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- Asa, Sylvia, et al.
(författare)
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2020 vision of digital pathology in action
- 2019
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Ingår i: Journal of Pathology Informatics. - : Medknow Publications. - 2229-5089 .- 2153-3539. ; 10:27
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 2. |
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| 3. |
- Clarke, Emily L., et al.
(författare)
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Development and Evaluation of a Novel Point-of-Use Quality Assurance Tool for Digital Pathology
- 2019
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Ingår i: Archives of Pathology & Laboratory Medicine. - : COLL AMER PATHOLOGISTS. - 0003-9985 .- 1543-2165. ; 143:10, s. 1246-1255
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Tidskriftsartikel (refereegranskat)abstract
- Context.-Flexible working at diverse or remote sites is a major advantage when reporting using digital pathology, but currently there is no method to validate the clinical diagnostic setting within digital microscopy. Objective.-To develop a preliminary Point-of-Use Quality Assurance (POUQA) tool designed specifically to validate the diagnostic setting for digital microscopy. Design.-We based the POUQA tool on the red, green, and blue (RGB) values of hematoxylin-eosin. The tool used 144 hematoxylin-eosin-colored, 5x5-cm patches with a superimposed random letter with subtly lighter RGB values from the background color, with differing levels of difficulty. We performed an initial evaluation across 3 phases within 2 pathology departments: 1 in the United Kingdom and 1 in Sweden. Results.-In total, 53 experiments were conducted across all phases resulting in 7632 test images viewed in all. Results indicated that the display, the users visual system, and the environment each independently impacted performance. Performance was improved with reduction in natural light and through use of medical-grade displays. Conclusions.-The use of a POUQA tool for digital microscopy is essential to afford flexible working while ensuring patient safety. The color-contrast test provides a standardized method of comparing diagnostic settings for digital microscopy. With further planned development, the color-contrast test may be used to create a "Verified Login" for diagnostic setting validation.
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| 4. |
- Falk, Martin, Dr.rer.nat. 1981-, et al.
(författare)
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Interactive Visualization of 3D Histopathology in Native Resolution
- 2019
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Ingår i: IEEE Transactions on Visualization and Computer Graphics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1077-2626 .- 1941-0506 .- 2160-9306. ; 25:1, s. 1008-1017
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Tidskriftsartikel (refereegranskat)abstract
- We present a visualization application that enables effective interactive visual analysis of large-scale 3D histopathology, that is, high-resolution 3D microscopy data of human tissue. Clinical work flows and research based on pathology have, until now, largely been dominated by 2D imaging. As we will show in the paper, studying volumetric histology data will open up novel and useful opportunities for both research and clinical practice. Our starting point is the current lack of appropriate visualization tools in histopathology, which has been a limiting factor in the uptake of digital pathology. Visualization of 3D histology data does pose difficult challenges in several aspects. The full-color datasets are dense and large in scale, on the order of 100,000 x 100,000 x 100 voxels. This entails serious demands on both rendering performance and user experience design. Despite this, our developed application supports interactive study of 3D histology datasets at native resolution. Our application is based on tailoring and tuning of existing methods, system integration work, as well as a careful study of domain specific demands emanating from a close participatory design process with domain experts as team members. Results from a user evaluation employing the tool demonstrate a strong agreement among the 14 participating pathologists that 3D histopathology will be a valuable and enabling tool for their work.
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| 5. |
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| 6. |
- Skoglund, Karin, 1980-, et al.
(författare)
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Annotations, ontologies, and whole slide images : Development of an annotated ontology-driven whole slide image library of normal and abnormal human tissue
- 2019
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Ingår i: Journal of Pathology Informatics. - : Medknow Publications. - 2229-5089 .- 2153-3539. ; 10:22
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Digital pathology is today a widely used technology, and the digitalization of microscopic slides into whole slide images (WSIs) allows the use of machine learning algorithms as a tool in the diagnostic process. In recent years, “deep learning” algorithms for image analysis have been applied to digital pathology with great success. The training of these algorithms requires a large volume of high-quality images and image annotations. These large image collections are a potent source of information, and to use and share the information, standardization of the content through a consistent terminology is essential. The aim of this project was to develop a pilot dataset of exhaustive annotated WSI of normal and abnormal human tissue and link the annotations to appropriate ontological information. Materials and Methods: Several biomedical ontologies and controlled vocabularies were investigated with the aim of selecting the most suitable ontology for this project. The selection criteria required an ontology that covered anatomical locations, histological subcompartments, histopathologic diagnoses, histopathologic terms, and generic terms such as normal, abnormal, and artifact. WSIs of normal and abnormal tissue from 50 colon resections and 69 skin excisions, diagnosed 2015-2016 at the Department of Clinical Pathology in Linköping, were randomly collected. These images were manually and exhaustively annotated at the level of major subcompartments, including normal or abnormal findings and artifacts. Results: Systemized nomenclature of medicine clinical terms (SNOMED CT) was chosen, and the annotations were linked to its codes and terms. Two hundred WSI were collected and annotated, resulting in 17,497 annotations, covering a total area of 302.19 cm2, equivalent to 107,7 gigapixels. Ninety-five unique SNOMED CT codes were used. The time taken to annotate a WSI varied from 45 s to over 360 min, a total time of approximately 360 h. Conclusion: This work resulted in a dataset of 200 exhaustive annotated WSIs of normal and abnormal tissue from the colon and skin, and it has informed plans to build a comprehensive library of annotated WSIs. SNOMED CT was found to be the best ontology for annotation labeling. This project also demonstrates the need for future development of annotation tools in order to make the annotation process more efficient.
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| 7. |
- Skoglund, Karin, 1980-, et al.
(författare)
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Colon data from the Visual Sweden project DROID
- 2019
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Annan publikationabstract
- The dataset consists of 101 H&E-stained colon whole slide images (WSI) - 52 abnormal and 49 benign cases. All significant abnormal findings identified are outlined and categorized into 15 types such as hyperplastic polyp, high grade adenocarcinoma and necrosis. Other tissue components such as mucosa, submucosa, as well as the surgical margin are delineated to create a complete histological map. In total, 756 separate annotations have been made to segment the different tissue structures and link them to ontological information.
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