A data model and database for high-resolution pathology analytical image informatics

作者：Fusheng Wang, Jun Kong, Lee Cooper, Tony C Pan, Tahsin Kurç, Wenjin Chen, Ashish Arunkumar Sharma, Cristobal Niedermayr, Tae W Oh, Daniel J. Brat, Alton B. Farris, David J. Foran, Joel Haskin Saltz · 发表于：Journal of Pathology Informatics · 年份：2011 · DOI：10.4103/2153-3539.83192 · 被引用次数：60 · 研究领域：AI in cancer detection、Cell Image Analysis Techniques、Digital Imaging for Blood Diseases

BACKGROUND: The systematic analysis of imaged pathology specimens often results in a vast amount of morphological information at both the cellular and sub-cellular scales. While microscopy scanners and computerized analysis are capable of capturing and analyzing data rapidly, microscopy image data remain underutilized in research and clinical settings. One major obstacle which tends to reduce wider adoption of these new technologies throughout the clinical and scientific communities is the challenge of managing, querying, and integrating the vast amounts of data resulting from the analysis of large digital pathology datasets. This paper presents a data model, which addresses these challenges, and demonstrates its implementation in a relational database system. CONTEXT: This paper describes a data model, referred to as Pathology Analytic Imaging Standards (PAIS), and a database implementation, which are designed to support the data management and query requirements of detailed characterization of micro-anatomic morphology through many interrelated analysis pipelines on whole-slide images and tissue microarrays (TMAs). AIMS: (1) Development of a data model capable of efficiently representing and storing virtual slide related image, annotation, markup, and feature information. (2) Development of a database, based on the data model, capable of supporting queries for data retrieval based on analysis and image metadata, queries for comparison of results from different analyses, and...