A direction‐selective local‐thresholding method,DSLT, in combination with a dye‐based method for automated three‐dimensional segmentation of cells and airspaces in developing leaves

Quantifying the anatomical data acquired from three‐dimensional (3D) images has become increasingly important in recent years. Visualization and image segmentation are essential for acquiring accurate and detailed anatomical data from images; however, plant tissues such as leaves are difficult to image by confocal or multi‐photon laser scanning microscopy because their airspaces generate optical aberrations. To overcome this problem, we established a staining method based on Nile Red in silicone‐oil solution. Our staining method enables color differentiation between lipid bilayer membranes and airspaces, while minimizing any damage to leaf development. By repeated applications of our staining method we performed time‐lapse imaging of a leaf over 5 days. To counteract the drastic decline in signal‐to‐noise ratio at greater tissue depths, we also developed a local thresholding method (direction‐selective local thresholding, DSLT) and an automated iterative segmentation algorithm. The segmentation algorithm uses the DSLT to extract the anatomical structures. Using the proposed methods, we accurately segmented 3D images of intact leaves to single‐cell resolution, and measured the airspace volumes in intact leaves.     

Automated classification and quantification of F-actin-containing ruffles in confocal micrographs

Membrane ruffles are actin-rich protrusions of the plasma membrane that can be observed on the surface of many cell types. Phase contrast and fluorescent microscopy are widely used in the analysis of ruffles, which are commonly identified in cells stained with fluorescently labeled phalloidin. Currently, comparison of cellular ruffle formation under different experimental conditions is generally qualitative or semiquantitative. Ruffle structures are often defined using manual tracing and thresholding methods. Here, we report the rapid and accurate segmentation of ruffles from two-dimensional confocal projections of cells using automated method based on well-established image processing and analysis methods. Line-shaped ruffles were detected using line detectors and were then separated from the filtered images. Automated categorizing of the segmented line structures enabled accurate quantification of the ruffles. This automated approach is efficient and reliable and hence can serve as a powerful tool in studies of the mechanism of ruffle formation.     

3D cell nuclei segmentation based on gradient flow tracking

Background  

Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding.     

A new high‐resolution computed tomography (CT) segmentation method for trabecular bone architectural analysis

In the last decade, high‐resolution computed tomography (CT) and microcomputed tomography (micro‐CT) have been increasingly used in anthropological studies and as a complement to traditional histological techniques. This is due in large part to the ability of CT techniques to nondestructively extract three‐dimensional representations of bone structures. Despite prior studies employing CT techniques, no completely reliable method of bone segmentation has been established. Accurate preprocessing of digital data is crucial for measurement accuracy, especially when subtle structures such as trabecular bone are investigated. The research presented here is a new, reproducible, accurate, and fully automated computerized segmentation method for high‐resolution CT datasets of fossil and recent cancellous bone: the Ray Casting Algorithm (RCA). We compare this technique with commonly used methods of image thresholding (i.e., the half‐maximum height protocol and the automatic, adaptive iterative thresholding procedure). While the quality of the input images is crucial for conventional image segmentation, the RCA method is robust regarding the signal to noise ratio, beam hardening, ring artifacts, and blurriness. Tests with data of extant and fossil material demonstrate the superior quality of RCA compared with conventional thresholding procedures, and emphasize the need for careful consideration of optimal CT scanning parameters. Am J Phys Anthropol 2009. © 2009 Wiley‐Liss, Inc.     

FogBank: a single cell segmentation across multiple cell lines and image modalities

Background

Many cell lines currently used in medical research, such as cancer cells or stem cells, grow in confluent sheets or colonies. The biology of individual cells provide valuable information, thus the separation of touching cells in these microscopy images is critical for counting, identification and measurement of individual cells. Over-segmentation of single cells continues to be a major problem for methods based on morphological watershed due to the high level of noise in microscopy cell images. There is a need for a new segmentation method that is robust over a wide variety of biological images and can accurately separate individual cells even in challenging datasets such as confluent sheets or colonies.

