Tools of the Trade: Image Analysis Programs for Confocal Laser-Scanning Microscopy Studies of Biofilms and Considerations for Their Use by Experimental Researchers

Confocal laser-scanning microscopy (CLSM) is the bedrock of the microscopic visualization of biofilms. Previous applications of CLSM in biofilm studies have largely focused on observations of bacterial or fungal elements of biofilms, often seen as aggregates or mats of cells. However, the field of biofilm research is moving beyond qualitative observations alone, toward the quantitative analysis of the structural and functional features of biofilms, across clinical, environmental, and laboratory conditions. In recent times, several image analysis programs have been developed to extract and quantify biofilm properties from confocal micrographs. These tools not only vary in their scope and relevance to the specific biofilm features under study but also with respect to the user interface, compatibility with operating systems, and raw image requirements. Understanding these considerations is important when selecting tools for quantitative biofilm analysis, including at the initial experimental stages of image acquisition. In this review, we provide an overview of image analysis programs for confocal micrographs of biofilms, with a focus on tool selection and image acquisition parameters that are relevant for experimental researchers to ensure reliability and compatibility with downstream image processing.

Structure and Fluorescence Intensity Measurements in Biofilms

Confocal laser scanning microscopy (CLSM) is one of the most relevant technologies for studying biofilms in situ. Several tools have been developed to investigate and quantify the architecture of biofilms. However, an approach to accurately quantify the intensity of a fluorescent signal over biofilm depth is still lacking. Here we present a tool developed in the ImageJ open-source software that can be used to extract both structure and fluorescence intensity from CLSM data: BIAM (Biofilm Intensity and Architecture Measurement). This is of utmost significance when studying the fundamental mechanisms of biofilm development, differentiation, and in situ gene expression or when aiming to understand the effect of external molecules on biofilm phenotypes.

Connected attribute morphology for unified vegetation segmentation and classification in precision agriculture

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Segmentation and classification pipeline fully relying on attribute morphology.

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Strong locality of the approach avoids resulting foreground noise.

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Segmentation outputs regions directly, avoiding the component labelling step.

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Max-tree structure enables feature calculation during segmentation.

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Competitive classification of plant regions into crop/weed for varying plant types.

Discriminating value crops from weeds is an important task in precision agriculture. In this paper, we propose a novel image processing pipeline based on attribute morphology for both the segmentation and classification tasks. The commonly used approaches for vegetation segmentation often rely on thresholding techniques which reach their decisions globally. By contrast, the proposed method works with connected components obtained by image threshold decomposition, which are naturally nested in a hierarchical structure called the max-tree, and various attributes calculated from these regions. Image segmentation is performed by attribute filtering, preserving or discarding the regions based on their attribute value and allowing for the decision to be reached locally. This segmentation method naturally selects a collection of foreground regions rather than pixels, and the same data structure used for segmentation can be further reused to provide the features for classification, which is realised in our experiments by a support vector machine (SVM). We apply our methods to normalised difference vegetation index (NDVI) images, and demonstrate the performance of the pipeline on a dataset collected by the authors in an onion field, as well as a publicly available dataset for sugar beets. The results show that the proposed segmentation approach can segment the fine details of plant regions locally, in contrast to the state-of-the-art thresholding methods, while providing discriminative features which enable efficient and competitive classification rates for crop/weed discrimination.

Label-free in situ imaging of oil body dynamics and chemistry in germination

Plant oleosomes are uniquely emulsified lipid reservoirs that serve as the primary energy source during seed germination. These oil bodies undergo significant changes regarding their size, composition and structure during normal seedling development; however, a detailed characterization of these oil body dynamics, which critically affect oil body extractability and nutritional value, has remained challenging because of a limited ability to monitor oil body location and composition during germination in situ Here, we demonstrate via in situ, label-free imaging that oil bodies are highly dynamic intracellular organelles that are morphologically and biochemically remodelled extensively during germination. Label-free, coherent Raman microscopy (CRM) combined with bulk biochemical measurements revealed the temporal and spatial regulation of oil bodies in native soya bean cotyledons during the first eight days of germination. Oil bodies undergo a cycle of growth and shrinkage that is paralleled by lipid and protein compositional changes. Specifically, the total protein concentration associated with oil bodies increases in the first phase of germination and subsequently decreases. Lipids contained within the oil bodies change in saturation and chain length during germination. Our results show that CRM is a well-suited platform to monitor in situ lipid dynamics and local chemistry and that oil bodies are actively remodelled during germination. This underscores the dynamic role of lipid reservoirs in plant development.

