Red blood cell phenotyping from 3D confocal images using artificial neural networks.

ABSTRACT: The investigation of cell shapes mostly relies on the manual classification of 2D images, causing a subjective and time consuming evaluation based on a portion of the cell surface. We present a dual-stage neural network architecture for analyzing fine shape details from confocal microscopy recordings in 3D. The system, tested on red blood cells, uses training data from both healthy donors and patients with a congenital blood disease, namely hereditary spherocytosis. Characteristic shape features are revealed from the spherical harmonics spectrum of each cell and are automatically processed to create a reproducible and unbiased shape recognition and classification. The results show the relation between the particular genetic mutation causing the disease and the shape profile. With the obtai

SUBMITTER: Simionato G 

PROVIDER: S-EPMC8118337 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature