Deep Learning Based Cell Segmentation of 3D Confocal Images of Wood for Enzymatic Hydrolysis Applications

An adapted 3D U-net trained with artificial longitudinal domain restriction reduces segmentation errors in partially imaged wood cells from 3D confocal microscopy during early enzymatic hydrolysis.

• Why it matters: Wood cell-wall recalcitrance limits enzymatic hydrolysis efficiency, and reliable morphological biomarkers require accurate 3D cell segmentation despite confocal penetration leaving open-ended structures.
• What they did: The framework trains a 3D U-net for wood cell segmentation using artificial longitudinal domain restriction, then is quantitatively evaluated on a dedicated benchmark dataset.
• The result: A selected model configuration reduces segmentation errors and yields more robust predictions during early enzymatic hydrolysis, enabling quantification of morphology linked to biomass conversion efficiency.