Learning-guided automatic three dimensional synapse quantification for drosophila neurons

BACKGROUND

The subcellular distribution of synapses is fundamentally important for the assembly, function, and plasticity of the nervous system. Automated and effective quantification tools are a prerequisite to large-scale studies of the molecular mechanisms of subcellular synapse distribution. Common practices for synapse quantification in neuroscience labs remain largely manual or semi-manual. This is mainly due to computational challenges in automatic quantification of synapses, including large volume, high dimensions and staining artifacts. In the case of confocal imaging, optical limit and xy-z resolution disparity also require special considerations to achieve the necessary robustness.

RESULTS

A novel algorithm is presented in the paper for learning-guided automatic recognition and quantification of synaptic markers in 3D confocal images. The method developed a discriminative model based on 3D feature descriptors that detected the centers of synaptic markers. It made use of adaptive thresholding and multi-channel co-localization to improve the robustness. The detected markers then guided the splitting of synapse clumps, which further improved the precision and recall of the detected synapses. Algorithms were tested on lobula plate tangential cells (LPTCs) in the brain of Drosophila melanogaster, for GABAergic synaptic markers on axon terminals as well as dendrites.

CONCLUSIONS

The presented method was able to overcome the staining artifacts and the fuzzy boundaries of synapse clumps in 3D confocal image, and automatically quantify synaptic markers in a complex neuron such as LPTC. Comparison with some existing tools used in automatic 3D synapse quantification also proved the effectiveness of the proposed method.

Efficient and robust pupil size and blink estimation from near-field video sequences for human-machine interaction

Monitoring pupil and blink dynamics has applications in cognitive load measurement during human-machine interaction. However, accurate, efficient, and robust pupil size and blink estimation pose significant challenges to the efficacy of real-time applications due to the variability of eye images, hence to date, require manual intervention for fine tuning of parameters. In this paper, a novel self-tuning threshold method, which is applicable to any infrared-illuminated eye images without a tuning parameter, is proposed for segmenting the pupil from the background images recorded by a low cost webcam placed near the eye. A convex hull and a dual-ellipse fitting method are also proposed to select pupil boundary points and to detect the eyelid occlusion state. Experimental results on a realistic video dataset show that the measurement accuracy using the proposed methods is higher than that of widely used manually tuned parameter methods or fixed parameter methods. Importantly, it demonstrates convenience and robustness for an accurate and fast estimate of eye activity in the presence of variations due to different users, task types, load, and environments. Cognitive load measurement in human-machine interaction can benefit from this computationally efficient implementation without requiring a threshold calibration beforehand. Thus, one can envisage a mini IR camera embedded in a lightweight glasses frame, like Google Glass, for convenient applications of real-time adaptive aiding and task management in the future.

Trim9 regulates activity-dependent fine-scale topography in Drosophila

Topographic projection of afferent terminals into 2D maps in the CNS is a general strategy used by the nervous system to encode the locations of sensory stimuli. In vertebrates, it is known that although guidance cues are critical for establishing a coarse topographic map, neural activity directs fine-scale topography between adjacent afferent terminals [1-4]. However, the molecular mechanism underlying activity-dependent regulation of fine-scale topography is poorly understood. Molecular analysis of the spatial relationship between adjacent afferent terminals requires reliable localization of the presynaptic terminals of single neurons as well as genetic manipulations with single-cell resolution in vivo. Although both requirements can potentially be met in Drosophila melanogaster [5, 6], no activity-dependent topographic system has been identified in flies [7]. Here we report a topographic system that is shaped by neuronal activity in Drosophila. With this system, we found that topographic separation of the presynaptic terminals of adjacent nociceptive neurons requires different levels of Trim9, an evolutionarily conserved signaling molecule [8-11]. Neural activity regulates Trim9 protein levels to direct fine-scale topography of sensory afferents. This study offers both a novel mechanism by which neural activity directs fine-scale topography of axon terminals and a new system to study this process at single-neuron resolution.

