ArticleData in brief2026
Experimentally paired high- and low-resolution confocal fluorescence microscopy dataset for deep-learning super-resolution imaging of tooth dentin porosity.
Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The proposed dataset provides experimentally acquired high- and low-resolution confocal laser scanning microscopy images of dentin porosity, split into classified image patches that can be used for paired or unpaired super-resolution training. Porous dentinal tubule and branch channels represent a vast network and can be used as a model for the mechanosensory system of teeth. The large quantity and small size of tubules and branches present an imaging challenge, requiring high resolution images at a large field of view in order to image statistically relevant regions of the tooth and fully model the porosity network. Imaging more quickly increases the potential of the field of view that can be acquired, however in return decreases resolution. Deep-learning based super-resolution could be used on low-resolution images to restore high resolution information from the image. A fluorescently stained tooth slice was first imaged with 100×100 nm
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