Contrast-independent curvilinear structure detection in biomedical images

Sustained immune activation and impaired epithelial barrier integrity in the ectocervix of women with chronic HIV infection

Chronic systemic immune activation significantly influences human immunodeficiency virus (HIV) disease progression. Despite evidence of a pro-inflammatory environment in the genital tract of HIV-infected women, comprehensive investigations into cervical tissue from this region remain limited. Similarly, the consequences of chronic HIV infection on the integrity of the female genital epithelium are poorly understood, despite its importance in HIV transmission and replication. Ectocervical biopsies were obtained from HIV-seropositive (n = 14) and HIV-seronegative (n = 47) female Kenyan sex workers. RNA sequencing and bioimage analysis of epithelial junction proteins (E-cadherin, desmoglein-1, claudin-1, and zonula occludens-1) were conducted, along with CD4 staining. RNA sequencing revealed upregulation of immunoregulatory genes in HIV-seropositive women, primarily associated with heightened T cell activity and interferon signaling, which further correlated with plasma viral load. Transcription factor analysis confirmed the upregulation of pro-inflammatory transcription factors, such as RELA, NFKB1, and IKZF3, which facilitates HIV persistence in T cells. Conversely, genes and pathways associated with epithelial barrier function and structure were downregulated in the context of HIV. Digital bioimage analysis corroborated these findings, revealing significant disruption of various epithelial junction proteins in ectocervical tissues of the HIV-seropositive women. Thus, chronic HIV infection associated with ectocervical inflammation, characterized by induced T cell responses and interferon signaling, coupled with epithelial disruption. These alterations may influence HIV transmission and heighten susceptibility to other sexually transmitted infections. These findings prompt exploration of therapeutic interventions to address HIV-related complications and mitigate the risk of sexually transmitted infection transmission.

Estradiol-mediated enhancement of the human ectocervical epithelial barrier correlates with desmoglein-1 expression in the follicular menstrual phase

Background

The cervicovaginal epithelial barrier is crucial for defending the female reproductive tract against sexually transmitted infections. Hormones, specifically estradiol and progesterone, along with their respective receptor expressions, play an important role in modulating this barrier. However, the influence of estradiol and progesterone on gene and protein expression in the ectocervical mucosa of naturally cycling women is not well understood.

Methods

Mucosal and blood samples were collected from Kenyan female sex workers at high risk of sexually transmitted infections. All samples were obtained at two time points, separated by two weeks, aiming for the follicular and luteal phases of the menstrual cycle. Ectocervical tissue biopsies were analyzed by RNA-sequencing and in situ immunofluorescence staining, cervicovaginal lavage samples (CVL) were evaluated using protein profiling, and plasma samples were analyzed for hormone levels.

Results

Unsupervised clustering of RNA-sequencing data was performed using Weighted gene co-expression network analysis (WGCNA). In the follicular phase, estradiol levels positively correlated with a gene module representing epithelial structure and function, and negatively correlated with a gene module representing cell cycle regulation. These correlations were confirmed using regression analysis including adjustment for bacterial vaginosis status. Using WGCNA, no gene module correlated with progesterone levels in the follicular phase. In the luteal phase, no gene module correlated with either estradiol or progesterone levels. Protein profiling on CVL revealed that higher levels of estradiol during the follicular phase correlated with increased expression of epithelial barrier integrity markers, including DSG1. This contrasted to the limited correlations of protein expression with estradiol levels in the luteal phase. In situ imaging analysis confirmed that higher estradiol levels during the follicular phase correlated with increased DSG1 expression.

Conclusion

We demonstrate that estradiol levels positively correlate with specific markers of ectocervical epithelial structure and function, particularly DSG1, during the follicular phase of the menstrual cycle. Neither progesterone levels during the follicular phase nor estradiol and progesterone levels during the luteal phase correlated with any specific sets of gene markers. These findings align with the expression of estradiol and progesterone receptors in the ectocervical epithelium during these menstrual phases.

A toolbox to analyze collective cell migration, proliferation and cellular organization simultaneously

BACKGROUND

Analyses of collective cell migration and orientation phenomena are needed to assess the behavior of multicellular clusters. While some tools to the authors' knowledge none is capable to analyze collective migration, cellular orientation and proliferation in phase contrast images simultaneously.

