Deep Learning for Automated Analysis of Cellular and Extracellular Components of the Foreign Body Response in Multiphoton Microscopy Images

Authors

Mattia Sarti
Maria Parlani
Luis Diaz-Gomez
Antonios G Mikos
Pietro Cerveri
Stefano Casarin
Eleonora Dondossola

Publication Date

1-1-2021

Journal

Frontiers in Bioengineering and Biotechnology

Abstract

The Foreign body response (FBR) is a major unresolved challenge that compromises medical implant integration and function by inflammation and fibrotic encapsulation. Mice implanted with polymeric scaffolds coupled to intravital non-linear multiphoton microscopy acquisition enable multiparametric, longitudinal investigation of the FBR evolution and interference strategies. However, follow-up analyses based on visual localization and manual segmentation are extremely time-consuming, subject to human error, and do not allow for automated parameter extraction. We developed an integrated computational pipeline based on an innovative and versatile variant of the U-Net neural network to segment and quantify cellular and extracellular structures of interest, which is maintained across different objectives without impairing accuracy. This software for automatically detecting the elements of the FBR shows promise to unravel the complexity of this pathophysiological process.

Keywords

foreign body response, deep learning, U-Net, intravital multiphoton microscopy, image analysis

DOI

10.3389/fbioe.2021.797555

PMID

35145962

PMCID

PMC8822221

PubMedCentral® Posted Date

1-25-2022

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes