Roundness variation in JPEG images affects the automated process of nuclear immunohistochemical quantification: correction with a linear regression model

The volume of digital image (DI) storage continues to be an important problem in computer-assisted pathology. DI compression enables the size of files to be reduced but with the disadvantage of loss of quality. Previous results indicated that the efficiency of computer-assisted quantification of immunohistochemically stained cell nuclei may be significantly reduced when compressed DIs are used. This study attempts to show, with respect to immunohistochemically stained nuclei, which morphometric parameters may be altered by the different levels of JPEG compression, and the implications of these alterations for automated nuclear counts, and further, develops a method for correcting this discrepancy in the nuclear count. For this purpose, 47 DIs from different tissues were captured in uncompressed TIFF format and converted to 1:3, 1:23 and 1:46 compression JPEG images. Sixty-five positive objects were selected from these images, and six morphological parameters were measured and compared for each object in TIFF images and those of the different compression levels using a set of previously developed and tested macros. Roundness proved to be the only morphological parameter that was significantly affected by image compression. Factors to correct the discrepancy in the roundness estimate were derived from linear regression models for each compression level, thereby eliminating the statistically significant differences between measurements in the equivalent images. These correction factors were incorporated in the automated macros, where they reduced the nuclear quantification differences arising from image compression. Our results demonstrate that it is possible to carry out unbiased automated immunohistochemical nuclear quantification in compressed DIs with a methodology that could be easily incorporated in different systems of digital image analysis.     

How to measure image quality in tissue-based diagnosis (diagnostic surgical pathology)

Background

Automated image analysis, measurements of virtual slides, and open access electronic measurement user systems require standardized image quality assessment in tissue-based diagnosis.

Aims

To describe the theoretical background and the practical experiences in automated image quality estimation of colour images acquired from histological slides.

Theory, material and measurements

Digital images acquired from histological slides should present with textures and objects that permit automated image information analysis. The quality of digitized images can be estimated by spatial independent and local filter operations that investigate in homogenous brightness, low peak to noise ratio (full range of available grey values), maximum gradients, equalized grey value distribution, and existence of grey value thresholds. Transformation of the red-green-blue (RGB) space into the hue-saturation-intensity (HSI) space permits the detection of colour and intensity maxima/minima. The feature distance of the original image to its standardized counterpart is an appropriate measure to quantify the actual image quality. These measures have been applied to a series of H&;E stained, fluorescent (DAPI, Texas Red, FITC), and immunohistochemically stained (PAP, DAB) slides. More than 5,000 slides have been measured and partly analyzed in a time series.

Results

Analysis of H&;E stained slides revealed low shading corrections (10%) and moderate grey value standardization (10 – 20%) in the majority of cases. Immunohistochemically stained slides displayed greater shading and grey value correction. Fluorescent stained slides are often revealed to high brightness. Images requiring only low standardization corrections possess at least 5 different statistically significant thresholds, which are useful for object segmentation. Fluorescent images of good quality only posses one singular intensity maximum in contrast to good images obtained from H&;E stained slides that present with 2 – 3 intensity maxima.

Conclusion

Evaluation of image quality and creation of formally standardized images should be performed prior to automatic analysis of digital images acquired from histological slides. Spatial dependent and local filter operations as well as analysis of the RGB and HSI spaces are appropriate methods to reproduce evaluated formal image quality.

     

Issues in using whole slide imaging for diagnostic pathology: “routine” stains,immunohistochemistry and predictive markers

The traditional microscope, together with the “routine” hematoxylin and eosin (H & E) stain, remains the “gold standard” for diagnosis of cancer and other diseases; remarkably, it and the majority of associated biological stains are more than 150 years old. Immunohistochemistry has added to the repertoire of “stains” available. Because of the need for specific identification and even measurement of “biomarkers,” immunohistochemistry has increased the demand for consistency of performance and interpretation of staining results. Rapid advances in the capabilities of digital imaging hardware and software now offer a realistic route to improved reproducibility, accuracy and quantification by utilizing whole slide digital images for diagnosis, education and research. There also are potential efficiencies in work flow and the promise of powerful new analytical methods; however, there also are challenges with respect to validation of the quality and fidelity of digital images, including the standard H & E stain, so that diagnostic performance by pathologists is not compromised when they rely on whole slide images instead of traditional stained tissues on glass slides.     

