Quantitative Approaches to Study Retinal Neurogenesis

The study of the development of the vertebrate retina can be addressed from several perspectives: from a purely qualitative to a more quantitative approach that takes into account its spatio-temporal features, its three-dimensional structure and also the regulation and properties at the systems level. Here, we review the ongoing transition toward a full four-dimensional characterization of the developing vertebrate retina, focusing on the challenges at the experimental, image acquisition, image processing and quantification. Using the developing zebrafish retina, we illustrate how quantitative data extracted from these type of highly dense, three-dimensional tissues depend strongly on the image quality, image processing and algorithms used to segment and quantify. Therefore, we propose that the scientific community that focuses on developmental systems could strongly benefit from a more detailed disclosure of the tools and pipelines used to process and analyze images from biological samples.

Improvement in systematic error in background-oriented schlieren results by using dynamic backgrounds

The use of electronic visual displays for background-oriented schlieren allows for the quick change of the reference images. In this study, we show that the quality of synthetic and background-oriented schlieren images can be improved by acquiring a set of images with different reference images and generating a median displacement field from it. To explore potential benefits, we studied different background changing strategies and their effect on the quality of the evaluation of the displacement field via artificial and experimental image distortions. Graphic abstract

Quantitative Approaches to Study Retinal Neurogenesis

The study of the development of the vertebrate retina can be addressed from several perspectives: from purely qualitative to a more quantitative approach that takes into account its spatiotemporal features, its three dimensional structure and also the regulation and properties at the systems level. Here we review the ongoing transition towards a full four-dimensional characterization of the developing vertebrate retina, focusing on the challenges at the experimental, image acquisition, image processing and quantification. Using the developing zebrafish retina, we illustrate how quantitative data extracted from these type of highly dense three-dimensional tissues depends strongly on the image quality, image processing and algorithms used to segment and quantify. Therefore, we propose that the scientific community that focuses on developmental systems could strongly benefit from a more detailed disclosure of the tools and pipelines used to process and analyze images from biological samples.

Deep learning-based estimation of Flory–Huggins parameter of A–B block copolymers from cross-sectional images of phase-separated structures

In this study, deep learning (DL)-based estimation of the Flory–Huggins χ parameter of A-B diblock copolymers from two-dimensional cross-sectional images of three-dimensional (3D) phase-separated structures were investigated. 3D structures with random networks of phase-separated domains were generated from real-space self-consistent field simulations in the 25–40 χN range for chain lengths (N) of 20 and 40. To confirm that the prepared data can be discriminated using DL, image classification was performed using the VGG-16 network. We comprehensively investigated the performances of the learned networks in the regression problem. The generalization ability was evaluated from independent images with the unlearned χN. We found that, except for large χN values, the standard deviation values were approximately 0.1 and 0.5 for A-component fractions of 0.2 and 0.35, respectively. The images for larger χN values were more difficult to distinguish. In addition, the learning performances for the 4-class problem were comparable to those for the 8-class problem, except when the χN values were large. This information is useful for the analysis of real experimental image data, where the variation of samples is limited.

Stochastic Neural Networks for Automatic Cell Tracking in Microscopy Image Sequences of Bacterial Colonies

We describe an automated analysis method to quantify the detailed growth dynamics of a population of bacilliform bacteria. We propose an innovative approach to frame-sequence tracking of deformable-cell motion by the automated minimization of a new, specific cost functional. This minimization is implemented by dedicated Boltzmann machines (stochastic recurrent neural networks). Automated detection of cell divisions is handled similarly by successive minimizations of two cost functions, alternating the identification of children pairs and parent identification. We validate this automatic cell tracking algorithm using recordings of simulated cell colonies that closely mimic the growth dynamics of E. coli in microfluidic traps. On a batch of 1100 image frames, cell registration accuracies per frame ranged from 94.5% to 100%, with a high average. Our initial tests using experimental image sequences of E. coli colonies also yield convincing results, with a registration accuracy ranging from 90% to 100%.

Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations

Controlling the properties of organic/inorganic materials requires detailed knowledge of their molecular adsorption geometries. This is often unattainable, even with current state-of-the-art tools. Visualizing the structure of complex non-planar adsorbates with atomic force microscopy (AFM) is challenging, and identifying it computationally is intractable with conventional structure search. In a fresh approach, we propose to integrate cross-disciplinary tools for a robust and automated identification of 3D adsorbate configurations. We employ Bayesian optimization with first-principles simulations for accurate and unbiased structure inference of multiple adsorbates. The corresponding AFM simulations then allow us to fingerprint adsorbate structures appearing in AFM experimental images. In the instance of bulky (1S)-camphor adsorbed on the Cu(111) surface, we found three matching AFM image contrasts, which allowed us to correlate experimental image features to distinct cases of molecular adsorption.

Contact Angles in Two-Phase Flow Images

In this work, we calculate contact angles in X-ray tomography images of two-phase flow in order to investigate the wettability. Triangulated surfaces, generated using the images, are smoothed to calculate the contact angles. As expected, the angles have a spread rather than being a constant value. We attempt to shed light on sources of the spread by addressing the overlooked mesh corrections prior to smoothing, poorly resolved image features, cluster-based analysis, and local variations of contact angles. We verify the smoothing algorithm by analytical examples with known contact angle and curvature. According to the analytical cases, point-wise and average contact angles, average mean curvature and surface area converge to the analytical values with increased voxel grid resolution. Analytical examples show that these parameters can reliably be calculated for fluid–fluid surfaces composed of roughly 3000 vertices or more equivalent to 1000 pixel2. In an experimental image, by looking into individual interfaces and clusters, we show that contact angles are underestimated for wetting fluid clusters where the fluid–fluid surface is resolved with less than roughly 500 vertices. However, for the fluid–fluid surfaces with at least a few thousand vertices, the mean and standard deviation of angles converge to similar values. Further investigation of local variations of angles along three-phase lines for large clusters revealed that a source of angle variations is anomalies in the solid surface. However, in the places least influenced by such noise, we observed that angles tend to be larger when the line is convex and smaller when the line is concave. We believe this pattern may indicate the significance of line energy in the free energy of the two-phase flow systems.

Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations

Controlling the properties of organic/inorganic materials requires detailed knowledge of their molecular adsorption geometries. This is often unattainable, even with current state-of-the-art tools. Visualizing the structure of complex non-planar adsorbates with atomic force microscopy (AFM) is challenging, and identifying it computationally is intractable with conventional structure search. In a fresh approach, we propose to integrate cross-disciplinary tools for a robust and automated identification of 3D adsorbate configurations. We employ Bayesian optimization with first-principles simulations for accurate and unbiased structure inference of multiple adsorbates. The corresponding AFM simulations then allow us to fingerprint adsorbate structures appearing in AFM experimental images. In the instance of bulky (1S)-camphor adsorbed on the Cu(111) surface, we found three matching AFM image contrasts, which allowed us to correlate experimental image features to distinct cases of molecular adsorption.

IMAGE PRE-PROCESSING STRATEGIES FOR ENHANCING PHOTOGRAMMETRIC 3D RECONSTRUCTION OF UNDERWATER SHIPWRECK DATASETS

Although underwater photogrammetry has become widely adopted, there are still significant unresolved issues that are worthy of attention. This article focuses on the 3D model generation of underwater shipwrecks and intends explicitly to address the problem of dealing with sub-optimal datasets. Even if the definition of best practices and standards to be adopted during the acquisition phase appears to be crucial, there is a massive amount of data gathered so far by professionals and the scientific community all over the world that cannot be ignored. The compelling idea is to attempt to achieve the best reconstruction results possible, even from sub-optimal or less-than-ideal image datasets. This work focuses on the investigation of different strategies and approaches for balancing the quality of the photogrammetric products, without neglecting their reliability concerning the surveyed object. The case study of this research is the Mandalay MHT, a 34 m long steel-hulled auxiliary schooner that sank in 1966 and now lies in the Biscayne National Park (Florida - USA). The dataset has been provided by the Submerged Resources Center (SRC) of the US National Park Service, in order to develop an experimental image enhancement method functional to the virtualization and visualization of the generated products, as a part of a sustainable, affordable, and reliable method of studying submerged artefacts and sites. The original images have been processed using different image enhancement approaches, and the outputs have been compared and analysed.

DIVA: natural navigation inside 3D images using virtual reality

As three-dimensional microscopy becomes commonplace in biological re-search, there is an increasing need for researchers to be able to view experimental image stacks in a natural three-dimensional viewing context. Through stereoscopy and motion tracking, commercial virtual reality headsets provide a solution to this important visualization challenge by allowing researchers to view volumetric objects in an entirely intuitive fashion. With this motivation, we present DIVA, a user-friendly software tool that automatically creates detailed three-dimensional reconstructions of raw experimental image stacks that are integrated in virtual reality. In DIVA’s immersive virtual environment, users can view, manipulate and perform volumetric measurements on their microscopy images as they would to real physical objects. In contrast to similar solutions, our software provides high-quality volume rendering with native TIFF file compatibility. We benchmark the software with diverse image types including those generated by confocal, light-sheet and electron microscopy. DIVA is available at https://diva.pasteur.fr and will be regularly updated.