New Automated Single-Cell Technique for Segmentation and Quantitation of Lipid Droplets

Lipid droplets are the major organelle for intracellular storage of triglycerides and cholesterol esters. Various methods have been attempted for automated quantitation of fluorescently stained lipid droplets using either thresholding or watershed methods. We find that thresholding methods deal poorly with clusters of lipid droplets, whereas watershed methods require a smoothing step that must be optimized to remove image noise. We describe here a novel three-stage hybrid method for automated segmentation and quantitation of lipid droplets. In this method, objects are initially identified by thresholding. They are then tested for circularity to distinguish single lipid droplets from clusters. Clusters are subjected to a secondary watershed segmentation. We provide a characterization of this method in simulated images. Additionally, we apply this method to images of fixed cells containing stained lipid droplets and GFP-tagged proteins to provide a proof-of-principle that this method can be used for colocalization studies. The circularity measure can additionally prove useful for the identification of inappropriate segmentation in an automated way; for example, of non-cellular material. We will make the programs and source code available to the community under the Gnu Public License. We believe this technique will be of interest to cell biologists for light microscopic studies of lipid droplet biology.     

Studies on Papanicolaou Staining. III. Quantitative Investigations of Orangeophilia and Cyanophilia

This paper provides data derived from the visible light absorbance spectra of Papanicolaou stained epithelial cells from the uterine cervix. Twenty-four types of spectra have been considered, namely, those derived from orangeophilic and cyanophilic nuclei and cytoplasms of superficial, intermediate, parabasal and dysplastic cells, and cells of carcinoma in situ and invasive carcinoma. Wavelengths of maximum absorbance and peak absorbances are tabulated. The proportions of bound orange G, eosin Y, aluminum-hematein and light green SF yellowish have been calculated. For the majority of cell types, dyebinding differences between orangeophilic and corresponding cyanophilic substrates were statistically significant. CIE coordinates were calculated from absorbance spectra; again differences between orangeophilic and cyanophilic cells were statistically significant in most cases. Although the designation of cells as orangeophilic or cyanophilic is made on the basis of cytoplasmic coloration, the nucleus is also usually orangeophilic or cyanophilic. These nuclear differences are real and not due to the effects of over- and underlying cytoplasm.     

Segmentation of transitional cell carcinoma nuclei by nonsupervised thresholding in different color spaces

OBJECTIVE: To develop a method for nonsupervised thresholding of transitional cell carcinoma nuclei of hematoxylin-eosin-stained histologic sections. STUDY DESIGN: For grayscale, RGB, HSL and L*a*b* thresholding we used an extension of a clustering method, based on a between-class/within-class criterion, applying optimal gray-level thresholding to distributions of R, G, B, or H and S or L* color domains. Algorithms were tested on 20 hematoxylin-eosin-stained sections of bladder carcinomas. RESULTS: Results were compared with corresponding results of manually selected nuclear areas. Images were compared pixel to pixel with matching reference images. Grayscale automatic thresholding presented unacceptably low pixel specificity, which complicated further nuclear segmentation. Nonsupervised thresholding in RGB or HSL, as well as semimanual thresholding in L*a*b* color space demonstrated significantly better accuracy and high values of pixel specificity and sensitivity, which permitted errors of only 4.27-5.83% in the subsequent mean area estimation of the transitional cell carcinoma nuclei. CONCLUSION: Nonsupervised multispectral thresholding in RGB or HSL color space extends single graylevel thresholding techniques to multilevel thresholding. This seems to be an effective, relatively simple and fast alternative to the widely used automatic grayscale or manual color thresholding for segmentation of nuclei in routine histologic sections.     

Relations between hydrophobicity tested by three methods and surface chemical composition of Escherichia coli

The cell surface hydrophobicity of three strains of Escherichia coli cultured in liquid medium and on solid medium was measured using various methods including adsorption to pxylene, partition of cells in a polyethylene glycol/dextran (PEG/DEX) two phase system and contact angle measurements. The percentage adsorbed to pxylene ranged from 1.6% to 67% and the percentage of cells in polyethylene glycol phase ranged from 19% to 64%. The contact angle data of less than 40 degrees C revealed a hydrophylic character of the E. coli strains studied here. No relations were found between paraxylene/water partitioning, PEG/DEX partioning and water contact angles. The linear correlation coefficients between the results of the three hydrophobicity assays and the elemental concentration ratios obtained by X-ray photoelectron spectroscopy (XPS) were calculated. A linear correlation was found between the contact angles and the O/C ratios (r=0.91) and the N/C ratios (0.67). The adsorption to pxylene correlates better with N/C ratios (0.88) but does not correlate with O/C ratios (0.46). However, this test correlates with N/P ratios (0.79). No relation was obtained between partition in PEG/DEX system and any elemental concentration ratios. The surface composition determined by XPS was converted into a molecular composition in terms of proteins, polysaccharides, and hydrocarbon-like compounds. The proteins/polysaccharides and the hydrocarbons/polysaccharides seems to determine the contact angle of E. coli but not the adsorption to paraxylene or partition in the PEG/DEX system.     

A Simple and Effective Approach for Digitization of the CTG Signals from CTG Traces

ObjectivesCardiotocography (CTG) is a useful tool for monitoring of the fetal heart rate (FHR) and uterine contractions (UC) during the intrauterine life. Generally, CTG is provided on a printed paper which is hard to save for future evaluations. So, digitization of CTG signals is in demand for future evaluations. A straightforward approach for digitization of the CTG signals is to apply image processing on the scanned CTG printed papers.Material and methodsIn this paper, an automatic procedure is proposed for digitization of the CTG signals. The proposed approach consists of four main stages such as pre-processing, image segmentation, signal extraction and signal calibration. The pre-processing stage covers median filtering and contrasts limited adaptive histogram equalization (CLAHE) for noise removal and contrast enhancement. Image segmentation is used to binarize the CTG images for signal determination using the Otsu's thresholding algorithm. The signal extraction is carried out by a two-stepped algorithm. The acquired CTG signals are then calibrated for obtaining the final CTG signals. We use the correlation coefficient to measure the similarity between the automatically digitized CTG signals and original signals.ResultsIn experimental works, an open-access database, which contains 552 CTG recordings, is employed. The results are quite impressive. According to the obtained results, the average correlation coefficients for FHR and UC signals are 0.9715 ± 0.0168 and 0.9717 ± 0.0465, respectively.ConclusionsThe obtained results show that the proposed method is quite efficient in digitization of the CTG signals. In future works, this tool will be used to digitize the recordings belonging to the antepartum period collected from the obstetrics clinics in Medical Park Hospital in Elazığ, Turkey.     

Unusual cytologic findings in low grade papillary transitional cell carcinoma

OBJECTIVE: To describe cases of low grade papillary transitional cell carcinoma (LG-pTCC) with a low nuclear cytoplasmic (N/C) ratio and unusual cytologic patterns with many isolated, single neoplastic cells. STUDY DESIGN: We defined the following unusual cytologic findings as "isolated, single cell pattern": (1) numerous single cells sometimes with a few flat cell clusters; (2) very low N/C ratio; (3) angulation of cytoplasmic contour; (4) pale, homogeneous cytoplasm; (5) hyperchromatic nuclei with an uneven contour; (6) monotonous cytologic appearance; and (7) clear background. We studied 2,956 cytologic specimens of voided urine from 114 LG-pTCC patients at our university hospital during a 10-year period. RESULTS: Thirty-six specimens had the isolated, single cell pattern. The isolated, single cell pattern showed less celllular atypia than does the typical pattern of LG-pTCC. On histology the cases with the isolated, single cell pattern showed a papillary structure with an erosive surface and were composed of mildly atypical neoplastic cells with very low N/C ratios. CONCLUSION: Some LG-pTCCs show many single, atypical transitional 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.     

Measurement of subvisual changes in cervical squamous metaplastic cells for detecting abnormality.

Quantitative measures of visually normal squamous metaplastic cells exfoliated from the uterine cervix were obtained to test the hypothesis that these cells, like intermediate squamous and endocervical columnar cells, show subvisual evidence of atypia in cases of bonafide squamous intraepithelial neoplasia. The cells identified as squamous metaplastic were obtained from 14 abnormal (dysplastic) and 9 diagnostically negative cases. Although the cell populations so grouped showed no statistically significant differences in overall cell size, nuclear area or nuclear/cytoplasmic ratios, there were significant differences in nuclear and cytoplasmic densitometric features and in nuclear texture features. A combination of three features (nuclear density, texture and cytoplasmic density) permitted 76% of the cells to be categorized correctly as originating in normal versus abnormal slides. It is concluded that selected quantitative features of exfoliated metaplastic cell populations may contribute to improved diagnostic accuracy in automated screening for cervical abnormalities.     

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.     

Characterization of eukaryotic cell surfaces prior to and after serum protein adsorption by X-ray photoelectron spectroscopy

Elemental surface concentration ratios N/C, O/C, and P/C of fibroblasts, HELA epithelial cells, and smooth muscle cells, prior to and after washing in the absence or presence of serum proteins, were determined by X-ray photoelectron spectroscopy. Cell surfaces appeared to adsorb hardly any serum proteins, and the relatively high P/C, as compared to N/C and O/C, elemental surface concentration ratio indicated that the cell surfaces consisted mainly of the phospholipid bilayer, with little or no proteins present. The lack of adsorption of serum proteins to the cell surfaces seems at odds with the common notion that cells require adhesive proteins in order to adhere and spread. However, the adsorption behavior of cellularly produced proteins may be completely different, particularly since they seem to be able to displace adsorbed serum proteins from biomaterials surfaces. Interestingly, only HELA epithelial cells (a tumor cell line) appeared to adsorb a very small amount of proteins.     

Introduction and evaluation of a gray-value voxel conversion technique

In micro finite element analyses (microFEA) of cancellous bone, the 3D-imaging data that the FEA-models are based on, contain a range of gray-values. In the construction of the eventual FEA-model, these gray-values are commonly thresholded. Although thresholding is successful at small voxel sizes, at larger voxel sizes there is substantial loss of trabecular connectivity. We propose a new method: the gray-value method, where the microFEA-models use the information within the 3D-imaging data directly, without prior thresholding. Our question was twofold. First, how does the gray-value method compare to both plain and mass-compensated thresholding? Second, what is the effect of element size on the results obtained with the gray-value method? We used nine microCT-scans of human vertebral cancellous bone. These were degraded to represent different resolutions, and converted into microFEA-models using plain thresholding, mass-compensated thresholding, and the gray-value method. The apparent elastic moduli of the specimens were determined using microFEA. The different methods were compared on the basis of the apparent elastic moduli, compared to those calculated for a 28 microm reference model. The results showed that the gray-value method greatly improves the results relative to other methods. The gray-value method gives accurate predictions of the apparent elastic moduli, for voxel sizes up to one trabecular thickness (Tb.Th.). For voxel sizes greater than one Tb.Th. the accuracy, although still better than for both thresholding methods, becomes increasingly worse.     

Studies on Papanicolaou staining. III. Quantitative investigations of orangeophilia and cyanophilia

This paper provides data derived from the visible light absorbance spectra of Papanicolaou stained epithelial cells from the uterine cervix. Twenty-four types of spectra have been considered, namely, those derived from orangeophilic and cyanophilic nuclei and cytoplasms of superficial, intermediate, parabasal and dysplastic cells, and cells of carcinoma in situ and invasive carcinoma. Wavelengths of maximum absorbance and peak absorbances are tabulated. The proportions of bound orange G, eosin Y, aluminum-hematein and light green SF yellowish have been calculated. For the majority of cell types, dyebinding differences between orangeophilic and corresponding cyanophilic substrates were statistically significant. CIE coordinates were calculated from absorbance spectra; again differences between organeophilic and cyanophilic cells were statistically significant in most cases. Although the designation of cells as orangeophilic or cyanophilic is made on the basis of cytoplasmic coloration, the nucleus is also usually orangeophilic or cyanophilic. These nuclear differences are real and not due to the effects of over- and underlying cytoplasm.     

Characterization of eukaryotic cell surfaces prior to and after serum protein adsorption by X-ray photoelectron spectroscopy. Fibroblasts, HELA epithelial, and smooth muscle cells.

Elemental surface concentration ratios N/C,O/C, and P/C of fibroblasts, HELA epithelial cells, and smooth muscle cells, prior to and after washing in the absence or presence of serum proteins, were determined by X-ray photoelectron spectroscopy. Cell surfaces appeared to adsorb hardly any serum proteins, and the relatively high P/C, as compared to N/C and O/C, elemental surface concentration ratio indicated that the cell surfaces consisted mainly of the phospholipid bilayer, with little or no proteins present. The lack of adsorption of serum proteins to the cell surfaces seems at odds with the common notion that cells require adhesive proteins in order to adhere and spread. However, the adsorption behavior of cellularly produced proteins may be completely different, particularly since they seem to be able to displace adsorbed serum proteins from biomaterials surfaces. Interestingly, only HELA epithelial cells (a tumor cell line) appeared to adsorb a very small amount of proteins.     

Evaluation of automated threshold selection methods for accurately sizing microscopic fluorescent cells by image analysis

The accurate measurement of bacterial and protistan cell biomass is necessary for understanding their population and trophic dynamics in nature. Direct measurement of fluorescently stained cells is often the method of choice. The tedium of making such measurements visually on the large numbers of cells required has prompted the use of automatic image analysis for this purpose. Accurate measurements by image analysis require an accurate, reliable method of segmenting the image, that is, distinguishing the brightly fluorescing cells from a dark background. This is commonly done by visually choosing a threshold intensity value which most closely coincides with the outline of the cells as perceived by the operator. Ideally, an automated method based on the cell image characteristics should be used. Since the optical nature of edges in images of light-emitting, microscopic fluorescent objects is different from that of images generated by transmitted or reflected light, it seemed that automatic segmentation of such images may require special considerations. We tested nine automated threshold selection methods using standard fluorescent microspheres ranging in size and fluorescence intensity and fluorochrome-stained samples of cells from cultures of cyanobacteria, flagellates, and ciliates. The methods included several variations based on the maximum intensity gradient of the sphere profile (first derivative), the minimum in the second derivative of the sphere profile, the minimum of the image histogram, and the midpoint intensity. Our results indicated that thresholds determined visually and by first-derivative methods tended to overestimate the threshold, causing an underestimation of microsphere size. The method based on the minimum of the second derivative of the profile yielded the most accurate area estimates for spheres of different sizes and brightnesses and for four of the five cell types tested. A simple model of the optical properties of fluorescing objects and the video acquisition system is described which explains how the second derivative best approximates the position of the edge.     

Evaluation of automated threshold selection methods for accurately sizing microscopic fluorescent cells by image analysis.

The accurate measurement of bacterial and protistan cell biomass is necessary for understanding their population and trophic dynamics in nature. Direct measurement of fluorescently stained cells is often the method of choice. The tedium of making such measurements visually on the large numbers of cells required has prompted the use of automatic image analysis for this purpose. Accurate measurements by image analysis require an accurate, reliable method of segmenting the image, that is, distinguishing the brightly fluorescing cells from a dark background. This is commonly done by visually choosing a threshold intensity value which most closely coincides with the outline of the cells as perceived by the operator. Ideally, an automated method based on the cell image characteristics should be used. Since the optical nature of edges in images of light-emitting, microscopic fluorescent objects is different from that of images generated by transmitted or reflected light, it seemed that automatic segmentation of such images may require special considerations. We tested nine automated threshold selection methods using standard fluorescent microspheres ranging in size and fluorescence intensity and fluorochrome-stained samples of cells from cultures of cyanobacteria, flagellates, and ciliates. The methods included several variations based on the maximum intensity gradient of the sphere profile (first derivative), the minimum in the second derivative of the sphere profile, the minimum of the image histogram, and the midpoint intensity. Our results indicated that thresholds determined visually and by first-derivative methods tended to overestimate the threshold, causing an underestimation of microsphere size. The method based on the minimum of the second derivative of the profile yielded the most accurate area estimates for spheres of different sizes and brightnesses and for four of the five cell types tested. A simple model of the optical properties of fluorescing objects and the video acquisition system is described which explains how the second derivative best approximates the position of the edge.     

Automatic segmentation of cell nuclei in Feulgen-stained histological sections of prostate cancer and quantitative evaluation of segmentation results

Digital image analysis of cell nuclei is useful to obtain quantitative information for the diagnosis and prognosis of cancer. However, the lack of a reliable automatic nuclear segmentation is a limiting factor for high-throughput nuclear image analysis. We have developed a method for automatic segmentation of nuclei in Feulgen-stained histological sections of prostate cancer. A local adaptive thresholding with an object perimeter gradient verification step detected the nuclei and was combined with an active contour model that featured an optimized initialization and worked within a restricted region to improve convergence of the segmentation of each nucleus. The method was tested on 30 randomly selected image frames from three cases, comparing the results from the automatic algorithm to a manual delineation of 924 nuclei. The automatic method segmented a few more nuclei compared to the manual method, and about 73% of the manually segmented nuclei were also segmented by the automatic method. For each nucleus segmented both manually and automatically, the accuracy (i.e., agreement with manual delineation) was estimated. The mean segmentation sensitivity/specificity were 95%/96%. The results from the automatic method were not significantly different from the ground truth provided by manual segmentation. This opens the possibility for large-scale nuclear analysis based on automatic segmentation of nuclei in Feulgen-stained histological sections.     

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.     

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.     

冬小麦生境中土壤养分对凋落物碳氮释放的影响

土壤养分影响植物生长, 进而影响凋落物质量和产量; 凋落物质量和产量影响凋落物分解过程。基于一个生长实验和一个相同环境分解实验, 研究了冬小麦(Triticum aestivum)生境中养分可利用性对凋落物碳(C)和氮(N)释放的影响。结果显示: (1)冬小麦凋落物产量、叶/根C:N比、C释放量和N释放量随土壤养分梯度呈单调变化; (2)土壤养分影响叶凋落物丢失率而不影响根凋落物丢失率; (3)初始叶/根C:N比与其C、N释放量之间存在负相关关系; (4)分解过程降低叶C:N比和根C:N比。结果表明: 生境中土壤养分的提高可加速凋落物C、N归还, 这反过来可能促进冬小麦生长, 因此这种效应是正反馈; 初始C:N比可预测凋落物C、N释放量。     

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.