ArticleScientific reports2021
ANMAF: an automated neuronal morphology analysis framework using convolutional neural networks.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 24 citations in OpenAlex.
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- A Deep Learning Approach for Neuronal Cell Body Segmentation in Neurons Expressing GCaMP Using a Swin Transformer.eNeuro · 2023Article
- Sensitivity of CNN image analysis to multifaceted measurements of neurite growth.BMC bioinformatics · 2023Article
- Role of NKCC1 and KCC2 during hypoxia-induced neuronal swelling in the neonatal neocortex.Neurobiology of disease · 2023Article
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Authors and funding
4 authors at 1 institution in 1 country.
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Abstract
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.
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