A Deep Learning Approach for Neuronal Cell Body Segmentation in Neurons Expressing GCaMP Using a Swin Transformer.

Autor:	Islam MS; School of Data Science, The University of Virginia, Charlottesville, VA 22903., Suryavanshi P; Department of Pediatrics, Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA 52242., Baule SM; Department of Biomedical Engineering, The University of Iowa, Iowa City, IA 52242., Glykys J; Department of Pediatrics, Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA 52242 baek@virginia.edu joseph-glykys@uiowa.edu.; Department of Neurology, The University of Iowa, Iowa City, IA 52242., Baek S; School of Data Science, The University of Virginia, Charlottesville, VA 22903 baek@virginia.edu joseph-glykys@uiowa.edu.
Jazyk:	angličtina
Zdroj:	ENeuro [eNeuro] 2023 Sep 26; Vol. 10 (9). Date of Electronic Publication: 2023 Sep 26 (Print Publication: 2023).
DOI:	10.1523/ENEURO.0148-23.2023
Abstrakt:	Neuronal cell body analysis is crucial for quantifying changes in neuronal sizes under different physiological and pathologic conditions. Neuronal cell body detection and segmentation mainly rely on manual or pseudo-manual annotations. Manual annotation of neuronal boundaries is time-consuming, requires human expertise, and has intra/interobserver variances. Also, determining where the neuron's cell body ends and where the axons and dendrites begin is taxing. We developed a deep-learning-based approach that uses a state-of-the-art shifted windows (Swin) transformer for automated, reproducible, fast, and unbiased 2D detection and segmentation of neuronal somas imaged in mouse acute brain slices by multiphoton microscopy. We tested our Swin algorithm during different experimental conditions of low and high signal fluorescence. Our algorithm achieved a mean Dice score of 0.91, a precision of 0.83, and a recall of 0.86. Compared with two different convolutional neural networks, the Swin transformer outperformed them in detecting the cell boundaries of GCamP6s expressing neurons. Thus, our Swin transform algorithm can assist in the fast and accurate segmentation of fluorescently labeled neuronal cell bodies in thick acute brain slices. Using our flexible algorithm, researchers can better study the fluctuations in neuronal soma size during physiological and pathologic conditions. Competing Interests: The authors declare no competing financial interests. (Copyright © 2023 Islam et al.)
Databáze:	MEDLINE
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