Deep Learning-Based Segmentation of Post-Mortem Human's Olfactory Bulb Structures in X-ray Phase-Contrast Tomography.

ABSTRACT: The human olfactory bulb (OB) has a laminar structure. The segregation of cell populations in the OB image poses a significant challenge because of indistinct boundaries of the layers. Standard 3D visualization tools usually have a low resolution and cannot provide the high accuracy required for morphometric analysis. X-ray phase contrast tomography (XPCT) offers sufficient resolution and contrast to identify single cells in large volumes of the brain. The numerous microanatomical structures detectable in XPCT image of the OB, however, greatly complicate the manual delineation of OB neuronal cell layers. To address the challenging problem of fully automated segmentation of XPCT images of human OB morphological layers, we propose a new pipeline for tomographic data processing. Convolutional

SUBMITTER: Meshkov A 

PROVIDER: S-EPMC9331385 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature