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383:, or transcriptional changes. This has the potential to reduce human error and improve accuracy of diagnoses. Digital slides can be easily shared, increasing the potential for data usage in education as well as in consultations between expert pathologists. Multiplexed imaging (staining multiple markers on the same slide) allows pathologists to understand finer distribution of cell-types and their relative locations. An understanding of the spatial distribution of cell-types or markers and pathways they express, can allow for prescription of targeted drugs or build combinational therapies in a personalized manner.
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243:, are used to identify medically significant regions and objects on digital slides. A GPU acceleration software for pathology imaging analysis, cross-comparing spatial boundaries of a huge amount of segmented micro-anatomic objects has been developed. The core algorithm of PixelBox in this software has been adopted in Fixstars' Geometric Performance Primitives (GPP) library as a part of NVIDIA Developer, which is a production geometry engine for advanced graphical information systems, electronic design automation, computer vision and motion planning solutions.
156:
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are advantages to WSI when creating digital data from glass slides, when it comes to real-time telepathology applications, WSI is not a strong choice for discussion and collaboration between multiple remote pathologists. Furthermore, unlike digital radiology where the elimination of film made return on investment (ROI) clear, the ROI on digital pathology equipment is less obvious. The strongest ROI justification includes improved quality of healthcare, increased efficiency for pathologists, and reduced costs in handling glass slides.
28:
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camera and a motorized stage to move the slide around while parts of the tissue are imaged. Tile scanners capture square field-of-view images covering the entire tissue area on the slide, while line-scanners capture images of the tissue in long, uninterrupted stripes rather than tiles. In both cases, software associated with the scanner stitch the tiles or lines together into a single, seamless image.
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Digital slides are created from glass slides using specialized scanning machines. All high quality scans must be free of dust, scratches, and other obstructions. There are two common methods for digital slide scanning, tile-based scanning and line-based scanning. Both technologies use an integrated
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can be used to automate the manual counting of structures, or for classifying the condition of tissue such as is used in grading tumors. They can additionally be used for feature detection of mitotic figures, epithelial cells, or tissue specific structures such as lung cancer nodules, glomeruli, or
348:
Digital pathology has been approved by the FDA for primary diagnosis. The approval was based on a multi-center study of 1,992 cases in which whole-slide imaging (WSI) was shown to be non-inferior to microscopy across a wide range of surgical pathology specimens, sample types and stains. While there
103:
has undergone the digital transformation almost 15 years ago, not because radiology is more advanced, but there are fundamental differences between digital images in radiology and digital pathology: The image source in radiology is the (alive) patient, and today in most cases, the image is even
357:
Validation of a digital microscopy workflow in a specific environment (see above) is important to ensure high diagnostic performance of pathologists when evaluating digital whole-slide images. There are different methods that can be used for this validation process. The
College of American
534:
Saillard, Charlie; Dubois, Rémy; Tchita, Oussama; Loiseau, Nicolas; Garcia, Thierry; Adriansen, Aurélie; Carpentier, Séverine; Reyre, Joelle; Enea, Diana; von Loga, Katharina; Kamoun, Aurélie; Rossat, Stéphane; Wiscart, Corentin; Sefta, Meriem; Auffret, Michaël (2023-11-06).
1575:
Fine, Jeffrey L.; Grzybicki, Dana M.; Silowash, Russell; Ho, Jonhan; Gilbertson, John R.; Anthony, Leslie; Wilson, Robb; Parwani, Anil V.; et al. (2008). "Evaluation of whole slide image immunohistochemistry interpretation in challenging prostate needle biopsies".
192:, for desktop and mobile, is the OpenSeadragon viewer. QuPath is another such open source software, which is often used for digital pathology applications because it offers a powerful set of tools for working with whole slide images. OpenSlide, on the other hand is a
94:
efforts in pathology. However, in 2000, the technical requirements (scanner, storage, network) were still a limiting factor for a broad dissemination of digital pathology concepts. This changed as new powerful and affordable scanner technology as well as mass /
1157:"Validating whole slide imaging systems for diagnostic purposes in pathology: guideline update from the College of American Pathologists in collaboration with the American Society for Clinical Pathology and the Association for Pathology Informatics"
167:
Whole slide image quality comparison, with a slide scanned with a 20x objective and about 0.8 gigabytes (GB) in size to the left, and a 40x objective and approximately 1.2 GB in size to the right. Each image shows a red blood
258:
stain calculation by QuPath in a pure seminoma, which gives a measure of the proliferation rate of the tumor. The colors represent the intensity of expression: blue-no expression, yellow-low, orange-moderate, and red-high
296:
Digital pathology workflow is integrated into the institution's overall operational environment. Slide digitization is expected to reduce the number of routine, manually reviewed slides, maximizing workload efficiency.
