Artifact Augmentation for Enhanced Tissue Detection in Microscope Scanner Systems.

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.

Calibration-Aimed Comparison of Image-Cytometry- and Flow-Cytometry-Based Approaches of Ploidy Analysis.

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.

Regression Based Iterative Illumination Compensation Method for Multi-Focal Whole Slide Imaging System.

Image quality, resolution and scanning time are critical in digital pathology. In order to create a high-resolution digital image, the scanner systems execute stitching algorithms to the digitized images. Due to the heterogeneity of the tissue sample, complex optical path, non-acceptable sample quality or rapid stage movement, the intensities on pictures can be uneven. The evincible and visible intensity distortions can have negative effect on diagnosis and quantitative analysis. Utilizing the common areas of the neighboring field-of-views, we can estimate compensations to eliminate the inhomogeneities. We implemented and validated five different approaches for compensating output images created with an area scanner system. The proposed methods are based on traditional methods such as adaptive histogram matching, regression-based corrections and state-of-the art methods like the background and shading correction (BaSiC) method. The proposed compensation methods are suitable for both brightfield and fluorescent images, and robust enough against dust, bubbles, and optical aberrations. The proposed methods are able to correct not only the fixed-pattern artefacts but the stochastic uneven illumination along the neighboring or above field-of-views utilizing iterative approaches and multi-focal compensations.

An optimized image analysis algorithm for detecting nuclear signals in digital whole slides for histopathology.

Nuclear estrogen receptor (ER), progesterone receptor (PR) and Ki-67 protein positive tumor cell fractions are semiquantitatively assessed in breast cancer for prognostic and predictive purposes. These biomarkers are usually revealed using immunoperoxidase methods resulting in diverse signal intensity and frequent inhomogeneity in tumor cell nuclei, which are routinely scored and interpreted by a pathologist during conventional light-microscopic examination. In the last decade digital pathology-based whole slide scanning and image analysis algorithms have shown tremendous development to support pathologists in this diagnostic process, which can directly influence patient selection for targeted- and chemotherapy. We have developed an image analysis algorithm optimized for whole slide quantification of nuclear immunostaining signals of ER, PR, and Ki-67 proteins in breast cancers. In this study, we tested the consistency and reliability of this system both in a series of brightfield and DAPI stained fluorescent samples. Our method allows the separation of overlapping cells and signals, reliable detection of vesicular nuclei and background compensation, especially in FISH stained slides. Detection accuracy and the processing speeds were validated in routinely immunostained breast cancer samples of varying reaction intensities and image qualities. Our technique supported automated nuclear signal detection with excellent efficacy: Precision Rate/Positive Predictive Value was 90.23 ± 4.29%, while Recall Rate/Sensitivity was 88.23 ± 4.84%. These factors and average counting speed of our algorithm were compared with two other open source applications (QuPath and CellProfiler) and resulted in 6-7% higher Recall Rate, while 4- to 30-fold higher processing speed. In conclusion, our image analysis algorithm can reliably detect and count nuclear signals in digital whole slides or any selected large areas i.e. hot spots, thus can support pathologists in assessing clinically important nuclear biomarkers with less intra- and interlaboratory bias inherent of empirical scoring. © 2017 International Society for Advancement of Cytometry.