Image informatics strategies for deciphering neuronal network connectivity

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies

Curvilinear Structure Enhancement in Biomedical Images

Curvilinear structures can appear in many different areas and at a variety of scales. They can be axons and dendrites in the brain, blood vessels in the fundus, streets, rivers or fractures in buildings, and others. So, it is essential to study curvilinear structures in many fields such as neuroscience, biology, and cartography regarding image processing. Image processing is an important field for the help to aid in biomedical imaging especially the diagnosing the disease. Image enhancement is the early step of image analysis. In this thesis, I focus on the research, development, implementation, and validation of 2D and 3D curvilinear structure enhancement methods, recently established. The proposed methods are based on phase congruency, mathematical morphology, and tensor representation concepts. First, I have introduced a 3D contrast independent phase congruency-based enhancement approach. The obtained results demonstrate the proposed approach is robust against the contrast variations in 3D biomedical images. Second, I have proposed a new mathematical morphology-based approach called the bowler-hat transform. In this approach, I have combined the mathematical morphology with a local tensor representation of curvilinear structures in images. The bowler-hat transform is shown to give better results than comparison methods on challenging data such as retinal/fundus images. The bowler-hat transform is shown to give better results than comparison methods on challenging data such as retinal/fundus images. Especially the proposed method is quite successful while enhancing of curvilinear structures at junctions. Finally, I have extended the bowler-hat approach to the 3D version to prove the applicability, reliability, and ability of it in 3D

Automated Three-Dimensional Detection and Shape Classification of Dendritic Spines from Fluorescence Microscopy Images

A fundamental challenge in understanding how dendritic spine morphology controls learning and memory has been quantifying three-dimensional (3D) spine shapes with sufficient precision to distinguish morphologic types, and sufficient throughput for robust statistical analysis. The necessity to analyze large volumetric data sets accurately, efficiently, and in true 3D has been a major bottleneck in deriving reliable relationships between altered neuronal function and changes in spine morphology. We introduce a novel system for automated detection, shape analysis and classification of dendritic spines from laser scanning microscopy (LSM) images that directly addresses these limitations. The system is more accurate, and at least an order of magnitude faster, than existing technologies. By operating fully in 3D the algorithm resolves spines that are undetectable with standard two-dimensional (2D) tools. Adaptive local thresholding, voxel clustering and Rayburst Sampling generate a profile of diameter estimates used to classify spines into morphologic types, while minimizing optical smear and quantization artifacts. The technique opens new horizons on the objective evaluation of spine changes with synaptic plasticity, normal development and aging, and with neurodegenerative disorders that impair cognitive function

Flame front analysis of ethanol, butanol, iso-octane and gasoline in a spark-ignition engine using laser tomography and integral length scale measurements

Direct-injection spark-ignition engines have become popular due to their flexibility in injection strategies and higher efficiency; however, the high-pressure in-cylinder injection process can alter the airflow field by momentum exchange, with different effects for fuels of diverse properties. The current paper presents results from optical studies of stoichiometric combustion of ethanol, butanol, iso-octane and gasoline in a direct-injection spark-ignition engine run at 1500 RPM with 0.5 bar intake plenum pressure and early intake stroke fuel injection for homogeneous mixture preparation. The analysis initially involved particle image velocimetry measurements of the flow field at ignition timing with and without fuelling for comparison. Flame chemiluminescence imaging was used to characterise the global flame behaviour and double-pulsed Laser-sheet flame tomography by Mie scattering to quantify the local topology of the flame front. The flow measurements with fuel injection showed integral length scales of the same order to those of air only on the tumble plane, but larger regions with scales up to 9 mm on the horizontal plane. Averaged length scales over both measurement planes were between 4 and 6 mm, with ethanol exhibiting the largest and butanol the smallest. In non-dimensional form, the integral length scales were up to 20% of the clearance height and 5–12% of the cylinder bore. Flame tomography showed that at radii between 8 and 12 mm, ethanol was burning the fastest, followed by butanol, iso-octane and gasoline. The associated turbulent burning velocities were 4.6–6.5 times greater than the laminar burning velocities and about 13–20% lower than those obtained by flame chemiluminescence imaging. Flame roundness was 10–15% on the tomography plane, with largest values for ethanol, followed by butanol, gasoline and iso-octane; chemiluminescence imaging showed larger roundness (18–25%), albeit with the same order amongst fuels. The standard deviation of the displacement of the instantaneous flame contour from one filtered by its equivalent radius was obtained as a measure of flame brush thickness and correlated strongly with the equivalent flame radius; when normalised by the radius, it was 4–6% for all fuels. The number of crossing points between instantaneous and filtered flame contour showed a strong negative correlation with flame radius, independent of fuel type. The crossing point frequency was 0.5–1.6 mm−1. The flame brush thickness was about 1/10th of the integral length scale. A positive correlation was found between integral length scale and flame brush thickness and a negative correlation with crossing frequency

Automatic Optic Nerve Measurement: A New Tool to Standardize Optic Nerve Assessment in Ultrasound B-Mode Images

Transorbital sonography provides reliable information about the estimation of intra-cranial pressure by measuring the optic nerve sheath diameter (ONSD), whereas the optic nerve (ON) diameter (OND) may reveal ON atrophy in patients with multiple sclerosis. Here, an AUTomatic Optic Nerve MeAsurement (AUTONoMA) system for OND and ONSD assessment in ultrasound B-mode images based on deformable models is presented. The automated measurements were compared with manual ones obtained by two operators, with no significant differences. AUTONoMA correctly segmented the ON and its sheath in 71 out of 75 images. The mean error compared with the expert operator was 0.06 ± 0.52 mm and 0.06 ± 0.35 mm for the ONSD and OND, respectively. The agreement between operators and AUTONoMA was good and a positive correlation was found between the readers and the algorithm with errors comparable with the inter-operator variability. The AUTONoMA system may allow for standardization of OND and ONSD measurements, reducing manual evaluation variability

Inferring cellular forces from image stacks

Although the importance of cellular forces to a wide range of embryogenesis and disease processes is widely recognized, measuring these forces is challenging, especially in three dimensions. Here, we introduce CellFIT-3D, a force inference technique that allows tension maps for three-dimensional cellular systems to be estimated from image stacks. Like its predecessors, video force microscopy and CellFIT, this cell mechanics technique assumes boundary-specific interfacial tensions to be the primary drivers, and it constructs force-balance equations based on triple junction (TJ) dihedral angles. The technique involves image processing, segmenting of cells, grouping of cell outlines, calculation of dihedral planes, averaging along three-dimensional TJs, and matrix equation assembly and solution. The equations tend to be strongly overdetermined, allowing indistinct TJs to be ignored and solution error estimates to be determined. Application to clean and noisy synthetic data generated using Surface Evolver gave tension errors of 1.6?7%, and analyses of eight-cell murine embryos gave estimated errors smaller than the 10% uncertainty of companion aspiration experiments. Other possible areas of application include morphogenesis, cancer metastasis and tissue engineering.authorsversionPeer reviewe

Customizable tubular model for n-furcating blood vessels and its application to 3D reconstruction of the cerebrovascular system

Understanding the 3D cerebral vascular network is one of the pressing issues impacting the diagnostics of various systemic disorders and is helpful in clinical therapeutic strategies. Unfortunately, the existing software in the radiological workstation does not meet the expectations of radiologists who require a computerized system for detailed, quantitative analysis of the human cerebrovascular system in 3D and a standardized geometric description of its components. In this study, we show a method that uses 3D image data from magnetic resonance imaging with contrast to create a geometrical reconstruction of the vessels and a parametric description of the reconstructed segments of the vessels. First, the method isolates the vascular system using controlled morphological growing and performs skeleton extraction and optimization. Then, around the optimized skeleton branches, it creates tubular objects optimized for quality and accuracy of matching with the originally isolated vascular data. Finally, it optimizes the joints on n-furcating vessel segments. As a result, the algorithm gives a complete description of shape, position in space, position relative to other segments, and other anatomical structures of each cerebrovascular system segment. Our method is highly customizable and in principle allows reconstructing vascular structures from any 2D or 3D data. The algorithm solves shortcomings of currently available methods including failures to reconstruct the vessel mesh in the proximity of junctions and is free of mesh collisions in high curvature vessels. It also introduces a number of optimizations in the vessel skeletonization leading to a more smooth and more accurate model of the vessel network. We have tested the method on 20 datasets from the public magnetic resonance angiography image database and show that the method allows for repeatable and robust segmentation of the vessel network and allows to compute vascular lateralization indices. Graphical abstract: [Figure not available: see fulltext.]</p