Design of steerable filters for feature detection using canny-like criteria

Grouping Boundary Proposals for Fast Interactive Image Segmentation

Geodesic models are known as an efficient tool for solving various image segmentation problems. Most of existing approaches only exploit local pointwise image features to track geodesic paths for delineating the objective boundaries. However, such a segmentation strategy cannot take into account the connectivity of the image edge features, increasing the risk of shortcut problem, especially in the case of complicated scenario. In this work, we introduce a new image segmentation model based on the minimal geodesic framework in conjunction with an adaptive cut-based circular optimal path computation scheme and a graph-based boundary proposals grouping scheme. Specifically, the adaptive cut can disconnect the image domain such that the target contours are imposed to pass through this cut only once. The boundary proposals are comprised of precomputed image edge segments, providing the connectivity information for our segmentation model. These boundary proposals are then incorporated into the proposed image segmentation model, such that the target segmentation contours are made up of a set of selected boundary proposals and the corresponding geodesic paths linking them. Experimental results show that the proposed model indeed outperforms state-of-the-art minimal paths-based image segmentation approaches.

Revisiting Mehrotra and Nichani's Corner Detection Method for Improvement with Truncated Anisotropic Gaussian Filtering

In the early 1990s, Mehrotra and Nichani developed a filtering-based corner detection method, which, though conceptually intriguing, suffered from limited reliability, leading to minimal references in the literature. Despite its underappreciation, the core concept of this method, rooted in the half-edge concept and directional truncated first derivative of Gaussian, holds significant promise. This article presents a comprehensive assessment of the enhanced corner detection algorithm, combining both qualitative and quantitative evaluations. We thoroughly explore the strengths, limitations, and overall effectiveness of our approach by incorporating visual examples and conducting evaluations. Through experiments conducted on both synthetic and real images, we demonstrate the efficiency and reliability of the proposed algorithm. Collectively, our experimental assessments substantiate that our modifications have transformed the method into one that outperforms established benchmark techniques. Due to its ease of implementation, our improved corner detection process has the potential to become a valuable reference for the computer vision community when dealing with corner detection algorithms. This article thus highlights the quantitative achievements of our refined corner detection algorithm, building upon the groundwork laid by Mehrotra and Nichani, and offers valuable insights for the computer vision community seeking robust corner detection solutions.

Quantifying Yeast Microtubules and Spindles Using the Toolkit for Automated Microtubule Tracking (TAMiT)

Fluorescently labeled proteins absorb and emit light, appearing as Gaussian spots in fluorescence imaging. When fluorescent tags are added to cytoskeletal polymers such as microtubules, a line of fluorescence and even non-linear structures results. While much progress has been made in techniques for imaging and microscopy, image analysis is less well-developed. Current analysis of fluorescent microtubules uses either manual tools, such as kymographs, or automated software. As a result, our ability to quantify microtubule dynamics and organization from light microscopy remains limited. Despite the development of automated microtubule analysis tools for in vitro studies, analysis of images from cells often depends heavily on manual analysis. One of the main reasons for this disparity is the low signal-to-noise ratio in cells, where background fluorescence is typically higher than in reconstituted systems. Here, we present the Toolkit for Automated Microtubule Tracking (TAMiT), which automatically detects, optimizes, and tracks fluorescent microtubules in living yeast cells with sub-pixel accuracy. Using basic information about microtubule organization, TAMiT detects linear and curved polymers using a geometrical scanning technique. Images are fit via an optimization problem for the microtubule image parameters that are solved using non-linear least squares in Matlab. We benchmark our software using simulated images and show that it reliably detects microtubules, even at low signal-to-noise ratios. Then, we use TAMiT to measure monopolar spindle microtubule bundle number, length, and lifetime in a large dataset that includes several S. pombe mutants that affect microtubule dynamics and bundling. The results from the automated analysis are consistent with previous work and suggest a direct role for CLASP/Cls1 in bundling spindle microtubules. We also illustrate automated tracking of single curved astral microtubules in S. cerevisiae, with measurement of dynamic instability parameters. The results obtained with our fully-automated software are similar to results using hand-tracked measurements. Therefore, TAMiT can facilitate automated analysis of spindle and microtubule dynamics in yeast cells.

Quantifying yeast microtubules and spindles using the Toolkit for Automated Microtubule Tracking (TAMiT)

Fluorescently labeled proteins absorb and emit light, appearing as Gaussian spots in fluorescence imaging. When fluorescent tags are added to cytoskeletal polymers such as microtubules, a line of fluorescence and even non-linear structures results. While much progress has been made in techniques for imaging and microscopy, image analysis is less well developed. Current analysis of fluorescent microtubules uses either manual tools, such as kymographs, or automated software. As a result, our ability to quantify microtubule dynamics and organization from light microscopy remains limited. Despite development of automated microtubule analysis tools for in vitro studies, analysis of images from cells often depends heavily on manual analysis. One of the main reasons for this disparity is the low signal-to-noise ratio in cells, where background fluorescence is typically higher than in reconstituted systems. Here, we present the Toolkit for Automated Microtubule Tracking (TAMiT), which automatically detects, optimizes and tracks fluorescent microtubules in living yeast cells with sub-pixel accuracy. Using basic information about microtubule organization, TAMiT detects linear and curved polymers using a geometrical scanning technique. Images are fit via an optimization problem for the microtubule image parameters that is solved using non-linear least squares in Matlab. We benchmark our software using simulated images and show that it reliably detects microtubules, even at low signal-to-noise ratios. Then, we use TAMiT to measure monopolar spindle microtubule bundle number, length, and lifetime in a large dataset that includes several S. pombe mutants that affect microtubule dynamics and bundling. The results from the automated analysis are consistent with previous work, and suggest a direct role for CLASP/Cls1 in bundling spindle microtubules. We also illustrate automated tracking of single curved astral microtubules in S. cerevisiae , with measurement of dynamic instability parameters. The results obtained with our fully-automated software are similar to results using hand-tracked measurements. Therefore, TAMiT can facilitate automated analysis of spindle and microtubule dynamics in yeast cells.

Velocity Vector Estimation of Two-Dimensional Flow Field Based on STIV

As an important part of hydrometry, river discharge monitoring plays an irreplaceable role in the planning and management of water resources and is an essential element and necessary means of river management. Due to its benefits of simplicity, efficiency and safety, Space-Time Image Velocimetry (STIV) has attracted attention from all around the world. The most crucial component of the STIV is the detection of the Main Orientation of Texture (MOT), and the precision of detection directly affects the results of calculations. However, due to the complicated river flow characteristics and the harsh testing environment in the field, a large amount of noise and interfering textures show up in the space-time images, which affects the detection results of the MOT. In response to the shortage of noise and interference texture, a new non-contact image analysis method is developed. Firstly, Multi-scale Retinex (MSR) is proposed to pre-process the images for contrast enhancement; secondly, a fourth-order Gaussian derivative steerable filter is employed to enhance the structure of the texture; next, based on the probability density distribution function and the orientations of the enhanced images, the noise suppression function and the orientation-filtering function are designed to filter out the noise to highlight the texture. Finally, the Fourier Maximum Angle Analysis (FMAA) is used to filter out the noise further and obtain the clear orientations to achieve the measurement of velocity and discharge. The experimental results show that, compared with the widely used image velocimetry measurements, the accuracy of our method in the average velocity and flow discharge is significantly improved, and the real-time performance is excellent.

ILEE: Algorithms and toolbox for unguided and accurate quantitative analysis of cytoskeletal images

The eukaryotic cytoskeleton plays essential roles in cell signaling and trafficking, broadly associated with immunity and diseases in humans and plants. To date, most studies describing cytoskeleton dynamics and function rely on qualitative/quantitative analyses of cytoskeletal images. While state-of-the-art, these approaches face general challenges: the diversity among filaments causes considerable inaccuracy, and the widely adopted image projection leads to bias and information loss. To solve these issues, we developed the Implicit Laplacian of Enhanced Edge (ILEE), an unguided, high-performance approach for 2D/3D-based quantification of cytoskeletal status and organization. Using ILEE, we constructed a Python library to enable automated cytoskeletal image analysis, providing biologically interpretable indices measuring the density, bundling, segmentation, branching, and directionality of the cytoskeleton. Our data demonstrated that ILEE resolves the defects of traditional approaches, enables the detection of novel cytoskeletal features, and yields data with superior accuracy, stability, and robustness. The ILEE toolbox is available for public use through PyPI and Google Colab.

Implicit image processing with ghost imaging

In computational ghost imaging, the object is illuminated with a sequence of known patterns and the scattered light is collected using a detector that has no spatial resolution. Using those patterns and the total intensity measurement from the detector, one can reconstruct the desired image. Here we study how the reconstructed image is modified if the patterns used for the illumination are not the same as the reconstruction patterns and show that one can choose how to illuminate the object, such that the reconstruction process behaves like a spatial filtering operation on the image. The ability to directly measure a processed image allows one to bypass the post-processing steps and thus avoid any noise amplification they imply. As a simple example we show the case of an edge-detection filter.

Unsharp Mask Guided Filtering

The goal of this paper is guided image filtering, which emphasizes the importance of structure transfer during filtering by means of an additional guidance image. Where classical guided filters transfer structures using hand-designed functions, recent guided filters have been considerably advanced through parametric learning of deep networks. The state-of-the-art leverages deep networks to estimate the two core coefficients of the guided filter. In this work, we posit that simultaneously estimating both coefficients is suboptimal, resulting in halo artifacts and structure inconsistencies. Inspired by unsharp masking, a classical technique for edge enhancement that requires only a single coefficient, we propose a new and simplified formulation of the guided filter. Our formulation enjoys a filtering prior from a low-pass filter and enables explicit structure transfer by estimating a single coefficient. Based on our proposed formulation, we introduce a successive guided filtering network, which provides multiple filtering results from a single network, allowing for a trade-off between accuracy and efficiency. Extensive ablations, comparisons and analysis show the effectiveness and efficiency of our formulation and network, resulting in state-of-the-art results across filtering tasks like upsampling, denoising, and cross-modality filtering. Code is available at https://github.com/shizenglin/Unsharp-Mask-Guided-Filtering.

Bioimage Analysis and Cell Motility

Bioimage analysis (BIA) has historically helped study how and why cells move; biological experiments evolved in intimate feedback with the most classical image processing techniques because they contribute objectivity and reproducibility to an eminently qualitative science. Cell segmentation, tracking, and morphology descriptors are all discussed here. Using ameboid motility as a case study, these methods help us illustrate how proper quantification can augment biological data, for example, by choosing mathematical representations that amplify initially subtle differences, by statistically uncovering general laws or by integrating physical insight. More recently, the non-invasive nature of quantitative imaging is fertilizing two blooming fields: mechanobiology, where many biophysical measurements remain inaccessible, and microenvironments, where the quest for physiological relevance has exploded data size. From relief to remedy, this trend indicates that BIA is to become a main vector of biological discovery as human visual analysis struggles against ever more complex data.

Adaptive multiorientation resolution analysis of complex filamentous network images

MOTIVATION

Microscopy images of cytoskeletal, nucleoskeletal and other structures contain complex junctions of overlapping filaments with arbitrary geometry. Yet, state-of-the-art algorithms generally perform single orientation analysis to segment these structures, resulting in gaps near junctions, or assume particular junction geometries to detect them.

RESULTS

We developed a fully automated image analysis approach to address the challenge of determining the number of orientations and their values at each point in space to detect both lines and their junctions. Our approach does not assume any fixed number of orientations or any particular geometry in the case of multiple coincident orientations. It is based on analytically resolving coincident orientations revealed by steerable ridge filtering in an adaptive manner that balances orientation resolution and spatial localization. Combining this multiorientation resolution information with a generalization of the concept of non-maximum suppression allowed us to then identify the centers of lines and their junctions in an image. We validated our approach using a wide array of synthetic junctions and by comparison to manual segmentation. We also applied it to light microscopy images of cytoskeletal and nucleoskeletal networks.

AVAILABILITY AND IMPLEMENTATION

https://github.com/mkitti/AdaptiveResolutionOrientationSpace.

SUPPLEMENTARY INFORMATION

Supplementary information is available at Bioinformatics online.

The nucleus acts as a ruler tailoring cell responses to spatial constraints

The microscopic environment inside a metazoan organism is highly crowded. Whether individual cells can tailor their behavior to the limited space remains unclear. In this study, we found that cells measure the degree of spatial confinement by using their largest and stiffest organelle, the nucleus. Cell confinement below a resting nucleus size deforms the nucleus, which expands and stretches its envelope. This activates signaling to the actomyosin cortex via nuclear envelope stretch-sensitive proteins, up-regulating cell contractility. We established that the tailored contractile response constitutes a nuclear ruler-based signaling pathway involved in migratory cell behaviors. Cells rely on the nuclear ruler to modulate the motive force that enables their passage through restrictive pores in complex three-dimensional environments, a process relevant to cancer cell invasion, immune responses, and embryonic development.

Multi-Channel Surface EMG Spatio-Temporal Image Enhancement Using Multi-Scale Hessian-Based Filters

Surface electromyography (sEMG) signals acquired with linear electrode array are useful in analyzing muscle anatomy and physiology. Most algorithms for signal processing, detection, and estimation require adequate quality of the input signals, however, multi-channel sEMG signals are commonly contaminated due to several noise sources. The sEMG signal needs to be enhanced prior to the digital signal and image processing to achieve the best results. This study is using spatio-temporal images to represent surface EMG signals. The motor unit action potential (MUAP) in these images looks like a linear structure, making certain angles with the x-axis, depending on the conduction velocity of the MU. A multi-scale Hessian-based filter is used to enhance the linear structure, i.e., the MUAP region, and to suppress the background noise. The proposed framework is compared with some of the existing algorithms using synthetic, simulated, and experimental sEMG signals. Results show improved detection accuracy of the motor unit action potential after the proposed enhancement as a preprocessing step.

A comprehensive study on feature types for osteoporosis classification in dental panoramic radiographs

BACKGROUND AND OBJECTIVE

Osteoporosis is a disease characterized by a decrease in bone density. It is often associated with fractures and severe pain. Previous studies have shown a high correlation between the density of the bone in the hip and in the mandibular bone in the jaw. This suggests that dental radiographs might be useful for detecting osteoporosis. Use of dental radiographs for this purpose would simplify early detection of osteoporosis. However, dental radiographs are not normally examined by radiologists. This paper explores the use of 13 different feature extractors for detection of reduced bone density in dental radiographs.

METHODS

The computed feature vectors are then processed with a Self-Organizing Map and Learning Vector Quantization as well as Support Vector Machines to produce a set of 26 predictive models.

RESULTS

The results show that the models based on Self-Organizing Map and Learning Vector Quantization using Gabor Filter, Edge Orientation Histogram, Haar Wavelet, and Steerable Filter feature extractors outperform the rest of the 22 models in detecting osteoporosis. The proposed Gabor-based algorithm achieved an accuracy of 92.6%, a sensitivity of 97.1%, and a specificity of 86.4%.

CONCLUSIONS

The oriented edges and textures in the upper and lower jaw regions are useful for distinguishing normal patients from patients with osteoporosis.

The Fourier-Argand Representation: An Optimal Basis of Steerable Patterns

Computing the convolution between a 2D signal and a corresponding filter with variable orientations is a basic problem that arises in various tasks ranging from low level image processing (e.g. ridge/edge detection) to high level computer vision (e.g. pattern recognition). Through decades of research, there still lacks an efficient method for solving this problem. In this paper, we investigate this problem from the perspective of approximation by considering the following problem: what is the optimal basis for approximating all rotated versions of a given bivariate function? Surprisingly, solely minimising the L2-approximation-error leads to a rotation-covariant linear expansion, which we name Fourier-Argand representation. This representation presents two major advantages: 1) rotation-covariance of the basis, which implies a "strong steerability" - rotating by an angle α corresponds to multiplying each basis function by a complex scalar e-ikα; 2) optimality of the Fourier-Argand basis, which ensures a few number of basis functions suffice to accurately approximate complicated patterns and highly direction-selective filters. We show the relation between the Fourier-Argand representation and the Radon transform, leading to an efficient implementation of the decomposition for digital filters. We also show how to retrieve accurate orientation of local structures/patterns using a fast frequency estimation algorithm.

Spatial integration of mechanical forces by α-actinin establishes actin network symmetry

Cell and tissue morphogenesis depend on the production and spatial organization of tensional forces in the actin cytoskeleton. Actin network architecture is made of distinct modules characterized by specific filament organizations. The assembly of these modules are well described, but their integration in a cellular network is less understood. Here, we investigated the mechanism regulating the interplay between network architecture and the geometry of the extracellular environment of the cell. We found that α-actinin, a filament crosslinker, is essential for network symmetry to be consistent with extracellular microenvironment symmetry. It is required for the interconnection of transverse arcs with radial fibres to ensure an appropriate balance between forces at cell adhesions and across the actin network. Furthermore, this connectivity appeared necessary for the ability of the cell to integrate and to adapt to complex patterns of extracellular cues as they migrate. Our study has unveiled a role of actin filament crosslinking in the spatial integration of mechanical forces that ensures the adaptation of intracellular symmetry axes in accordance with the geometry of extracellular cues.This article has an associated First Person interview with the first author of the paper.

Robust and automated detection of subcellular morphological motifs in 3D microscopy images

Rapid developments in live-cell 3D microscopy enable imaging of cell morphology and signaling with unprecedented detail. However, tools to systematically measure and visualize the intricate relationships between intracellular signaling, cytoskeletal organization, and downstream cell morphological outputs do not exist. Here we introduce u-shape3D, a computer graphics and machine-learning pipeline to probe molecular mechanisms underlying 3D cell morphogenesis and to test the intriguing possibility that morphogenesis itself affects intracellular signaling. We demonstrate a generic morphological motif detector that automatically finds lamellipodia, filopodia, blebs, and other motifs. Combining motif detection with molecular localization, we measure the differential association of PIP₂ and Kras^V12 with blebs. Both signals associate with bleb edges, as expected for membrane-localized proteins, but only PIP₂ is enhanced on blebs. This indicates that sub-cellular signaling processes are differentially modulated by local morphological motifs. Overall, our computational workflow enables the objective, 3D analysis of the coupling of cell shape and signaling.

Nessys: A new set of tools for the automated detection of nuclei within intact tissues and dense 3D cultures

Methods for measuring the properties of individual cells within their native 3D environment will enable a deeper understanding of embryonic development, tissue regeneration, and tumorigenesis. However, current methods for segmenting nuclei in 3D tissues are not designed for situations in which nuclei are densely packed, nonspherical, or heterogeneous in shape, size, or texture, all of which are true of many embryonic and adult tissue types as well as in many cases for cells differentiating in culture. Here, we overcome this bottleneck by devising a novel method based on labelling the nuclear envelope (NE) and automatically distinguishing individual nuclei using a tree-structured ridge-tracing method followed by shape ranking according to a trained classifier. The method is fast and makes it possible to process images that are larger than the computer's memory. We consistently obtain accurate segmentation rates of >90%, even for challenging images such as mid-gestation embryos or 3D cultures. We provide a 3D editor and inspector for the manual curation of the segmentation results as well as a program to assess the accuracy of the segmentation. We have also generated a live reporter of the NE that can be used to track live cells in 3 dimensions over time. We use this to monitor the history of cell interactions and occurrences of neighbour exchange within cultures of pluripotent cells during differentiation. We provide these tools in an open-access user-friendly format.

Quantitative Analysis of Nuclear Lamins Imaged by Super-Resolution Light Microscopy

The nuclear lamina consists of a dense fibrous meshwork of nuclear lamins, Type V intermediate filaments, and is ~14 nm thick according to recent cryo-electron tomography studies. Recent advances in light microscopy have extended the resolution to a scale allowing for the fine structure of the lamina to be imaged in the context of the whole nucleus. We review quantitative approaches to analyze the imaging data of the nuclear lamina as acquired by structured illumination microscopy (SIM) and single molecule localization microscopy (SMLM), as well as the requisite cell preparation techniques. In particular, we discuss the application of steerable filters and graph-based methods to segment the structure of the four mammalian lamin isoforms (A, C, B1, and B2) and extract quantitative information.

3-D Quantification of Filopodia in Motile Cancer Cells

We present a 3D bioimage analysis workflow to quantitatively analyze single, actin-stained cells with filopodial protrusions of diverse structural and temporal attributes, such as number, length, thickness, level of branching, and lifetime, in time-lapse confocal microscopy image data. Our workflow makes use of convolutional neural networks trained using real as well as synthetic image data, to segment the cell volumes with highly heterogeneous fluorescence intensity levels and to detect individual filopodial protrusions, followed by a constrained nearest-neighbor tracking algorithm to obtain valuable information about the spatio-temporal evolution of individual filopodia. We validated the workflow using real and synthetic 3-D time-lapse sequences of lung adenocarcinoma cells of three morphologically distinct filopodial phenotypes and show that it achieves reliable segmentation and tracking performance, providing a robust, reproducible and less time-consuming alternative to manual analysis of the 3D+t image data.

Minimal Paths for Tubular Structure Segmentation With Coherence Penalty and Adaptive Anisotropy

The minimal path method has proven to be particularly useful and efficient in tubular structure segmentation applications. In this paper, we propose a new minimal path model associated with a dynamic Riemannian metric embedded with an appearance feature coherence penalty and an adaptive anisotropy enhancement term. The features that characterize the appearance and anisotropy properties of a tubular structure are extracted through the associated orientation score. The proposed the dynamic Riemannian metric is updated in the course of the geodesic distance computation carried out by the efficient single-pass fast marching method. Compared to the state-of-the-art minimal path models, the proposed minimal path model is able to extract the desired tubular structures from a complicated vessel tree structure. In addition, we propose an efficient prior path-based method to search for vessel radius value at each centerline position of the target. Finally, we perform the numerical experiments on both synthetic and real images. The quantitive validation is carried out on retinal vessel images. The results indicate that the proposed model indeed achieves a promising performance.

Automated profiling of growth cone heterogeneity defines relations between morphology and motility

Growth cones are complex, motile structures at the tip of an outgrowing neurite. They often exhibit a high density of filopodia (thin actin bundles), which complicates the unbiased quantification of their morphologies by software. Contemporary image processing methods require extensive tuning of segmentation parameters, require significant manual curation, and are often not sufficiently adaptable to capture morphology changes associated with switches in regulatory signals. To overcome these limitations, we developed Growth Cone Analyzer (GCA). GCA is designed to quantify growth cone morphodynamics from time-lapse sequences imaged both in vitro and in vivo, but is sufficiently generic that it may be applied to nonneuronal cellular structures. We demonstrate the adaptability of GCA through the analysis of growth cone morphological variation and its relation to motility in both an unperturbed system and in the context of modified Rho GTPase signaling. We find that perturbations inducing similar changes in neurite length exhibit underappreciated phenotypic nuance at the scale of the growth cone.

Multimodal imaging and high-throughput image-processing for drug screening on living organisms on-chip

A major step for the validation of medical drugs is the screening on whole organisms, which gives the systemic information that is missing when using cellular models. Caenorhabditis elegans is a soil worm that catches the interest of researchers who study systemic physiopathology (e.g., metabolic and neurodegenerative diseases) because: (1) its large genetic homology with humans supports translational analysis; (2) worms are much easier to handle and grow in large amounts compared with rodents, for which (3) the costs and (4) the ethical concerns are substantial. Here, we demonstrate how multimodal optical imaging on such an organism can provide high-content information relevant to the drug development pipeline (e.g., mode-of-action identification, dose-response analysis), especially when combined with on-chip multiplexing capability. After designing a microfluidic array to select small separated populations of C. elegans, we combine fluorescence and bright-field imaging along with high-throughput feature recognition and signal detection to enable the identification of the mode-of-action of an antibiotic. For this purpose, we use a genetically encoded fluorescence reporter of mitochondrial stress, which we studied in living specimens during their entire development. Furthermore, we demonstrate real-time, very large field-of-view capability on multiplexed motility assays for the assessment of the dose-response relation of an anesthetic.

A key function for microtubule-associated-protein 6 in activity-dependent stabilisation of actin filaments in dendritic spines

Emerging evidence indicates that microtubule-associated proteins (MAPs) are implicated in synaptic function; in particular, mice deficient for MAP6 exhibit striking deficits in plasticity and cognition. How MAP6 connects to plasticity mechanisms is unclear. Here, we address the possible role of this protein in dendritic spines. We find that in MAP6-deficient cortical and hippocampal neurons, maintenance of mature spines is impaired, and can be restored by expressing a stretch of the MAP6 sequence called Mc modules. Mc modules directly bind actin filaments and mediate activity-dependent stabilisation of F-actin in dendritic spines, a key event of synaptic plasticity. In vitro, Mc modules enhance actin filament nucleation and promote the formation of stable, highly ordered filament bundles. Activity-induced phosphorylation of MAP6 likely controls its transfer to the spine cytoskeleton. These results provide a molecular explanation for the role of MAP6 in cognition, enlightening the connection between cytoskeletal dysfunction, synaptic impairment and neuropsychiatric illnesses.

Microtubule-associated protein 6 (MAP6) is known to be important for synaptic plasticity and cognition, supposedly via interaction with microtubules. Here, the authors found that MAP6 is crucial for the stabilisation of enlarged synapses through its association with a different cytoskeletal element, actin.

Characterization of the role of collagen network structure and composition in cancer cell migration

The geometry of 3D collagen networks is a key factor that influences the behavior of live cells within extra-cellular matrices. This paper presents a method for automatic quantification of the 3D collagen network geometry with fiber resolution in confocal reflection microscopy images. The proposed method is based on a smoothing filter and binarization of the collagen network followed by a fiber reconstruction algorithm. The method is validated on 3D collagen gels with various collagen and Matrigel concentrations. The results reveal that Matrigel affects the collagen network geometry by decreasing the network pore size while preserving the fiber length and fiber persistence length. The influence of network composition and geometry, especially pore size, is preliminarily analyzed by quantifying the migration patterns of lung cancer cells within microfluidic devices filled with three different hydrogel types. The experiments reveal that Matrigel, while decreasing pore size, stimulates cell migration. Further studies on this relationship could be instrumental for the study of cancer metastasis and other biological processes involving cell migration.

Modeling, validation and verification of three-dimensional cell-scaffold contacts from terabyte-sized images

Background

Cell-scaffold contact measurements are derived from pairs of co-registered volumetric fluorescent confocal laser scanning microscopy (CLSM) images (z-stacks) of stained cells and three types of scaffolds (i.e., spun coat, large microfiber, and medium microfiber). Our analysis of the acquired terabyte-sized collection is motivated by the need to understand the nature of the shape dimensionality (1D vs 2D vs 3D) of cell-scaffold interactions relevant to tissue engineers that grow cells on biomaterial scaffolds.

Results

We designed five statistical and three geometrical contact models, and then down-selected them to one from each category using a validation approach based on physically orthogonal measurements to CLSM. The two selected models were applied to 414 z-stacks with three scaffold types and all contact results were visually verified. A planar geometrical model for the spun coat scaffold type was validated from atomic force microscopy images by computing surface roughness of 52.35 nm ±31.76 nm which was 2 to 8 times smaller than the CLSM resolution. A cylindrical model for fiber scaffolds was validated from multi-view 2D scanning electron microscopy (SEM) images. The fiber scaffold segmentation error was assessed by comparing fiber diameters from SEM and CLSM to be between 0.46% to 3.8% of the SEM reference values. For contact verification, we constructed a web-based visual verification system with 414 pairs of images with cells and their segmentation results, and with 4968 movies with animated cell, scaffold, and contact overlays. Based on visual verification by three experts, we report the accuracy of cell segmentation to be 96.4% with 94.3% precision, and the accuracy of cell-scaffold contact for a statistical model to be 62.6% with 76.7% precision and for a geometrical model to be 93.5% with 87.6% precision.

Conclusions

The novelty of our approach lies in (1) representing cell-scaffold contact sites with statistical intensity and geometrical shape models, (2) designing a methodology for validating 3D geometrical contact models and (3) devising a mechanism for visual verification of hundreds of 3D measurements. The raw and processed data are publicly available from https://isg.nist.gov/deepzoomweb/data/ together with the web -based verification system.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1928-x) contains supplementary material, which is available to authorized users.

Anisotropic Müller glial scaffolding supports a multiplex lattice mosaic of photoreceptors in zebrafish retina

Background

The multiplex, lattice mosaic of cone photoreceptors in the adult fish retina is a compelling example of a highly ordered epithelial cell pattern, with single cell width rows and columns of cones and precisely defined neighbor relationships among different cone types. Cellular mechanisms patterning this multiplex mosaic are not understood. Physical models can provide new insights into fundamental mechanisms of biological patterning. In earlier work, we developed a mathematical model of photoreceptor cell packing in the zebrafish retina, which predicted that anisotropic mechanical tension in the retinal epithelium orients planar polarized adhesive interfaces to align the columns as cone photoreceptors are generated at the retinal margin during post-embryonic growth.

Methods

With cell-specific fluorescent reporters and in vivo imaging of the growing retinal margin in transparent juvenile zebrafish we provide the first view of how cell packing, spatial arrangement, and cell identity are coordinated to build the lattice mosaic. With targeted laser ablation we probed the tissue mechanics of the retinal epithelium.

Results

Within the lattice mosaic, planar polarized Crumbs adhesion proteins pack cones into a single cell width column; between columns, N-cadherin-mediated adherens junctions stabilize Müller glial apical processes. The concentration of activated pMyosin II at these punctate adherens junctions suggests that these glial bands are under tension, forming a physical barrier between cone columns and contributing to mechanical stress anisotropies in the epithelial sheet. Unexpectedly, we discovered that the appearance of such parallel bands of Müller glial apical processes precedes the packing of cones into single cell width columns, hinting at a possible role for glia in the initial organization of the lattice mosaic. Targeted laser ablation of Müller glia directly demonstrates that these glial processes support anisotropic mechanical tension in the planar dimension of the retinal epithelium.

Conclusions

These findings uncovered a novel structural feature of Müller glia associated with alignment of photoreceptors into a lattice mosaic in the zebrafish retina. This is the first demonstration, to our knowledge, of planar, anisotropic mechanical forces mediated by glial cells.

Electronic supplementary material

The online version of this article (10.1186/s13064-017-0096-z) contains supplementary material, which is available to authorized users.

Derivative Kernels: Numerics and Applications

A generalized framework for numerical differentiation (ND) is proposed for constructing a finite impulse response (FIR) filter in closed form. The framework regulates the frequency response of ND filters for arbitrary derivative-order and cutoff frequency selected parameters relying on interpolating power polynomials and maximally flat design techniques. Compared with the state-of-the-art solutions, such as Gaussian kernels, the proposed ND filter is sharply localized in the Fourier domain with ripple-free artifacts. Here, we construct 2D MaxFlat kernels for image directional differentiation to calculate image differentials for arbitrary derivative order, cutoff level and steering angle. The resulted kernel library renders a new solution capable of delivering discrete approximation of gradients, Hessian, and higher-order tensors in numerous applications. We tested the utility of this library on three different imaging applications with main focus on the unsharp masking. The reported results highlight the high efficiency of the 2D MaxFlat kernel and its versatility with respect to robustness and parameter control accuracy.

Fgf8 Expression and Degradation of Retinoic Acid Are Required for Patterning a High-Acuity Area in the Retina

Species that are highly reliant on their visual system have a specialized retinal area subserving high-acuity vision, e.g., the fovea in humans. Although of critical importance for our daily activities, little is known about the mechanisms driving the development of retinal high-acuity areas (HAAs). Using the chick as a model, we found a precise and dynamic expression pattern of fibroblast growth factor 8 (Fgf8) in the HAA anlage, which was regulated by enzymes that degrade retinoic acid (RA). Transient manipulation of RA signaling, or reduction of Fgf8 expression, disrupted several features of HAA patterning, including photoreceptor distribution, ganglion cell density, and organization of interneurons. Notably, patterned expression of RA signaling components was also found in humans, suggesting that RA also plays a role in setting up the human fovea.

An Improved Neuron Segmentation Model for Crack Detection – Image Segmentation Model

It is still very challenging to establish a unified and robust framework to perform accurate and complete crack extraction from images with cluttered background, various morphological differences and even with shadow influence. In this paper, an improved neuron segmentation model with two stages is proposed for crack segmentation. Firstly, a robust crack indicator function is designed based on local directional filtering; it makes up for the traditional function based on hessian matrix, which is resulting in problem of local structure discontinuities. After obtaining the indicator function, the crack detection is performed in an integrated mode; it is incorporating the automated directional region growing without manual intervention by adopting level sets; then efficient and complete crack segmentation is realized by iterative contour evolution. The performance of the proposed model is demonstrated by experiments on three kinds of grouped crack sample images and the quantitative evaluation. We also argue that the proposed model is applicable for biomedical image segmentation.

Automated Anatomy-Based Tracking of Systemic Arteries in Arbitrary Field-of-View CTA Scans

We propose an automated pipeline for vessel centerline extraction in 3-D computed tomography angiography (CTA) scans with arbitrary fields of view. The principal steps of the pipeline are body part detection, candidate seed selection, segment tracking, which includes centerline extraction, and vessel tree growing. The final tree-growing step can be instantiated in either a semi- or fully automated fashion. The fully automated initialization is carried out using a vessel position regression algorithm. Both semi-and fully automated methods were evaluated on 30 CTA scans comprising neck, abdominal, and leg arteries in multiple fields of view. High detection rates and centerline accuracy values for 38 distinct vessels demonstrate the effectiveness of our approach.

Vimentin fibers orient traction stress

The intermediate filament vimentin is required for cells to transition from the epithelial state to the mesenchymal state and migrate as single cells; however, little is known about the specific role of vimentin in the regulation of mesenchymal migration. Vimentin is known to have a significantly greater ability to resist stress without breaking in vitro compared with actin or microtubules, and also to increase cell elasticity in vivo. Therefore, we hypothesized that the presence of vimentin could support the anisotropic mechanical strain of single-cell migration. To study this, we fluorescently labeled vimentin with an mEmerald tag using TALEN genome editing. We observed vimentin architecture in migrating human foreskin fibroblasts and found that network organization varied from long, linear bundles, or "fibers," to shorter fragments with a mesh-like organization. We developed image analysis tools employing steerable filtering and iterative graph matching to characterize the fibers embedded in the surrounding mesh. Vimentin fibers were aligned with fibroblast branching and migration direction. The presence of the vimentin network was correlated with 10-fold slower local actin retrograde flow rates, as well as spatial homogenization of actin-based forces transmitted to the substrate. Vimentin fibers coaligned with and were required for the anisotropic orientation of traction stresses. These results indicate that the vimentin network acts as a load-bearing superstructure capable of integrating and reorienting actin-based forces. We propose that vimentin's role in cell motility is to govern the alignment of traction stresses that permit single-cell migration.

FlyLimbTracker: An active contour based approach for leg segment tracking in unmarked, freely behaving Drosophila

Understanding the biological underpinnings of movement and action requires the development of tools for quantitative measurements of animal behavior. Drosophila melanogaster provides an ideal model for developing such tools: the fly has unparalleled genetic accessibility and depends on a relatively compact nervous system to generate sophisticated limbed behaviors including walking, reaching, grooming, courtship, and boxing. Here we describe a method that uses active contours to semi-automatically track body and leg segments from video image sequences of unmarked, freely behaving D. melanogaster. We show that this approach yields a more than 6-fold reduction in user intervention when compared with fully manual annotation and can be used to annotate videos with low spatial or temporal resolution for a variety of locomotor and grooming behaviors. FlyLimbTracker, the software implementation of this method, is open-source and our approach is generalizable. This opens up the possibility of tracking leg movements in other species by modifications of underlying active contour models.

Dissecting the actin cortex density and membrane-cortex distance in living cells by super-resolution microscopy

Nanoscale spacing between the plasma membrane and the underlying cortical actin cytoskeleton profoundly modulates cellular morphology, mechanics, and function. Measuring this distance has been a key challenge in cell biology. Current methods for dissecting the nanoscale spacing either limit themselves to complex survey design using fixed samples or rely on diffraction-limited fluorescence imaging whose spatial resolution is insufficient to quantify distances on the nanoscale. Using dual-color super-resolution STED (stimulated-emission-depletion) microscopy, we here overcome this challenge and accurately measure the density distribution of the cortical actin cytoskeleton and the distance between the actin cortex and the membrane in live Jurkat T-cells. We found an asymmetric cortical actin density distribution with a mean width of 230 (+105/-125) nm. The spatial distances measured between the maximum density peaks of the cortex and the membrane were bi-modally distributed with mean values of 50  ±  15 nm and 120  ±  40 nm, respectively. Taken together with the finite width of the cortex, our results suggest that in some regions the cortical actin is closer than 10 nm to the membrane and a maximum of 20 nm in others.

Characterization of three-dimensional cancer cell migration in mixed collagen-Matrigel scaffolds using microfluidics and image analysis

Microfluidic devices are becoming mainstream tools to recapitulate in vitro the behavior of cells and tissues. In this study, we use microfluidic devices filled with hydrogels of mixed collagen-Matrigel composition to study the migration of lung cancer cells under different cancer invasion microenvironments. We present the design of the microfluidic device, characterize the hydrogels morphologically and mechanically and use quantitative image analysis to measure the migration of H1299 lung adenocarcinoma cancer cells in different experimental conditions. Our results show the plasticity of lung cancer cell migration, which turns from mesenchymal in collagen only matrices, to lobopodial in collagen-Matrigel matrices that approximate the interface between a disrupted basement membrane and the underlying connective tissue. Our quantification of migration speed confirms a biphasic role of Matrigel. At low concentration, Matrigel facilitates migration, most probably by providing a supportive and growth factor retaining environment. At high concentration, Matrigel slows down migration, possibly due excessive attachment. Finally, we show that antibody-based integrin blockade promotes a change in migration phenotype from mesenchymal or lobopodial to amoeboid and analyze the effect of this change in migration dynamics, in regards to the structure of the matrix. In summary, we describe and characterize a robust microfluidic platform and a set of software tools that can be used to study lung cancer cell migration under different microenvironments and experimental conditions. This platform could be used in future studies, thus benefitting from the advantages introduced by microfluidic devices: precise control of the environment, excellent optical properties, parallelization for high throughput studies and efficient use of therapeutic drugs.

Vimentin Intermediate Filaments Template Microtubule Networks to Enhance Persistence in Cell Polarity and Directed Migration

Increased expression of vimentin intermediate filaments (VIFs) enhances directed cell migration, but the mechanism behind VIFs' effect on motility is not understood. VIFs interact with microtubules, whose organization contributes to polarity maintenance in migrating cells. Here, we characterize the dynamic coordination of VIF and microtubule networks in wounded monolayers of retinal pigment epithelial cells. By genome editing, we fluorescently labeled endogenous vimentin and α-tubulin, and we developed computational image analysis to delineate architecture and interactions of the two networks. Our results show that VIFs assemble an ultrastructural copy of the previously polarized microtubule network. Because the VIF network is long-lived compared to the microtubule network, VIFs template future microtubule growth along previous microtubule tracks, thus providing a feedback mechanism that maintains cell polarity. VIF knockdown prevents cells from polarizing and migrating properly during wound healing. We suggest that VIFs' templating function establishes a memory in microtubule organization that enhances persistence in cell polarization in general and migration in particular.

