ArticleJournal of pathology informatics2019
Annotations, Ontologies, and Whole Slide Images - Development of an Annotated Ontology-Driven Whole Slide Image Library of Normal and Abnormal Human Tissue.
Article in Journal of pathology informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 30 citations in OpenAlex.
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- Clinicians' Agreement on Extrapulmonary Radiographic Findings in Chest X-Rays Using a Diagnostic Labelling Scheme.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence in liver imaging: methods and applications.Hepatology international · 2024Review
- A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer.Scientific data · 2023Article
- Modelling digital health data: The ExaMode ontology for computational pathology.Journal of pathology informatics · 2023Article
- Inter- and Intra-Observer Agreement When Using a Diagnostic Labeling Scheme for Annotating Findings on Chest X-rays-An Early Step in the Development of a Deep Learning-Based Decision Support System.Diagnostics (Basel, Switzerland) · 2022Article
- The state of the art for artificial intelligence in lung digital pathology.The Journal of pathology · 2022Review
- Artificial intelligence in head and neck cancer diagnosis.Journal of pathology informatics · 2022Review
- Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training.Journal of digital imaging · 2021Article
- Artificial intelligence driven next-generation renal histomorphometry.Current opinion in nephrology and hypertension · 2020Review
- TissueWand, a Rapid Histopathology Annotation Tool.Journal of pathology informatics · 2020Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveDigital 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
methodsSeveral 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.
resultsSystemized 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 cm
conclusionThis 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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