Abstract
While astrocytes have been traditionally described as passive supportive cells, studies during the last decade have shown they are active players in many aspects of CNS physiology and function both in normal and disease states. However, the precise mechanisms regulating astrocytes function and interactions within the CNS are still poorly understood. This knowledge gap is due in large part to the limitations of current image analysis tools that cannot process astrocyte images efficiently and to the lack of methods capable of quantifying their complex morphological characteristics. To provide an unbiased and accurate framework for the quantitative analysis of fluorescent images of astrocytes, we introduce a new automated image processing pipeline whose main novelties include an innovative module for cell detection based on multiscale directional filters and a segmentation routine that leverages deep learning and sparse representations to reduce the need of training data and improve performance. Extensive numerical tests show that our method performs very competitively with respect to state-of-the-art methods also in challenging images where astrocytes are clustered together. Our code is released open source and freely available to the scientific community.
Highlights
While astrocytes have been traditionally described as passive supportive cells, studies during the last decade have shown they are active players in many aspects of CNS physiology and function both in normal and disease states
Automated analysis of microscopy images of astrocytes is challenging due to their large variability in size and shapes, the complex topology of processes occurring over multiple scales and the highly entangled nature of networks formed by such cells
We used a collection of 5 GFAP-stained fluorescent images of mice astrocyte cells in the cortex collected by the laboratory of Dr Tang from the Department of Neuroscience and Cell Biology at the University of Texas Medical Branch
Summary
While astrocytes have been traditionally described as passive supportive cells, studies during the last decade have shown they are active players in many aspects of CNS physiology and function both in normal and disease states. Astrocytes have been shown to reflect their diverse abilities and functions on their special structural design, and alterations in astrocyte morphology are known to correlate to traumatic brain injury, infection, ischemia, autoimmune responses, and neurodegenerative diseases[5,7,8] Their intricate arborization and ramifications allow them to enwrap synaptic terminals and modulate synaptic processes. Authors show that reconstructed traces are useful to define quantitative arbor measurements via Scorcioni’s L-measures[15], a rich collection of neuroanatomical parameters for the quantitative characterization of neuronal morphology This method does not generate voxel-level segmentation and does not address the problem of cell separation. The recent work by Suleymanova et al.[17] introduces a method for astrocyte detection based on a CNN that provides very accurate result even though training requires a large number of training samples This method does not address the problem of segmentation
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