1196:
Aeffner, Famke; Zarella, Mark D.; Buchbinder, Nathan; Bui, Marilyn M.; Goodman, Matthew R.; Hartman, Douglas J.; Lujan, Giovanni M.; Molani, Mariam A.; Parwani, Anil V.; Lillard, Kate; Turner, Oliver C. (2019-03-08).
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Nirmal, Ajit J.; Maliga, Zoltan; Vallius, Tuulia; Quattrochi, Brian; Chen, Alyce A.; Jacobson, Connor A.; Pelletier, Roxanne J.; Yapp, Clarence; Arias-Camison, Raquel; Chen, Yu-An; Lian, Christine G. (2022-04-11).
112:
content, the required storage in digital pathology is two to three orders of magnitude higher than in radiology. However, the advantages anticipated through digital pathology are similar to those in radiology:
1105:
Bertram, Christof A; Stathonikos, Nikolas; Donovan, Taryn A; Bartel, Alexander; Fuchs-Baumgartinger, Andrea; Lipnik, Karoline; can Diest, Paul J; Bonsembiante, Federico; Klopfleisch, Robert (September 2021).
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for digital calculation of bone marrow cellularity in QuPath: The system is trained on the appearance of immune cells versus other tissue, and uses this to give an overall percentage of each type.
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Digital pathology also allows internet information sharing for education, diagnostics, publication and research. This may take the form of publicly available datasets or open source access to
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emerged in the 1990s across various areas of life science research. At the turn of the century the scientific community more and more agreed on the term "digital pathology" to denote
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as well as in research projects. Digital pathology allows to share and annotate slides in a much easier way and to download annotated lecture sets generates new opportunities for
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to view, manage, share, and analyze digital slides on computer monitors. This field has applications in diagnostic medicine and aims to achieve more efficient and cost-effective
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In this case, there is no clear distinction between tumor cells and surrounding large stromal cells, requiring delimitation before applying automatic stain quantification tools.
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Capability to access past specimen from the same patients and similar cases for comparison and review, with much less effort than retrieving slides from the archive shelves.
987:"Whole slide imaging versus microscopy for primary diagnosis in surgical pathology: a multicenter randomized blinded noninferiority study of 1992 cases (pivotal study)"
104:
primarily captured in digital format. In pathology the scanning is done from preserved and processed specimens, for retrospective studies even from slides stored in a
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Pathologists has published a guideline with minimal requirements for validation of whole slide imaging systems for diagnostic purposes in human pathology.
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Serag, Ahmed; Ion-Margineanu, Adrian; Qureshi, Hammad; McMillan, Ryan; Saint Martin, Marie-Judith; Diamond, Jim; O'Reilly, Paul; Hamilton, Peter (2019).
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Digital slides are accessible for viewing via a computer monitor and viewing software either locally or remotely via the
Internet. An example of an
854:"Ki67 and LSD1 Expression in Testicular Germ Cell Tumors Is Not Associated with Patient Outcome: Investigation Using a Digital Pathology Algorithm"
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Zwonitzer, R; Kalinski, T; Hofmann, H; Roessner, A; Bernarding, J (2007). "Digital pathology: DICOM-conform draft, testbed, and first results".
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461:"Twenty Years of Digital Pathology: An Overview of the Road Travelled, What is on the Horizon, and the Emergence of Vendor-Neutral Archives"
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910:"Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent"
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Capability to compare different areas of multiple slides simultaneously (slide by slide mode) with the help of a virtual microscope.
197:
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Potts, Steven J.; Young, G. David; Voelker, Frank A. (2010). "The role and impact of quantitative discovery pathology".
1503:"Automatic Annotation of Histopathological Images Using a Latent Topic Model Based On Non-negative Matrix Factorization"
963:
700:"Implementation of Whole Slide Imaging for Clinical Purposes: Issues to Consider From the Perspective of Early Adopters"
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Digital slides are maintained in an information management system that allows for archival and intelligent retrieval.
201:
1199:"Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association"
537:"Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides"
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Digital slides are often stored and delivered over the
Internet or private networks, for viewing and consultation.
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Siegel, Gabriel; Regelman, Dan; Maronpot, Robert; Rosenstock, Moti; Hayashi, Shim-mo; Nyska, Abraham (Oct 2018).
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Lourenço BC, Guimarães-Teixeira C, Flores BCT, Miranda-Gonçalves V, Guimarães R, Cantante M, et al. (2022).
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Tolksdorf, Robert; Bontas, Elena
Paslaru (2004). "Organizing Knowledge in a Semantic Web for Pathology".
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Potts, Steven J. (2009). "Digital pathology in drug discovery and development: Multisite integration".
835:
1365:"The spatial landscape of progression and immunoediting in primary melanoma at single cell resolution"
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Trained pathologists traditionally view tissue slides under a microscope. These tissue slides may be
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Capability to transmit digital slides over distances quickly, which enables telepathology scenarios.
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bindings are also available) that provides a simple interface to read and view whole-slide images.
1258:"Predicting tumour mutational burden from histopathological images using multiscale deep learning"
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to highlight cellular structures. When slides are digitized, they are able to be shared through
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Image analysis tools are used to derive objective quantification measures from digital slides.
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Capability to annotate areas directly in the slide and share this for teaching and research.
1607:"New Developments in Digital Pathology: from Telepathology to Virtual Pathology Laboratory"
908:
Cruz-Roa A, Gilmore H, Basavanhally A, Feldman M, Ganesan S, Shih NNC, et al. (2017).
1606:
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Z-stacking is the scanning of a slide at multiple focal planes along the vertical z-axis.
1613:. Studies in Health Technology and Informatics. Vol. 105. IOS Press. pp. 61–9.
1427:"From telepathology to virtual pathology institution: The new world of digital pathology"
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Ferreira, R; Moon, J; Humphries, J; Sussman, A; Saltz, J; Miller, R; Demarzo, A (1997).
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949:"This work is licensed under a Creative Commons Attribution 4.0 International License."
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35:, and encompasses topics including slide scanning, digital imaging, image analysis and
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Kaibo Wang; Yin Huai; Rubao Lee; Fusheng Wang; Xiaodong Zhang; Joel H. Saltz (2012).
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Digital pathology is today widely used for educational purposes in telepathology and
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1108:"Validation of digital microscopy: Review of validation methods and sources of bias"
786:
Proceedings of the VLDB Endowment. International
Conference on Very Large Data Bases
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960:"FDA allows marketing of first whole slide imaging system for digital pathology"
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Figure 2 - available via license: Creative
Commons Attribution 4.0 International
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Kayser, Klaus; Kayser, Gian; Radziszowski, Dominik; Oehmann, Alexander (2004).
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782:"Accelerating Pathology Image Data Cross-Comparison on CPU-GPU Hybrid Systems"
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McCullough, Bruce; Ying, Xiaoyou; Monticello, Thomas; Bonnefoi, Marc (2004).
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1084:"How to Build a Business Case to Justify the Investment in Digital Pathology"
1036:"Utilizing novel telepathology system in preclinical studies and peer review"
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1490:"Feeding OWL: Extracting and Representing the Content of Pathology Reports"
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Hamilton, Peter W.; Wang, Yinhai; McCullough, Stephen J.; Sussman (2012).
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332:, the automatic camera is focused on a fold (left in image), resulting in
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109:
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Cruz-Roa, Angel; DĂaz, Gloria; Romero, Eduardo; González, Fabio (2011).
1455:"Digital Microscopy Imaging and New Approaches in Toxicologic Pathology"
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66:, and disease predictions through advancements in machine learning and
1648:. Lecture Notes in Computer Science. Vol. 3263. pp. 115–56.
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vessels, or estimation of molecular biomarkers such as mutated genes,
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635:"Virtual microscopy and digital pathology in training and education"
1609:. In Duplaga, Mariusz; Zieliński, Krzysztof; Ingram, David (eds.).
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that focuses on managing and analyzing information generated from
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International
Journal of Computer Assisted Radiology and Surgery
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Schlangen, David; Stede, Manfred; Bontas, Elena
Paslaru (2004).
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Mukhopadhyay, Sanjay; Feldman, Michael; Abels, Esther (2017).
1305:"Translational AI and Deep Learning in Diagnostic Pathology"
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specimen slides. It utilizes computer-based technology and
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Transformation of
Healthcare with Information Technologies
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Kayser, K; Kayser, G; Radziszowski, D; Oehmann, A (1999).
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and knowledge sharing in pathology. Digital pathology in
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Evans AJ, Salama ME, Henricks WH, Pantanowitz L (2017).
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and are numerically analyzed using computer algorithms.
188:, web-based viewer for this purpose implemented in pure
1155:
Evans, Andrew J; Brown, Richard; Bui, Marilyn (2021).
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NLPXML '04 Proceedings of the
Workshop on NLP and XML
685:"Informatics, digital & computational pathology"
336:(blur) of the surrounding tissue (right in image).
99:technologies appeared on the market. The field of
788:. Vol. 5, no. 11. pp. 1543–1554.
1646:Object-Oriented and Internet-Based Technologies
108:. Besides this difference in pre-analytics and
31:Digital pathology is a major part of pathology
1434:Romanian Journal of Morphology and Embryology
598:Romanian Journal of Morphology and Embryology
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1739:Computer Methods and Programs in Biomedicine
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818:"Geometric Performance Primitives (GPP)"
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991:American Journal of Surgical Pathology
511:"Whole Slide Imaging | MBF Bioscience"
68:artificial intelligence in healthcare
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142:is an emerging and upcoming field.
82:trace back to the 1960s with early
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1507:Journal of Pathology Informatics
1203:Journal of Pathology Informatics
1040:Journal of Toxicologic Pathology
654:10.1111/j.1600-0463.2011.02869.x
465:Journal of Pathology Informatics
324:tissue is particularly prone to
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286:Breast cancer prediction by AI.
1554:(Supplement 1): 119–35. 2006.
784:. Proceedings VLDB Endowment.
1:
1590:10.1016/j.humpath.2007.08.007
1546:"E-Health and Telemedicine".
1722:10.1016/j.drudis.2010.09.001
1693:10.1016/j.drudis.2009.06.013
1381:10.1158/2159-8290.CD-21-1357
1003:10.1097/PAS.0000000000000948
86:experiments. The concept of
1654:10.1007/978-3-540-30196-7_4
1262:Nature Machine Intelligence
307:machine learning algorithms
159:A microscopy slide scanner.
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1751:10.1016/j.cmpb.2007.05.010
553:10.1038/s41467-023-42453-6
236:, often implemented using
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1472:10.1080/01926230490451734
1274:10.1038/s42256-020-0190-5
1174:10.5858/arpa.2020-0723-CP
1124:10.1177/03009858211040476
717:10.5858/arpa.2016-0074-OA
234:classification algorithms
1086:. Sectra Medical Systems
594:"The virtual microscope"
1520:10.4103/2153-3539.92031
1322:10.3389/fmed.2019.00185
381:tumor mutational burden
836:"Letter to Kaibo Wang"
459:Pantanowitz L (2018).
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1459:Toxicologic Pathology
1309:Frontiers in Medicine
1216:10.4103/jpi.jpi_82_18
1052:10.1293/tox.2018-0032
541:Nature Communications
515:www.mbfbioscience.com
478:10.4103/jpi.jpi_69_18
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78:The roots of digital
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1710:Drug Discovery Today
1681:Drug Discovery Today
1496:. Nlpxml '04: 43–50.
1112:Veterinary Pathology
871:10.3390/life12020264
394:Anatomical pathology
1161:Arch Pathol Lab Med
704:Arch Pathol Lab Med
272:Tissue segmentation
439:Virtual microscopy
424:Surgical pathology
409:Medical laboratory
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230:Image segmentation
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88:virtual microscopy
56:virtual microscopy
46:is a sub-field of
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1716:(21–22): 943–50.
1687:(19–20): 935–41.
1663:978-3-540-23201-8
1620:978-1-58603-438-2
962:(Press release).
926:10.1038/srep46450
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44:Digital pathology
18:Digital Pathology
16:(Redirected from
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