MOTH: Memory-Efficient On-the-Fly Tiling of Histological Image Annotations Using QuPath

dc.contributor.authorKauer, Thomas
dc.contributor.authorSehring, Jannik
dc.contributor.authorSchmid, Kai
dc.contributor.authorBartkuhn, Marek
dc.contributor.authorWiebach, Benedikt
dc.contributor.authorCrnkovic, Slaven
dc.contributor.authorKwapiszewska, Grazyna
dc.contributor.authorAcker, Till
dc.contributor.authorAmsel, Daniel
dc.date.accessioned2024-12-12T10:56:34Z
dc.date.available2024-12-12T10:56:34Z
dc.date.issued2024
dc.description.abstractThe 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.en
dc.identifier.urihttps://jlupub.ub.uni-giessen.de/handle/jlupub/20058
dc.identifier.urihttps://doi.org/10.22029/jlupub-19413
dc.language.isoen
dc.rightsNamensnennung 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddcddc:610
dc.titleMOTH: Memory-Efficient On-the-Fly Tiling of Histological Image Annotations Using QuPath
dc.typearticle
local.affiliationFB 11 - Medizin
local.source.articlenumber292
local.source.epage10
local.source.journaltitleJournal of imaging
local.source.number11
local.source.spage1
local.source.urihttps://doi.org/10.3390/jimaging10110292
local.source.volume10

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