nils-herrmann/clean_scirepeval huggingface dataset:
Automated image-based screening of cell cultures for cell therapy

The goal of this project is to develop an automated computer vision system to acquire and analyze images of adherent cell cultures. These cultures are involved in the establishment of a cell therapy protocol using primary cells. Cultures are stained to facilitate the image segmentation based on color information. Nevertheless, the high density and the uneven dispersion of cells favor nuclei clustering which are identified by a nonparametric classification. This approach automatically fits the individual nuclei features which vary according to cultures. A split and merge post-processing is used to divide aggregates into individual nuclei. This automatic system is applied to the improvements of cell therapy protocol and to control the characteristics of cells during the clinical trial
Interacting multiple model based method to track moving fluorescent biological spots

The study of cell and pathogen motility in biology requires computerized methods to enable objective quantitative analysis of large amounts of data. In this paper, we propose a method to detect and track multiple moving biological objects showing different kind of dynamics in image sequences acquired through fluorescence video microscopy. It enables the extraction and analysis of informations such as number, position, speed and movement phases of, e.g., endosomes and viral particles. The method consists of four stages. After a detection stage performed by an undecimated wavelet transform, we compute, for each detected spot, several predictions of its future state in the next frame. This is accomplished thanks to an interacting multiple model (IMM) algorithm which includes several models corresponding to different movement types. Tracks are constructed thereafter by a data association algorithm based on the maximization of the estimated likelihood of each IMM. The last stage consists in updating the IMM filters in order to compute final estimations for the present image and to improve predictions for the next image. The performance of the method is illustrated on synthetic image data.
Combining Local Filtering and Multiscale Analysis for Edge, Ridge, and Curvilinear Objects Detection

This paper presents a general method for detecting curvilinear structures, like filaments or edges, in noisy images. This method relies on a novel technique, the feature-adapted beamlet transform (FABT) which is the main contribution of this paper. It combines the well-known beamlet transform (BT), introduced by Donoho , with local filtering techniques in order to improve both detection performance and accuracy of the BT. Moreover, as the desired feature detector is chosen to belong to the class of steerable filters, our transform requires only O(N log(N)) operations, where N = n 2 is the number of pixels. Besides providing a fast implementation of the FABT on discrete grids, we present a statistically controlled method for curvilinear objects detection. To extract significant objects, we propose an algorithm in four steps: 1) compute the FABT, 2) normalize beamlet coefficients, 3) select meaningful beamlets thanks to a fast energy-based minimization, and 4) link beamlets together in order to get a list of objects. We present an evaluation on both synthetic and real data, and demonstrate substantial improvements of our method over classical feature detectors.
Blind deconvolution for diffraction-limited fluorescence microscopy

Optical Sections of biological samples obtained from a fluorescence Confocal Laser Scanning Microscopes (CLSM) are often degraded by out-of-focus blur and photon counting noise. Such physical constraints on the observation are a result of the diffraction-limited nature of the optical system, and the reduced amount of light detected by the photomultiplier respectively. Hence, the image stacks can benefit from postprocessing restoration methods based on deconvolution. The parameters of the acquisition system's Point Spread Function (PSF) may vary during the course of experimentation, and so they have to be estimated directly from the observation data. We describe here an alternate minimization algorithm for the simultaneous blind estimation of the specimen 3D distribution of fluorescent sources and the PSF. Experimental results on real data show that the algorithm provides very good de- convolution results in comparison to theoretical microscope PSF models.
Unsupervised analysis of mouse social behaviour reveals how hyperactivity impacts social aspects in a mouse model of autism

Unsupervised classification of animal behaviour triggers growing interest. Tracking algorithms are allowing more and more efficient recording and thus produce increasing amount of data to be classified. In this context, we propose two levels of analysis to describe the behaviour of mouse models of neuropsychiatric disorders. We studied mice mutated in Shank2, a gene associated with autism spectrum disorders. We compared them with their wildtype littermates, regarding the proportions of behavioural events classified along time, and also the thresholds used to define these events. The developed algorithm allowed to understand to what extent hyperactivity could explain social deficits in Shank2 mutant mice.
Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: a tool for cell-based drug testing

