Automatic 3D cell segmentation of fruit parenchyma tissue from X-ray micro CT images using deep learning.

ABSTRACT:

Background

High quality 3D information of the microscopic plant tissue morphology-the spatial organization of cells and intercellular spaces in tissues-helps in understanding physiological processes in a wide variety of plants and tissues. X-ray micro-CT is a valuable tool that is becoming increasingly available in plant research to obtain 3D microstructural information of the intercellular pore space and individual pore sizes and shapes of tissues. However, individual cell morphology is difficult to retrieve from micro-CT as cells cannot be segmented properly due to negligible density differences at cell-to-cell interfaces. To address this, deep learning-based models were trained and tested to segment individual cells using X-ray micro-CT images of parenchyma tissue samples from a

SUBMITTER: Van Doorselaer L 

PROVIDER: S-EPMC10799452 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature