nils-herrmann/clean_scirepeval huggingface dataset:
A dynamic model coupling photoacclimation and photoinhibition in microalgae

Microalgae are often considered a promising alternative for production of renewable energy, particularly as a potential producer of biodiesel. In order to improve large-scale, industrial culturing systems, the development of mathematical models that are capable of predicting photosynthetic productivity under dynamic conditions is crucial. Especially important are the processes of growth inhibition due to excess light, known as photoinhibition, and of adjustment of the light harvesting capacity to photon flux, known as photoacclimation. In this paper, we develop a dynamic model that accounts for the processes of photoinhibition, photoacclimation and growth in microalgae, thereby spanning multiple time scales. The properties of the model are investigated under quasi steady-state conditions and the model is validated against several experimental data sets from the literature. We also discuss how the model can provide new insights into the mechanisms underlying photoacclimation.
Affine phase based motion estimation applied to echocardiography

The purpose of this paper is to introduce an analytic affine transformation estimator based on the phase of two multidimensional analytical signals. Adapted to RF with transverse oscillations image model, the proposed estimator is shown to be more accurate than classical intensity based affine estimation. Applied to cardiac motion estimation, the proposed approach is shown to decrease by 20% the standard deviation errors reported to a recent phase-based method.
A fully automatic and multi-structural segmentation of the left ventricle and the myocardium on highly heterogeneous 2D echocardiographic data

2D echocardiography remains nowadays the main clinical imaging modality in daily practice for assessing the cardiac function. This task requires an accurate segmentation of the left ventricle (LV) and myocardium at end diastole (ED) and systole (ES). Because of intrinsic high variability in image quality in ultrasound data, manual interactions are still needed to obtain a precise delineation of the heart structures. This is both time consuming for specialists and not reproducible. In this study, we investigate a machine learning solution based on the Structured Random Forest algorithm to fully automate the segmentation of the myocardium and LV on heterogeneous clinical data. We compare its performance to the semi-automatic state of the art Active Appearance Model (AAM). The competitive results that were achieved lead us to believe that supervised learning may be the key to automatic heart segmentation.
A Virtual Imaging Platform for Multi-Modality Medical Image Simulation

This paper presents the Virtual Imaging Platform (VIP), a platform accessible at http://vip.creatis.insa-lyon.fr to facilitate the sharing of object models and medical image simulators, and to provide access to distributed computing and storage resources. A complete overview is presented, describing the ontologies designed to share models in a common repository, the workίow template used to integrate simulators, and the tools and strategies used to exploit computing and storage resources. Simulation results obtained in four image modalities and with different models show that VIP is versatile and robust enough to support large simulations. The platform currently has 200 registered users who consumed 33 years of CPU time in 2011.
Variational B-spline level-set method for fast image segmentation

In the field of image segmentation, most of level-set-based active contour approaches are based on a discrete representation of the associated implicit function. We present in this paper a different formulation where the level-set is modelled as a continuous parametric function expressed on a B-spline basis. Starting from the Mumford-Shah energy functional, we show that this formulation allows computing the solution as a restriction of the variational problem on the space spanned by the B-splines. As a consequence, the minimization of the functional is directly obtained in terms of the B-splines parameters. We also show that each step of this minimization may be expressed through a convolution operation. Because the B-spline functions are separable, this convolution may in turn be performed as a sequence of simple 1D convolutions, which yields a very efficient algorithm. The behaviour of this approach is illustrated on biomedical images from various fields.
A Framework for the Generation of Realistic Synthetic Cardiac Ultrasound and Magnetic Resonance Imaging Sequences From the Same Virtual Patients

The use of synthetic sequences is one of the most promising tools for advanced in silico evaluation of the quantification of cardiac deformation and strain through 3-D ultrasound (US) and magnetic resonance (MR) imaging. In this paper, we propose the first simulation framework which allows the generation of realistic 3-D synthetic cardiac US and MR (both cine and tagging) image sequences from the same virtual patient. A state-of-the-art electromechanical (E/M) model was exploited for simulating groundtruth cardiac motion fields ranging from healthy to various pathological cases, including both ventricular dyssynchrony and myocardial ischemia. The E/M groundtruth along with template MR/US images and physical simulators were combined in a unified framework for generating synthetic data. We efficiently merged several warping strategies to keep the full control of myocardial deformations while preserving realistic image texture. In total, we generated 18 virtual patients, each with synthetic 3-D US, cine MR, and tagged MR sequences. The simulated images were evaluated both qualitatively by showing realistic textures and quantitatively by observing myocardial intensity distributions similar to real data. In particular, the US simulation showed a smoother myocardium/background interface than the state-of-the-art. We also assessed the mechanical properties. The pathological subjects were discriminated from the healthy ones by both global indexes (ejection fraction and the global circumferential strain) and regional strain curves. The synthetic database is comprehensive in terms of both pathology and modality, and has a level of realism sufficient for validation purposes. All the 90 sequences are made publicly available to the research community via an open-access database.
A GPU level-set segmentation framework for 3D Echocardiography

Real-time 3D Echocardiography (RT3DE), providing truly volumetric images of the heart, is a promising imaging modality for cardiac morphology and function assessment. Fast and automatic segmentation of the left ventricle (LV) is essential to enable an efficient quantitative analysis of these 3D data. Recently, we proposed a GPU implementation of the Level-Set (LS) Sparse Field algorithm for 3D image segmentation which combines high computational efficiency and flexibility in the interface evolution. In this work, we make our GPU LS solver able to deal with strongly inhomogeneous images such as the myocardial wall in RT3DE. The applicability of our method in clinical environment was evaluated by measuring LV volumetric parameters on 23 RT3DE exams, and comparing them with reference values from manual contouring. Results show the effectiveness of our framework in performing accurate LV myocardium segmentation in RT3DE near real-time.
A restoration framework for ultrasonic tissue characterization

Ultrasonic tissue characterization has become an area of intensive research. This procedure generally relies on the analysis of the unprocessed echo signal. Because the ultrasound echo is degraded by the non-ideal system point spread function, a deconvolution step could be employed to provide an estimate of the tissue response that could then be exploited for a more accurate characterization. In medical ultrasound, deconvolution is commonly used to increase diagnostic reliability of ultrasound images by improving their contrast and resolution. Most successful algorithms address deconvolution in a maximum a posteriori estimation framework; this typically leads to the solution of ℓ2-norm or ℓ1-norm constrained optimization problems, depending on the choice of the prior distribution. Although these techniques are sufficient to obtain relevant image visual quality improvements, the obtained reflectivity estimates are, however, not appropriate for classification purposes. In this context, we introduce in this paper a maximum a posteriori deconvolution framework expressly derived to improve tissue characterization. The algorithm overcomes limitations associated with standard techniques by using a nonstandard prior model for the tissue response. We present an evaluation of the algorithm performance using both computer simulations and tissue-mimicking phantoms. These studies reveal increased accuracy in the characterization of media with different properties. A comparison with state-of-the-art Wiener and ℓ1-norm deconvolution techniques attests to the superiority of the proposed algorithm.
Fast and fully automatic 3D left ventricular segmentation using shape-based B-spline explicit active surfaces

Cardiac function assessment is a critical step in cardiology and 3D ultrasound plays an increasingly important role. Automatic left ventricular (LV) segmentation remains however challenging particularly in the presence of artifacts and for images with low contrast-to-noise ratio (CNR). It is thus crucial to give segmentation tools prior information on the LV shape in order to fill in the gaps when image content is low. In this work, a fast automatic framework for full cycle LV segmentation is proposed that uses shape information from cardiac magnetic resonance (cMR) for a more accurate segmentation.
Observers for the biotechnological processes with unknown kinetics. Application to wastewater treatment

We present nonlinear observers for a class of biotechnological processes. These observers are an extension of the asymptotic observers (observers with unknown inputs) devoted to biotechnological systems for which some parts of the model are unknown. We take benefit of the outputs which are nonlinear functions of the state to design a closed loop observer. The global convergence of these nonlinear observers is proven. The method is illustrated with an experimental application to a water treatment process.
3D Strain Assessment in Ultrasound (Straus): A Synthetic Comparison of Five Tracking Methodologies

This paper evaluates five 3D ultrasound tracking algorithms regarding their ability to quantify abnormal deformation in timing or amplitude. A synthetic database of B-mode image sequences modeling healthy, ischemic and dyssynchrony cases was generated for that purpose. This database is made publicly available to the community. It combines recent advances in electromechanical and ultrasound modeling. For modeling heart mechanics, the Bestel-Clement-Sorine electromechanical model was applied to a realistic geometry. For ultrasound modeling, we applied a fast simulation technique to produce realistic images on a set of scatterers moving according to the electromechanical simulation result. Tracking and strain accuracies were computed and compared for all evaluated algorithms. For tracking, all methods were estimating myocardial displacements with an error below 1 mm on the ischemic sequences. The introduction of a dilated geometry was found to have a significant impact on accuracy. Regarding strain, all methods were able to recover timing differences between segments, as well as low strain values. On all cases, radial strain was found to have a low accuracy in comparison to longitudinal and circumferential components.
Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography

Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE. A database consisting of 45 multivendor cardiac ultrasound recordings acquired at different centers with corresponding reference measurements from three experts are made available. The algorithms from nine research groups were quantitatively evaluated and compared using the proposed online platform. The results showed that the best methods produce promising results with respect to the experts' measurements for the extraction of clinical indices, and that they offer good segmentation precision in terms of mean distance error in the context of the experts' variability range. The platform remains open for new submissions.
Elastic registration vs. block matching for quantification of cardiac function with 3D ultrasound: Initial results of a direct comparison in silico based on a new evaluation pipeline

This paper presents a comparison between block matching and elastic registration for the estimation of cardiac deformation and strain from 3D ultrasound. The comparison study exploits a new evaluation pipeline recently developed by the authors. The pipeline generates synthetic sequences that are extremely similar to real ultrasound recording while the synthetic cardiac motion is fully controlled by an electro-mechanical model of the heart. Hereto, five synthetic sequences were generated corresponding to one healthy heart and four ischemic ones. Elastic registration returned both the smaller tracking errors and the most robust strain estimates. Although with limitations, this study brings further evidence that the new technique might be ready for extensive clinical testing.
Phase-based registration of cardiac tagged MR images using anatomical deformation model

We developed a phase-based registration method applied to cardiac motion and strain quantification from tagged MR images. A parametric deformation model defined in an anatomical space of the heart was incorporated into the registration framework. The model parameters were optimized by minimizing phase-based similarity criteria. The proposed method was compared to a previously developed algorithm where point-wise HARP tracking results were regularized a posteriori by the same parametric deformation model. On synthetic data, the two methods achieved similar results on motion and circumferential strain accuracies. However, our method achieved better accuracy on longitudinal strain. On volunteer data, our method gave smaller median and variance values on tracking errors with statistical significance.
Statistical modeling of the radio-frequency signal in echocardiographic images based on generalized Gaussian distribution

We study in this work the statistics of the radio frequency (RF) signal for both fully and partially developed speckle in echocardiographic images in the context of image segmentation and classification. From physical image formation model, we first derive the probability density function (PDF) of the RF signal using the K distribution framework. We then show that this pdf may be reliably approximated through a generalized Gaussian distribution. The ability of the proposed distribution to model RD echocardiographic signals from cardiac tissue and blood region is demonstrated on data acquired in vivo
Motion decorrelation in echocardiography: Analysis from a realistic simulation

Echocardiography has been widely used to perform motion estimation in real time for heart function analysis. However, this imaging modality may induce a decorrelation between the real tissue motion and its ultrasonic speckle in the corresponding echocardiographic images. Most of the studies that investigate this problem are based on the assumption of a spatially invariant point spread function (PSF) which represents an oversimplification of the echographic acquisition process. Moreover, these studies use linear scanning which is not realistic in the context of echocardiography where the acquisitions are performed using sectorial probes. In this paper, we thus study the influence of echocardiographic equipment and acquisition geometry on the apparent motion through a realistic simulations using Field II. Contrary to the results obtained from an invariant PSF assumption, we show that decorrelation appears even in the case of a simple axial translation. Axial deformation is also studied and we show that the amount of decorrelation depends on the geometry of the probe.
Generation of ultra-realistic synthetic echocardiographic sequences to facilitate standardization of deformation imaging