This paper presents a segmentation and tracking method for quantitative analysis of cell dynamics from in vitro videomicroscopy data. The method is based on parametric active contours and includes several adaptations that address important difficulties of cellular imaging, particularly the presence of low-contrast boundary deformations known as pseudopods, and the occurence of multiple contacts between cells. First, we use an edge map based on the average intensity dispersion that takes advantage of relative background homogeneity to facilitate the detection of both pseudopods and interfaces between adjacent cells. Second, we introduce a repulsive interaction between contours that allows correct segmentation of objects in contact and overcomes the shortcomings of previously reported techniques to enforce contour separation. Our tracking technique was validated on a realistic data set by comparison with a manually defined ground-truth and was successfully applied to study the motility of amoebae in a biological research project.
Using physics engines to track objects in images

This paper tackles the problem of tracking multiple articulated objects undergoing frequent contacts in a video sequence. Conventional tracking methods usually fail to distinguish objects during contact, implying the use of disambiguation techniques to recover the identity of each object. Moreover, such methods do not provide detailed shape information at the articulation level. We address these limitations by proposing a novel approach to track multiple articulated objects using the mean-shift technique and physics engines. By defining a model of each object using geometrical primitives and physical constraints, we exploit the physics engine force solver as a control layer of the mean-shift process and follow the model and its deformation along the sequence. The method is applied to track mice observed with a webcam and two sporozoites observed in reflection interference contrast microscopy, highlighting the flexibility and genericity of the framework.
Point-spread function model for fluorescence MACROscopy imaging

In this paper, we model the point-spread function (PSF) of a fluorescence MACROscope with a field aberration. The MACROscope is an imaging arrangement that is designed to directly study small and large specimen preparations without physically sectioning them. However, due to the different optical components of the MACROscope, it cannot achieve the condition of lateral spatial invariance for all magnifications. For example, under low zoom settings, this field aberration becomes prominent, the PSF varies in the lateral field, and is proportional to the distance from the center of the field. On the other hand, for larger zooms, these aberrations become gradually absent. A computational approach to correct this aberration often relies on an accurate knowledge of the PSF. The PSF can be defined either theoretically using a scalar diffraction model or empirically by acquiring a three-dimensional image of a fluorescent bead that approximates a point source. The experimental PSF is difficult to obtain and can change with slight deviations from the physical conditions. In this paper, we model the PSF using the scalar diffraction approach, and the pupil function is modeled by chopping it. By comparing our modeled PSF with an experimentally obtained PSF, we validate our hypothesis that the spatial variance is caused by two limiting optical apertures brought together on different conjugate planes.
Characterization of cell shape and deformation in 3D using Spherical Harmonics

Understanding the mechanisms involved in cell deformation and motility is of major interest in numerous areas of life sciences. Precise quantification of cell shape requires robust shape description tools to be amenable to subsequent analysis and classification. The main difficulty lies in the great variability of cell shapes within so-called ”homogeneous” populations. While basic shape descriptors fail to provide sufficient information and lack robustness to small shape variabilities, here we investigate the use of the Spherical Harmonics transform to efficiently extract and quantify cell shape and deformation. Using real 3D+t biological imaging data sets, we show that this tool allows to precisely characterize the cell shape both in a static and a dynamic manner, allowing to extract a wide range of qualitative and quantitative parameters, such as outliers individuals, redundant shape configurations and spatiotemporal deformation patterns.
A deconvolution method for confocal microscopy with total variation regularization

Confocal laser scanning microscopy is a powerful and increasingly popular technique for 3D imaging of biological specimens. However the acquired images are degraded by blur from out-of-focus light and Poisson noise due to photon-limited detection. Several deconvolution methods have been proposed to reduce these degradations, including the Richardson-Lucy algorithm, which computes a maximum likelihood estimation adapted to Poisson statistics. However this method tends to amplify noise if used without regularizing constraint. Here, we propose to combine the Richardson-Lucy algorithm with a regularizing constraint based on total variation, whose smoothing avoids oscillations while preserving edges. We show on simulated images that this constraint improves the deconvolution result both visually and using quantitative measures.
Morphological source separation for particle tracking in complex biological environments

Tracking nano-metric particles in a biological environment is a very difficult task because of the low signal intensity and the high mobility of these small objects. The task becomes nearly impossible for classical tracking procedures when the targets are labeled with a marker that is not strictly specific, because in this case dynamic structures in the cell are also visible. To address this limitation, we propose to use a source separation technique based on sparsity principles which allows the discrimination of objects with different morphologies. We prove in a real case that tracking in the source separated images allows to track particles that interact with other sources, something which was not feasible until now. This capability opens up new perspectives for the analysis documenting intricate interactions between cellular compartments.
Numerical evaluation of sampling bounds for near-optimal reconstruction in compressed sensing

In this paper, we propose an empirical review of the conditions under which the compressed sensing framework allows to achieve exact image reconstruction. After a short presentation of the theoretical results related to this subject, we investigate the relevance and the limits of these theoretical results through several numerical reconstructions of some benchmark images. In particular, we discuss quantitative and qualitative artifacts that affect the reconstructed image when reducing the number of measurements in the Fourier domain. Finally, we conclude our study by extending our results to some real microscopic images.
Denoising of Microscopy Images: A Review of the State-of-the-Art, and a New Sparsity-Based Method

This paper reviews the state-of-the-art in denoising methods for biological microscopy images and introduces a new and original sparsity-based algorithm. The proposed method combines total variation (TV) spatial regularization, enhancement of low-frequency information, and aggregation of sparse estimators and is able to handle simple and complex types of noise (Gaussian, Poisson, and mixed), without any a priori model and with a single set of parameter values. An extended comparison is also presented, that evaluates the denoising performance of the thirteen (including ours) state-of-the-art denoising methods specifically designed to handle the different types of noises found in bioimaging. Quantitative and qualitative results on synthetic and real images show that the proposed method outperforms the other ones on the majority of the tested scenarios.
Video reconstruction using compressed sensing measurements and 3d total variation regularization for bio-imaging applications

The theory of compressed sensing (CS) predicts that random (or pseudo-random) linear measurements together with non-linear reconstruction can be used to sample and recover structured signals in a compressive manner. Lots of previous results demonstrated the efficiency of CS in recovering 2D images acquired using dedicated CS devices (single-pixel camera, accelerated MRI, etc...). In this paper, we investigate how this framework can be extended to perform an efficient joint reconstruction of a sequence of time-correlated 2D images, using 3D total variation regularization. We also evaluate the performances of this framework on test sequences issued from the bio-imaging field.
Bayesian Estimation for Optimized Structured Illumination Microscopy

Structured illumination microscopy is a recent imaging technique that aims at going beyond the classical optical resolution by reconstructing high-resolution (HR) images from low-resolution (LR) images acquired through modulation of the transfer function of the microscope. The classical implementation has a number of drawbacks, such as requiring a large number of images to be acquired and parameters to be manually set in an ad-hoc manner that have, until now, hampered its wide dissemination. Here, we present a new framework based on a Bayesian inverse problem formulation approach that enables the computation of one HR image from a reduced number of LR images and has no specific constraints on the modulation. Moreover, it permits to automatically estimate the optimal reconstruction hyperparameters and to compute an uncertainty bound on the estimated values. We demonstrate through numerical evaluations on simulated data and examples on real microscopy data that our approach represents a decisive advance for a wider use of HR microscopy through structured illumination.
Active contours for the movement and motility analysis of biological objects

We present a complete framework for automating the objective and reliable assessment of biological objects motility. We address, in the context of one particular application, some of the main problems in dealing with the automatic tracking of biological objects, which are: (1) the robust segmentation of individual objects which is quite often hindered by the poor contrast of the imaging technique; (2) the proper initialisation of multiple objects description; and (3) the problem of temporary object aggregation, during which objects loose their individuality. We have implemented the active contour model based on the gradient vector field model (Xu and Prince, 1998) and adapted it in order to overcome some of its limitations in dealing with biological related objects. Results are presented of the analysis of video sequences of a living amoeba.
3-D Active Meshes: Fast Discrete Deformable Models for Cell Tracking in 3-D Time-Lapse Microscopy

Variational deformable models have proven over the past decades a high efficiency for segmentation and tracking in 2-D sequences. Yet, their application to 3-D time-lapse images has been hampered by discretization issues, heavy computational loads and lack of proper user visualization and interaction, limiting their use for routine analysis of large data-sets. We propose here to address these limitations by reformulating the problem entirely in the discrete domain using 3-D active meshes, which express a surface as a discrete triangular mesh, and minimize the energy functional accordingly. By performing computations in the discrete domain, computational costs are drastically reduced, whilst the mesh formalism allows to benefit from real-time 3-D rendering and other GPU-based optimizations. Performance evaluations on both simulated and real biological data sets show that this novel framework outperforms current state-of-the-art methods, constituting a light and fast alternative to traditional variational models for segmentation and tracking applications.
Fluid dynamics modeling of cell and membrane deformations

We propose a realistic model of cell and membrane deformations based on fluid mechanics. We focus on the blebbing process, a key mechanism of motility for several cell types. It is seen as a rupture of symmetry in the surface tension forces affecting the membrane of the cell. It produces sudden protrusions in regions where the tension forces vanish, inducing large displacements of cell material and whole-cell movement. We describe the cell and the external medium as fluids subject to Stokes equations, and the cell membrane as an interface defined by an advected level set function and subject to surface tension. We setup a numerical implementation in two dimensions using finite elements and show that the model successfully produces whole-cell movement, without involving substrate adhesion nor specific organelles. This work paves the way for further studies on the shape and dynamics of the blebs, in direct relation with live cell imaging.
An Overview of Image Analysis in Multidimensional Biological Microscopy

Following an overview of image analysis applications in 2D and 3D dynamic biological microscopy, we present work developed in our laboratory dedicated to two central aspects of cell biology of infection, cell shape and motility analysis and particle tracking. We describe a fully automatic segmentation and tracking method designed to enable quantitative analyses of cellular shape and motion from 4D (3D+t) microscopy data. To get at a better understanding of pathogens/host cell interactions and to document infectious disease processes in living systems, it is necessary to characterise the dynamic properties of pathogens. We describe a method to detect and track multiple moving biological spot-like particles showing different kind of dynamics in image sequences acquired through multidimensional fluorescence microscopy
Tracking of multiple fluorescent biological objects in three dimensional video microscopy

A method which allows for the first time to perform automatically the detection and the tracking of microscopic objects directly from three dimensional image data is presented. It enables to analyse biological moving objects in three dimensional fluorescence image sequences coming from biological immunomicroscopy experiments, and get quantitative data such as the number of objects, their position, movement phases and speed. After a detection step is performed through the multiscale analysis of images using a shift-invariant wavelet transform, the tracking is achieved using a Kalman filter and an association which enable the position of the moving objects to be predicted, refined and updated. Trajectories are analysed in terms of different parameters relevant for the motility analysis of biological objects.
A Guided Tour of Selected Image Processing and Analysis Methods for Fluorescence and Electron Microscopy

Microscopy imaging, including fluorescence micro scopy and electron microscopy, has taken a prominent role in life science research and medicine due to its ability to investigate the 3D interior of live cells and organisms. A long-term research in bio-imaging at the sub-cellular and cellular scales consists then in inferring the relationships between the dynamics of macromolecules and their functions. In this area, image processing and analysis methods are now essential to understand the dynamic organization of groups of interacting molecules inside molecular machineries and to address issues in fundamental biology driven by advances in molecular biology, optics and technology. In this paper, we present recent advances in fluorescence and electron microscopy and we focus on dedicated image processing and analysis methods required to quantify phenotypes for a limited number but typical studies in cell imaging.
Multiple particle tracking in 3-D+t microscopy: method and application to the tracking of endocytosed quantum dots

We propose a method to detect and track multiple moving biological spot-like particles showing different kinds of dynamics in image sequences acquired through multidimensional fluorescence microscopy. It enables the extraction and analysis of information such as number, position, speed, movement, and diffusion phases of, e.g., endosomal particles. The method consists of several stages. After a detection stage performed by a three-dimensional (3-D) undecimated wavelet transform, we compute, for each detected spot, several predictions of its future state in the next frame. This is accomplished thanks to an interacting multiple model (IMM) algorithm which includes several models corresponding to different biologically realistic movement types. Tracks are constructed, thereafter, by a data association algorithm based on the maximization of the likelihood of each IMM. The last stage consists of updating the IMM filters in order to compute final estimations for the present image and to improve predictions for the next image. The performances of the method are validated on synthetic image data and used to characterize the 3-D movement of endocytic vesicles containing quantum dots.
Particle tracking in fluorescent microscopy images improved by morphological source separation

Particle detection and tracking methods generally assume a simplistic image model that is rarely valid when imaging biological processes in fluorescence microscopy. The tracking task may become nearly impossible when complex biological structures are visible and interfere with the signal of interest. To address this limitation we have adapted a source separation technique based on sparsity principles to the characteristics of fluorescent biological images. Since it allows the discrimination of objects with different morphologies, we present an approach to detect and track particles that exploits its results. The tracking algorithm resolves particles that temporarily aggregate by exploiting the proposed model of image. We prove in a real case the ability of the method to track numerous particles in a complex and dynamic background, something which was not feasible until now, hence offering new tools to document interactions between cellular compartments.