No icon

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

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

Abstract:

Differential interference contrast (DIC) microscopy is widely used for observing unstained biological samples that are otherwise optically transparent. Combining this optical technique with machine vision could enable the automation of many life science experiments; however, identifying relevant features under DIC is challenging. In particular, precise tracking of cell boundaries in a thick (>100μm) slice of tissue has not previously been accomplished. We present a novel 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 incorporates a filtering mechanism to handle organic tissue interference and a  robust edge-sparsity regularizer that integrates dynamic edge tracking capabilities. As a secondary contribution, this paper 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 understand the image statistics (to improve algorithms), as well as quantitatively test cell segmentation and tracking algorithms in scenarios, where ground truth data is fully known.

Existing System:

Using machine vision to automatically segment individual cells under DIC optics in real time would be highly useful for microscopy automation of life science experiments. However, precise cell segmentation is challenging and the vast majority of existing algorithms are not directly applicable to segmentation under DIC in tissue. General purpose segmentation algorithms in the computer vision literature typically assume statistical homogeneity within (or outside) a segmentation region that would be lost under contrast-enhancing optical approaches such as DIC. While it is possible that these algorithms could be applied after application-specific preprocessing, these existing approaches are not directly applicable to the target application without being combined with deconvolution. There are additionally a number of specific cell segmentation and tracking methods that are also not directly applicable to cell segmentation under DIC in tissue.

Proposed System:

For this purpose, real-time tracking of the target cell boundary is essential. This application presents several challenges that make cell membrane localization very difficult: (1) heavy interference from the presence of organic tissue around the target cell, (2) low SNR due to scattering of light characteristic of thick tissue samples, and (3) cell motion induced by the glass probe. Extending the framework provided, our proposed algorithm is formulated as a regularized least-squares optimization that incorporates a filtering mechanism to handle organic tissue interference and a robust edge-sparsity regularizer that integrates dynamic edge-tracking capabilities.

We specifically note that the proposed algorithm is performing a deconvolution of the complex effects of DIC imaging integrated into a segmentation process and is not a direct segmentation of raw DIC images. Toward this end, our focus is specifically on cell boundary tracking instead of more typical deconvolution metrics such as least-square image reconstruction. Ancillary to our main contribution, this paper 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.

Comment As:

Comment (0)