Despite the overwhelming availability of techniques for computation of cardiac deformation and strain with 2D echocardiography, their widespread dissemination in clinical practice is still held back by the reported low reproducibility between different solutions. This can in part be attributed to the lack of a solid and open quality assurance framework to assess and compare their performance. Building upon our previous work, we present here a new pipeline for the benchmarking of such algorithms making use of realistic 2D synthetic sequences. While the synthetic motion is fully controlled by a computational heart model, the visual appearance is extremely similar to a real ultrasound recording. The pipeline is used to generate an initial library of four sequences.
Myocardial Motion Estimation From Medical Images Using the Monogenic Signal

We present a method for the analysis of heart motion from medical images. The algorithm exploits monogenic signal theory, recently introduced as an N-dimensional generalization of the analytic signal. The displacement is computed locally by assuming the conservation of the monogenic phase over time. A local affine displacement model is considered to account for typical heart motions as contraction/expansion and shear. A coarse-to-fine B-spline scheme allows a robust and effective computation of the model's parameters, and a pyramidal refinement scheme helps to handle large motions. Robustness against noise is increased by replacing the standard point-wise computation of the monogenic orientation with a robust least-squares orientation estimate. Given its general formulation, the algorithm is well suited for images from different modalities, in particular for those cases where time variant changes of local intensity invalidate the standard brightness constancy assumption. This paper evaluates the method's feasibility on two emblematic cases: cardiac tagged magnetic resonance and cardiac ultrasound. In order to quantify the performance of the proposed method, we made use of realistic synthetic sequences from both modalities for which the benchmark motion is known. A comparison is presented with state-of-the-art methods for cardiac motion analysis. On the data considered, these conventional approaches are outperformed by the proposed algorithm. A recent global optical-flow estimation algorithm based on the monogenic curvature tensor is also considered in the comparison. With respect to the latter, the proposed framework provides, along with higher accuracy, superior robustness to noise and a considerably shorter computation time.
Compressive sensing in medical ultrasound

One of the fundamental theorem in information theory is the so-called sampling theorem also known as Shannon-Nyquist theorem. This theorem aims at giving the minimal frequency needed to sample and reconstruct perfectly an analog band-limited signal. Compressive sensing (or compressed sensing, compressive sampling) or CS in short is a recent theory that allows, if the signal to be reconstructed satisfies a number of conditions, to decrease the amount of data needed to reconstruct the signal. As a result this theory can be used for at least two purposes: i) accelerate the acquisition rate without decreasing the reconstructed signal quality (e.g. in terms of resolution, SNR, contrast ...) ii) improve the image quality without increasing the quantity of needed data. Even if medical ultrasound is a domain where several potential applications can be highlighted, the use of this theory in this domain is extremely recent. In this paper we review the basic theory of compressive sensing. Then, a review of the existing CS studies in the field of medical ultrasound is given: reconstruction of sparse scattering maps, pre-beamforming channel data, post-beamforming signals and slow time Doppler data. Finally the open problems and challenges to be tackled in order to make the application of CS to medical US a reality will be given.
Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.
Identification of reaction schemes for bioprocesses: Determination of an incompletely known yield matrix

In this paper we propose a methodology to determine the structure of the yield coefficient matrix K in a mass balance based model and to identify its coefficients from a set of available data. The first step consists in estimating the number of reactions that must be taken into account to represent the main mass transfer within the bioreactor. This provides the dimension of K. Then we propose a method to directly determine the structure of the matrix (i.e. mainly its zeros and the signs of its coefficients). These methods are illustrated with simulations of a process of lipase production from olive oil by Candida rugosa.