Vessel-wall imaging and quantification of flow-mediated dilation using water-selective 3D SSFP-echo

BACKGROUND

To introduce a new, efficient method for vessel-wall imaging of carotid and peripheral arteries by means of a flow-sensitive 3D water-selective SSFP-echo pulse sequence.

METHODS

Periodic applications of RF pulses will generate two transverse steady states, immediately after and before an RF pulse; the latter being referred to as the SSFP-echo. The SSFP-echo signal for water protons in blood is spoiled as a result of moving spins losing phase coherence in the presence of a gradient pulse along the flow direction. Bloch equation simulations were performed over a wide range of velocities to evaluate the flow sensitivity of the SSFP-echo signal. Vessel walls of carotid and femoral and popliteal arteries were imaged at 3 T. In two patients with peripheral artery disease the femoral arteries were imaged bilaterally to demonstrate method's potential to visualize atherosclerotic plaques. The method was also evaluated as a means to measure femoral artery flow-mediated dilation (FMD) in response to cuff-induced ischemia in four subjects.

RESULTS

The SSFP-echo pulse sequence, which does not have a dedicated blood signal suppression preparation, achieved low blood signal permitting discrimination of the carotid and peripheral arterial walls with in-plane spatial resolution ranging from 0.5 to 0.69 mm and slice thickness of 2 to 3 mm, i.e. comparable to conventional 2D vessel-wall imaging techniques. The results of the simulations were in good agreement with analytical solution and observations for both vascular territories examined. Scan time ranged from 2.5 to 5 s per slice yielding a contrast-to-noise ratio between the vessel wall and lumen from 3.5 to 17. Mean femoral FMD in the four subjects was 9%, in good qualitative agreement with literature values.

CONCLUSIONS

Water-selective 3D SSFP-echo pulse sequence is a potential alternative to 2D vessel-wall imaging. The proposed method is fast, robust, applicable to a wide range of flow velocities, and straightforward to implement.

A nonlinear image contrast sharpening approach based on Munsell's scale

Contrast is a measure of the variation in intensity or gray value in a specified region of an image. The region can be most or all of the image, giving rise to a global concept of contrast. The region might, on the other hand, be a small window in which case the concept of contrast is a locally defined expression. In this work, we introduce a nonlinear local contrast enhancement method. This method utilizes the Munsell value scale which is based upon human visual perception. Use of the Munsell value scale allows for the partitioning of the gray scale into ten discrete subintervals. Subsequent local processing occurs within each of these subintervals. Inside each subinterval, this method constructs a contrast enhancement function that is a smooth approximation to the threshold step function and which maps a given subinterval into itself. This function then thresholds the gray values in a subinterval in a smooth manner about a locally computed quantity called the mean edge gray value. By enhancing the contrast in this way, the original shades of gray are preserved. That is, the groupings of the gray values by subinterval are preserved. As a result, no gray value distortion is introduced into the image.

Automatic segmentation of diatom images for classification

A general framework for automatic segmentation of diatom images is presented. This segmentation is a critical first step in contour-based methods for automatic identification of diatoms by computerized image analysis. We review existing results, adapt popular segmentation methods to this difficult problem, and finally develop a method that substantially improves existing results. This method is based on the watershed segmentation from mathematical morphology, and belongs to the class of hybrid segmentation techniques. The novelty of the method is the use of connected operators for the computation and selection of markers, a critical ingredient in the watershed method to avoid over-segmentation. All methods considered were used to extract binary contours from a large database of diatom images, and the quality of the contours was evaluated both visually and based on identification performance.