Cell Membrane Tracking in Living Brain Tissue Using Differential Interference Contrast Microscopy - 2018


Differential interference distinction (DIC) microscopy is widely used for observing unstained biological samples that are otherwise optically transparent. Combining this optical technique with machine vision might enable the automation of the many life science experiments; but, identifying relevant options beneath DIC is difficult. In particular, precise tracking of cell boundaries in a thick (>100µm) slice of tissue has not previously been accomplished. We present a completely unique deconvolution algorithm that achieves the state-of-the-art performance at identifying and tracking these membrane locations. Our proposed algorithm is formulated as a regularized least squares optimization that includes a filtering mechanism to handle organic tissue interference and a sturdy edge-sparsity regularizer that integrates dynamic edge tracking capabilities. As a secondary contribution, this Project also describes new community infrastructure in the form of a MATLAB toolbox for accurately simulating DIC microscopy images of in vitro brain slices. Building on existing DIC optics modeling, our simulation framework additionally contributes an accurate representation of interference from organic tissue, neuronal cell-shapes, and tissue motion due to the action of the pipette. This simulator allows us to better perceive the image statistics (to boost algorithms), as well as quantitatively check cell segmentation and tracking algorithms in eventualities, where ground truth information is totally known.

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