A multi-stage random forest classifier for phase contrast cell segmentation

We present a machine learning based approach to automatically detect and segment cells in phase contrast images. The proposed method consists of a multi-stage classification scheme based on random forest (RF) classifier. Both low level and mid level image features are used to determine meaningful cell regions. Pixel-wise RF classification is first carried out to categorize pixels into 4 classes (dark cell, bright cell, halo artifact, and background) and generate a probability map for cell regions. K-means clustering is then applied on the probability map to group similar pixels into candidate cell regions. Finally, cell validation is performed by another RF to verify the candidate cell regions. The proposed method has been tested on U2-OS human osteosarcoma phase contrast images. The experimental results show better performance of the proposed method with precision 92.96% and recall 96.63% compared to a state-of-the-art segmentation technique.

Determination of Statistical Properties of Microtubule Populations

Microtubules are structures within the cell that form a transportation network along which motor proteins tow cargo to destinations. To establish and maintain a structure capable of serving the cell’s tasks, microtubules undergo deconstruction and reconstruction regularly. This change in structure is critical to tasks like wound repair and cell motility. Images of fluorescing microtubule networks are captured in grayscale at different wavelengths, displaying different tagged proteins. The analysis of these polymeric structures involves identifying the presence of the protein and the direction of the structure in which it resides. This study considers the problem of finding statistical properties of sections of microtubules. We consider the research done on directional filters and utilize a basic solution to find the center of a ridge. The method processes the captured image by centering a circle around pre-determined pixel locations so that the highest possible average pixel intensity is found within the circle, thus marking the center of the microtubule. The location of these centers allows us to estimate angular direction and curvature of the microtubules, statistically estimate the direction of microtubules in a region of the cell, and compare properties of different types of microtubule networks in the same region. To verify accuracy, we study the results of the method on a test image.

Quantitative Multiscale Cell Imaging in Controlled 3D Microenvironments

The microenvironment determines cell behavior, but the underlying molecular mechanisms are poorly understood because quantitative studies of cell signaling and behavior have been challenging due to insufficient spatial and/or temporal resolution and limitations on microenvironmental control. Here we introduce microenvironmental selective plane illumination microscopy (meSPIM) for imaging and quantification of intracellular signaling and submicrometer cellular structures as well as large-scale cell morphological and environmental features. We demonstrate the utility of this approach by showing that the mechanical properties of the microenvironment regulate the transition of melanoma cells from actin-driven protrusion to blebbing, and we present tools to quantify how cells manipulate individual collagen fibers. We leverage the nearly isotropic resolution of meSPIM to quantify the local concentration of actin and phosphatidylinositol 3-kinase signaling on the surfaces of cells deep within 3D collagen matrices and track the many small membrane protrusions that appear in these more physiologically relevant environments.

Multiscale Centerline Detection

Finding the centerline and estimating the radius of linear structures is a critical first step in many applications, ranging from road delineation in 2D aerial images to modeling blood vessels, lung bronchi, and dendritic arbors in 3D biomedical image stacks. Existing techniques rely either on filters designed to respond to ideal cylindrical structures or on classification techniques. The former tend to become unreliable when the linear structures are very irregular while the latter often has difficulties distinguishing centerline locations from neighboring ones, thus losing accuracy. We solve this problem by reformulating centerline detection in terms of a regression problem. We first train regressors to return the distances to the closest centerline in scale-space, and we apply them to the input images or volumes. The centerlines and the corresponding scale then correspond to the regressors local maxima, which can be easily identified. We show that our method outperforms state-of-the-art techniques for various 2D and 3D datasets. Moreover, our approach is very generic and also performs well on contour detection. We show an improvement above recent contour detection algorithms on the BSDS500 dataset.

Quality-Related Monitoring and Grading of Granulated Products by Weibull-Distribution Modeling of Visual Images with Semi-Supervised Learning

The topic of online product quality inspection (OPQI) with smart visual sensors is attracting increasing interest in both the academic and industrial communities on account of the natural connection between the visual appearance of products with their underlying qualities. Visual images captured from granulated products (GPs), e.g., cereal products, fabric textiles, are comprised of a large number of independent particles or stochastically stacking locally homogeneous fragments, whose analysis and understanding remains challenging. A method of image statistical modeling-based OPQI for GP quality grading and monitoring by a Weibull distribution(WD) model with a semi-supervised learning classifier is presented. WD-model parameters (WD-MPs) of GP images' spatial structures, obtained with omnidirectional Gaussian derivative filtering (OGDF), which were demonstrated theoretically to obey a specific WD model of integral form, were extracted as the visual features. Then, a co-training-style semi-supervised classifier algorithm, named COSC-Boosting, was exploited for semi-supervised GP quality grading, by integrating two independent classifiers with complementary nature in the face of scarce labeled samples. Effectiveness of the proposed OPQI method was verified and compared in the field of automated rice quality grading with commonly-used methods and showed superior performance, which lays a foundation for the quality control of GP on assembly lines.

Hermite Snakes With Control of Tangents

We introduce a new model of parametric contours defined in a continuous fashion. Our curve model relies on Hermite spline interpolation and can easily generate curves with sharp discontinuities; it also grants direct access to the tangent at each location. With these two features, the Hermite snake distinguishes itself from classical spline-snake models and allows one to address certain bioimaging problems in a more efficient way. More precisely, the Hermite snake construction allows introducing sharp corners in the snake curve and designing directional energy functionals relying on local orientation information in the input image. Using the formalism of spline theory, the model is shown to meet practical requirements such as invariance to affine transformations and good approximation properties. Finally, the dependence on initial conditions and the robustness to the noise is studied on synthetic data in order to validate our Hermite snake model, and its usefulness is illustrated on real biological images acquired using brightfield, phase-contrast, differential-interference-contrast, and scanning-electron microscopy.