![]() The central idea is that cell contours, the cellular interior or any objects within the field of view do not have the exact same intensity profile throughout the z-dimension. a z-stack) of various cells, and use the information contained within the full z-stack to identify the focal region of the cells in the images. Instead of relying on the in-focus image, we systematically acquired multiple stacks of images around the focal plane ( i.e. Here, we propose a different segmentation strategy inspired by hyperspectral imaging. This is an important constraint, which – in practice – requires periodic auto-focusing or a control system to automatically maintain perfect focus. Notably, segmentation critically depends on obtaining high-quality images with a constant focus that outlines the borders and main morphological features of the cell. This is a considerable waste of time and energy, and highlights the need for a simple, versatile strategy to segment cells, irrespective of experimental design or cellular characteristics. As a result, research groups design and tweak image analysis software 5, 6, 7, 8, 9, 10, 11, 12, 13 to match their specific segmentation problem. Although efficient for specific problems, these methods are not versatile, and usually fail when applied to different cell types or other experimental conditions. The numerous existing methods were designed for specific cell types and usually rely on specific morphological features ( e.g., size, shape, fluorescent labeling). Despite years of development, a universal method to segment cells from microscopy images has not yet been established. However, a robust and efficient cell segmentation method is required to obtain high quality single cell traces 1, 2. In recent years, longitudinal time-lapse studies have emerged as key methods in quantitative biology and are essential to understand the dynamics of cellular processes 2, 3, 4. 2017 8:51.Thanks to the development of microfluidics and microscopy, it is now possible to measure the dynamics of single cells over time 1, 2. Routine digital pathology workflow: the Catania experience. įraggetta F, Garozzo S, Zannoni G, Pantanowitz L, Rossi E. Complete digital pathology for routine histopathology diagnosis in a multicenter hospital network. Retamero JA, Aneiros-Fernandez J, del Moral RG. Advantage of Z-stacking for teleconsultation between the USA and Colombia. Mosquera-Zamudio A, Hanna MG, Parra-Medina R, Piedrahita AC, Rodriguez-Urrego PA, Pantanowitz L. Use of whole slide imaging (WSI) for distance teaching. Įvans AJ, Depeiza N, Allen SG, Fraser K, Shirley S, Chetty R. Use of whole slide imaging in surgical pathology quality assurance: design and pilot validation studies. Ho J, Parwani A V, Jukic DM, Yagi Y, Anthony L, Gilbertson JR. The Paris System for Reporting Urinary Cytology ThinPrep Z-stack digital cytology urine cytology urothelial carcinoma whole slide images. In addition, Z-stacked urine WSI does not justify the prolonged scan times and larger storage needs compared to those without Z-stack. Z-stack images provide minimal diagnostic benefit for urine ThinPrep WSI. Surveys demonstrated a range in comfort and use with slightly more favorable opinions for Z-stacked cases. The average scan time and file size for slides without Z-stack and with Z-stack are 6.27 minute/0.827 GB and 14.06 minute/2.650 GB, respectively. Intraobserver CK was 0.697-1.0 (P < 0.05), indicating substantial to perfect concordance. For both rounds, interobserver CK was moderate-to-perfect (0.417-1.0, P <. 05) with Z-stack both indicating substantial-to-perfect concordance. ![]() A survey after each round evaluated participant experience.ĬK with the original report ranged from 0.606 to 1.0 (P <. A Cohen's Kappa (CK) calculated concordance rates. Six cytopathologists and 1 cytotechnologist evaluated the cases in 2 rounds with a 2-week wash-out period in a blinded manner. Slides were scanned at 40× magnification without Z-stack and with Z-stack at 3 layers, 1 μm each. Thirty ThinPrep urine cases of high-grade urothelial carcinoma (n = 22) and cases of negative for high-grade urothelial carcinoma (n = 8) were included. This study investigates the use of Z-stacked images for diagnostic assessment and the experience of evaluating urine ThinPrep WSI. ThinPrep and other liquid-based preparations may alleviate the issue by reducing clusters in a concentrated area. Whole slide imaging (WSI) adoption has been slower in cytopathology due, in part, to challenges in multifocal plane scanning on 3-dimensional cell clusters.
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