Segmentation Analyses to Identify and Quantify Microglia in 3D Time Series Image Stacks
Abstract
Using time-lapse confocal microscopy to record and observe microglia behavior in living zebrafish embryos, the Mitchell lab investigates the molecular basis of dynamic migration and phagocytic behavior of microglia in the central nervous system. Microglial cells regulate brain development, maintain neuronal networks, and repair neural injuries. Timelapse imaging provides crucial insight into the behavior of these cells. Other methods, such as manual cell counting, are subject to human bias and are tedious, repetitive, and time-consuming. The Long computer science lab utilizes Python and open-source modules to develop an automated pipeline as a programmatic solution. These methods aim to expedite and optimize the lab’s manual processes. This research project seeks to segment microglia when applied to 3D time series image stacks. Computer segmentation of microglia imaging relies on pixel values to generate bitmasks. Microglia segmentation is challenging because the cell’s irregular shape and size can be obscured, resulting in omitted pixel intensity values. Two programs were developed to test the viability of Otsu, multi-Otsu, and Yen automatic thresholding. Both methods separate the raw image into two layers: the foreground and background. Numerical data is generated via histograms to predict optimal threshold values for each algorithm. Prototype implementations of these programs demonstrate viability for microglia counting. However, misclassification of microglia occurs when the predicted pixel threshold is outside the histogram’s range. Further refinement of these programs is crucial as they will be foundational for future object classification methods and microglia tracking, optimizing the lab’s data processing capabilities and time.
Segmentation Analyses to Identify and Quantify Microglia in 3D Time Series Image Stacks
Using time-lapse confocal microscopy to record and observe microglia behavior in living zebrafish embryos, the Mitchell lab investigates the molecular basis of dynamic migration and phagocytic behavior of microglia in the central nervous system. Microglial cells regulate brain development, maintain neuronal networks, and repair neural injuries. Timelapse imaging provides crucial insight into the behavior of these cells. Other methods, such as manual cell counting, are subject to human bias and are tedious, repetitive, and time-consuming. The Long computer science lab utilizes Python and open-source modules to develop an automated pipeline as a programmatic solution. These methods aim to expedite and optimize the lab’s manual processes. This research project seeks to segment microglia when applied to 3D time series image stacks. Computer segmentation of microglia imaging relies on pixel values to generate bitmasks. Microglia segmentation is challenging because the cell’s irregular shape and size can be obscured, resulting in omitted pixel intensity values. Two programs were developed to test the viability of Otsu, multi-Otsu, and Yen automatic thresholding. Both methods separate the raw image into two layers: the foreground and background. Numerical data is generated via histograms to predict optimal threshold values for each algorithm. Prototype implementations of these programs demonstrate viability for microglia counting. However, misclassification of microglia occurs when the predicted pixel threshold is outside the histogram’s range. Further refinement of these programs is crucial as they will be foundational for future object classification methods and microglia tracking, optimizing the lab’s data processing capabilities and time.