Year: 2024
Type: article
Abstract: The emerging usage of digitalized histopathological images is leading to a novel possibility for data analysis. With the help of artificial intelligence algorithms, it is now possible to detect certain structures and morphological features on whole slide images automatically. This enables algorithms to count, measure, or evaluate those areas when trained properly. To achieve suitable training, datasets must be annotated and curated by users in programs like QuPath. The extraction of this data for artificial intelligence algorithms is still rather tedious and needs to be saved on a local hard drive. We developed a toolkit for integration into existing pipelines and tools, like U-net, for the on-the-fly extraction of annotation tiles from existing QuPath projects. The tiles can be directly used as input for artificial intelligence algorithms, and the results are directly transferred back to QuPath for visual inspection. With the toolkit, we created a convenient way to incorporate QuPath into existing AI workflows.
Source: Journal of Imaging
Institutions: Justus-Liebig-Universität Gießen, Medical University of Graz
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,046
Awards: G1688000358, G2614417093, MIRACUM - Medizininformatik-Konsortium - Nachwuchsgruppe KI-gestützte morphomolekulare Präzisions-Medizin in Neuroonkologie (AI-RON): Universität Gießen, G7731084911, PM4Onco - Medizininformatik-Use Case 'Personalisierte Medizin für die Onkologie' - Teilvorhaben: Integration Krebsregisterdaten, erweiterte Analytik, Roll-out, Koordinierung und Öffentlichkeitsarbeit: UK Gießen, G8904012666
OpenAlex ID: W4404415313