nils-herrmann/clean_scirepeval huggingface dataset:
Automated super-resolution detection of fluorescent rods in 2D

We describe a method designed to detect fluorescent rods from 2D microscopy images. It is motivated by the desire to study the dynamics of bacteria such as Shigella. The methodology is adapted from a super-resolution spot detection algorithm and is based on a parametric model of the rods and the microscope PSF. The algorithm consists of two parts: 1) a pre-detection step, based on thresholding a score computed from the product of the mean curvature and the local intensity of the filtered image, 2) an iterative procedure, where a mixture model of blurred segments is fitted to the image, and segments are removed, then added under the control of hypothesis tests. An upper bound is provided for the probability of erroneously detecting rods in noise. We show that the algorithm can reliably detect and accurately localize rods from low SNR images and can distinguish rods separated by subresolution distances. We also illustrate its ability to identify and separate overlapping bacteria on a real image
Phase retrieval with sparsity priors and application to microscopy video reconstruction

The theory of compressed sensing (CS) predicts that structured images can be sampled in a compressive manner with very few nonadaptive linear measurements, made in a proper adjacent domain. However, is such a recovery still possible with non-linear measurements, such as optical-based Fourier modulus? In this paper, we study the problem of Fourier phase retrieval required for optical Fourier CS imaging. We propose an algorithm to solve this problem, exploiting a specific TV-based regularization constraint. We demonstrate the performance of the proposed method on synthetic and real test sequences, in the context of microscopy video reconstructions.
A statistical analysis of spatial clustering along cell filaments using Ripley's K function

The analysis of the spatial distribution of molecules along one dimensional structures, such as filaments of the cell's cytoskeleton, gives essential information on intracellular transport mechanisms. The standard tool for analyzing molecules' organizationis the Ripley's K function, which permits to statistically test the hypothesis of molecules' random distribution against clustering or dispersion. However, the computation of the critical quantiles of Ripley's K function is currently based on Monte-Carlo simulations, which induces a high computational load and hinders its use. Here, we present an analytical expression of these quantiles for 1D filaments, leading to a fast and robust statistical test. Thereafter, we used our statistical test to analyze the spatial distribution of proteins involved in intraflagellar transport along the flagellum of the parasite Typanosoma brucei.
Bioimaging: A new frontier area for signal processing research

It is now a common consensus that in-depth understanding of biological systems requires robust and systematic quantification of their spatiotemporal characteristics. This endeavour has been significantly enlightened over the past decades thanks to the numerous advances in fluorescent probes, labeling techniques and microscopy systems. It is likewise a consensus that more powerful and specific signal/image processing methods are henceforth required to process the large amounts and variety of signals in biology and biomedicine. Here we give a panorama of some state-of-the-art image processing methods and also point at signal processing challenges and outline topics for future research.
Detection of linear structures in biological images

We present a method for detecting linear structures in biological microscopy images. The contribution of this paper is to unify the Beamlet transform with linear filtering techniques and propose a new detector, the feature-adpated beamlet transform. Our detector is able to incorporate knowledge about the desired line-profile lying along curves, like edges or ridges. We propose an efficient implementation in the case the profile is designed as a steerable filter. Preliminary results on biological images containing both edge- and ridge-like profiles have shown significant improvements over linear detector techniques and multiscale detection techniques based on traditional Beamlet transform.
Sparsity-based simplification of spectral-domain optical coherence tomography images of cardiac samples

We propose a sparsity-based simplification method for Spectral Domain Optical Coherence Tomography (SD-OCT) images of cardiac samples, displaying layers of tissue. Inspired by the Compressed Sensing (CS) theory, we implement a dedicated sparse sampling of SD-OCT samples achieving image simplification suited for layers segmentation, which is the target application. We compare our simplified images to state of the art denoising approaches. We also validate a straightforward segmentation approach on the variance map of the simplified images against manual delineation on raw SD-OCT images of in-vitro biological samples from four human hearts. Finally, we correlate average layer thickness with histopatho-logical measures.
Statistical color texture descriptors for histological images analysis

In this paper we compare different approaches to combine color and statistical texture descriptors. Previous studies on this topic were conducted on natural images only. We focus on the particular case of histological datasets where color plays an important role due to the staining process of the biological samples. We also introduce two new variants of the well-known Local Binary Patterns (LBP) operator. We test these approaches on three diversified histological datasets. We show that combining color and texture features extracted separately is preferable on datasets having a large variability in the staining, while simultaneous extraction of color and texture information is recommended only for more standardized stainings.
Color image segmentation based on Markov random field clustering for histological image analysis

In order to characterise the virulence factors of different Mycobacterium tuberculosis strains responsible for the tuberculosis disease, the quantification, by cell counting, of immune cell recruitment is necessary. However this task by microscopic observations is very tedious and difficult to reproduce. Hence we propose an automatic counting approach, consisting in color image segmentation to discriminate three regions: cell nuclei, immune cells and background, followed by the extraction of each cell entity. For color segmentation, a Markov random field clustering approach taking simultaneously into account both color and spatial information is chosen. Our technique was successfully applied to several color images of different strains, and an evaluation of the results has been performed, showing the robustness of the method against noise, marker color changes, illumination changes and blurring.
Improving single particle localization with an empirically calibrated Gaussian kernel

Accurate computational localization of single fluorescent particles is of interest to many biophysical studies and underlies recent approaches to high resolution microscopy using photo-switchable fluorophores. The position of individual particles is typically computed by least-squares fitting of a Gaussian intensity profile to the image, whose band-width is either derived from an idealized theoretical model of the point spread function (PSF), or itself fitted to the image. However, the band-width best approximating the actual PSF may differ significantly from its theoretical value, while fitting it is expected to degrade localization accuracy. Here, instead, we measure the real PSF bandwidth using fluorescent beads as calibration probes, and use this new bandwidth in a Gaussian model fitting algorithm. We show on simulated and real images that this simple modification of the standard localization procedure results in significant improvement of the 3D accuracy in the nanometer range.
3D shape analysis using overcomplete spherical wavelets: Application to BLEB detection in cell biology

Amoeboid cell motility is characterised by the emission of protrusions at the cellular surface (also known as “blebs”). Detection and counting of these protrusions is a crucial step towards the understanding of the deformation and motility machinery. We propose an automated technique to detect protrusions at the surface of cells observed in 3D fluorescence microscopy using over-complete spherical wavelets. The framework permits intuitive manipulation of wavelets on the sphere, thanks to a straightforward analogy with traditional wavelets on the plane. We illustrate detection results on a real data set of protruding cells, indicating the reliability of the method. Moreover, the flexibility of the approach makes it easily amenable to other shape analysis problems.
Multi-staining registration of large histology images

Quantifying T cells inside tumorous tissue can help identifying immune profiles in order to improve prognosis and possibly develop immunotherapy. However, to identify T cells and cancerous cells in two consecutive staining slides is challenging: the tissue preparation introduces the problem of alignment on large size images with poor visual common information. This work presents a framework for aligning whole slide images by extracting their common information and performing non-rigid registration based on B-splines to solve this problem. Experiments show good results with a mean error of 20.34 ± 12.20µm on our images even if some developments are still needed. This preliminary work is publicly available as part of our open-source Icy platform.
Detection of full length microtubules in live microscopy images

The common way to study microtubule dynamics in microscopy image sequences is to track the growing ends of microtubules. However, this strategy may fail for dense microtubules due to numerous ambiguities in point association. We suggest that detecting and tracking full length microtules, instead of tracking their extremities only, would provide substantial information to resolve these ambiguities. In this paper, we propose a first part toward that end by introducting a fully automated detection method of full length microtubules, with a statistical control of false detections. It is based on the Feature-adapted Beamlet transform which has been successfully used for filament detection . We provide three improvements to our previous work: i) a normalization of beamlet coefficients, ii) a scale-dependent thresholding of beamlets and iii) a novel beamlet chaining algorithm, adapted to microtubules images.
On the digital trail of mobile cells

Cell migration is a field of intense current research, where biologists increasingly rely on methods and expertise from physics and engineering. Signal processing approaches can contribute significantly to this research, most notably to help analyze the exploding quantity of imaging data produced with standard and new microscopy techniques. In this article, we first provide a brief background on the importance of understanding cell movements, then review a selection of current efforts on tracking moving cells, with an emphasis on deformable model approaches (some ideas expressed in this article have been previously discussed. We will point out some of the main difficulties posed by cellular imaging, discuss advantages and limitations of different tracking techniques, and suggest a few directions for future advances
Split and merge data association filter for dense multi-target tracking

Bayesian target tracking methods consist in filtering successive measurements coming from a detector. In the presence of clutter or multiple targets, the filter must be coupled with an association procedure. The classical Bayesian multitarget tracking methods rely on the hypothesis that a target can generate at most one measurement per scan and that a measurement originates from at most one target. When tracking a high number of deformable sources, the previous assumptions are often not met that leads to the failure of the existing methods. Here, we propose an algorithm which allows to perform the tracking in the cases when a single target generates several measurements or several targets generate a single measurement. The novel idea presented in this paper is the introduction of a set that we call virtual measurement set which supersedes and extends the set of measurements. This set is chosen to optimally fit the set of the predicted measurements at each time step. This is done in two stages: i) a set of feasible joint association events is built from virtual measurements that are created by successively splitting and merging the real measurements; ii) the joint probability is maximized over all feasible joint association events. The method has been tested on microscopy image sequences which typically contains densely moving objects and gives satisfactory preliminary results.
Multiple hypothesis tracking for cluttered biological image sequences.

In this paper, we present a method for simultaneously tracking thousands of targets in biological image sequences, which is of major importance in modern biology. The complexity and inherent randomness of the problem lead us to propose a unified probabilistic framework for tracking biological particles in microscope images. The framework includes realistic models of particle motion and existence and of fluorescence image features. For the track extraction process per se, the very cluttered conditions motivate the adoption of a multiframe approach that enforces tracking decision robustness to poor imaging conditions and to random target movements. We tackle the large-scale nature of the problem by adapting the multiple hypothesis tracking algorithm to the proposed framework, resulting in a method with a favorable tradeoff between the model complexity and the computational cost of the tracking procedure. When compared to the state-of-the-art tracking techniques for bioimaging, the proposed algorithm is shown to be the only method providing high-quality results despite the critically poor imaging conditions and the dense target presence. We thus demonstrate the benefits of advanced Bayesian tracking techniques for the accurate computational modeling of dynamical biological processes, which is promising for further developments in this domain.
A compressed sensing approach for biological microscopic image processing

In fluorescence microscopy the noise level and the photobleaching are cross-dependent problems since reducing exposure time to reduce photobleaching degrades image quality while increasing noise level. These two problems cannot be solved independently as a post-processing task, hence the most important contribution in this work is to a-priori denoise and reduce photobleaching simultaneously by using the Compressed Sensing framework (CS). In this paper, we propose a CS-based denoising framework, based on statistical properties of the CS optimality, noise reconstruction characteristics and signal modeling applied to microscopy images with low signal-to-noise ratio (SNR). Our approach has several advantages over traditional denoising methods, since it can under-sample, recover and denoise images simultaneously. We demonstrate with simulated and practical experiments on fluorescence image data that thanks to CS denoising we can obtain images with similar or increased SNR while still being able to reduce exposition times.
Automated quantification of cell endocytosis using active contours and wavelets

Cellular endocytosis is a mechanism of great interest in biology, for it regulates the communication between the cell and the external medium. With recent advances in fluorescence microscopy, endocytosis has become a popular candidate for image-based high content screening campains. In this context, we have developed an automated framework comprising robust cell segmentation using coupled shape-constrained active contours and efficient endosome extraction using an isotropic undecimated wavelet transform. The resulting method has few parameters and is able to analyze tens of cells per image in the order of seconds. Validation is performed by experimentally confirming previously published results obtained through manual analysis.
Introduction to the Issue on Advanced Signal Processing in Microscopy and Cell Imaging

Microscopy imaging, including fluorescence microscopy and electron microscopy, has a prominent role in life science and medical research. During the past two decades, biological imaging has undergone a revolution by way of the development of new microscopy techniques that allow the visualization of tissues, cells, proteins and macromolecular structures at all levels of resolution, physiological states, chemical composition and dynamic analysis. Thanks to recent advances in optics, digital sensing technologies and labeling probes (i.e., XFP—Colored Fluorescence Protein), we can now visualize subcellular components and organelles at the scale of a few nanometers to several hundreds of nanometers. As a result, fluorescent microscopy has become the workhorse of modern biology. Further technological advances include structured and coherent light sources, faster and more sensitive detectors, smaller and more specific molecular probes and automation processes for image acquisition. Additionally, there is a push towards multimodal imaging in order to gather complementary information such as the concentration of fluorophores at various wavelengths and the refractive index of a given sample (phase imaging).
Feature-aided particle tracking

We present a new feature-aided tracking algorithm dedicated to the task of tracking multiple and closely-spaced biological particles. We propose a new function to score associations, based on kinetic models, and enriched with an additional feature. This feature is based on adaptive profiles and the physical properties of the acquisition system. A key property is that this feature definition allows to resolve the challenging task of tracking particles that appear fused. Results on simulations show improved performances over existing methods both on tracking and on the resolution of fused particles.
Denoising in fluorescence microscopy using compressed sensing with multiple reconstructions and non-local merging

The cross-dependency of noise level and photobleaching in microscopy was discussed in a previous work and an efficient compressed sensing (CS) method was proposed to simultaneously reduce the noise level and the photobleaching. Here we present an improved CS denoising framework for fluorescence microscopy images, exploiting Non-Local means filtering to merge multiple reconstructions. This framework enables high-quality reconstruction of low exposed microscopy images based on random Fourier sampling schemes and multiple CS reconstructions. Practical experiments on fluorescence images demonstrate that even performing 10% of the measurements, the signal-to-noise ratio can be significantly improved while keeping reduced exposure time, preserving edges and the image sharpness.
Improving active contours for segmentation and tracking of motile cells in videomicroscopy

We describe a method to segment and track motile biological cells in videomicroscopy data, based on parametric active contours. We propose improvements designed to overcome obstacles in applications to cellular imaging. To allow detection of low contrast pseudopodia, we use an edge map based on the local intensity deviation that better reflects cell/background boundaries. To allow correct segmentations of cells in contact, we introduce a repulsion effect between contours. The method performance is illustrated on real data.
Statistical colocalization in biological imaging with false discovery control

In this paper, we present a novel object-based statistical colocalization method. Our colocalization relies on multiple hypothesis tests on the distances between all pairs of the (spot-shaped) objects from the two markers. We wish to test among all these pairs how many are significantly close to each other such that they cannot occur just "by chance". Two objects are decided to be co-localized if the test on their distance is significant. For this purpose, we first extract the objects by applying a wavelet-based spot detection approach which fully takes into account the mixed-Poisson-Gaussian noise process of confocal fluorescence images. Then, we build a null hypothesis model in which the distribution of the distance between two independently randomly drawn detections in the cell is estimated by a kernel method. The observed distances are tested against this null model. Our tests control the false discovery rate (FDR) of the co-localizations. Simulations show that this approach has a good specificity. Furthermore, our method has been successfully applied in a real problem of protein colocalization analysis during the endocytic process.
Using steerable wavelets and minimal paths to reconstruct automatically filaments in fluorescence imaging

The accurate detection of filamentous structures in fluorescence microscopy, such as stained cytoskeleton and cilia, is an important technical issue in bioimage analysis. We propose here a two-steps approach that combines image thresholding in steerable wavelets domain and minimal path reconstruction to robustly detect and quantify filaments in their entire length. Indeed, the first steerable transformation enhances bright and anisotropic structures such as stained filaments, but local variations of fluorescence intensity often leads to line breaks in segmented filaments. We thus used a minimal path algorithm in a second step to close these gaps and reconstruct the whole filaments. Thereafter, we used our two-steps approach to detect and quantify the flagellum of the parasite Typanosoma brucei at a population level.
Compressed sensing applications for biological microscopy

This paper provides an overview of some compressed sensing contributions to biological microscopy developed in our laboratory. They are mainly on four topics: (i) a CS-based denoising framework exploiting a Total Variation sparsity prior and very limited number of measurements in the Fourier domain, (ii) practical experiments on fluorescence image data demonstrating that thanks to CS the signal-to-noise ratio can be improved while still reducing the photobleaching effect, (iii) a CS reconstruction framework combining Fourier magnitude measurements and Fourier phase estimation for sequential microscopy image acquisition, (iv) a microscopy acquisition scheme successfully combining Compressed Sensing (CS) and digital holography.
Quantitative analysis of cellular shape and motility with improved active contours

Quantitative studies of cellular motility and morphology require a method to segment and track individual cells in videomicroscopy data. We present an active contour method adapted to address important difficulties of cellular imaging. Detection of low contrast pseudopods is facilitated by using an edge map based on the average intensity deviation field. Separation of cells in contact is facilitated by introducing a repulsion effect between contours. Our method was successfully applied to assess the influence of a particular protein on the motility of the parasite Entamoeba histolytica, and in an ongoing study of chemotaxis.
Improving histology images segmentation through spatial constraints and supervision

We introduce two approaches to improve an existing color segmentation technique based on a Split and Merge quantization process for the study of stained histological images. We propose to modify the merge criterion : first, we include a spatial constraints heuristic; then we suggest the use of supervision and a more elaborated visual features representation. We tested these approaches on a renal biopsies dataset to automatically quantify interstitial fibrosis and show that supervision brings very significant improvements.