Quantification of vascular density using a semiautomated technique for immunostained specimens

OBJECTIVE: To develop a semiautomated, quantitative techniquefor the assessment of vascular density in immunohistochemically stained tissue sections using diaminobenzidine tetrahydrochloride (DAB) and hematoxylin as chromagens. STUDY DESIGN: A semiautomated thresholding technique was developed to quantitate vascular density in tissue sections stained with anti-CD31 (1 degrees antibody). The immunohistochemically stained specimens were digitally imaged using a 24-bit color camera. The blue component of the RGB image was segmented using a variable high-pass filter. After thresholding, the segmented areas (CD31 positive) were quantified and vascular density determined. The validity of the method was verified by calculating the precision of the technique using the coefficient of repeatability and by quantifying its agreement with manual analysis according to the Bland-Altman approach. RESULTS: Vascular endothelial cells were specifically selected using anti-CD31 as the primary antibody and the appropriate horseradish peroxidase-conjugated secondary antibody. Utilizing the semiautomated thresholding technique, the separation of DAB-stained tissuefrom non-DAB-stained tissue was achieved. The method developed possesses a low coefficient of repeatability (0.49%), agrees well with manual assessment (mean difference = -0.29 +/- 0.92%), is highly automated and is user friendly. CONCLUSION: A novel semiautomated technique for the quantification of vascular density was developed. This technique provides a method for reproducible measurement of immunostaining procedures (immunohistochemistry, immunocytochemistry and in situ hybridization) utilizing immunoperoxidase techniques with DAB as a chromagen.     

Automated analysis of muscle fibre images

A technique for studying the distribution and size of different fibre types in muscles is proposed for automated analysis of individual fibres in optical density images from ATPase-stained muscle sections. After delineation, fibres may be classified into different histological types (1, 2A, 2B and 2C) using the measurement of their mean optical density (mOD). The densitometric measurements were obtained from three serial histological slides stained under different conditions. The delineation procedure is performed on one of the images: the resulting mask is fitted to the other images using a linear coordinate transform. Along with densitometric measurements, the lesser diameter of the fibres is computed. Both in processing and in analysis, extensive use was made of mathematical morphology tools. All software was implemented on a VICOM digital image processor, extended with a VISIOMORPH morphoprocessor board.     

Influence of staining on fast automated cell segmentation, feature extraction and cell image analysis

FAZYTAN, a system for fast automated cell segmentation, cell image analysis and extraction of nuclear features, was used to analyze cervical cell images variously stained by the conventional Papanicolaou stain, the new Papanicolaou stain and hematoxylin and thionin only; the last two dyes are used as the nuclear stains in the two versions of the Papanicolaou stain. Other dyes were also tried in cell classification experiments. All cell images in the variously stained samples could be described by the same nuclear features as had been adapted for the discrimination of conventional-Papanicolaou-stained cells. Variances were lower for thionin-stained cells as compared with hematoxylin-stained cells. By application of spectrophotometry, it was confirmed that the spectra of the cytoplasmic counterstains are superimposed on those of the nuclear stains. It appears that a variety of dyes are suitable as cytologic stains for cell classification by the FAZYTAN system, provided that they achieve sufficiently strong nuclear-cytoplasmic contrast by precisely delineating the chromatin texture.     

Combined confocal and wide-field high-resolution cytometry of fluorescent in situ hybridization-stained cells

BACKGROUND: The recently developed technique of high-resolution cytometry (HRCM) enables automated acquisition and analysis of fluorescent in situ hybridization (FISH)-stained cell nuclei using conventional wide-field fluorescence microscopy. The method has now been extended to confocal imaging and offers the opportunity to combine the advantages of confocal and wide-field modes. METHODS: We have automated image acquisition and analysis from a standard inverted fluorescence microscope equipped with a confocal module with Nipkow disk and a cooled digital CCD camera. The system is fully controlled by a high-performance computer that performs both acquisition and related on-line image analysis. The system can be used either for an automatic two (2D) and three-dimensional (3D) analysis of FISH- stained interphase nuclei or for a semiautomatic 3D analysis of FISH-stained cells in tissues. The user can select which fluorochromes are acquired using wide-field mode and which using confocal mode. The wide-field and confocal images are overlaid automatically in computer memory. The developed software compensates automatically for both chromatic color shifts and spatial shifts caused by switching to a different imaging mode. RESULTS: Using the combined confocal and wide-field HRCM technique, it is possible to take advantage of both imaging modes. Images of some dyes (such as small hybridization dots or counterstain images of individual interphase nuclei) do not require confocal quality and can be acquired quickly in wide-field mode. On the contrary, images of other dyes (such as chromosome territories or counterstain images of cells in tissues) do require improved quality and are acquired in confocal mode. The dual-mode approach is two to three times faster compared with the single-mode confocal approach and the spectrum of its applications is much broader compared with both single-mode confocal and single-mode wide-field systems. CONCLUSIONS: The combination of high speed specific to the wide-field mode and high quality specific to the confocal mode gives optimal system performance.     

Quantification of histochemical staining by color deconvolution

OBJECTIVE: To develop a flexible method of separation and quantification of immunohistochemical staining by means of color image analysis. STUDY DESIGN: An algorithm was developed to deconvolve the color information acquired with red-green-blue (RGB) cameras and to calculate the contribution of each of the applied stains based on stain-specific RGB absorption. The algorithm was tested using different combinations of diaminobenzidine, hematoxylin and eosin at different staining levels. RESULTS: Quantification of the different stains was not significantly influenced by the combination of multiple stains in a single sample. The color deconvolution algorithm resulted in comparable quantification independent of the stain combinations as long as the histochemical procedures did not influence the amount of stain in the sample due to bleaching because of stain solubility and saturation of staining was prevented. CONCLUSION: This image analysis algorithm provides a robust and flexible method for objective immunohistochemical analysis of samples stained with up to three different stains using a laboratory microscope, standard RGB camera setup and the public domain program NIH Image.     

Quantification of fluorescence in situ hybridization signals by image cytometry.

In this study we aimed at the development of a cytometric system for quantification of specific DNA sequences using fluorescence in situ hybridization (ISH) and digital imaging microscopy. The cytochemical and cytometric aspects of a quantitative ISH procedure were investigated, using human peripheral blood lymphocyte interphase nuclei and probes detecting high copy number target sequences as a model system. These chromosome-specific probes were labeled with biotin, digoxigenin, or fluorescein. The instrumentation requirements are evaluated. Quantification of the fluorescence ISH signals was performed using an epi-fluorescence microscope with a multi-wavelength illuminator, equipped with a cooled charge couple device (CCD) camera. The performance of the system was evaluated using fluorescing beads and a homogeneously fluorescing specimen. Specific image analysis programs were developed for the automated segmentation and analysis of the images provided by ISH. Non-uniform background fluorescence of the nuclei introduces problems in the image analysis segmentation procedures. Different procedures were tested. Up to 95% of the hybridization signals could be correctly segmented using digital filtering techniques (min-max filter) to estimate local background intensities. The choice of the objective lens used for the collection of images was found to be extremely important. High magnification objectives with high numerical aperture, which are frequently used for visualization of fluorescence, are not optimal, since they do not have a sufficient depth of field. The system described was used for quantification of ISH signals and allowed accurate measurement of fluorescence spot intensities, as well as of fluorescence ratios obtained with double-labeled probes.     

Automated image analysis of live/dead staining of the fungus Aureobasidium pullulans on microscope slides and leaf surfaces

An image analysis program and protocol for the identification and enumeration of live versus dead cells of the yeast-like fungus Aureobasidium pullulans was developed for both populations on microscope slides and leaf surfaces. Live cells took up CellTracker Blue, while nonviable cells stained with DEAD Red. Image analysis macro programs running under Optimas software were used to acquire images and to differentiate and enumerate viable from nonviable cells. The software was capable of discriminating green as a third parameter for identification and quantification of green fluorescent protein-expressing cells in a wild-type population.     

SlideToolkit: An Assistive Toolset for the Histological Quantification of Whole Slide Images

The demand for accurate and reproducible phenotyping of a disease trait increases with the rising number of biobanks and genome wide association studies. Detailed analysis of histology is a powerful way of phenotyping human tissues. Nonetheless, purely visual assessment of histological slides is time-consuming and liable to sampling variation and optical illusions and thereby observer variation, and external validation may be cumbersome. Therefore, within our own biobank, computerized quantification of digitized histological slides is often preferred as a more precise and reproducible, and sometimes more sensitive approach. Relatively few free toolkits are, however, available for fully digitized microscopic slides, usually known as whole slides images. In order to comply with this need, we developed the slideToolkit as a fast method to handle large quantities of low contrast whole slides images using advanced cell detecting algorithms. The slideToolkit has been developed for modern personal computers and high-performance clusters (HPCs) and is available as an open-source project on github.com. We here illustrate the power of slideToolkit by a repeated measurement of 303 digital slides containing CD3 stained (DAB) abdominal aortic aneurysm tissue from a tissue biobank. Our workflow consists of four consecutive steps. In the first step (acquisition), whole slide images are collected and converted to TIFF files. In the second step (preparation), files are organized. The third step (tiles), creates multiple manageable tiles to count. In the fourth step (analysis), tissue is analyzed and results are stored in a data set. Using this method, two consecutive measurements of 303 slides showed an intraclass correlation of 0.99. In conclusion, slideToolkit provides a free, powerful and versatile collection of tools for automated feature analysis of whole slide images to create reproducible and meaningful phenotypic data sets.     

