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Digital Pathology: Path into the Future
Published in Gerd Binnig, Ralf Huss, Günter Schmidt, Tissue Phenomics, 2018
Peter D. Caie, David J. Harrison
As the field is still in its infancy, there are multiple terms for the different aspects of the technology. In this chapter, we refer to digital pathology as encompassing the entire infrastructure needed to effectively view and manage digital images of tissue sections within a clinical setting; from scanners through to laboratory information systems and digital archiving of specimens. While whole-slide imaging is the high-resolution digitization of whole tissue sections mounted on glass microscopy slides and is arguably one of the most essential components for the success of digital pathology, without high-quality images, the field falls at the first hurdle. These digital images can be viewed and navigated using ergonomic high-definition computer interfaces while being easily shared with colleagues on a global scale with the click of a button.
Professionalism: Development and Career Progression in Informatics
Published in Alexander Peck, Clark’s Essential PACS, RIS and Imaging Informatics, 2017
In the UK, the main area where rapid growth is currently being experienced is in pathology, a service whose departments are beginning to receive significant investment across the UK to begin the modernisation from manual slide-based processing towards digital pathology. As a result of this, the field of digital pathology is developing in much the same manner as was observed within radiology during the 2004–2006 era of the NPf IT, and in similar ways as imaging departments transitioned successfully away from the analogue film and chemical era. Career pathways for pathology informatics personnel are less formatively defined at present, but appear to be following the same pathway as discussed here.
Image segmentation
Published in Rodrigo Rojas Moraleda, Nektarios A. Valous, Wei Xiong, Niels Halama, Computational Topology for Biomedical Image and Data Analysis, 2019
Rodrigo Rojas Moraleda, Nektarios A. Valous, Wei Xiong, Niels Halama
The chapter presents a computational topology implementation in the context of segmenting cell nuclei from histological images. The proposed approach is carried out in the following steps: An external process retrieves digital images from a database. In digital pathology, these images often consist of digitized glass slides known as whole-slide images.A change in the color model is carried out in order to create a feature space that captures well the properties of the region-of-interest (ROI), e.g., cell nuclei. A feature space is sought where the persistent homology of the ROIs is different from the persistent homology of other structures in the image.Images are deconstructed into connected components at different scales.An inclusion tree is built that stores information between connected components.Persistent homology is used to summarize the Betti number dimension-0 changes when step (iv) occurs.Standard statistical methods are used to define a confidence interval for the birth and death of homological classes. In many cases, this interval is enough to identify an ROI in the image which makes it a kind of topological signature.Segmentation masks are obtained; this is a post-processing step for transforming selected points over the persistent diagram into a binary mask.
Disruptive innovations in the clinical laboratory: catching the wave of precision diagnostics
Published in Critical Reviews in Clinical Laboratory Sciences, 2021
Ziyad Khatab, George M. Yousef
The concept of digitizing pathology practice that got introduced in the late 1990s is a great example of disruptive technology in laboratory medicine. Digital pathology has many advantages and an extended scope of applications, as outlined in recent reviews [22]. These include easy sharing of slides among institutions, which can have a great impact on improving the efficiency of consultations (through digital consults by pathologists with unique specializations worldwide). Taking into consideration that pathology is heading toward specialized practice is of special importance, but in many community hospitals, there is a lack of specialization compared to academic institutions where pathologists practice mainly one or a limited number of specialties like genitourinary or breast pathology. Some pathologists are even sub-specialized, such as “pediatric gastrointestinal pathology.” Thanks to the introduction of digital pathology, we can now obtain digital second opinions from expert pathologists across the globe in almost no time.
Laser Capture Proteomics: spatial tissue molecular profiling from the bench to personalized medicine
Published in Expert Review of Proteomics, 2021
Lance A. Liotta, Philip A. Pappalardo, Alan Carpino, Amanda Haymond, Marissa Howard, Virginia Espina, Julie Wulfkuhle, Emanuel Petricoin
Digital pathology in the future can incorporate emerging generations of LCM and other spatial tissue profiling technologies. A diagnostic biopsy pathologic tissue section histologic image is digitally captured at high resolution, sent to the internet cloud, and then marked up remotely on a scientist or clinician’s computer screen. Once the specific histopathology regions are marked for interrogation on-screen (including groups of cells, single cells, immune cells, stromal cells, etc.), these regions are automatically and remotely microdissected, followed by proteomic, mutational, and transcriptomic evaluation. The data from the molecular analysis of each selected region is then ported back through the cloud and can be viewed on-screen as a separate laboratory report for each individual histologic region that is highlighted.
Prognostic image-based quantification of CD8CD103 T cell subsets in high-grade serous ovarian cancer patients
Published in OncoImmunology, 2021
S. T. Paijens, A. Vledder, D. Loiero, E. W. Duiker, J. Bart, A. M. Hendriks, M. Jalving, H. H. Workel, H. Hollema, N. Werner, A. Plat, G. B. A. Wisman, R. Yigit, H. Arts, A. J. Kruse, N.M. de Lange, V. H. Koelzer, M. de Bruyn, H. W. Nijman
In order to translate CD8CD103 TRM quantity and location into a diagnostic tool, the development of immune scores are needed. However, manual TIL quantification by pathologists is hampered by interobserver variability and is time-consuming.16 The rise of digital pathology, including image-based quantification and machine learning algorithms, provides an opportunity to overcome these limitations. Machine learning algorithms apply statistical methods to process data and have shown to be reproducible and reliable for the analysis of tissue composition in cancer.17 The deep characterization of the tumor microenvironment, through spatial analysis and multiplexing, makes image-based quantification an efficient tool to extract comprehensive information on biomarker expression levels, co-localization, and compartmentalization.18,19 Horeweg et al. demonstrated the successful application of image-based CD8CD103 TRM quantification in early-stage endometrial cancer, by demonstrating concordance between automatic machine learning and assessment by expert pathologists. The study showed greater sensitivity of automatic machine learning compared to manual quantification.20