nils-herrmann/clean_scirepeval huggingface dataset:
Prostate segmentation in echographic images: A variational approach using deformable super-ellipse and rayleigh distribution

We present a simple, robust and computationally efficient method for the semi-automatic segmentation of the prostate in TRUS (Transrectal Ultrasound) image. The method relies on a variational formulation based on a deformable super- ellipse and a region energy based on the assumption of a Rayleigh distribution. Instead of using the classical level-set approach, we directly insert the implicit representation of a deformable super-ellipse into the energy to minimize. This yields a super-ellipse evolution able to accurately segment prostate and surrounding tissues while handling boundary gaps on the contour.
Level-set segmentation of myocardium and epicardium in ultrasound images using localized Bhattacharyya distance

Level-sets methods have successfully been used for segmentation of the endocardial border in cardiac US images. Robust methods have been proposed within a Bayesian framework  . However, segmentation of the whole myocardial wall is still challenging given that the epicardial boundary is highly heterogeneous and discontinuous. The presence of papillary muscles may also complicate endocardial boundary detection. We propose a novel level-set technique for combined endo- and epicardial boundaries segmentation. Hereto a localizing approach is used to overcome limitations associated with global techniques. A comparison with such a global level-set approach presented in  is made.
Simulation of realistic echocardiographic sequences for ground-truth validation of motion estimation

We present a framework for the simulation of realistic cardiac ultrasound sequences. Both the visual aspect and the synthesized motion mimic a real echocardiography sequence used as template. The resulting simulation appears virtually indistinguishable from a real scan. As the true tissue motion is known, these synthetic sequences can provide a trustful benchmark for the analysis of heart motion. This possibility is illustrated by comparing the performance of two well known motion estimation algorithms.
A fully automatic and multi-structural segmentation of the left ventricle and the myocardium on highly heterogeneous 2D echocardiographic data

Although 3D ultrasound plays an increasingly important role, 2D echocardiography remains the main clinical imaging modality for cardiac function assessment in daily practice. This requires precise delineation of the myocardium at end diastole (ED) and systole (ES). Because of intrinsic high variability in image quality, manual interactions are still needed. In this study, we investigate a machine learning solution to fully automate the segmentation of the myocardium on heterogeneous dataset.
Tangential oscillations for motion estimation in echocardiography

In this paper we present a feasibility study of transverse oscillations in the field of echocardiography. Transverse oscillations beamforming methods have been shown to be able to improve the accuracy of local motion estimation with ultrasound images. This has only been studied for linear geometry while this paper shows the feasibility for sectorial geometry used in echocardiography. The possibility to obtain point spread functions showing transverse oscillations is demonstrated and the application for motion estimation is also studied. The improvement is mostly in terms of robustness of the lateral motion estimated. Indeed the standard deviation of the estimated lateral motion is decreased by more than a factor of 2 when the axial motion is less than 0.04 mm.
Coupled B-spline active geometric functions for myocardial segmentation: A localized region-based approach

Although echocardiographic image segmentation has been a hot topic for long and several methods exist for robust endocardial segmentation, little attention has been directed to full myocardial segmentation. In fact, full myocardial segmentation is particularly challenging due to the high heterogeneity in the appearance of the epicardial boundary in US images. In the present paper, we propose an extension to the recently introduced Active Geometric Functions segmentation framework in order to cope with data inhomogeneities. This method has been successfully applied to short axis B-mode images acquired in a clinical setting. The mean absolute distance between the algorithm and the reference contours provided by an expert physician is 2.81±0.7 pixels. The computation time per frame is around 1 second, in a non-optimized MATLAB implementation, which shows that this method can be extended to achieve realtime performance.
Compactly Supported Radial Basis Functions Based Collocation Method for Level-Set Evolution in Image Segmentation

The partial differential equation driving level-set evolution in segmentation is usually solved using finite differences schemes. In this paper, we propose an alternative scheme based on radial basis functions (RBFs) collocation. This approach provides a continuous representation of both the implicit function and its zero level set. We show that compactly supported RBFs (CSRBFs) are particularly well suited to collocation in the framework of segmentation. In addition, CSRBFs allow us to reduce the computation cost using a kd-tree-based strategy for neighborhood representation. Moreover, we show that the usual reinitialization step of the level set may be avoided by simply constraining the l1-norm of the CSRBF parameters. As a consequence, the final solution is topologically more flexible, and may develop new contours (i.e., new zero-level components), which are difficult to obtain using reinitialization. The behavior of this approach is evaluated from numerical simulations and from medical data of various kinds, such as 3-D CT bone images and echocardiographic ultrasound images.
Near optimal interval observers bundle for uncertain bioreactors

In this paper we design an interval observer for the estimation of unmeasured variables of uncertain bioreactors. The observer is based on a bounded error observer, as proposed in , that considers a loose approximation of the growing rate. We first show how to generate guaranteed upper and lower bounds on the state, provided that known intervals for the initial condition and the uncertainties are available. These so called “framers” depend on a tuning gain. They can be run in parallel and the envelope provides the best estimate. An optimality criterion is introduced leading to the definition of an optimal observer. We show that this criterion provides straightforwardly a gain set containing the best framers. The method is applied to the estimation of the total biomass of an industrial wastewater treatment plant, demonstrating its efficiency.
Interval observers for planar systems with complex poles

In some parametric domains, the problem of designing an exponentially stable interval observer for an exponentially stable two dimensional time-invariant linear system is an open problem. We show that, in some cases, no linear time-invariant change of coordinates can help to construct exponentially stable interval observers. Next, we exhibit a time-varying change of coordinates transforming the original system into a form for which exponentially stable interval observers can be easily constructed. An interval observer is then presented in the case where additional disturbances are present in the dynamics, and applied to the chaotic Chua's system.
Cardiac motion assessment from echocardiographic image sequences by means of the structure multivector

We recently contributed an algorithm for the estimation of cardiac deformation from echocardiographic image sequences based on the monogenic signal. By exploiting the phase information instead of the pixel intensity, the algorithm was robust to the temporal contrast variations normally encountered in cardiac ultrasound. In this paper we propose an improvement of that framework making use of an extension of the monogenic signal formalism, called structure multivector. The structure multivector models the image as the superposition of two perpendicular waves with associated amplitude, phase and orientation. Such a model is well adapted to describe the granular pattern of the characteristic speckle noise. The displacement is computed by solving the optical flow equation jointly for the two image phases. A local affine model accounts for typical cardiac motions as contraction/expansion and shearing; a coarse-to-fine B-spline scheme allows for a robust and effective computation of the model parameters and a pyramidal refinement scheme helps deal with large motions. Performance was evaluated on realistic simulated cardiac ultrasound sequences and compared to our previous monogenic-based algorithm and classical speckle tracking. Endpoint-error was used as accuracy metric. With respect to them we achieved error reductions of 13% and 30% respectively.
Creaseg: A free software for the evaluation of image segmentation algorithms based on level-set

This paper describes a free open source software in Matlab (named Creaseg, http://www.creatis.insa-lyon. fr/∼bernard/creaseg) for the evaluation of the performance of different level-set based algorithms in the context of 2D image segmentation. The platform gives access to the implementation of six level-set methods that have been chosen in order to cover a wide range of data attachment terms (contour, region and localized approaches). The software also gives the possibility to compare the performance of the proposed algorithms on any kind of images. The performance can be evaluated either visually, or from similarity measurements between a reference and the results of the segmentation.
Variational B-Spline Level-Set: A Linear Filtering Approach for Fast Deformable Model Evolution

In the field of image segmentation, most level-set-based active-contour approaches take advantage of a discrete representation of the associated implicit function. We present in this paper a different formulation where the implicit function is modeled as a continuous parametric function expressed on a B-spline basis. Starting from the active-contour energy functional, we show that this formulation allows us to compute 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-spline coefficients. 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 1-D convolutions, which yields an efficient algorithm. As a further consequence, each step of the level-set evolution may be interpreted as a filtering operation with a B-spline kernel. Such filtering induces an intrinsic smoothing in the algorithm, which can be controlled explicitly via the degree and the scale of the chosen B-spline kernel. We illustrate the behavior of this approach on simulated as well as experimental images from various fields.
Using a geometric formulation of annular-like shape priors for constraining variational level-sets

In this paper we address the segmentation of shapes which may be approximated by two elliptical contours. Such patterns are indeed recurrent in many image processing applications. In this context, we develop a level-set framework especially dedicated to the detection of annular-like shapes. The behavior of this approach is illustrated on images from different fields. An evaluation is then performed for myocardium detection in medical images.
Compressed delay-and-sum beamforming for ultrafast ultrasound imaging

The theory of compressed sensing (CS) leverages upon structure of signals in order to reduce the number of samples needed to reconstruct a signal, compared to the Nyquist rate. Although CS approaches have been proposed for ultrasound (US) imaging with promising results, practical implementations are hard to achieve due to the impossibility to mimic random sampling on a US probe and to the high memory requirements of the measurement model. In this paper, we propose a CS framework for US imaging based on an easily implementable acquisition scheme and on a delay-and-sum measurement model.
B-Spline Explicit Active Surfaces: An Efficient Framework for Real-Time 3-D Region-Based Segmentation

A new formulation of active contours based on explicit functions has been recently suggested. This novel framework allows real-time 3-D segmentation since it reduces the dimensionality of the segmentation problem. In this paper, we propose a B-spline formulation of this approach, which further improves the computational efficiency of the algorithm. We also show that this framework allows evolving the active contour using local region-based terms, thereby overcoming the limitations of the original method while preserving computational speed. The feasibility of real-time 3-D segmentation is demonstrated using simulated and medical data such as liver computer tomography and cardiac ultrasound images.
Towards real-time 3D region-based segmentation: B-spline explicit active surfaces

We introduce in this paper a formulation of region-based active contours using explicit surfaces. By formally relating the explicit formulation to the usual implicit formulation, we show that it allows to take advantage of the local or global region-based functionals designed in the level-set framework, while providing a fast algorithm yielding close to real-time segmentation of 3D objects. We compare the segmentation performance of the proposed method with the fast level-set based method of Shi . In similar conditions, the proposed method is 26x to 46x faster. Furthermore, we show that it can handle challenging, inhomogeneous data, while keeping a low computational burden.
Influence of system geometry on motion tracking in echocardiographic image sequences

In this paper, we present a study on motion tracking in echocardiographic ultrasound images. The difficulty of motion estimation is linked to the fact that echographic image formation induces decorrelation between the underlying motion of tissue and the observed speckle motion. We investigate in this work the influence of speckle decorrelation on motion estimation from realistic simulations using Field II [6]. In particular, the analysis is performed for two simple motions: a translation and an incompressible deformation. The influence of decorrelation on speckle tracking accuracy is quantified using a conventional block matching (BM) algorithm and a more recent algorithm based on bilinear deformable block matching (BDBM). For all the simulations, the influence of the sectorial acquisition geometry is also investigated by performing motion estimation on Cartesian and polar data. Results show that decorrelation and loss of accuracy are significantly larger when motion estimation is performed from the conventional interpolated Cartesian data. In this perspective, we also show that the accuracy of motion estimation can be improved by using the BDBM algorithm, which may to some extent compensate for the deformation of the speckle patterns induced by decorrelation and sectorial geometry
Multiview myocardial tracking in echocardiographic 2D sequences using shape and motion constrained level-set

Segmentation of the myocardium in echocardiographic images is an important task for the diagnosis of heart disease. This task is difficult due to the inherent problems of echographic images (i.e. low contrast, speckle noise, signal dropout, presence of shadows). In this article, we extend a level-set method recently proposed in  in order to track the whole myocardium in echocardiographic sequences. To this end, we formulate a new motion prior energy that constrains the zero-level of the implicit function to satisfy the optical flow hypothesis. The algorithm is then compared to experts references and to another method on a dataset of 12 sequences (more than 700 images) acquired in the four main echocardiographic views.
Realistic Vendor-Specific Synthetic Ultrasound Data for Quality Assurance of 2-D Speckle Tracking Echocardiography: Simulation Pipeline and Open Access Database

Two-dimensional (2-D) echocardiography is the modality of choice in the clinic for the diagnosis of cardiac disease. Hereto, speckle tracking (ST) packages complement visual assessment by the cardiologist by providing quantitative diagnostic markers of global and regional cardiac function (e.g., displacement, strain, and strain-rate). Yet, the reported high vendor-dependence between the outputs of different ST packages raises clinical concern and hampers the widespread dissemination of the ST technology. In part, this is due to the lack of a solid commonly accepted quality assurance pipeline for ST packages. Recently, we have developed a framework to benchmark ST algorithms for 3-D echocardiography by using realistic simulated volumetric echocardiographic recordings. Yet, 3-D echocardiography remains an emerging technology, whereas the compelling clinical concern is, so far, directed to the standardization of 2-D ST only. Therefore, by building upon our previous work, we present in this paper a pipeline to generate realistic synthetic sequences for 2-D ST algorithms. Hereto, the synthetic cardiac motion is obtained from a complex electromechanical heart model, whereas realistic vendor-specific texture is obtained by sampling a real clinical ultrasound recording. By modifying the parameters in our pipeline, we generated an open-access library of 105 synthetic sequences encompassing: 1) healthy and ischemic motion patterns; 2) the most common apical probe orientations; and 3) vendor-specific image quality from seven different systems. Ground truth deformation is also provided to allow performance analysis. The application of the provided data set is also demonstrated in the benchmarking of a recent academic ST algorithm.
Extension of Ultrasound Fourier Slice Imaging theory to sectorial acquisition

Ultrasound image reconstruction from the echoes received by an ultrasound probe after the transmission of diverging waves is an active area of research because of its capacity to insonify at ultra-high frame rate with large regions of interest using small phased arrays as the ones used in echocardiography. Current state-of-the-art techniques are based on the emission of diverging waves and the use of delay and sum strategies applied on the received signals to reconstruct the desired image (DW/DAS). Recently, we have introduced the concept of Ultrasound Fourier Slice Imaging (UFSI) theory for the reconstruction of ultrafast imaging for linear acquisition. In this study, we extend this theory to sectorial acquisition thanks to the introduction of an explicit and invertible spatial transform. Starting from a diverging wave, we show that the direct use of UFSI theory along with the application of the proposed spatial transform allows reconstructing the insonified medium in the conventional Cartesian space. Simulations and experiments reveal the capacity of this new approach in obtaining competitive quality of ultrafast imaging when compared with the current reference method.
Compressed sensing reconstruction of 3D ultrasound data using dictionary learning

In this paper we propose a compressed sensing (CS) method adapted to 3D ultrasound imaging (US). In contrast to previous work, we propose a new approach based on the use of learned overcomplete dictionaries. Such dictionaries allow for much sparser representations of the signals since they are optimized for a particular class of images such as US images. We will investigate two undersampling patterns of the 3D US imaging: a spatially uniform random acquisition and a line-wise random acquisition. The latter being extremely interesting for 3D imaging: it would indeed allow skipping the acquisition of many lines among the several thousands required in 3D acquisitions, thus, speeding up the whole acquisition process and incrementing the imaging rate. In this study, the dictionary was learned using the K-SVD algorithm on patches extracted from a training dataset constituted of simulated 3D non-log envelope US volumes. Experiments were performed on a testing dataset made of a simulated 3D US log-envelope volume not included in the testing dataset. CS reconstruction was performed by removing 20% to 80% of the original samples according to the two undersampling patterns. Reconstructions using a K-SVD dictionary previously trained dictionary indicate minimal information loss, thus showing the potential of the overcomplete dictionaries.
Speckle decorrelation of motion in Ultrasound Fourier images

Ultrasound Fourier imaging was firstly proposed by J-Y Lu  during the 90's to obtain very high frame rate and recently revisited by Gueth et al.  and Garcia et al. . These methods were evaluated by measuring the static characteristics of the reconstructed images, but no study attempted to evaluate their dynamic behavior, i.e. the influence of this image formation on motion estimation. This study focuses on the speckle decorrelation when different kinds of motions are applied. Simulation results show that speckle decorrelation of Fourier imaging methods is close to the conventional delay and sum (DAS) method and even better in the case of high frame rate. This study shows that Fourier imaging has strong potential to improve motion estimation compared to methods applied in conventional imaging.
Sparse regularization methods in ultrafast ultrasound imaging

Ultrafast ultrasound (US) imaging based on plane wave (PW) insonification is a widely used modality nowadays. Two main types of approaches have been proposed for image reconstruction either based on classical delay-and-sum (DAS) or on Fourier reconstruction. Using a single PW, these methods lead to a lower image quality than DAS with multi-focused beams. In this paper we review recent beamforming approaches based on sparse regularization methods. The imaging problem, either spatial-based (DAS) or Fourier-based, is formulated as a linear inverse problem and convex optimization algorithms coupled with sparsity priors are used to solve the ill-posed problem. We describe two applications of the framework namely the sparse inversion of the beamforming problem and the compressed beamforming in which the framework is combined with compressed sensing. Based on numerical simulations and experimental studies, we show the advantage of the proposed methods in terms of image quality compared to classical methods.
Reduction of the Anaerobic Digestion Model No1 for an industrial wastewater treatment plant by principal component analysis

The Anaerobic Digestion Model N°1 (ADM1) is accepted widely as a common platform for anaerobic process modelling and simulation. However, this model has an excessive complexity and a large number of parameters and states. This hinders its calibration and its use in control applications, and may render it inadequate for development of state observers. Therefore, a technique of model reduction for biological models based in principal component analysis was applied to identifying a new reduced stoichiometric matrix. The method shows that the main model features can be obtained with 2 reactions. Then, the kinetic parameters have been estimated on the base of representative known biological kinetics (Haldane or Monod). The reduced developed model takes into account the measured states in anaerobic wastewater treatment plants and captures the good qualitative behaviour of the ADM1. The reduced model accurately reproduces the dynamics of the process.