An Image Retrieval Pipeline in a Medical Data Integration Center.

Autor:	Cheng KY; Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany., Pazmino S; Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany., Bergh B; Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany., Lange-Hegermann M; inIT-Institute Industrial IT, OWL University of Applied Sciences and Arts, Lemgo, Germany., Schreiweis B; Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany.
Jazyk:	angličtina
Zdroj:	Studies in health technology and informatics [Stud Health Technol Inform] 2024 Jan 25; Vol. 310, pp. 1388-1389.
DOI:	10.3233/SHTI231208
Abstrakt:	Medical images need annotations with high-level semantic descriptors, so that domain experts can search for the desired dataset among an enormous volume of visual media within a Medical Data Integration Center. This article introduces a processing pipeline for storing and annotating DICOM and PNG imaging data by applying Elasticsearch, S3 and Deep Learning technologies. The proposed method processes both DICOM and PNG images to generate annotations. These image annotations are indexed in Elasticsearch with the corresponding raw data paths, where they can be retrieved and analyzed.
Databáze:	MEDLINE
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