Panoptic segmentation for complete labeling of fruit microstructure in 3D micro-CT images with deep learning.

ABSTRACT: Metabolic processes in plant organs involving transport of water, metabolic gasses, and nutrients depend on the three-dimensional (3D) microscopic tissue morphology. However, imaging and quantifying this microstructure, including the spatial layout of parenchyma cells, pores, vascular bundles and special features such as stone cell clusters (brachysclereids), is challenging. To address this, a 3D deep learning-based panoptic segmentation model, combining semantic and instance segmentation, was developed to accelerate and improve microstructure characterization of apple and pear fruit tissue in X-ray micro-computed tomography (CT) images. In addition, various training datasets and data augmentation techniques, including synthetic data, were explored to enhance segmentation quality. The 3D p

SUBMITTER: Van Doorselaer L 

PROVIDER: S-EPMC12709988 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature