ANMAF: an automated neuronal morphology analysis framework using convolutional neural networks

Measurement of neuronal size is challenging due to their complex histology. Current practice includes manual or pseudo-manual measurement of somatic areas, which is labor-intensive and prone to human biases and intra-/inter-observer variances. We developed a novel high-throughput neuronal morphology...

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Bibliographic Details
Published in:Scientific reports 2021-04, Vol.11 (1), p.8179-8179, Article 8179
Main Authors: Tong, Ling, Langton, Rachel, Glykys, Joseph, Baek, Stephen
Format: Article
Language:English
Subjects:
631/114/116
631/114/1564
Brain slice preparation
Histology
Humanities and Social Sciences
Morphology
multidisciplinary
Neural networks
Neuroimaging
Quantitative research
Science
Science (multidisciplinary)
Segmentation
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Summary:Measurement of neuronal size is challenging due to their complex histology. Current practice includes manual or pseudo-manual measurement of somatic areas, which is labor-intensive and prone to human biases and intra-/inter-observer variances. We developed a novel high-throughput neuronal morphology analysis framework (ANMAF), using convolutional neural networks (CNN) to automatically contour the somatic area of fluorescent neurons in acute brain slices. Our results demonstrate considerable agreements between human annotators and ANMAF on detection, segmentation, and the area of somatic regions in neurons expressing a genetically encoded fluorophore. However, in contrast to humans, who exhibited significant variability in repeated measurements, ANMAF produced consistent neuronal contours. ANMAF was generalizable across different imaging protocols and trainable even with a small number of humanly labeled neurons. Our framework can facilitate more rigorous and quantitative studies of neuronal morphology by enabling the segmentation of many fluorescent neurons in thick brain slices in a standardized manner.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-87471-w