Lukasz Roszkowiak

Validation of various adaptive threshold methods of segmentation applied to follicular lymphoma digital images stained with 3,3’-Diaminobenzidine&Haematoxylin

AbstractThe comparative study of the results of various segmentation methods for the digital images of the follicular lymphoma cancer tissue section is described in this paper. The sensitivity and specificity and some other parameters of the following adaptive threshold methods of segmentation: the Niblack method, the Sauvola method, the White method, the Bernsen method, the Yasuda method and the Palumbo method, are calculated. Methods are applied to three types of images constructed by extraction of the brown colour information from the artificial images synthesized based on counterpart experimentally captured images. This paper presents usefulness of the microscopic image synthesis method in evaluation as well as comparison of the image processing results. The results of thoughtful analysis of broad range of adaptive threshold methods applied to: (1) the blue channel of RGB, (2) the brown colour extracted by deconvolution and (3) the ’brown component’ extracted from RGB allows to select some pairs: method and type of image for which this method is most efficient considering various criteria e.g. accuracy and precision in area detection or accuracy in number of objects detection and so on. The comparison shows that the White, the Bernsen and the Sauvola methods results are better than the results of the rest of the methods for all types of monochromatic images. All three methods segments the immunopositive nuclei with the mean accuracy of 0.9952, 0.9942 and 0.9944 respectively, when treated totally. However the best results are achieved for monochromatic image in which intensity shows brown colour map constructed by colour deconvolution algorithm. The specificity in the cases of the Bernsen and the White methods is 1 and sensitivities are: 0.74 for White and 0.91 for Bernsen methods while the Sauvola method achieves sensitivity value of 0.74 and the specificity value of 0.99. According to Bland-Altman plot the Sauvola method selected objects are segmented without undercutting the area for true positive objects but with extra false positive objects. The Sauvola and the Bernsen methods gives complementary results what will be exploited when the new method of virtual tissue slides segmentation be develop.Virtual SlidesThe virtual slides for this article can be found here: slide 1: http://diagnosticpathology.slidepath.com/dih/webViewer.php?snapshotId=13617947952577 and slide 2: http://diagnosticpathology.slidepath.com/dih/webViewer.php?snapshotId=13617948230017.

Comparison of the Manual, Semiautomatic, and Automatic Selection and Leveling of Hot Spots in Whole Slide Images for Ki-67 Quantification in Meningiomas

Background. This paper presents the study concerning hot-spot selection in the assessment of whole slide images of tissue sections collected from meningioma patients. The samples were immunohistochemically stained to determine the Ki-67/MIB-1 proliferation index used for prognosis and treatment planning. Objective. The observer performance was examined by comparing results of the proposed method of automatic hot-spot selection in whole slide images, results of traditional scoring under a microscope, and results of a pathologists manual hot-spot selection. Methods. The results of scoring the Ki-67 index using optical scoring under a microscope, software for Ki-67 index quantification based on hot spots selected by two pathologists (resp., once and three times), and the same software but on hot spots selected by proposed automatic methods were compared using Kendalls tau-b statistics. Results. Results show intra- and interobserver agreement. The agreement between Ki-67 scoring with manual and automatic hot-spot selection is high, while agreement between Ki-67 index scoring results in whole slide images and traditional microscopic examination is lower. Conclusions. The agreement observed for the three scoring methods shows that automation of area selection is an effective tool in supporting physicians and in increasing the reliability of Ki-67 scoring in meningioma.

Improvements to Segmentation Method of Stained Lymphoma Tissue Section Images

We present the METINUS (METhod of Immunohistochemical NUclei Segmentation) , which is a improved and modified version of supporting tool for pathologists from 2010. The method supports examination of immunohistochemically stained thin tissue sections from biopsy of follicular lymphoma patients. The software localizes and counts FOXP3 expression in the cells’ nuclei supporting standard procedure of diagnosis and prognosis. The algorithm performs colour separation followed by object extraction and validation. Objects with statistical parameters not in specified range are disqualified from further assessment. To calculate the statistics we use the following: three channels of RGB, three channels of Lab colour space, brown channel and three layers completed with colour deconvolution. Division of the objects is done with support of watershed and colour deconvolution algorithm. Evaluation was performed on arbitrarily chosen 20 images with moderate quality of most typical tissues. We compared results of improved method with the previous version in the context of semiautomatic, pathologist controlled, computer-aided result of quantification as reference. Comparison is based on quantity of nuclei located per image using Kendall’s tau-b correlation coefficient. It shows concordance of 0.91 between results of proposed method and reference, while with previous version it is only 0.71.

Color standardization for the immunohistochemically stained tissue section images

Refining the information about the tissue is achieved by the standardization of color in whole-slide images and digital images of tissue sections. The investigation described in this paper proposes a solution to the problems of color dissimilarity for microscope slides digitalized by various equipment. A method of color standardization using color calibration microscope slide is proposed and a piece of software is provided. The proposed method is verified on microscope slides digitalized by various digitalization tools. It is shown that the resulting colors in two images of the same fragments are more similar to each other. The prepared microscope color calibration slide is adjusted to the standardization of immunohistochemically stained tissue samples but it can be easily adjusted to other types of staining. The software is independent from color, shape and location of color spots on the calibration slide so it can be easily used with the other calibration slides designed for different needs.