Results

We present a new automated segmentation method called FogBank that accurately separates cells when confluent and touching each other. This technique is successfully applied to phase contrast, bright field, fluorescence microscopy and binary images. The method is based on morphological watershed principles with two new features to improve accuracy and minimize over-segmentation.First, FogBank uses histogram binning to quantize pixel intensities which minimizes the image noise that causes over-segmentation. Second, FogBank uses a geodesic distance mask derived from raw images to detect the shapes of individual cells, in contrast to the more linear cell edges that other watershed-like algorithms produce.We evaluated the segmentation accuracy against manually segmented datasets using two metrics. FogBank achieved segmentation accuracy on the order of 0.75 (1 being a perfect match). We compared our method with other available segmentation techniques in term of achieved performance over the reference data sets. FogBank outperformed all related algorithms. The accuracy has also been visually verified on data sets with 14 cell lines across 3 imaging modalities leading to 876 segmentation evaluation images.

Conclusions

FogBank produces single cell segmentation from confluent cell sheets with high accuracy. It can be applied to microscopy images of multiple cell lines and a variety of imaging modalities. The code for the segmentation method is available as open-source and includes a Graphical User Interface for user friendly execution.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0431-x) contains supplementary material, which is available to authorized users.     

Segmentation improvement through denoising of PET images with 3D-context modelling in wavelet domain

Positron emission tomography (PET) images have been incorporated into the radiotherapy process as a powerful tool to assist in the contouring of lesions, leading to the emergence of a broad spectrum of automatic segmentation schemes for PET images (PET-AS). However, not all proposed PET-AS algorithms take into consideration the previous steps of image preparation. PET image noise has been shown to be one of the most relevant affecting factors in segmentation tasks. This study demonstrates a nonlinear filtering method based on spatially adaptive wavelet shrinkage using three-dimensional context modelling that considers the correlation of each voxel with its neighbours. Using this noise reduction method, excellent edge conservation properties are obtained. To evaluate the influence in the segmentation schemes of this filter, it was compared with a set of Gaussian filters (the most conventional) and with two previously optimised edge-preserving filters. Five segmentation schemes were used (most commonly implemented in commercial software): fixed thresholding, adaptive thresholding, watershed, adaptive region growing and affinity propagation clustering. Segmentation results were evaluated using the Dice similarity coefficient and classification error. A simple metric was also included to improve the characterisation of the filters used for induced blurring evaluation, based on the measurement of the average edge width. The proposed noise reduction procedure improves the results of segmentation throughout the performed settings and was shown to be more stable in low-contrast and high-noise conditions. Thus, the capacity of the segmentation method is reinforced by the denoising plan used.     

Novel Methods of Automated Quantification of Gap Junction Distribution and Interstitial Collagen Quantity from Animal and Human Atrial Tissue Sections

Background

Gap junctions (GJs) are the principal membrane structures that conduct electrical impulses between cardiac myocytes while interstitial collagen (IC) can physically separate adjacent myocytes and limit cell-cell communication. Emerging evidence suggests that both GJ and interstitial structural remodeling are linked to cardiac arrhythmia development. However, automated quantitative identification of GJ distribution and IC deposition from microscopic histological images has proven to be challenging. Such quantification is required to improve the understanding of functional consequences of GJ and structural remodeling in cardiac electrophysiology studies.

Methods and Results

Separate approaches were employed for GJ and IC identification in images from histologically stained tissue sections obtained from rabbit and human atria. For GJ identification, we recognized N-Cadherin (N-Cad) as part of the gap junction connexin 43 (Cx43) molecular complex. Because N-Cad anchors Cx43 on intercalated discs (ID) to form functional GJ channels on cell membranes, we computationally dilated N-Cad pixels to create N-Cad units that covered all ID-associated Cx43 pixels on Cx43/N-Cad double immunostained confocal images. This approach allowed segmentation between ID-associated and non-ID-associated Cx43. Additionally, use of N-Cad as a unique internal reference with Z-stack layer-by-layer confocal images potentially limits sample processing related artifacts in Cx43 quantification. For IC quantification, color map thresholding of Masson''s Trichrome blue stained sections allowed straightforward and automated segmentation of collagen from non-collagen pixels. Our results strongly demonstrate that the two novel image-processing approaches can minimize potential overestimation or underestimation of gap junction and structural remodeling in healthy and pathological hearts. The results of using the two novel methods will significantly improve our understanding of the molecular and structural remodeling associated functional changes in cardiac arrhythmia development in aged and diseased hearts.     

Segmentation of Fluorescence Microscopy Images for Quantitative Analysis of Cell Nuclear Architecture

Considerable advances in microscopy, biophysics, and cell biology have provided a wealth of imaging data describing the functional organization of the cell nucleus. Until recently, cell nuclear architecture has largely been assessed by subjective visual inspection of fluorescently labeled components imaged by the optical microscope. This approach is inadequate to fully quantify spatial associations, especially when the patterns are indistinct, irregular, or highly punctate. Accurate image processing techniques as well as statistical and computational tools are thus necessary to interpret this data if meaningful spatial-function relationships are to be established. Here, we have developed a thresholding algorithm, stable count thresholding (SCT), to segment nuclear compartments in confocal laser scanning microscopy image stacks to facilitate objective and quantitative analysis of the three-dimensional organization of these objects using formal statistical methods. We validate the efficacy and performance of the SCT algorithm using real images of immunofluorescently stained nuclear compartments and fluorescent beads as well as simulated images. In all three cases, the SCT algorithm delivers a segmentation that is far better than standard thresholding methods, and more importantly, is comparable to manual thresholding results. By applying the SCT algorithm and statistical analysis, we quantify the spatial configuration of promyelocytic leukemia nuclear bodies with respect to irregular-shaped SC35 domains. We show that the compartments are closer than expected under a null model for their spatial point distribution, and furthermore that their spatial association varies according to cell state. The methods reported are general and can readily be applied to quantify the spatial interactions of other nuclear compartments.     

Single Cell Analysis of Drug Distribution by Intravital Imaging

Recent advances in the field of intravital imaging have for the first time allowed us to conduct pharmacokinetic and pharmacodynamic studies at the single cell level in live animal models. Due to these advances, there is now a critical need for automated analysis of pharmacokinetic data. To address this, we began by surveying common thresholding methods to determine which would be most appropriate for identifying fluorescently labeled drugs in intravital imaging. We then developed a segmentation algorithm that allows semi-automated analysis of pharmacokinetic data at the single cell level. Ultimately, we were able to show that drug concentrations can indeed be extracted from serial intravital imaging in an automated fashion. We believe that the application of this algorithm will be of value to the analysis of intravital microscopy imaging particularly when imaging drug action at the single cell level.     

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.     

Segmentation of microscopic cell scenes

Different methods for the automated segmentation of microscopic cell scenes are presented with examples. The techniques discussed include edge detection by thresholding, "blob" detection by split-and-merge algorithm, global thresholding using gray-level histograms, hierarchic thresholding using color information, global thresholding using two-dimensional histograms and segmentation by "blob" labeling. Methods are more robust against insignificant changes in the scene and perform more reliably as more a priori knowledge about the scene is incorporated in the segmentation algorithm. The inclusion of both photometric and geometric a priori knowledge can result in a high level of correct segmentations, the cost of which is increased computation time.     

Automatic Nuclei Segmentation in H&E Stained Breast Cancer Histopathology Images

The introduction of fast digital slide scanners that provide whole slide images has led to a revival of interest in image analysis applications in pathology. Segmentation of cells and nuclei is an important first step towards automatic analysis of digitized microscopy images. We therefore developed an automated nuclei segmentation method that works with hematoxylin and eosin (H&E) stained breast cancer histopathology images, which represent regions of whole digital slides. The procedure can be divided into four main steps: 1) pre-processing with color unmixing and morphological operators, 2) marker-controlled watershed segmentation at multiple scales and with different markers, 3) post-processing for rejection of false regions and 4) merging of the results from multiple scales. The procedure was developed on a set of 21 breast cancer cases (subset A) and tested on a separate validation set of 18 cases (subset B). The evaluation was done in terms of both detection accuracy (sensitivity and positive predictive value) and segmentation accuracy (Dice coefficient). The mean estimated sensitivity for subset A was 0.875 (±0.092) and for subset B 0.853 (±0.077). The mean estimated positive predictive value was 0.904 (±0.075) and 0.886 (±0.069) for subsets A and B, respectively. For both subsets, the distribution of the Dice coefficients had a high peak around 0.9, with the vast majority of segmentations having values larger than 0.8.     

Encephalitozoon cuniculi: quantitation of parasites and evaluation of viability

Two methods (manual and automated) for quantitation of viable versus dead Encephalitozoon cuniculi are reported. The manual method uses ethidium bromide and acridine orange to stain dead and viable organisms, respectively. The stained organisms are visually differentiated with the aid of a fluorescence microscope. The automated method uses propidium iodide to stain dead parasites, which are differentiated from viable unstained parasites with the aid of a flow cytometer. An automated cell counter (Coulter Counter) was used to count rapidly large numbers of samples and to improve the sensitivity of counting low concentrations of parasites. These methods will enhance investigators' abilities to conduct quantitative experiments on host defense mechanisms against E. cuniculi.     

Automatic quantification of microvessels using unsupervised image analysis.

An automatic method for quantification of images of microvessels by computing area proportions and number of objects is presented. The objects are segmented from the background using dynamic thresholding of the average component size histogram. To be able to count the objects, fragmented objects are connected, all objects are filled, and touching objects are separated using a watershed segmentation algorithm. The method is fully automatic and robust with respect to illumination and focus settings. A test set consisting of images grabbed with different focus and illumination for each field of view, was used to test the method, and the proposed method showed less variation than the intraoperator variation using manual threshold. Further, the method showed good correlation to manual object counting (r = 0.80) on an other test set.     

Automated detection of tunneling nanotubes in 3D images.

BACKGROUND: This paper presents an automated method for the identification of thin membrane tubes in 3D fluorescence images. These tubes, referred to as tunneling nanotubes (TNTs), are newly discovered intercellular structures that connect living cells through a membrane continuity. TNTs are 50-200 nm in diameter, crossing from one cell to another at their nearest distance. In microscopic images, they are seen as straight lines. It now emerges that the TNTs represent the underlying structure of a new type of cell-to-cell communication. METHODS: Our approach for the identification of TNTs is based on a combination of biological cell markers and known image processing techniques. Watershed segmentation and edge detectors are used to find cell borders, TNTs, and image artifacts. Mathematical morphology is employed at several stages of the processing chain. Two image channels are used for the calculations to improve classification of watershed regions into cells and background. One image channel displays cell borders and TNTs, the second is used for cell classification and displays the cytoplasmic compartments of the cells. The method for cell segmentation is 3D, and the TNT detection incorporates 3D information using various 2D projections. RESULTS: The TNT- and cell-detection were applied to numerous 3D stacks of images. A success rate of 67% was obtained compared with manual identification of the TNTs. The digitalized results were used to achieve statistical information of selected properties of TNTs. CONCLUSION: To further explore these structures, automated detection and quantification is desirable. Consequently, this automated recognition tool will be useful in biological studies on cell-to-cell communication where TNT quantification is essential.     

Automatic image segmentation of nuclear stained breast tissue sections using color active contour model and an improved watershed method

Automatic image segmentation of immunohistologically stained breast tissue sections helps pathologists to discover the cancer disease earlier. The detection of the real number of cancer nuclei in the image is a very tedious and time consuming task. Segmentation of cancer nuclei, especially touching nuclei, presents many difficulties to separate them by traditional segmentation algorithms. This paper presents a new automatic scheme to perform both classification of breast stained nuclei and segmentation of touching nuclei in order to get the total number of cancer nuclei in each class. Firstly, a modified geometric active contour model is used for multiple contour detection of positive and negative nuclear staining in the microscopic image. Secondly, a touching nuclei method based on watershed algorithm and concave vertex graph is proposed to perform accurate quantification of the different stains. Finally, benign nuclei are identified by their morphological features and they are removed automatically from the segmented image for positive cancer nuclei assessment. The proposed classification and segmentation schemes are tested on two datasets of breast cancer cell images containing different level of malignancy. The experimental results show the superiority of the proposed methods when compared with other existing classification and segmentation methods. On the complete image database, the segmentation accuracy in term of cancer nuclei number is over than 97%, reaching an improvement of 3–4% over earlier methods.     

Extended-resolution imaging of the interaction of lipid droplets and mitochondria

Physical contacts between organelles play a pivotal role in intracellular trafficking of metabolites. Monitoring organelle interactions in living cells using fluorescence microscopy is a powerful approach to functionally assess these cellular processes. However, detailed target acquisition is typically limited due to light diffraction. Furthermore, subcellular compartments such as lipid droplets and mitochondria are highly dynamic and show significant subcellular movement. Thus, high-speed acquisition of these organelles with extended-resolution is appreciated. Here, we present an imaging informatics pipeline enabling spatial and time-resolved analysis of the dynamics and interactions of fluorescently labeled lipid droplets and mitochondria in a fibroblast cell line. The imaging concept is based on multispectral confocal laser scanning microscopy and includes high-speed resonant scanning for fast spatial acquisition of organelles. Extended-resolution is achieved by the recording of images at minimized pinhole size and by post-processing of generated data using a computational image restoration method. Computation of inter-organelle contacts is performed on basis of segmented spatial image data. We show limitations of the image restoration and segmentation part of the imaging informatics pipeline. Since both image processing methods are implemented in other related methodologies, our findings will help to identify artifacts and the false-interpretation of obtained morphometric data. As a proof-of-principle, we studied how lipid load and overexpression of PLIN5, considered to be involved in the tethering of LDs and mitochondria, affects organelle association.     

Habitat-Net: Segmentation of habitat images using deep learning

Understanding environmental factors that influence forest health, as well as the occurrence and abundance of wildlife, is a central topic in forestry and ecology. However, the manual processing of field habitat data is time-consuming and months are often needed to progress from data collection to data interpretation. To shorten the time to process the data we propose here Habitat-Net: a novel deep learning application based on Convolutional Neural Networks (CNN) to segment habitat images of tropical rainforests. Habitat-Net takes color images as input and after multiple layers of convolution and deconvolution, produces a binary segmentation of the input image. We worked on two different types of habitat datasets that are widely used in ecological studies to characterize the forest conditions: canopy closure and understory vegetation. We trained the model with 800 canopy images and 700 understory images separately and then used 149 canopy and 172 understory images to test the performance of Habitat-Net. We compared the performance of Habitat-Net to the performance of a simple threshold based method, manual processing by a second researcher and a CNN approach called U-Net, upon which Habitat-Net is based. Habitat-Net, U-Net and simple thresholding reduced total processing time to milliseconds per image, compared to 45 s per image for manual processing. However, the higher mean Dice coefficient of Habitat-Net (0.94 for canopy and 0.95 for understory) indicates that accuracy of Habitat-Net is higher than that of both the simple thresholding (0.64, 0.83) and U-Net (0.89, 0.94). Habitat-Net will be of great relevance for ecologists and foresters, who need to monitor changes in their forest structures. The automated workflow not only reduces the time, it also standardizes the analytical pipeline and, thus, reduces the degree of uncertainty that would be introduced by manual processing of images by different people (either over time or between study sites).     

Segmentation and detection of fluorescent 3D spots

The 3D spatial organization of genes and other genetic elements within the nucleus is important for regulating gene expression. Understanding how this spatial organization is established and maintained throughout the life of a cell is key to elucidating the many layers of gene regulation. Quantitative methods for studying nuclear organization will lead to insights into the molecular mechanisms that maintain gene organization as well as serve as diagnostic tools for pathologies caused by loss of nuclear structure. However, biologists currently lack automated and high throughput methods for quantitative and qualitative global analysis of 3D gene organization. In this study, we use confocal microscopy and fluorescence in-situ hybridization (FISH) as a cytogenetic technique to detect and localize the presence of specific DNA sequences in 3D. FISH uses probes that bind to specific targeted locations on the chromosomes, appearing as fluorescent spots in 3D images obtained using fluorescence microscopy. In this article, we propose an automated algorithm for segmentation and detection of 3D FISH spots. The algorithm is divided into two stages: spot segmentation and spot detection. Spot segmentation consists of 3D anisotropic smoothing to reduce the effect of noise, top-hat filtering, and intensity thresholding, followed by 3D region-growing. Spot detection uses a Bayesian classifier with spot features such as volume, average intensity, texture, and contrast to detect and classify the segmented spots as either true or false spots. Quantitative assessment of the proposed algorithm demonstrates improved segmentation and detection accuracy compared to other techniques.     

Encephalitozoon cuniculi: Quantitation of Parasites and Evaluation of Viability1

Two methods (manual and automated) for quantitation of viable versus dead Encephalitozoon cuniculi are reported. The manual method uses ethidium bromide and acridine orange to stain dead and viable organisms, respectively. The stained organisms are visually differentiated with the aid of a fluorescence microscope. The automated method uses propidium iodide to stain dead parasites, which are differentiated from viable unstained parasites with the aid of a flow cytometer. An automated cell counter (Coulter Counter) was used to count rapidly large numbers of samples and to improve the sensitivity of counting low concentrations of parasites. These methods will enhance investigators' abilities to conduct quantitative experiments on host defense mechanisms against E. cuniculi.