Core-Shell Capsule Image Segmentation through Deep Learning with Synthetic Training Data

verfasst von

Leon Budde, Julia Dreger, Dominik Egger, Thomas Seel

Abstract

Core-shell capsules (CSC) are a promising approach for 3D cell culture because they overcome the challenges of traditional large-scale cell cultivation techniques used in tissue engineering. Currently, CSC are segmented from microscopic images in a cumbersome manual procedure to evaluate their properties, such as size or complete encapsulation of the core compartment. In this paper, we propose an automated segmentation process of CSC based on an unmodified YOLOv8 instance segmentation model. We train the model exclusively on synthetic CSC images created from 10 manually annotated real images and evaluate its performance using the common Intersection over Union (IoU) metric on a test set consisting of 181 real images. Without modifying the model or tuning the hyperparameters, we achieve a mean IoU of 0.86, underlining the potential of deep-learning-based CSC segmentation relying entirely on synthetic training data.

Organisationseinheit(en)

Institut für Mechatronische Systeme
Medizintechnik & Bildverarbeitung
Institut für Zellbiologie und Biophysik
Biofabrikation für Wirkstofftestung

Typ

Artikel

Journal

Current Directions in Biomedical Engineering

Band

10

Seiten

123–126

Anzahl der Seiten

4

Publikationsdatum

19.12.2024

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Biomedizintechnik

Elektronische Version(en)

https://doi.org/10.1515/cdbme-2024-2030 (Zugang: Offen)