Abstract
The automated in vitro segmentation of axonal phase-contrast images to allow axonal tracing over time is highly desirable to understand axonal biology in the context of health and disease. While deep learning has become a powerful tool in biomedical image analysis for semantic segmentation tasks, segmentation performance has been limited so far since axons are long and thin objects that are sensitive to under- and/or over-segmentation. We here propose the use of an ensemble-based convolutional neural network (CNN) framework for the segmentation of axons on phase-contrast microscopic images. The mean ResNet-50 ensemble performed better than the max u-net ensemble on the axon segmentation task. We estimated an upper limit for the expected improvement using an oracle-machine. Additionally, we introduced a soft version of the Dice coefficient that describes the visually perceived quality of axon segmentation better than the standard Dice. Importantly, the mean ResNet-50 ensemble reached the performance level of human experts. Taken together, we developed a CNN to robustly segment axons in phase-contrast microscopy that will foster further investigations of axonal biology in health and disease.
Originalsprache | Englisch |
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Titel | MobiHealth 2020: Wireless Mobile Communication and Healthcare |
Seitenumfang | 11 |
Band | 362 |
Herausgeber (Verlag) | Springer, Cham |
Erscheinungsdatum | 2021 |
Seiten | 274-284 |
ISBN (Print) | 978-3-030-70568-8 |
ISBN (elektronisch) | 978-3-030-70569-5 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2021 |
Veranstaltung | 9th EAI International Conference on Wireless Mobile Communication and Healthcare - Virtual Event Dauer: 19.11.2020 → 19.11.2020 |
Strategische Forschungsbereiche und Zentren
- Zentren: Zentrum für Künstliche Intelligenz Lübeck (ZKIL)
- Querschnittsbereich: Intelligente Systeme
DFG-Fachsystematik
- 4.43-05 Bild- und Sprachverarbeitung, Computergraphik und Visualisierung, Human Computer Interaction, Ubiquitous und Wearable Computing