We resume our journey and further discuss the various aspects of whole slide scanning such as virtual slide generation, image compression, pyramid representation along with storage and access. We hope you enjoy reading this part of our series and stay tuned for the upcoming parts on applications of digital pathology.
TECHNOLOGY
The process of digitization includes four sequential parts: image acquisition (scanning), storage, editing, and display of images (11). WSI uses slide scanners that consist of four main components: light source, slide stage, objective lenses, and a high-resolution camera for image capture (8,12-14).
Whole Slide Scanning
Whole slide scanners capture images of tissue sections tile by tile or in a line-scanning fashion. The multiple images (tiles or lines) are captured and digitally assembled to generate a digital image of the entire slide (15,16). WSI can be categorized as bright field, fluorescent, and multispectral and some scanners can accommodate more than one modality. Bright field scanning emulates standard bright field microscopy and is the most common and cost-effective approach. Fluorescent scanning is akin to fluorescent microscopy and is used to digitize fluorescently labelled slides (i.e., fluorescent immunohistochemistry [IHC], fluorescent in situ hybridization) (15,16). Multispectral imaging captures spectral information across the spectrum of light and can be applied to both the bright field and fluorescent settings (15-19). Line scanning exclusively uses focus maps; however, these can also be used with tile scanning. More recently, scanning processes have been developed that incorporate continuous automatic refocusing processes, further increasing the quality of scans (15-19). They have also incorporated tissue recognition features that allow for automatic detection of the histology specimen via a low-magnification overview scan (16). Different scanners vary in their scanning modality, slide-loading capacity and scan time with a capacity of holding 400 slides in high-throughput scanners (15,20). Scanning times per slide range mainly from 30 seconds to several minutes (21-23). If the camera sensor has a lower resolution than the objective’s numerical aperture allows for, information is lost. Therefore, quality of the capturing camera within a digital scanner should be taken into consideration (15-20). After digitisation, quality of scans need to be assessed as scanning artefacts can affect downstream results, and can be caused by improper cleaning of slides prior to scanning, poorly focused scans, or compensation lines from improper stitching of lines or tiles (24,25).
Virtual Slides
Whole slide scanning generates digital representations of glass slides that can be navigated in an interactive manner. The slide must be captured at sufficiently high resolution and with adequate color depth.
Image Compression
Many methods to reduce file size using image compression are available in WSI. Many vendors use picture formats like JPEG, JPEG 2000, or LZW compression to reduce file size, often resulting in a reduction of file size by a factor of 7 or more (26). However, information is lost in the conversion that cannot be recovered. Although morphologic assessments appear to be less affected, densitometric assessments are increasingly sensitive to this loss (27). Thus, users are discouraged from applying JPEG compression successively on the same image, as it further degrades image quality. Discarding blank regions of the slide reduces file sizes as well as scan times by identifying regions in the initial macro snapshot that do not need to be scanned (28).
Pyramid Representation
Despite methods of reducing file size, a single whole slide image in practice often exceeds 1 GB in size which can be prohibitive to download and load into memory. This problem can be tackled by noting the intrinsic relationship between image scale and field of view. For large fields of view, resolution is limited by the computer monitor and therefore the image does not need to be loaded at the highest resolution. Conversely, when users examine tissue at high magnification, only a small field of view is visible on the monitor at any given time, and so the image does not need to be loaded in its entirety. Whole slide images are stored at multiple resolutions to accommodate a streamlined method for loading images. This multi-resolution representation is commonly referred to as an image pyramid. In this way, a viewer can retrieve a much smaller low-resolution component of the file when attempting to render large fields of view, therefore requiring less bandwidth to view the image. (23-28).
Storage and Access
The strategy for storing virtual slides is largely dependent on intended use. For applications with very few users and with no need for retention, local storage is often sufficient. However, if retention is important, a complete backup strategy including off-site storage, (RAID) storage, or optical/tape storage may be used. Hybrid solutions that involve local and cloud-based storage and access, or hub-and-spoke models for multisite organizations, can also be effective strategies (27-30).
Viewing and Managing Virtual Slides
WSI offers an opportunity to expand the tools available for users to include digital annotation, rapid navigation/magnification, and computer-assisted viewing and analysis (30). For example, when whole slide images are used for educational purposes, access to a dedicated image viewer enables us to annotate images for quick identification and navigation to regions of interest in the slide (30). Similarly, the use of WSI to support clinical diagnostics is often aided by the ability to view images in association with the patient’s clinical history, or alongside other slides or images that may have been acquired from the same patient (e.g., serial sections, IHC, gross photos, radiology) (31). For users who wish to apply image analysis algorithms to whole slide images, some of the viewers are packaged with algorithms that can detect cells, compute positive staining, perform regional segmentation, or perform nuclear segmentation in Hematoxylin and Eosin (H&E) images (32). Viewers often support the ability to annotate images, save regions of interest, take snapshots of selected regions, and export images to other formats. These can be integrated into department’s workflow in a seamless manner, providing on demand image analysis in conjunction with whole slide viewing (30,32,33).
Image Management Systems
Image management systems are software platforms that offer the ability to organize and access images using image metadata, patient information, or some other characteristic that can associate images into meaningful groups. For example, a common clinical workflow may organize slides in a hierarchy that provides users access to images in a manner not unlike laboratory information systems (LIS). Advanced features often include integrated image viewers and analysis routines, the ability to save and recall slide annotations, integration with information systems, storage of computed data (e.g., Human epidermal growth receptor 2 (HER2/neu score), authentication and user management, and modules that provide reports of results. As a result, image management systems are often a central component of a WSI system (28,30).
Preservation of Color Through the WSI Pipeline
Differences in color can have an influence on the diagnostic performance of pathologists. In a set of experiments examining the effect of a computer display’s age on color, Avanaki et al. (34) found that aging reduced the color saturation and luminosity of the display and produced a shift in the color point of white. Consequently, they found that the average time for pathologists to score digital slides increased from 41 to 50 seconds. Intersession percentage agreement of diagnostic scores for slides shown on a non-aged display was about 20% higher than that of aged slides. These findings indicate that preventing the degradation of color in the digitization and display process is important for optimal results. Additionally, color differences are commonly introduced by inter-display differences, which in turn can cause differences in color perception when the same slide, acquired by the same scanner and visualized in the same viewer application, is viewed on different displays. Absolute color calibration can be achieved with the use of the International Color Consortium (ICC) framework, an open, vendor-neutral, cross platform color management protocol. It begins by characterizing the whole slide scanner with a color calibration slide that contains a number of semi-transparent colored patches with known color attributes, such as that described by Yagi (35). After scanning the calibration slide, the relationship between the original color attributes of the reference patches and the values produced by the scanner is determined. This can then be characterized in the ICC source and destination profile and automatically attached to subsequently scanned slides, providing a complete reference to describe the color transformation introduced by the digitization process (36).
References:
1. Aeffner F, Zarella MD, Buchbinder N, Bui MM, Goodman MR, Hartman DJ, et al. Introduction to digital image analysis in whole-slide imaging: a white paper from the digital pathology association. Journal of pathology informatics. 2019;10.
2. Hamilton PW, Wang Y, McCullough SJ. Virtual microscopy and digital pathology in training and education. Apmis. 2012 Apr;120(4):305-15.
3. Ferreira R, Moon J, Humphries J, Sussman A, Saltz J, Miller R, et al. "The virtual microscope". Romanian Journal of Morphology and Embryology. 1997;45: 449–453.
4. Ho J, Parwani AV, Jukic DM, Yagi Y, Anthony L, Gilbertson JR. Use of whole slide imaging in surgical pathology quality assurance: design and pilot validation studies. Human pathology. 2006 Mar 1;37(3):322-31.
5. Pantanowitz L. Digital images and the future of digital pathology. Journal of pathology informatics. 2010;1.
6. Pantanowitz L, Sharma A, Carter AB, Kurc T, Sussman A, Saltz J. Twenty years of digital pathology: an overview of the road travelled, what is on the horizon, and the emergence of vendor-neutral archives. Journal of pathology informatics. 2018;9.
7. Saco A, Bombi JA, Garcia A, Ramírez J, Ordi J. Current status of whole-slide imaging in education. Pathobiology. 2016;83(2-3):79-88.
8. Abels E, Pantanowitz L. Current state of the regulatory trajectory for whole slide imaging devices in the USA. Journal of pathology informatics. 2017;8.
9. Wilbur DC. Digital cytology: current state of the art and prospects for the future. Acta cytologica. 2011;55(3):227-38.
10. Cucoranu IC, Parwani AV, Pantanowitz L. Digital whole slide imaging in cytology. Archives of Pathology and Laboratory Medicine. 2014 Mar;138(3):300.
11. Farris AB, Cohen C, Rogers TE, Smith GH. Whole slide imaging for analytical anatomic pathology and telepathology: practical applications today, promises, and perils. Archives of pathology & laboratory medicine. 2017 Apr;141(4):542-50.
12. Gilbertson JR, Ho J, Anthony L, Jukic DM, Yagi Y, Parwani AV. Primary histologic diagnosis using automated whole slide imaging: a validation study. BMC clinical pathology. 2006 Dec;6(1):1-9.
13. Farahani N, Parwani AV, Pantanowitz L. Whole slide imaging in pathology: advantages, limitations, and emerging perspectives. Pathol Lab Med Int. 2015 Jun 11;7(23-33):4321.
14. Pantanowitz L, Sinard JH, Henricks WH, Fatheree LA, Carter AB, Contis L, et al. Validating whole slide imaging for diagnostic purposes in pathology: guideline from the College of American Pathologists Pathology and Laboratory Quality Center. Archives of Pathology and Laboratory Medicine. 2013 Dec;137(12):1710-22.
15. Bueno G, Déniz O, Fernández‐Carrobles MD, Vállez N, Salido J. An automated system for whole microscopic image acquisition and analysis. Microscopy research and technique. 2014 Sep;77(9):697-713.
16. Montironi R, Cimadamore A, Massari F, Montironi MA, Lopez-Beltran A, Cheng L, et al. Whole slide imaging of large format histology in prostate pathology: potential for information fusion. Archives of Pathology & Laboratory Medicine. 2017 Nov;141(11):1460-1.
17. Indu M, Rathy R, Binu MP. “Slide less pathology”: Fairy tale or reality? Journal of oral and maxillofacial pathology: JOMFP. 2016 May;20(2):284.
18. Hamilton PW, Bankhead P, Wang Y, Hutchinson R, Kieran D, McArt DG, et al. Digital pathology and image analysis in tissue biomarker research. Methods. 2014 Nov 1;70(1):59-73.
19. Higgins C. Applications and challenges of digital pathology and whole slide imaging.
Biotech Histochem. 2015 Jul;90(5):341-7.
20. Feng Z, Puri S, Moudgil T, Wood W, Hoyt CC, Wang C, et al. Multispectral imaging of formalin-fixed tissue predicts ability to generate tumor-infiltrating lymphocytes from melanoma. Journal for immunotherapy of cancer. 2015 Dec;3(1):1-1.
21. Montalto MC, McKay RR, Filkins RJ. Autofocus methods of whole slide imaging systems and the introduction of a second-generation independent dual sensor scanning method. Journal of pathology informatics. 2011;2.
22. Boyce BF. Whole slide imaging: uses and limitations for surgical pathology and teaching. Biotechnic&Histochemistry. 2015 Jul 4;90(5):321-30.
23. Al‐Janabi S, Huisman A, Van Diest PJ. Digital pathology: current status and future perspectives. Histopathology. 2012 Jul;61(1):1-9.
24. Laurent C, Guérin M, Frenois FX, Thuries V, Jalabert L, Brousset P, et al. Whole-slide imaging is a robust alternative to traditional fluorescent microscopy for fluorescence in situ hybridization imaging using break-apart DNA probes. Human Pathology. 2013 Aug 1;44(8):1544-55.
25. Bertram CA, Klopfleisch R. The pathologist 2.0: an update on digital pathology in veterinary medicine. Veterinary pathology. 2017 Sep;54(5):756-66.
26. Neil DA, Demetris AJ. Digital pathology services in acute surgical situations. Journal of British Surgery. 2014 Sep;101(10):1185-6.
27. Sellaro TL, Filkins R, Hoffman C, Fine JL, Ho J, Parwani AV, et al. Relationship between magnification and resolution in digital pathology systems. Journal of pathology informatics. 2013;4.
28. Johnson JP, Krupinski EA, Nafziger JS, Yan M, Roehrig H. Visually lossless compression of breast biopsy virtual slides for telepathology. InMedical Imaging 2009: Image Perception, Observer Performance, and Technology Assessment 2009 Mar 12 (Vol. 7263, pp. 206-213). SPIE.
29. Pantanowitz L, Szymas J, Yagi Y, Wilbur D. Whole slide imaging for educational purposes. Journal of pathology informatics. 2012;3.
30. Pantanowitz L, Wiley CA, Demetris A, Lesniak A, Ahmed I, Cable W, et al. Experience with multimodality telepathology at the University of Pittsburgh Medical Center. Journal of pathology informatics. 2012;3.
31. Isaacs M, Lennerz JK, Yates S, Clermont W, Rossi J, Pfeifer JD. Implementation of whole slide imaging in surgical pathology: A value added approach. Journal of Pathology Informatics. 2011;2.
32. Saco A, Diaz A, Hernandez M, Martinez D, Montironi C, Castillo P, et al. Validation of whole-slide imaging in the primary diagnosis of liver biopsies in a university hospital. Digestive and Liver Disease. 2017 Nov 1;49(11):1240-6.
33. Krupinski EA, Johnson JP, Jaw S, Graham AR, Weinstein RS. Compressing pathology whole-slide images using a human and model observer evaluation. Journal of pathology informatics. 2012;3.
34. Avanaki AR, Espig KS, Sawhney S, Pantanowitzc L, Parwani AV, Xthona A, et al. Aging display's effect on interpretation of digital pathology slide. InMedical Imaging 2015: Digital Pathology 2015 Mar 19 (Vol. 9420, p. 942006). International Society for Optics and Photonics.
35. Yagi Y. Color standardization and optimization in whole slide imaging. In: Diagnostic pathology 2011 Dec (Vol. 6, No. 1, pp. 1-12). BioMed Central.
36. Hanna MG, Parwani A, Sirintrapun SJ. Whole slide imaging: technology and applications. Advances in Anatomic Pathology. 2020 Jul 12;27(4):251-9.
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Sambit K Mohanty, MD Nupur Sharma, MD