nils-herrmann/clean_scirepeval huggingface dataset:
A multi-threaded program architecture for an asynchronous and highly responsive GUI for automatic neuronal survival quantification

The goal of this paper is to present a multi-threaded program architecture to monitor and control, through the use of a highly responsive GUI, the results of automatically generated spot detections in neurobiology images. Spot detection is used in this context to investigate through the analysis of hundreds of images the spatiotemporal representation of the decrease of neuronal survival depending on the age of newborn neurons in the olfactive bulb stained with BrdU. Existing solutions to this task are plagued with time-consuming image processing steps, tedious interactions with the program and extensive user inputs and outputs. To increase the productivity of the whole process, we have focused our development on the optimisation of a dedicated GUI, which seperates the interactive process from the image processing background task, so that the user has real-time feedback on settings and results. It results in a dedicated solution that integrates in a single and user friendly application all the processes needed to count and classify spots according to biologists' requirements. Section 1 presents the biological context and computational requirements while Section 2 presents the biological protocol. Section 3 gives a short review of existing solutions and methods. Section 4 describes the suggested solution. Section 5 illustrates the application of quantitative data extracted from image analysis.
Spherical Harmonics based extraction and annotation of cell shape in 3D time-lapse microscopy sequences

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 a given homogeneous population. In this work, we propose a framework for cell shape extraction and classification for 3D time-lapse sequences of living cells, based on the SPherical HARMonics transform (SPHARM). Starting from an initial segmentation of the cell surface over time, this mathematical representation enables us to represent each extracted surface by a unique set of coefficients, while taking into account invariance properties such as translation or orientation. Then, unsupervised classification is conducted using a multi-class K-Means approach, so as to extract the most pertinent number of classes representing the different phases of the cell deformation. Experimental results on several sequences give encouraging results, and show that the proposed approach can be used to perform automated sequence annotation, and can be further applied to compare shape characteristics across different cell populations.
Tracking fluorescent cells with coupled geometric active contours

This paper presents a method based on geometric active contours to segment and track biological cells in fluorescence videomicroscopy. To achieve robust segmentation of fluorescent cells with fuzzy boundaries, we use the region-based model introduced by Chan and Vese. The level set approach allows fully automatic initialization, detection of dividing cells and of cells entering the field of view during the sequence. However, the standard approach using only one level set function leads to undesired merging of touching cells and failure of tracking. To prevent this, we represent each cell by its own level set function and introduce a coupling constraint that minimizes overlaps of active contours. This technique maintains the separation of contours and allows tracking to proceed even when the boundary between cells is weak or invisible.
Compressed Sensing in microscopy with random projections in the Fourier domain

In this paper we propose a Compressed Sensing-based image acquisition and recovery method that combines Fourier magnitude measurements and Fourier phase estimation for sequential microscopy image acquisition. The main idea is to combine sequential Optical Fourier Transform (OTF) magnitude measurements with Fourier phase estimation from complete keyframes acquisition. For images with homogeneous objects and background, Compressed Sensing (CS) provides indeed an optimal reconstruction framework from a set of random projections in Fourier domain, while constraining bounded variations in the spatial domain. As in many others optical systems, in microscopy we can observe the magnitude of the Fourier coefficients. However, getting the phase of these coefficients can be an very expensive task. Initial experiments simulating the proposed microscopy image acquisition protocol confirm the feasibility of the CS computational framework to recover image sequences in microscopy with a very high frame rate while preserving high SNR levels.
An Unbiased Risk Estimator for Image Denoising in the Presence of Mixed Poisson–Gaussian Noise

The behavior and performance of denoising algorithms are governed by one or several parameters, whose optimal settings depend on the content of the processed image and the characteristics of the noise, and are generally designed to minimize the mean squared error (MSE) between the denoised image returned by the algorithm and a virtual ground truth. In this paper, we introduce a new Poisson-Gaussian unbiased risk estimator (PG-URE) of the MSE applicable to a mixed Poisson-Gaussian noise model that unifies the widely used Gaussian and Poisson noise models in fluorescence bioimaging applications. We propose a stochastic methodology to evaluate this estimator in the case when little is known about the internal machinery of the considered denoising algorithm, and we analyze both theoretically and empirically the characteristics of the PG-URE estimator. Finally, we evaluate the PG-URE-driven parametrization for three standard denoising algorithms, with and without variance stabilizing transforms, and different characteristics of the Poisson-Gaussian noise mixture.
Reducing data acquisition for fast Structured Illumination Microscopy using Compressed Sensing

In this work, we introduce an original strategy to apply the Compressed Sensing (CS) framework to a super-resolution Structured Illumination Microscopy (SIM) technique. We first define a framework for direct domain CS, that exploits the sparsity of fluorescence microscopy images in the Fourier domain. We then propose an application of this method to a fast 4-images SIM technique, which allows to reconstruct super-resolved fluorescence microscopy images using only 25% of the camera pixels for each acquisition.
Automatic detection of 3D cell protrusions using spherical wavelets

The deformation process orchestrated by the cell machinery is particularly complex and characterized by shape changes at multiple geometrical scales to drive cell deformation and movement. Precise cell shape description tools have become necessary to detect shape changes in a robust manner to allow subsequent morpho-dynamic profiling. Here we investigate the use of spherical wavelets to perform a multi-scale analysis of the cell shape, in order to automatically detect and track protrusions at the cell surface. Examples on real biological data highlight encouraging results and open the way to a powerful description of the cell deformation process.
Unbiased analysis of mouse social behaviour using unsupervised machine learning

Mouse models are broadly used to study the mechanisms of neuropsychiatric disorders and to test potential treatments. In these models, automation to monitor behavioural differences during social interactions is currently limited. We propose in the present study a new method to conduct automatic behavioural classification, using an original unsupervised machine learning. We applied the proposed method to mice mutated in Shank2, a gene associated with autism spectrum disorders. We validated our results by comparing automatically extracted results to rule-based classifier labelling. We discovered seven behavioural states matching from 80 to 95% previous rule-based classification, and two unsuspected behaviours. Interestingly, we also highlighted genotype-related differences in two behavioural categories, namely locomotion and facing the conspecific.
Fluid optical flow for forces and pressure field estimation in cellular biology

We aim at analysing fluorescence microscopy sequences of moving cells, so as to estimate the velocities that define the movement, the forces that drive this movement, and the pressure that characterises the material state. We use an optical flow method constrained by Stokes flow mechanics equations that define the dynamics of a fluid that is incompressible, homogeneous and viscous. We present a finite element formulation that leads to a quadratic programming problem. We apply the method to real biological data of amoebas observed by fluorescence microscopy.
Adaptive gating in Gaussian Bayesian multi-target tracking

Bayesian target tracking methods consist of filtering successive measurements coming from a detector. Linear and nonlinear Gaussian Bayesian filters are well adapted to estimate the successive a posteriori state distributions of a single moving target from a sequence of observations. However, when tracking several targets in a cluttered environment, previous techniques must be combined with dedicated procedures for validating and associating the measurements to their predictions. Gating validation techniques are used to increase the reliability of the association technique by retaining only the measurements that could be originated from predicted measurements. In standard techniques, the only constraint imposed on the gate is to contain the correct measurement. However, as the shape of the validation gate is related to the covariance of the transition noise, it is of major importance to estimate it in a reliable manner. We therefore review several methods to update the covariance of transition noise and we propose a new one that enables the validation gate to be adapted both to the smoothly evolving dynamic of a moving target and to an abruptly changing dynamic. All the methods are compared for performance on microscopy image sequences which typically contain objects that abruptly change their behavior.
Quad-edge active contours for biomedical image segmentation

We investigate a novel, parallel implementation of active contours for image segmentation combining a multi-agent system with a quad-edge representation of the contour. The control points of the contour evolve independently from one another in a parallel fashion, handling contour deformation, and convergence, while the quad-edge representation simplifies contour manipulation and local re-sampling during its evolution. We illustrate this new approach on biological images, and compare results with a conventional active contour implementation, discussing its benefits and limitations. This preliminary work is made freely available as a plug-in for our open-source Icy platform, where it will be developed with future extensions.
Multiple hypothesis tracking in cluttered condition

Multiple hypothesis tracking (MHT) is a preferred technique for solving the data association problem in modern multiple targets tracking systems. However its computational cost is generally considered prohibitive for tracking numerous objects in cluttered environments due to numerous targets and spurious measurements. We present in this paper a new MHT formulation in which target perceivability is modeled whereby automatic early track termination and false measurements exclusion reduce the problem complexity and improve the method robustness to clutter. Moreover we propose a MHT implementation exploiting the tree structure of the potential tracks to take full advantages of recent parallel computing technologies. We provide experimental results showing that both the track model and algorithmic design make the algorithm fast and robust even in highly complex situations such as tracking numerous particles in fluorescent microscopy images.
Multiple hypothesis tracking in microscopy images

Multiple hypothesis tracking (MHT) is a preferred technique for solving the data association problem in modern multiple target tracking systems. However in bioimaging applications, its use has long been thought impossible due to the prohibitive cost induced by the high number of objects that need to be tracked and the poor quality of images. We show in this paper that this broadly accepted view should change. We propose a MHT algorithm (fMHT) that is fast even when dealing with very noisy images of very numerous targets. We have applied the method to the analysis of two sets of real microscopy images that contain thousands of biological targets. By doing so we prove the benefits of the approach when tracking in very noisy environments such as low-light level fluorescent microscopy images.
A fast and automated framework for extraction of nuclei from cluttered 3D images in fluorescence microscopy

Confocal fluorescence microscopy has become a standard tool to image thick 3D tissue samples, permitting the observation of cell behaviour, such as cell division within developing organs. However, robust and automatic extraction of nuclear shape and mitotic orientation may be hindered by a highly cluttered environment as for example in mammalian tissues. We propose a fast and automated framework for the segmentation of nuclei from cluttered 3D images, allowing robust quantification of various parameters such as number of cells, number of mitoses and mitotic orientation. We have applied this framework to scans of the developing mouse heart, and manual validation on three independent experiments indicates a detection rate of 93% in all cases. Moreover, the proposed tool permits fast, real-time 3D rendering of the data set during the analysis, and can be easily adapted to other applications related to dense tissue analysis.
Biophysical measurements in 2D and 3D live cell imaging using fluid dynamics and optical flow

Measuring the biophysical properties of living cells is essential to understand their underlying cytoskeletal machinery. Here we describe a robust extension of our previous algorithm for the intra-cellular measurement of pressure, velocity and forces inside living cells. We first consider a compressible fluid model to cope with out-of-plane intracellular flow in the 2D case, and also propose a 3D version of the algorithm for multi-plane imaging. Results demonstrate the high potential of the approach, giving access to biophysical measures that cannot be achieved using other experimental techniques.
Parametric Blind Deconvolution for Confocal Laser Scanning Microscopy

In this paper, we propose a method for the iterative restoration of fluorescence confocal laser scanning microscopic (CLSM) images and parametric estimation of the acquisition system's point spread function (PSF). The CLSM is an optical fluorescence microscope that scans a specimen in 3D and uses a pinhole to reject most of the out-of-focus light. However, the quality of the images suffers from two basic physical limitations. The diffraction-limited nature of the optical system, and the reduced amount of light detected by the photomultiplier cause blur and photon counting noise respectively. These images can hence benefit from post-processing restoration methods based on deconvolution. An efficient method for parametric blind image deconvolution involves the simultaneous estimation of the specimen 3D distribution of fluorescent sources and the microscope PSF. By using a model for the microscope image acquisition physical process, we reduce the number of free parameters describing the PSF and introduce constraints. The parameters of the PSF may vary during the course of experimentation, and so they have to be estimated directly from the observed data. A priori model of the specimen is further applied to stabilize the alternate minimization algorithm and to converge to the solutions.
Image-based representation and modeling of spatiotemporal cell dynamics

Successful completion of dynamic cellular processes ranging from intracellular transport to embryonic development depends critically on precise spatial and temporal coordination of the molecules and cells involved. Understanding spatiotemporal behaviors of these molecules and cells is therefore essential to understanding mechanisms of their associated cellular processes. Over the past two decades, a wide variety of image feature tracking techniques have been developed for recovery of spatiotemporal trajectories of molecules and cells from their images. From these trajectories, simple descriptors such as velocities or diffusion coefficients are often calculated for behavior characterization. However, for complex cellular processes, these descriptors cannot fully characterize or represent spatiotemporal behaviors of the molecules and cells involved. Instead, comprehensive computational representations and models become essential. This short paper introduces a special session that we organize to invite investigators to present their work related to this important emerging topic in biological imaging. We illustrate the necessity for computational representation and modeling of spatiotemporal cell dynamics using a specific example and review briefly some of the key technical challenges to be addressed. We conclude with an overview of the invited presentations.
Detection of Curvilinear Objects in Noisy Image using Feature-Adapted Beamlet Transform

This paper addresses the problem of detecting features running along lines or piecewise constant curves. Our method is adapted either for common image features like edges or ridges as well as any kind of features that can be designed by a priori knowledge. The main contribution of this paper is to unify the well-known Beamlet transform, introduced by Donoho et al, with linear filtering technique in order to define what we call the feature-adapted Beamlet transform. If the desired feature is chosen to belong to the class of steerable filters, our method can be achieved in linear time and can be easily implemented on a parallel machine. We present some experimental results both on edge- and ridge-like features that demonstrate the substantial improvement over classical feature detectors.
Feature-Adapted Fast Slant Stack

This paper presents a new method for computing the feature-adapted Radon and beamlet transforms in a fast and accurate way. These two transforms can be used for detecting features running along lines or piecewise constant curves. The main contribution of this paper is to unify the fast slant stack method, introduced in , with linear filtering technique in order to define what we call the feature-adapted fast slant stack. If the desired feature is chosen to belong to the class of steerable filters, our method can be achieved in  log(iV)), where N = n2 is the number of pixels. This new method leads to an efficient implementation of both feature-adapted radon and beamlet transforms, that outperforms our previous works. Our method has been developed in the context of biological imaging to detect image features lying along curves like edges or ridges as well as any kind of features that can be designed by a priori knowledge.
Curvelet analysis of kymograph for tracking bi-directional particles in fluorescence microscopy images

In this paper we present a new procedure for tracking bi-directional objects in kymographs. The proposed technique is based on a novel adaptive and directional band-pass filtering method which allows us to separate particles which move in opposite directions. The filtering method exploits the curvelet analysis of the kymograph image to automatically adapt to the objects trails characteristics and select oriented features. The separation of bi-directional objects in separated images allows us to reliably detect and track fluorescent particles in fluorescence image sequences, despite numerous crossroad points in the kymograph space. The new abilities provided by the proposed technique are highlighted by the analysis of biological images which were previously impossible to analyze reliably.
Compressed sensing for digital holographic microscopy

This paper describes an original microscopy imaging framework successfully employing Compressed Sensing for digital holography. Our approach combines a sparsity minimization algorithm to reconstruct the image and digital holography to perform quadrature-resolved random measurements of an optical field in a diffraction plane. Compressed Sensing is a recent theory establishing that near-exact recovery of an unknown sparse signal is possible from a small number of non-structured measurements. We demonstrate with practical experiments on holographic microscopy images of cerebral blood flow that our CS approach enables optimal reconstruction from a very limited number of measurements while being robust to high noise levels.
Image filtering using anisotropic structure tensor for cell membrane enhancement in 3D microscopy

Image filtering methods are crucial in a large number of applications where the relevant information is either noisy or partially missing. More particularly in 3D microscopy, cell membranes appear as structures of co-dimension 2, and suffer from discontinuities in intensity and orientation. Anisotropic PDE-based approaches offer an elegant solution to these problems, by integrating spatial and orientation information in a flexible and robust manner. In this work we propose a new tensor-based anisotropic model specifically adapted to 2D planar structures able to perform noise reduction and contrast enhancement simultaneously. Evaluation on synthetic and real data as well as comparison with previous works show that the model is well adapted for cell membrane structures.
Tracking live cells in 4D microscopy: From active surfaces to active meshes

Deformable models have proven to be particularly adapted to multiple cell segmentation and tracking in biology, thanks to their flexibility, robustness to noise and quantitative interpretability. In the context of 4D microscopy, two concurrent techniques have been developed: an active surfaces model, based on an implicit contour formulation, and an active meshes model, based on an explicit formulation. This paper describes and confronts both approaches in terms of quantitative results and computational cost on simulated and real biological sequences, in order to provide an objective review of their strengths and weaknesses.
A statistical analysis of spatial colocalization using Ripley's K function

The author of this work present an appendix paper entitled: "A statistical analysis of spatial colocalization using Ripley's K function." Thibault Lagache *, Vannary Meas-Yedid, lean-Christophe Olivo-Marin Institut Pasteur, Quantitative Image Analysis Unit, F-75015 Paris, France CNRS URA 2582, F-75015 Paris, France
Wavefront sensing for aberration modeling in fluorescence macroscopy

In this paper, we present an approach to calculate the wavefront in the back pupil plane of an objective in a fluorescent MACROscope. We use the three-dimensional image of a fluorescent bead because it contains potential pupil information in the ‘far’ out-of-focus planes for sensing the wavefront at the back focal plane of the objective. Wavefront sensing by phase retrieval technique is needed for several reasons. Firstly, the point-spread function of the imaging system can be calculated from the estimated pupil phase and used for image restoration. Secondly, the aberrations in the optics of the objective can be determined by studying this phase. Finally, the estimated wavefront can be used to correct the aberrated optical path without a wavefront sensor. In this paper, we estimate the wavefront of a MACROscope optical system by using Bayesian inferencing and derive the Gerchberg-Saxton algorithm as a special case.