Survey: interpolation methods for whole slide image processing

Evaluating whole slide images of histological and cytological samples is used in pathology for diagnostics, grading and prognosis . It is often necessary to rescale whole slide images of a very large size. Image resizing is one of the most common applications of interpolation. We collect the advantages and drawbacks of nine interpolation methods, and as a result of our analysis, we try to select one interpolation method as the preferred solution. To compare the performance of interpolation methods, test images were scaled and then rescaled to the original size using the same algorithm. The modified image was compared to the original image in various aspects. The time needed for calculations and results of quantification performance on modified images were also compared. For evaluation purposes, we used four general test images and 12 specialized biological immunohistochemically stained tissue sample images. The purpose of this survey is to determine which method of interpolation is the best to resize whole slide images, so they can be further processed using quantification methods. As a result, the interpolation method has to be selected depending on the task involving whole slide images.

The Method of Teeth Region Detection in Panoramic Dental Radiographs

Radiographs, which visualise human dentition as a single panorama photograph, are commonly used in prognosis and diagnosis in dental care and biometric individual identification. The automatics analysis of these images can provide useful information about patients. So, this paper proposes a method of an introductory phase of teeth segmentation: selection of a region of interest which contains every tooth in the pantomograph. The proposed method finds an area containing all the teeth and the line dividing the upper and lower jaws, called teeth line, using adaptive power-law transformation, Holder exponent, energy and entropy of the pantomograph. The teeth area is found correctly in 81.9%, while teeth line in 34.0%. The method is the introductory step in a single teeth segmentation, dentition evaluation according to presence of strong dental interventions and automatics generation of dental score for a patient.

The Analysis of the Shape of the Genetically Modified Human Skin Fibroblasts in Culture

The aim of this study is the comparison of the movement of various types of human dermal fibroblasts with and without genetic modification. Three groups of fibroblasts were cultured and monitored: (1) control group of fibroblasts isolated from human skin (not transduced); (2) transduced with lentivirus bearing EGFP fluorescent marker; (3) transduced with lentivirus bearing DsRed2 fluorescent marker. The experimental sequences of images documenting cells’ movement have been analyzed using image processing methods. The cells’ movement was described by: (1) distance between each two consecutive images/frames, (2) distance and displacement covered by each fibroblast in 30 minutes (3) length of cell crawling cycle, (4) the movement tortuosity coefficient for all fibroblasts in each group. Also shape features such as: area, perimeter, eccentricity, length of the major axis have been analyzed. It appears that the general movement behavior is not changed by the process of transduction but some of its aspects are modified. The efficiency of movement in the sense of distance covered and region penetration is decreased because of changes in cells’ morphology. Transduced cells are less polarized and develop extra podia during their crawling.

The METINUS Plus method for nuclei quantification in tissue microarrays of breast cancer and axillary node tissue section

Abstract This paper presents the METINUS Plus (METhod of Immunohistochemical NUclei Segmentation Plus) method that has been developed for localization and quantification of the regulatory T cells’ nuclei. The proposed methodology performs color separation followed by the extraction and validation of objects. Objects categorized as clusters, based on the area and shape, are divided with locally applied watershed supported by color deconvolution. Objects are validated based on RGB, Lab color space, and color deconvolution data. The chosen validation criteria allow quantification and differentiation between the subpopulations of regulatory T cells and breast cancer cells, which express FOXP3. The evaluation is based on the quantity and quality of nuclei segmentation in comparison with the results of the manual counting, done by four pathologists on a set of 20 images. The results were analyzed in comparison with human evaluation using Kendalls tau-b correlation coefficient and the Wilcoxon signed-rank test. The main achievement of this study is a possibility to find criteria that allow differentiation between two types of immunopositive cells’ nuclei: regulatory T cells with homogeneous stained nuclei and all cells with FOXP3 expression; in both tumor and axillary node biopsies. A major advantage of computer image processing is the reproducibility of achieved results, thus minimizing human intervention, and providing traceable clinical information.

PATMA: parser of archival tissue microarray

Tissue microarrays are commonly used in modern pathology for cancer tissue evaluation, as it is a very potent technique. Tissue microarray slides are often scanned to perform computer-aided histopathological analysis of the tissue cores. For processing the image, splitting the whole virtual slide into images of individual cores is required. The only way to distinguish cores corresponding to specimens in the tissue microarray is through their arrangement. Unfortunately, distinguishing the correct order of cores is not a trivial task as they are not labelled directly on the slide. The main aim of this study was to create a procedure capable of automatically finding and extracting cores from archival images of the tissue microarrays. This software supports the work of scientists who want to perform further image processing on single cores. The proposed method is an efficient and fast procedure, working in fully automatic or semi-automatic mode. A total of 89% of punches were correctly extracted with automatic selection. With an addition of manual correction, it is possible to fully prepare the whole slide image for extraction in 2 min per tissue microarray. The proposed technique requires minimum skill and time to parse big array of cores from tissue microarray whole slide image into individual core images.