METHODS

We provide a tool based to analyze phase contrast images of dense cell layers. PIV is used to calculatevelocity fields, while the structure tensor provides cellular orientation. An artificial neural network is used to identify cell division events, allowing to correlate migratory and organizational phenomena with cell density.

CONCLUSION

The presented tool allows the simultaneous analysis of collective cell behavior from phase contrast images in terms of migration, (self-)organization and proliferation.

Separable Paravector Orientation Tensors for Enhancing Retinal Vessels

Robust detection of retinal vessels remains an unsolved research problem, particularly in handling the intrinsic real-world challenges of highly imbalanced contrast between thick vessels and thin ones, inhomogeneous background regions, uneven illumination, and complex geometries of crossing/bifurcations. This paper presents a new separable paravector orientation tensor that addresses these difficulties by characterizing the enhancement of retinal vessels to be dependent on a nonlinear scale representation, invariant to changes in contrast and lighting, responsive for symmetric patterns, and fitted with elliptical cross-sections. The proposed method is built on projecting vessels as a 3D paravector valued function rotated in an alpha quarter domain, providing geometrical, structural, symmetric, and energetic features. We introduce an innovative symmetrical inhibitory scheme that incorporates paravector features for producing a set of directional contrast-independent elongated-like patterns reconstructing vessel tree in orientation tensors. By fitting constraint elliptical volumes via eigensystem analysis, the final vessel tree is produced with a strong and uniform response preserving various vessel features. The validation of proposed method on clinically relevant retinal images with high-quality results, shows its excellent performance compared to the state-of-the-art benchmarks and the second human observers.

A Benchmark Framework for Multiregion Analysis of Vesselness Filters

Vessel enhancement (aka vesselness) filters, are part of angiographic image processing for more than twenty years. Their popularity comes from their ability to enhance tubular structures while filtering out other structures, especially as a preliminary step of vessel segmentation. Choosing the right vesselness filter among the many available can be difficult, and their parametrization requires an accurate understanding of their underlying concepts and a genuine expertise. In particular, using default parameters is often not enough to reach satisfactory results on specific data. Currently, only few benchmarks are available to help the users choosing the best filter and its parameters for a given application. In this article, we present a generic framework to compare vesselness filters. We use this framework to compare seven gold standard filters. Our experiments are performed on three public datasets: the hepatic Ircad dataset (CT images), the Bullit dataset (brain MRA images) and the synthetic VascuSynth dataset. We analyse the results of these seven filters both quantitatively and qualitatively. In particular, we assess their performances in key areas: the organ of interest, the whole vascular network neighbourhood and the vessel neighbourhood split into several classes, based on their diameters. We also focus on the vessels bifurcations, which are often missed by vesselness filters. We provide the code of the benchmark, which includes up-to-date C++ implementations of the seven filters, as well as the experimental setup (parameter optimization, result analysis, etc.). An online demonstrator is also provided to help the community apply and visually compare these vesselness filters.

Local Intensity Order Transformation for Robust Curvilinear Object Segmentation

Segmentation of curvilinear structures is important in many applications, such as retinal blood vessel segmentation for early detection of vessel diseases and pavement crack segmentation for road condition evaluation and maintenance. Currently, deep learning-based methods have achieved impressive performance on these tasks. Yet, most of them mainly focus on finding powerful deep architectures but ignore capturing the inherent curvilinear structure feature (e.g., the curvilinear structure is darker than the context) for a more robust representation. In consequence, the performance usually drops a lot on cross-datasets, which poses great challenges in practice. In this paper, we aim to improve the generalizability by introducing a novel local intensity order transformation (LIOT). Specifically, we transfer a gray-scale image into a contrast-invariant four-channel image based on the intensity order between each pixel and its nearby pixels along with the four (horizontal and vertical) directions. This results in a representation that preserves the inherent characteristic of the curvilinear structure while being robust to contrast changes. Cross-dataset evaluation on three retinal blood vessel segmentation datasets demonstrates that LIOT improves the generalizability of some state-of-the-art methods. Additionally, the cross-dataset evaluation between retinal blood vessel segmentation and pavement crack segmentation shows that LIOT is able to preserve the inherent characteristic of curvilinear structure with large appearance gaps. An implementation of the proposed method is available at https://github.com/TY-Shi/LIOT.

Regular use of depot medroxyprogesterone acetate causes thinning of the superficial lining and apical distribution of HIV target cells in the human ectocervix

BACKGROUND

The hormonal contraceptive depot medroxyprogesterone acetate (DMPA) may be associated with an increased risk of acquiring human immunodeficiency virus (HIV). We hypothesize that DMPA use influences the ectocervical tissue architecture and HIV target cell localization.

METHODS

Quantitative image analysis workflows were developed to assess ectocervical tissue samples collected from DMPA users and control subjects not using hormonal contraception.

RESULTS

Compared to controls, the DMPA group exhibited a significantly thinner apical ectocervical epithelial layer and a higher proportion of CD4+CCR5+ cells with a more superficial location. This localization corresponded to an area with a non-intact E-cadherin net structure. CD4+Langerin+ cells were also more superficially located in the DMPA group, while fewer in number compared to the controls. Natural plasma progesterone levels did not correlate with any of these parameters, whereas estradiol levels were positively correlated with E-cadherin expression and a more basal location for HIV target cells of the control group.

CONCLUSIONS

DMPA users have a less robust epithelial layer and a more apical distribution of HIV target cells in the human ectocervix, which could confer a higher risk of HIV infection. Our results highlight the importance of assessing intact genital tissue samples to gain insights into HIV susceptibility factors.

The multiscale top-hat tensor enables specific enhancement of curvilinear structures in 2D and 3D images

Quantification and modelling of curvilinear structures in 2D and 3D images is a common challenge in a wide range of biomedical applications. Image enhancement is a crucial pre-processing step for curvilinear structure quantification. Many of the existing state-of-the-art enhancement approaches still suffer from contrast variations and noise. In this paper, we propose to address such problems via the use of a multiscale image processing approach, called Multiscale Top-Hat Tensor (MTHT). MTHT produces a better quality enhancement of curvilinear structures in low contrast and noisy images compared with other approaches in a range of 2D and 3D biomedical images. The proposed approach combines multiscale morphological filtering with a local tensor representation of curvilinear structure. The MTHT approach is validated on 2D and 3D synthetic and real images, and is also compared to the state-of-the-art curvilinear structure enhancement approaches. The obtained results demonstrate that the proposed approach provides high-quality curvilinear structure enhancement, allowing high accuracy segmentation and quantification in a wide range of 2D and 3D image datasets.

When Dijkstra Meets Vanishing Point: A Stereo Vision Approach for Road Detection

In this paper, we propose a vanishing-point constrained Dijkstra road model for road detection in a stereo-vision paradigm. First, the stereo-camera is used to generate the u- and v-disparity maps of road image, from which the horizon can be extracted. With the horizon and ground region constraints, we can robustly locate the vanishing point of road region. Second, a weighted graph is constructed using all pixels of the image, and the detected vanishing point is treated as the source node of the graph. By computing a vanishing-point constrained Dijkstra minimum-cost map, where both disparity and gradient of gray image are used to calculate cost between two neighbor pixels, the problem of detecting road borders in image is transformed into that of finding two shortest paths that originate from the vanishing point to two pixels in the last row of image. The proposed approach has been implemented and tested over 2600 grayscale images of different road scenes in the KITTI data set. The experimental results demonstrate that this training-free approach can detect horizon, vanishing point, and road regions very accurately and robustly. It can achieve promising performance.

System-wide organization of actin cytoskeleton determines organelle transport in hypocotyl plant cells

The actin cytoskeleton is an essential intracellular filamentous structure that underpins cellular transport and cytoplasmic streaming in plant cells. However, the system-level properties of actin-based cellular trafficking remain tenuous, largely due to the inability to quantify key features of the actin cytoskeleton. Here, we developed an automated image-based, network-driven framework to accurately segment and quantify actin cytoskeletal structures and Golgi transport. We show that the actin cytoskeleton in both growing and elongated hypocotyl cells has structural properties facilitating efficient transport. Our findings suggest that the erratic movement of Golgi is a stable cellular phenomenon that might optimize distribution efficiency of cell material. Moreover, we demonstrate that Golgi transport in hypocotyl cells can be accurately predicted from the actin network topology alone. Thus, our framework provides quantitative evidence for system-wide coordination of cellular transport in plant cells and can be readily applied to investigate cytoskeletal organization and transport in other organisms.

The Mycelium as a Network

The characteristic growth pattern of fungal mycelia as an interconnected network has a major impact on how cellular events operating on a micron scale affect colony behavior at an ecological scale. Network structure is intimately linked to flows of resources across the network that in turn modify the network architecture itself. This complex interplay shapes the incredibly plastic behavior of fungi and allows them to cope with patchy, ephemeral resources, competition, damage, and predation in a manner completely different from multicellular plants or animals. Here, we try to link network structure with impact on resource movement at different scales of organization to understand the benefits and challenges of organisms that grow as connected networks. This inevitably involves an interdisciplinary approach whereby mathematical modeling helps to provide a bridge between information gleaned by traditional cell and molecular techniques or biophysical approaches at a hyphal level, with observations of colony dynamics and behavior at an ecological level.

Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons

Digital reconstruction of neuronal cell morphology is an important step toward understanding the functionality of neuronal networks. Neurons are tree-like structures whose description depends critically on the junctions and terminations, collectively called critical points, making the correct localization and identification of these points a crucial task in the reconstruction process. Here we present a fully automatic method for the integrated detection and characterization of both types of critical points in fluorescence microscopy images of neurons. In view of the majority of our current studies, which are based on cultured neurons, we describe and evaluate the method for application to two-dimensional (2D) images. The method relies on directional filtering and angular profile analysis to extract essential features about the main streamlines at any location in an image, and employs fuzzy logic with carefully designed rules to reason about the feature values in order to make well-informed decisions about the presence of a critical point and its type. Experiments on simulated as well as real images of neurons demonstrate the detection performance of our method. A comparison with the output of two existing neuron reconstruction methods reveals that our method achieves substantially higher detection rates and could provide beneficial information to the reconstruction process.

Evolution of Electronic Circuits using Carbon Nanotube Composites

Evolution-in-materio concerns the computer controlled manipulation of material systems using external stimuli to train or evolve the material to perform a useful function. In this paper we demonstrate the evolution of a disordered composite material, using voltages as the external stimuli, into a form where a simple computational problem can be solved. The material consists of single-walled carbon nanotubes suspended in liquid crystal; the nanotubes act as a conductive network, with the liquid crystal providing a host medium to allow the conductive network to reorganise when voltages are applied. We show that the application of electric fields under computer control results in a significant change in the material morphology, favouring the solution to a classification task.

Making microscopy count: quantitative light microscopy of dynamic processes in living plants

Cell theory has officially reached 350 years of age as the first use of the word 'cell' in a biological context can be traced to a description of plant material by Robert Hooke in his historic publication 'Micrographia: or some physiological definitions of minute bodies'. The 2015 Royal Microscopical Society Botanical Microscopy meeting was a celebration of the streams of investigation initiated by Hooke to understand at the subcellular scale how plant cell function and form arises. Much of the work presented, and Honorary Fellowships awarded, reflected the advanced application of bioimaging informatics to extract quantitative data from micrographs that reveal dynamic molecular processes driving cell growth and physiology. The field has progressed from collecting many pixels in multiple modes to associating these measurements with objects or features that are meaningful biologically. The additional complexity involves object identification that draws on a different type of expertise from computer science and statistics that is often impenetrable to biologists. There are many useful tools and approaches being developed, but we now need more interdisciplinary exchange to use them effectively. In this review we show how this quiet revolution has provided tools available to any personal computer user. We also discuss the oft-neglected issue of quantifying algorithm robustness and the exciting possibilities offered through the integration of physiological information generated by biosensors with object detection and tracking.

Detecting Curvilinear Features Using Structure Tensors

Few published articles on curvilinear structures exist compared with works on detecting lines or corners with high accuracy. In medical ultrasound imaging, the structures that need to be detected appear as a collection of microstructures correlated along a path. In this paper, we investigated techniques that extract meaningful low-level information for curvilinear structures, using techniques based on structure tensor. We proposed a novel structure tensor enhancement inspired by bilateral filtering. We compared the proposed approach with five state-of-the-art curvilinear structure detectors. We tested the algorithms against simulated images with known ground truth and real images from three different domains (medical ultrasound, scanning electron microscope, and astronomy). For the real images, we employed experts to delineate the ground truth for each domain. Techniques borrowed from machine learning robustly assessed the performance of the methods (area under curve and cross validation). As a practical application, we used the proposed method to label a set of 5000 ultrasound images. We conclude that the proposed tensor-based approach outperforms the state-of-the-art methods in providing magnitude and orientation information for curvilinear structures. The evaluation methodology ensures that the employed feature-detection method will yield reproducible performance on new, unseen images. We published all the implemented methods as open-source software.