Technique and feasibility of a dual staining method for estrogen receptors and AgNORs.

A new staining method for dual demonstration of Estrogen receptors (ER) and argyrophilc Nucleolus-Organizer Regions (AgNORs) was developed. To rule out possible reciprocal effects, serial slides of 10 invasive ductale breast cancers were stained with either the single staining method or the simultaneous ER/AgNOR-staining method and investigated comparatively. By measuring the slides with the image analysis system AMBA, reciprocal effects could be excluded. It was proven that dual staining of both markers results in a reproducible and specific staining result. We concluded that it is justified to measure AgNORs in immunohistochemically stained cells.     

Automated quantification of nuclear immunohistochemical markers with different complexity

Manual quantification of immunohistochemically stained nuclear markers is still laborious and subjective and the use of computerized systems for digital image analysis have not yet resolved the problems of nuclear clustering. In this study, we designed a new automatic procedure for quantifying various immunohistochemical nuclear markers with variable clustering complexity. This procedure consisted of two combined macros. The first, developed with a commercial software, enabled the analysis of the digital images using color and morphological segmentation including a masking process. All information extracted with this first macro was automatically exported to an Excel datasheet, where a second macro composed of four different algorithms analyzed all the information and calculated the definitive number of positive nuclei for each image. One hundred and eighteen images with different levels of clustering complexity was analyzed and compared with the manual quantification obtained by a trained observer. Statistical analysis indicated a great reliability (intra-class correlation coefficient > 0.950) and no significant differences between the two methods. Bland–Altman plot and Kaplan–Meier curves indicated that the results of both methods were concordant around 90% of analyzed images. In conclusion, this new automated procedure is an objective, faster and reproducible method that has an excellent level of accuracy, even with digital images with a high complexity.     

Robust automated tumour segmentation on histological and immunohistochemical tissue images

Tissue microarray (TMA) is a high throughput analysis tool to identify new diagnostic and prognostic markers in human cancers. However, standard automated method in tumour detection on both routine histochemical and immunohistochemistry (IHC) images is under developed. This paper presents a robust automated tumour cell segmentation model which can be applied to both routine histochemical tissue slides and IHC slides and deal with finer pixel-based segmentation in comparison with blob or area based segmentation by existing approaches. The presented technique greatly improves the process of TMA construction and plays an important role in automated IHC quantification in biomarker analysis where excluding stroma areas is critical. With the finest pixel-based evaluation (instead of area-based or object-based), the experimental results show that the proposed method is able to achieve 80% accuracy and 78% accuracy in two different types of pathological virtual slides, i.e., routine histochemical H&E and IHC images, respectively. The presented technique greatly reduces labor-intensive workloads for pathologists and highly speeds up the process of TMA construction and provides a possibility for fully automated IHC quantification.     

Automated fluorescence image cytometry. DNA quantification and detection of chlamydial infections

Digitized fluorescence microscopy in conjunction with automated image segmentation is a promising approach for screening clinical specimens quickly and reliably. This paper describes the hardware and software of a prototype image-based cytometer that can identify fluorescent objects, discriminate true objects from artifacts and divide overlapping pairs of objects. The use of this image cytometer is discussed for: (1) the measurement of the DNA ploidy distribution of isolated mature rat liver nuclei labeled with 4',6-diamidine-2-phenylindole; (2) the comparison of the DNA ploidy distributions of the same samples measured by image cytometry (ICM) and flow cytometry (FCM); and (3) the quantification of chlamydial infection by double labeling cells with antichlamydiae antibody and Hoechst 33258 for nuclear DNA analysis. Ploidy distributions measured by the automated image cytometer compared favorably to those obtained by FCM. All pairs of overlapping nuclei were automatically detected by an additional computer algorithm, and those pairs that were clearly more than one nucleus by visual inspection were correctly divided. The irregular morphology of the chlamydiae-infected cells meant that 26% of them were not correctly identified in the fluorescein-stained images (as judged by manual inspection), but all cells were nevertheless detected correctly from the images of the Hoechst-stained samples. Automated fluorescence ICM yielded results similar to those obtained with FCM and had the additional benefit of maintaining cell and tissue architecture while preserving the opportunity for subsequent manual inspection of the specimen.     

