Ki-67 proliferation indices (PIs) define the grading of GastroEnteroPancreatic NeuroEndocrine Neoplasms (GEPNENs) and are crucial for therapeutic decisions. The precise Ki-67 assessment relies on manual counting, which is time-consuming, hardly accessible during routine pathological signout and thus usually replaced by the easier eye-estimation/balling method prone to interobserver variability and differences originating from the hot-spot size, localisation and tumor heterogeneity. These discrepancies can significantly affect the final PI resulting in misgrading of GEPNENs with potential adverse patient outcomes. In the era of digital pathology more and more applications are available to overcome this problem. In our retrospective study of 60 surgically resected GEPNEN cases, we tested the equivalence of traditional clinical (C) grading, manual counting with a MarkerCounter (MC) application and automatic grading with tumor recognition PatternQuant application with subsequent NuclearQuant (NQ) PI-assessment within 3DHistechs digital pathology platform. We found almost perfect agreement between the various grading methods (Spearman rank-order correlations: C vs. MC: ρ = 0.912, C vs. NQ: ρ = 0.883, MC vs NQ: ρ = 0.953) without clinically significant misgradings. Also the numerical values of the PIs derived with the various methods showed close correlations (Linear regression: C vs. MC: r = 0.952, C vs. NQ: r = 0.925, MC vs NQ: r = 0.978). The automated PI-assessment involved a mean 5-fold more tumor cells, better approximating the global/total Ki-67 PI, which was earlier shown to deliver more robust prognostic power and decreased interobserver variability. Furthermore, G3 tumors differed from G2 and G1 tumors in their cytomorphological parameterers: high grade tumors had significantly larger and more polymorphic, less regular tumor cell nuclei, which parameters could be also utilized for grading and/or prognostication purposes. Our study applied a simple, quick, easy-to-use, Machine Learning-based method that could be incorporated into routine digital pathology signout alleviating pathologists’ workload and increasing precision and recall rate.

Digitization in pathology and cytology labs is now widespread, a significant shift from a decade ago when few doctors used image processing tools. Despite unchanged scanning times due to excitation in fluorescent imaging, advancements in computing power and software have enabled more complex algorithms, yielding better-quality results. This study evaluates three nucleus segmentation algorithms for ploidy analysis using propidium iodide-stained digital WSI slides. Our goal was to improve segmentation accuracy to more closely match DNA histograms obtained via flow cytometry, with the ultimate aim of enhancing the calibration method we proposed in a previous study, which seeks to align image cytometry results with those from flow cytometry. We assessed these algorithms based on raw segmentation performance and DNA histogram similarity, using confusion-matrix-based metrics. Results indicate that modern algorithms perform better, with F1 scores exceeding 0.845, compared to our earlier solution’s 0.807, and produce DNA histograms that more closely resemble those from the reference FCM method.

As the field of routine pathology transitions into the digital realm, there is a surging demand for the full automation of microscope scanners, aiming to expedite the process of digitizing tissue samples, and consequently, enhancing the efficiency of case diagnoses. The key to achieving seamless automatic imaging lies in the precise detection and segmentation of tissue sample regions on the glass slides. State-of-the-art approaches for this task lean heavily on deep learning techniques, particularly U-Net convolutional neural networks. However, since samples can be highly diverse and prepared in various ways, it is almost impossible to be fully prepared for and cover every scenario with training data. We propose a data augmentation step that allows artificially modifying the training data by extending some artifact features of the available data to the rest of the dataset. This procedure can be used to generate images that can be considered synthetic. These artifacts could include felt pen markings, speckles of dirt, residual bubbles in covering glue, or stains. The proposed approach achieved a 1–6% improvement for these samples according to the F1 Score metric.

Nowadays the use of 3D visualization in pathology is already an existing technology, but its widespread is hindered by several technical obstacles. One of these is the memory required to store the samples required for 3D display and the computing capacity essential for display. In our article, we present a solution developed by us to solve these problems, which can be used to minimize the hardware resources required for 3D visualization. Our development enables users to perform 3D visualization on digitized pathological serial sections even with the help of their everyday laptop.

Using digital microscope scanners, gigapixel-scale images for tissue samples are scanned in a minute, which provides an opportunity for quantitative evaluation at the cellular or gene level.However, to make an accurate diagnosis for clinical or research cases, it is necessary to make serial sections and stain them using different reagents.Since digital scanning and processing are preceded by manual workflows, the orientations between the images are lost.In the absence of adjustment, we cannot compare them to each other, for colocalization or correlation analysis.A registration method is needed that organizes the samples in the same orientation.The proposed method is inspired by the traditional and deep-learning based registration methods (SURF, SIFT, ORB, SuperPoint, SuperGlue) and further developed to manage the tearing, creasing and other deformations between the samples.Based on the validation results, the basic methods give moderate results, however, by utilizing a grid-based approach and by choosing the appropriate number of recursive iterations and resolution, the methods can be improved.The proposed stain-independent, iterative, non-rigid registration method can manage not only tears, creases and deformations, but also correct structural changes between series sections.

Nowadays, digital pathology is an unavoidable element in making a medical diagnosis. This discipline makes it possible to carry out studies that in the past were only inaccurate or not possible at all. An example of such a test is the volumetric measurement, in which the medical professional is able to determine the volume of a piece of tissue. New technologies such as 3D visualization make it possible to perform such medical examinations quickly and accurately. In this paper, we present some of the imaging modalities currently used in medicine to perform volumetric measurement, and we present our own volumetric measurement solution, which we created with a graphics engine, in order to take advantage of polygon-based visualization. At the end of the paper, we present test results for our own volumetric measurement solution.

Nowadays, the examination of digitized samples with a 2 dimensional visualization software is a common solution in digital pathology. In addition to these solutions, a new and emerging display technology is 3 dimensional visualization. Using 3 dimensional visualization technology, doctors and researchers may be able to examine areas of tissue samples that 2 dimensional imaging programs do not allow. Examples include the internal structure of a tissue sample, the interconnection of individual tissue portions, and even volumetric lesions. Basically, 3 dimensional visualization currently has 2 major branches, all of which are covered in this paper. The first is the polygon-based visualization, the second is the voxel-based visualization. The goal of visualization is basically the same in both methods, to be able to enable 3 dimensional visualization using 2 dimensional data, but their implementation and operation are different.

Nowadays, the flow of information is paramount in order for everyone to have the knowledge they need to make decisions as soon as possible. It is no different in digital pathology either. There is still research and development today to make relevant medical information available to right holders as soon as possible. One possible direction of development is the use of real-time multi-user software. With these programs, users are able to collaborate in real time from around the world, set up a diagnosis together, and share it with others. By using this type of software, users can make decisions together by receiving the same data. These data can be, among other things, various measurement data, image data, morphological data. Implementing this software is difficult in several ways, such as processing huge amounts of data and ensuring smooth operation for multiple users. Some possible uses in the future include online medical training, preparation for and consultation on surgeries. In this paper, we present similar software solutions currently available on the market and under development, as well as our team’s solution to the problem described.

Ploidy analysis is the fundamental method of measuring DNA content. For decades, the principal way of conducting ploidy analysis was through flow cytometry. A flow cytometer is a specialized tool for analyzing cells in a solution. This is convenient in laboratory environments, but prohibits measurement reproducibility and the complete detachment of sample preparation from data acquisition and analysis, which seems to have become paramount with the constant decrease in the number of pathologists per capita all over the globe. As more open computer-aided systems emerge in medicine, the demand for overcoming these shortcomings, and opening access to even more (and more flexible) options, has also emerged. Image-based analysis systems can provide an alternative to these types of workloads, placing the abovementioned problems in a different light. Flow cytometry data can be used as a reference for calibrating an image-based system. This article aims to show an approach to constructing an image-based solution for ploidy analysis, take measurements for a basic comparison of the data produced by the two methods, and produce a workflow with the ultimate goal of calibrating the image-based system.

Virtual reality (VR) has grown in popularity in industry and science. While the development of virtual reality applications is progressing at a fast pace, even today it is a challenge to precisely define the aspects that make virtual reality software of high quality, user-friendly and usable. The biggest issue is that traditional software testing standards can only be partly followed. Using virtual reality for scientific purposes can provide researchers with new possibilities and change human-computer interaction. In this paper, we conducted a user study and identified a number of aspects that are the basic factors in determining the usability of virtual reality software. The results indicate that excessive head and arm movements have an impact on the VR experience.

Today, 2D visualization programs became more common in digital pathology. The use of these programs makes it possible to overcome the difficulties that were present in previous microscopic examinations. With these programs, you no longer have to worry about damaging the sample placed on the glass plate and no need to deal with physical samples for security or infection reasons, or perhaps the biggest advantage of such software, that the test is no longer stationary. Virtual reality technology is evolving at an ever-increasing rate and became more and more available for the average person. The purpose of this paper is to demonstrate the structure and operation of a software called PathoVrthat, in addition to the benefits of 2D visualization solutions, also uses virtual reality in a 3D visualization program. The program provides the ability to load two-dimensional digitized serial sections in virtual reality and is able to visualize various laboratory results on the samples displayed in virtual reality. We used the so-called Godot game engine when developing the software.

Cancer research and diagnostics is an important frontier to apply the power of computers. Researchers use image processing techniques for a few years now, but diagnostics only start to explore its possibilities. Pathologists specialized in this area usually diagnose by visual inspection, typically through a microscope, or more recently on a computer screen. They examine at tissue specimen or a sample consisting of a population cells extracted from it. The latter area is the area of cytometry that researchers started to support by creating image processing algorithms. The validation of an image processing approach like that is an expensive task both financially and time-wise. This paper aims to show a semi-automatized method to simplify this task, by reducing the amount of human interaction necessary.

Cancer research and diagnostics is an important frontier to apply the power of computers. Image processing gains more and more territory in pathology, our current study aims to apply image cytometry to this area. One of the most fundamental methods is ploidy analysis that aims to measure the pace of proliferation in the tissue. This is proven to be a good marker of tumor presence and aggressivity. Ploidy analysis traditionally is a flow cytometry area. The samples used in our study are the same as those used in a flow cytometer: a droplet of the suspension containing stained nuclei caught on a glass slide, covered and scanned. The resulting digital sample is analyzed by an image processing algorithm to detect cell nuclei, and measure their DNA content for the ploidy analysis. This article aims to show our progress in nucleus detection on these samples, and how this improvement to the detection of low intensity nuclei affects segmentation accuracy.

Fluorescence in situ hybridization is a widely used diagnostic procedure in pathology. This method can reveal the genetic background of malignant lesions. The aim of our study was to develop and optimize an image segmentation algorithm specifically for FISH quantification in breast cancer tissue. Moreover, we aimed to validate the results of our algorithm and to compare them with a semi-automated assessment (i.e. scoring on a computer screen) and the results of the conventional (i.e. manual microscopic) IHC quantification.

BACKGROUND:The immunohistochemical detection of estrogen (ER) and progesterone (PR) receptors in breast cancer is routinely used for prognostic and predictive testing. Whole slide digitalization supported by dedicated software tools allows quantization of the image objects (e.g. cell membrane, nuclei) and an unbiased analysis of immunostaining results. Validation studies of image analysis applications for the detection of ER and PR in breast cancer specimens provided strong concordance between the pathologist's manual assessment of slides and scoring performed using different software applications.METHODS:The effectiveness of two connected semi-automated image analysis software (NuclearQuant v. 1.13 application for Pannoramic™ Viewer v. 1.14) for determination of ER and PR status in formalin-fixed paraffin embedded breast cancer specimens immunostained with the automated Leica Bond Max system was studied. First the detection algorithm was calibrated to the scores provided an independent assessors (pathologist), using selected areas from 38 small digital slides (created from 16 cases) containing a mean number of 195 cells. Each cell was manually marked and scored according to the Allred-system combining frequency and intensity scores. The performance of the calibrated algorithm was tested on 16 cases (14 invasive ductal carcinoma, 2 invasive lobular carcinoma) against the pathologist's manual scoring of digital slides.RESULTS:The detection was calibrated to 87 percent object detection agreement and almost perfect Total Score agreement (Cohen's kappa 0.859, quadratic weighted kappa 0.986) from slight or moderate agreement at the start of the study, using the un-calibrated algorithm. The performance of the application was tested against the pathologist's manual scoring of digital slides on 53 regions of interest of 16 ER and PR slides covering all positivity ranges, and the quadratic weighted kappa provided almost perfect agreement (κ = 0.981) among the two scoring schemes.CONCLUSIONS:NuclearQuant v. 1.13 application for Pannoramic™ Viewer v. 1.14 software application proved to be a reliable image analysis tool for pathologists testing ER and PR status in breast cancer.

Slide-based image cytometry (SBC) has several advantages over flow cytometry but it is not widely used because of its low throughput, complicated workflow, and high price. Fully automated microscopes became affordable with the advent of whole slide imaging (WSI) and they can be transformed into a cytometer. A MIRAX MIDI automated whole slide imager was used with metal-halide and light emitting diode (LED)-based fluorescent illumination, filter block changer, and a cooled monochrome charge coupled device camera. The MIRAX control software was further developed for fluorescent sample detection, autofocusing, multichannel digitization, and signal correction due to nonuniform illumination. Fluorescent calibration beads were used to verify the linearity of the system. The HistoQuant software package of the MIRAX viewer was used for image segmentation and quantitative analysis. The data was displayed by the histogram, scatter plot, and gallery functions of the same program. Fluorescent samples can be reliably detected, focused, and scanned. The measured integrated fluorescence showed linearity with exposure time and staining intensity. Automated fluorescent WSI with stable LED illumination and high-quality homogeneous fluorescent slides can be used conveniently for SBC.