A robust and automatic method for human parasite egg recognition in microscopic images

With the accelerated movement of population, human parasitoses become an increasingly serious public health's problem. Currently, detections of parasite eggs through microscopic images are still the golden standard for diagnoses. However, this conventional method relies heavily on the experiences of inspectors, thus giving rise to misdiagnoses and missed diagnoses occasionally. And, as the number of clinical specimens increases rapidly, manual identification seems impractical. Hence, a fully automatic method is in desperate need. In this paper, we propose a robust method to segment and recognize the parasite eggs. Their contours are extracted using phase coherence technology, and the support vector machine (SVM) method based on shape and texture features is employed to classification of parasite eggs. Our novel method was comparable to the traditional method. The overall recognition rate was up to 95%, and the overall robustness indexes, including si, fnvf, fvpf, tpvf, were 95.7, 4.9, 3.7, 95.1, respectively, suggesting that our method is effective and the robustness is good, which has great potential to become a diagnostic method in the parasitological clinic.

Quantitative morphological analysis of curvilinear network for microscopic image based on individual fibre segmentation (IFS)

Microscopic images of curvilinear fibre network structure like cytoskeleton are traditionally analysed by qualitative observation, which can hardly provide quantitative information of their morphological properties. However, such information is crucially contributive to the understanding of important biological events, even helps to learn about the inner relations hard to perceive. Individual fibre segmentation-based curvilinear structure detector proposed in this study can identify each individual fibre in the network, as well as connections between different fibres. Quantitative information of each individual fibre, including length, orientation and position, can be extracted; so are the connecting modes in the fibre network, such as bifurcation, intersection and overlap. Distribution of fibres with different morphological properties is also presented. No manual intervening or subjective judging is required in the analysing process. Both synthesized and experimental microscopic images have verified that the detector is capable to segment curvilinear network at the subcellular level with strong noise immunity. The proposed detector is finally applied to the morphological study on cytoskeleton. It is believed that the individual fibre segmentation-based curvilinear structure detector can greatly enhance our understanding of those biological images generated from tons of biological experiments.

Phase-based binarization of ancient document images: model and applications

In this paper, a phase-based binarization model for ancient document images is proposed, as well as a postprocessing method that can improve any binarization method and a ground truth generation tool. Three feature maps derived from the phase information of an input document image constitute the core of this binarization model. These features are the maximum moment of phase congruency covariance, a locally weighted mean phase angle, and a phase preserved denoised image. The proposed model consists of three standard steps: 1) preprocessing; 2) main binarization; and 3) postprocessing. In the preprocessing and main binarization steps, the features used are mainly phase derived, while in the postprocessing step, specialized adaptive Gaussian and median filters are considered. One of the outputs of the binarization step, which shows high recall performance, is used in a proposed postprocessing method to improve the performance of other binarization methodologies. Finally, we develop a ground truth generation tool, called PhaseGT, to simplify and speed up the ground truth generation process for ancient document images. The comprehensive experimental results on the DIBCO'09, H-DIBCO'10, DIBCO'11, H-DIBCO'12, DIBCO'13, PHIBD'12, and BICKLEY DIARY data sets show the robustness of the proposed binarization method on various types of degradation and document images.

3-D curvilinear structure detection filter via structure-ball analysis

Curvilinear structure detection filters are crucial building blocks in many medical image processing applications, where they are used to detect important structures, such as blood vessels, airways, and other similar fibrous tissues. Unfortunately, most of these filters are plagued by an implicit single structure direction assumption, which results in a loss of signal around bifurcations. This peculiarity limits the performance of all subsequent processes, such as understanding angiography acquisitions, computing an accurate segmentation or tractography, or automatically classifying image voxels. This paper presents a new 3-D curvilinear structure detection filter based on the analysis of the structure ball, a geometric construction representing second order differences sampled in many directions. The structure ball is defined formally, and its computation on a discreet image is discussed. A contrast invariant diffusion index easing voxel analysis and visualization is also introduced, and different structure ball shape descriptors are proposed. A new curvilinear structure detection filter is defined based on the shape descriptors that best characterize curvilinear structures. The new filter produces a vesselness measure that is robust to the presence of X- and Y-junctions along the structure by going beyond the single direction assumption. At the same time, it stays conceptually simple and deterministic, and allows for an intuitive representation of the structure's principal directions. Sample results are provided for synthetic images and for two medical imaging modalities.