Measurement of markers for breast cancer in a model system using laser scanning cytometry

BACKGROUND: A variety of markers, including Ki67, estrogen receptors (ER), and progesterone receptors (PgR), are frequently measured in fine needle aspirates (FNA) from human breast carcinomas. We used a human breast carcinoma cell line, MCF7, as a model system to investigate the use of laser scanning cytometry (LSC) for the measurement of these markers. Additionally, we measured the number of apoptotic cells. METHODS: Cells were treated with drugs to vary the expression of markers and the number of apoptotic cells. They were then fixed on microscope slides. For LSC, the cells were stained for the different markers with fluorescein using immunofluorescence and for apoptotic cells using the TUNEL assay. The nuclei were counterstained with propidium iodide. A parallel set of slides was stained using horseradish peroxidase and diaminobenzidine and scored manually by conventional light microscopy. RESULTS: The results from the LSC closely paralleled those obtained by manual scoring of immunohistochemical stains. CONCLUSIONS: It should be possible to use LSC for the routine measurement of nuclear markers in FNAs from human breast carcinomas.     

A system for the on-line determination of glucose concentration

An off-line glucose analyzer, Yellow Springs Instrument (YSI) model 27 was modified and coupled to various peripheral components to produce a fast, fully automated system for the online determination of glucose concentration. The amount of time required to accomplish each measurement was in the order of two minutes. To demonstrate the utility of this system, various tests were performed. First, a stream containing known amounts of glucose was monitored on-line and the system was calibrated. The calibration curve was shown to be described by Michaelis-Menten kinetics. Once the system was properly calibrated, it was used to monitor the glucose concentration in the effluent stream of two different enzyme reactor systems. The glucose concentrations were within experimental error of those obtained via standard off-line techniques.     

Discrepancies in ploidy determination due to specimen sampling errors

Two techniques are described to enhance the detection of low frequency aneuploid cells in automated cell analysis. One method concerns a cell preparation technique; the other is focused on specific cell selection at the measurement level. The cell preparation method has been designed to select and process the tumour areas in paraffin blocks and can be used for image as well as for flow cytometry. The technique uses incident fluorescence microscopy for visual inspection of the surface of the fluorescently stained tissue block to select the specific tumour parts. Using image cytometry, it is shown that in tissue sections with very small tumour foci and many normal cells, aneuploidy could only be detected after enrichment of the cell sample with the specifically selected areas. The cell selection at the measurement level is directed towards detection of low frequency aneuploid cells on microscope slides using the specific capacities of LEYTAS (Leyden Television Analysis System). With this system, cells of interest can be selected by means of minimum size and intensity thresholds. In addition to measurement of the total cell population, all cells above a minimum DNA value can thus be specifically selected and measured. The advantage of both enrichment techniques is the possibility to detect and measure aneuploid cell lines in cases where normal, diploid cells dominate the paraffin tissue.     

Quantitative image analysis of connective tissue progenitors

OBJECTIVE: To develop an image analysis system to automatically identify colony-forming units (CFUs) in in vitro cell cultures of connective tissue progenitors. This system was designed to quantitatively assess colony morphology and number of colonies in 4-cm(2) culture wells. STUDY DESIGN: Large field-of-view high-resolution fluorescence images of 4',6-diamidino-2-phenylindole (DAPI)- and alkaline phosphatase (AP)-stained bone marrow cell cultures were obtained using an epi-fluorescence microscope and automated scanning stage. Cell nuclei were identified in the DAPI-stained images after removal of fluorescent debris from the image. An Euclidean distance map (EDM) of the segmented cell nuclei was used to cluster cell nuclei into colonies. The automated system was evaluated using 40 tissue culture wells of bone marrow aspirate samples. The results of the automated analysis were compared to the manual tracings of colonies by 3 reviewers. RESULTS: The automated method agreed with all 3 reviewers on average 87.5% of the time. Additionally, reviewers identified other colonies not outlined by the reviewers on average 2.7 times more than the automated method. CONCLUSION: The automated method is a less biased method for identifying CFUs than individual reviewers, it provides more quantitative information about colony morphology than can be obtained manually and it is less time consuming.     

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.