Cell quantification at the osteochondral interface from synchrotron radiation phase contrast micro-computed tomography images using a deep learning approach.

ABSTRACT: Osteochondral interface consists of two tissues: the calcified cartilage (CC) containing chondrocytes, and subchondral bone (SCB) containing osteocytes that interact with each other. In this study, we propose a new method for the three-dimensional (3D) segmentation of chondrocyte and osteocyte lacunae in CC and SCB from human knees, imaged using high resolution (650 nm) synchrotron radiation phase contrast micro-computed tomography (SR phase contrast micro-CT). Our approach is based on marker-controlled watershed (MCW) algorithm combined with a deep learning method (nnU-Net). We demonstrate that incorporating nnU-Net into the MCW process improves the identification and segmentation of cell lacunae. Using this method, we analyzed a subsample of fifteen cores extracted from the central area

SUBMITTER: Xu H 

PROVIDER: S-EPMC11604923 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature