sensing-based reconstruction of Doppler signal allowed reducing the number of Doppler emissions and yielded better results than traditional interpolation. However, in this study the reduction of Doppler emission had to be limited to 60% in order to produce satisfying reconstruction (i.e. a PSNR > 20 dB). We propose here to improve over this study by using a block sparse Bayesian learning (BSBL) framework for randomly interleaving Doppler and US emissions. The interest of using such framework in the context of duplex Doppler is linked to the unique ability of BSBL to exploit block-correlated signals and to recover non-sparse signals. The performance of the technique is evaluated for simulated data and compared to the recent results in . The results show that with this method the subsampling rate can be increased up to 80% while producing satisfying reconstruction quality.
Medical ultrasound image reconstruction using distributed compressive sampling

This paper investigates ultrasound (US) radiofrequency (RF) signal recovery using the distributed compressed sampling framework. The “correlation” between the RF signals forming a RF image is exploited by assuming that they have the same sparse support in the 1D Fourier transform, with different coefficient values. The method is evaluated using an experimental US image. The results obtained are shown to improve a previously proposed recovery method, where the correlation between RF signals was taken into account by assuming the 2D Fourier transform of the RF image sparse.
Semi-automatic left-atrial segmentation from volumetric ultrasound using B-spline explicit active surfaces

Segmentation of the left atrium (LA) of the heart allows quantification of LA volume dynamics which can give insight into cardiac function. However, very little attention has been given to LA segmentation from 3D ultrasound (US), most efforts being focused on the segmentation of the left ventricle (LV). The B-spline explicit active surfaces (BEAS) framework has been shown to be a very robust and efficient methodology to perform LV segmentation. In this study, the BEAS framework was adapted to allow for semi-automatic segmentation of the LA endocardium, with little user input. The method was tested in 20 volumetric sequences of echocardiographic data from healthy subjects and the segmentation results were evaluated against manual reference delineations of the LA. Relevant LA morphological and functional parameters were derived from the segmented surfaces, in order to assess the performance of the proposed method on its clinical usage. The results showed that the modified BEAS framework is capable of accurate semi-automatic LA segmentation in 3D transthoracic US, providing reliable quantification of the LA morphology and function.
A compressed beamforming framework for ultrafast ultrasound imaging

Classical beamforming methods, based on Delay-And-Sum (DAS) require an extensive number of samples and delay calculations to obtain high-quality images. Compressed Beamforming (CB) proposes an alternative to DAS, based on compressed sensing, which aims at reducing the data rate. However, proposed CB approaches induce a computationally heavy measurement model that hampers their attractiveness for iterative image reconstruction. In this paper, a CB framework, applicable to either radio-frequency or in-phase quadrature data and for both plane wave and diverging wave compounding, is described. The proposed framework exploits a computationally light measurement model which leads to tractable reconstruction. It solves a convex problem and assumes sparsity in a wavelet-based model to achieve high-quality image reconstruction from measurements acquired with only few transducer elements.
Extension of Fourier-Based Techniques for Ultrafast Imaging in Ultrasound With Diverging Waves

Ultrafast ultrasound imaging has become an intensive area of research thanks to its capability in reaching high frame rates. In this paper, we propose a scheme that allows the extension of the current Fourier-based techniques derived for planar acquisition to the reconstruction of sectorial scan with wide angle using diverging waves. The flexibility of the proposed formulation was assessed through two different Fourier-based techniques. The performance of the derived approaches was evaluated in terms of resolution and contrast from both simulations and in vitro experiments. The comparisons of the current state-of-the-art method with the conventional delay-and-sum technique illustrated the potential of the derived methods for producing competitive results with lower computational complexity.
P5C-3 Field Simulation Parameters Design for Realistic Statistical Parameters of Radio - Frequency Ultrasound Images

In this paper we present a study for designing realistic ultrasound image simulations from a statistical point of view. Indeed it is extremely important to be able to compute realistic simulated images for validating segmentation or classification methods based on the statistics of ultrasound images. The statistics of the radio frequency (RF) signals is modeled by a distribution called K-RF distribution. The scatterers modeling the medium are uniformly spatially distributed and have scattering coefficients modeled by a K-distribution. The concentration of scatterers and the statistics of their amplitude determine the statistics of the RF signals. We show how scale and shape parameters of the K-RF distribution modeling the RF signal are influenced by concentration, scale and shape parameters of the K distribution modeling the scatterers echogenecity. This study is carried out with the field II simulation program.
Interaction between time scales in microalgae based processes

Microalgae are often seen as a potential biofuel producer. In order to predict achievable productivities in the so called raceway culturing system, the dynamics of photosynthesis has to be taken into account. In particular, the dynamical effect of inhibition by an excess of light (photoinhibition) must be represented. We propose a model considering both photosynthesis and growth dynamics. This model involves three different time scales. We study the response of this model to fluctuating light with different frequencies by slow fast approximations. Therefore, we identify three different regimes for which a simplified expression for the model can be derived. These expressions give a hint on productivity improvement which can be expected by stimulating photosynthesis with a faster hydrodynamics.
Hybrid Strategy to Simulate 3-D Nonlinear Radio-Frequency Ultrasound Using a Variant Spatial PSF

There are several simulators for medical ultrasound (US) applications that can fully compute the nonlinear propagation on the transmitted pulse and the corresponding radio-frequency (RF) images. Creanuis is one recent model used to generate nonlinear RF images; however, the time requirements are long compared with linear models using a convolution strategy. In this paper, we describe an approach using convolution coupled with nonlinear information to create a pseudoacoustic tool that is able to quickly generate realistic US images. Several point-spread functions (PSFs) are computed with Creanuis. These PSFs are extracted at different depths in order to take into account variation in the resolution and apparition of harmonics during propagation. One convolution is then conducted for each PSF to generate a set of nonlinear raw RF images. The final image is obtained by merging these raw images using a PSF-weighting function. This hybrid Creanuis strategy was extended to 2-D, 2-D + t, 3-D, and 3-D + t images for both linear and phased-array geometries. We validated h-Creanuis using the mean deviation between the proposed images and those created using Creanuis and examined their statistical distributions. The mean deviations of Creanuis and h-Creanuis are below 2.5% for fundamental and second-harmonic images. The 3-D + t images obtained demonstrate the correct motion characteristics for speckle in sequences of both fundamental and second-harmonic images.
A Sparse regularization approach for ultrafast ultrasound imaging

Ultrafast imaging based on plane-wave (PW) insonification is an active area of research due to its capability of reaching high frame rates. Several approaches have been proposed either based on either of Fourier-domain reconstruction or on delay-and-sum (DAS) reconstruction. Using a single PW, these techniques achieve low quality, in terms of resolution and contrast, compared to the classic DAS method with focused beams. To overcome this drawback, compounding of several steered PWs is needed, which currently decreases the high frame rate limit that could be reached by such techniques. Based on a compressed sensing (CS) framework, we propose a new method that allows the reconstruction of high quality ultrasound (US) images from only 1 PW at the expense of augmenting the computational complexity at the reconstruction.
Impedance cardiography filtering using scale fourier linear combiner based on RLS algorithm

The Cardiac Output (CO) can be calculated from the thoracic cardio-impedance signal from several methods, and all of them are linked to the frequency information, information that is limited by the type of filtering used before. A methodology is proposed to evaluate the effect of the commonly used methods of filtering, and an improvement of the SFLC LMS-based algorithm by the use of RLS algorithm is also tested. Performances of algorithms are then evaluated considering different types of noise such as white noise or combination of sinusoidal noises to simulate the effect of respiration and body movements.
Tracking quality in plane-wave versus conventional cardiac ultrasound: A preliminary evaluation in-silico based on a state-of-the-art simulation pipeline

Fast cardiac ultrasound (US) imaging based on plane wave (PW) transmits is fostering significant research efforts. Coupled with a technique for deformation imaging, the new modality potentially allows for a more thorough assessment of cardiac function due to its higher temporal resolution. However, although the higher frame rate eases the tracking problem by enforcing speckle coherency, the loss in spatial resolution makes it harder. Moreover, at higher frame rate, a bias in the motion estimator will accumulate faster. It thus remains unclear whether myocardial tracking on PW recordings outperforms tracking on conventional images or vice versa. The aim of this study was therefore to directly contrast the intrinsic tracking quality of both acquisition schemes using a testing environment recently proposed by our lab.
An integrated solution for semi-automatic segmentation of volumetric ultrasound data based on B-spline explicit active surfaces

Real-time 3D echocardiography (RT3DE) is a noninvasive imaging modality used to provide diagnostic information about cardiac morphology and function. Unfortunately, manual analysis of these datasets remains cumbersome and time-consuming. Our team recently proposed a generic framework for automatic 3D segmentation of volumetric datasets (B-spline explicit active surfaces, BEAS). However, as fully automatic segmentation can (locally) fail, the option of manually correcting the segmentation result should be available for optimal clinical routine use. The aim of this study was to develop an interactive segmentation method by embedding the user input in the segmentation framework. In order to validate the proposed method, a database consisting of 10 3D echocardiographic images from open-chest sheep experiments was used. Two experts segmented the data, using a purely manual approach as well as the proposed interactive framework. Using the interactive approach, the RT3DE data could be analyzed in a more time efficient manner (analysis time, interactive: 44.7 ± 11.9s vs. manual: 181.5 ± 66.2s; p <; 0.001), while being an equally accurate alternative to manual contouring. Moreover, it improved the reproducibility of the extracted measurements (inter-observer variability, interactive: 3.1 ± 2.4ml vs. manual: 6.0 ± 2.7ml; p <; 0.05).
Nonlinear observers for a class of biological systems: application to validation of a phytoplanktonic growth model

The authors construct nonlinear observers in order to discuss the validity of biological models. They consider a class of systems including many classical models used in biological modeling. They formulate the nonlinear observers corresponding to these systems and prove the conditions necessary for their exponential convergence. They apply these observers on the well-known Droop model which describes the growth of a population of phytoplanktonic cells. The validity of this model is discussed based on the performance of the observers working on experimental data.
Estimation of lipid accumulation in microalgae with dynamic interval observers

Microalgae are a promising alternative for biofuel production since they can synthesize and accumulate considerable amounts of lipids. In this paper, we propose an interval observer for the estimation of the microalgal lipid quota. The observer is based on a change of coordinate that involves a time varying gain. We introduce a dynamics for the gain, whose trajectory converges toward a predefined optimal value (which maximizes the convergence rate of the observer). The observer performance is illustrated with experimental data of Isochrysis affinis galbana (clone T-iso) cultures.
Ultrasound Fourier slice imaging: a novel approach for ultrafast imaging technique

Ultrafast imaging based on plane-wave (PW) has become an intense area of research thanks to its capability of reaching frame rate higher than a thousand of frames per second. Several proposed approaches are based on Fourier-domain reconstruction. In these techniques, the Fourier transform of the received echoes is projected to the k-space corresponding to the Fourier transform of the object function. For one emitted PW, N lines along the kz axis direction are reconstructed in the k-space. We propose in this study a new acquisition scheme which allows acquiring the non-null part of the ultrasound spectrum with finer resolution. We show that this strategy allows obtaining images with slightly better lateral resolution and higher contrast-to-noise ratio (CNR) when compared to other Fourier-based techniques.
Coupling framers to get enhanced interval observers. Application to growth rate estimation in a photobioreactor

This paper proposes strategies to couple a set of framers (called bundle of framers), that all give a state bounding provided that a bounding of the initial condition is known. We first show that the best state estimate can be used at any time to enhance the estimate of each framer. Then we show how to build a framer which provides the best estimate at any time. We introduce a bounded estimate within the framer bundle in order to ensure the boundness of the estimate and finally get an interval observer. This enhanced interval observer design is applied to the estimate of state variables in a photobioreactor, and especially to the specific growth rate evaluation. The microalgal model is Droop model, from which the dynamics of the growth and substrate uptake rates are derived. Various framers can be derived from this model, guarantying bounds on the prediction error. These framers are then coupled together and lead to an improved estimate.
Fast and Fully Automatic Left Ventricular Segmentation and Tracking in Echocardiography Using Shape-Based B-Spline Explicit Active Surfaces

Cardiac volume/function assessment remains a critical step in daily cardiology, and 3-D ultrasound plays an increasingly important role. Fully automatic left ventricular segmentation is, however, a challenging task due to the artifacts and low contrast-to-noise ratio of ultrasound imaging. In this paper, a fast and fully automatic framework for the full-cycle endocardial left ventricle segmentation is proposed. This approach couples the advantages of the B-spline explicit active surfaces framework, a purely image information approach, to those of statistical shape models to give prior information about the expected shape for an accurate segmentation. The segmentation is propagated throughout the heart cycle using a localized anatomical affine optical flow. It is shown that this approach not only outperforms other state-of-the-art methods in terms of distance metrics with a mean average distances of 1.81±0.59 and 1.98±0.66 mm at end-diastole and end-systole, respectively, but is computationally efficient (in average 11 s per 4-D image) and fully automatic.
A RBF-Based Multiphase Level Set Method for Segmentation in Echocardiography using the Statistics of the Radiofrequency Signal

This work presents an algorithm for the segmentation of myocardial regions in echocardiography imaging based on the statistics of the radiofrequency image. We formulate the problem of segmentation in a maximum likelihood framework using the Generalized Gaussian as an a priori distribution. We minimize the resulting functional using a radial basis functions-based multiphase level set model. Numerical results obtained on both simulation and in vivo data demonstrate the capacity of our approach to segment myocardial regions in echocardiography imaging.
Interval observers for linear systems with delay

This paper focuses on the analysis of some classes of observers for linear systems with a point-wise delay. First, it is pointed out that, classical interval observers for systems without delays are not robust with respect to the presence of delays that appear in a specific structure location, no matter how small it is. Next, it is shown that, in general, for linear systems classical interval observers endowed with a point-wise delay are unstable. A new type of design enabling to circumvent this obstacle is proposed. It relies on framers that incorporate distributed delay terms.
Real-time region-based segmentation of 3D inhomogeneous objects in medical images

Active geometric functions were recently introduced as a tool to perform real time 3-D segmentation. In the present paper we propose a B-spline formulation of the problem, which further improves the computational efficiency of the algorithm. We also introduce local region based energies, overcoming the limitations of the original method. The feasibility of real-time 3D segmentation in challenging images using the proposed algorithm is demonstrated in liver tumor data. The segmentation accuracy is comparable to a recently proposed state-of-the-art algorithm, but with CPU times below 0.1s.