Spatial-Temporal Speckle Variance in the En-Face View as a Contrast for Optical Coherence Tomography Angiography (OCTA)

Dynamic contrast optical coherence tomography (DyC-OCT) for label-free live cell imaging

Dynamic contrast optical coherence tomography (DyC-OCT), an emerging imaging method, utilizes fluctuation patterns in OCT signals to enhance contrast, thereby enabling non-invasive label-free volumetric live cell imaging. In this mini review, we explain the core concepts behind DyC-OCT image formation and its system configurations, serving as practical guidance for future DyC-OCT users. Subsequently, we explore its applications in delivering high-quality, contrast-enhanced images of cellular morphology, as well as in monitoring changes in cellular activity/viability assay experiments.

Application of ImageJ in Optical Coherence Tomography Angiography (OCT-A): A Literature Review

Background

This study aimed to review the literature on the application of ImageJ in optical coherence tomography angiography (OCT-A) images.

Methods

A general search was performed in PubMed, Google Scholar, and Scopus databases. The authors evaluated each of the selected articles in order to assess the implementation of ImageJ in OCT-A images.

Results

ImageJ can aid in reducing artifacts, enhancing image quality to increase the accuracy of the process and analysis, processing and analyzing images, generating comparable parameters such as the parameters that assess perfusion of the layers (vessel density (VD), skeletonized density (SD), and vessel length density (VLD)) and the parameters that evaluate the structure of the layers (fractal dimension (FD), vessel density index (VDI), and lacunarity (LAC)), and the foveal avascular zone (FAZ) that are used widely in the retinal and choroidal studies), and establishing diagnostic criteria. It can help to save time when the dataset is huge with numerous plugins and options for image processing and analysis with reliable results. Diverse studies implemented distinct binarization and thresholding techniques, resulting in disparate outcomes and incomparable parameters. Uniformity in methodology is required to acquire comparable data from studies employing diverse processing and analysis techniques that yield varied outcomes.

Conclusion

Researchers and professionals might benefit from using ImageJ because of how quickly and correctly it processes and analyzes images. It is highly adaptable and potent software, allowing users to evaluate images in a variety of ways. There exists a diverse range of methodologies for analyzing OCTA images through the utilization of ImageJ. However, it is imperative to establish a standardized strategy to ensure the reliability and consistency of the method for research purposes.

Biometric Technologies Based on Optical Coherence Tomography

Optical coherence tomography (OCT) is one of the newest and most important optical non-invasive methods for the investigation and testing of various materials (e [...].

Concentration-associated pathology of alkali burn in a mouse model using anterior segment optical coherence tomography with angiography

Pathological features of alkali concentration-associated burn were studied using non-invasive anterior segment optical coherence tomography (AS-OCT) and OCT angiography (OCTA). Alkali burn was induced in C57BL/6J mice (n = 20) by placing filter paper soaked in 0.1, 0.25, 0.5, and 1 M NaOH for 30s on the right eye (left eye control). Longitudinal imaging was performed with AS-OCT/OCTA and fluorescein angiography over 14 days, after which eyes were enucleated at 7 and 14 days for histology and immunofluorescence. Concentration-associated corneal swelling was maximal at 0.5M, increasing linearly in a concentration-dependent fashion at 0.1, 0.25, and 0.5 M NaOH, to levels of 50%, 100%, and 175% of control, respectively. At 0.1M, corneal swelling and surface erosions were prominent, while at 0.25M, deep tissue damage, limbal neovascularization, and stromal haze were evident at 7 days. At 0.5M and 1M, severe exacerbation of the corneal swelling, angle closure, Descemet's membrane detachment, hyphema, and profuse central neovascularization were noted as early as day 3, which further progressed to inflammation, fibrosis, and opacity by day 7. We conclude that alkali concentration-dependent burn intensity biomarkers can be assessed by non-invasive AS-OCT/OCTA, distinguishing between mild, moderate, and severe ocular injury, with potential relevance toward clinical utilization in human eyes.

Performance and Robustness of Regional Image Segmentation Driven by Selected Evolutionary and Genetic Algorithms: Study on MR Articular Cartilage Images

The analysis and segmentation of articular cartilage magnetic resonance (MR) images belongs to one of the most commonly routine tasks in diagnostics of the musculoskeletal system of the knee area. Conventional regional segmentation methods, which are based either on the histogram partitioning (e.g., Otsu method) or clustering methods (e.g., K-means), have been frequently used for the task of regional segmentation. Such methods are well known as fast and well working in the environment, where cartilage image features are reliably recognizable. The well-known fact is that the performance of these methods is prone to the image noise and artefacts. In this context, regional segmentation strategies, driven by either genetic algorithms or selected evolutionary computing strategies, have the potential to overcome these traditional methods such as Otsu thresholding or K-means in the context of their performance. These optimization strategies consecutively generate a pyramid of a possible set of histogram thresholds, of which the quality is evaluated by using the fitness function based on Kapur's entropy maximization to find the most optimal combination of thresholds for articular cartilage segmentation. On the other hand, such optimization strategies are often computationally demanding, which is a limitation of using such methods for a stack of MR images. In this study, we publish a comprehensive analysis of the optimization methods based on fuzzy soft segmentation, driven by artificial bee colony (ABC), particle swarm optimization (PSO), Darwinian particle swarm optimization (DPSO), and a genetic algorithm for an optimal thresholding selection against the routine segmentations Otsu and K-means for analysis and the features extraction of articular cartilage from MR images. This study objectively analyzes the performance of the segmentation strategies upon variable noise with dynamic intensities to report a segmentation's robustness in various image conditions for a various number of segmentation classes (4, 7, and 10), cartilage features (area, perimeter, and skeleton) extraction preciseness against the routine segmentation strategies, and lastly the computing time, which represents an important factor of segmentation performance. We use the same settings on individual optimization strategies: 100 iterations and 50 population. This study suggests that the combination of fuzzy thresholding with an ABC algorithm gives the best performance in the comparison with other methods as from the view of the segmentation influence of additive dynamic noise influence, also for cartilage features extraction. On the other hand, using genetic algorithms for cartilage segmentation in some cases does not give a good performance. In most cases, the analyzed optimization strategies significantly overcome the routine segmentation methods except for the computing time, which is normally lower for the routine algorithms. We also publish statistical tests of significance, showing differences in the performance of individual optimization strategies against Otsu and K-means method. Lastly, as a part of this study, we publish a software environment, integrating all the methods from this study.