Precision cell boundary tracking on DIC microscopy video for patch clamping

Abstract

One method of patch clamping on brain tissue slices in vitro requires a human operator to visually track a cell's boundary and delicately make contact with a cell's membrane using a micropipette's tip. This type of patch clamping may be automated with computer vision methods; yet this is challenging since it requires precision cell-boundary tracking in the presence of heavy noise and interference. In this work, we present a cell-boundary tracking computer vision system which employs a novel deconvolution algorithm specifically created for this application. The deconvolution algorithm was designed to exploit static and dynamic structure in the cell's edges using a reweighted edge-sparsity prior. Quantitative results on simulated data demonstrate the superiority of the proposed algorithm against previous state-of-the-art algorithms. Lastly, the algorithm is applied on real patch clamping video data and qualitative results are discussed.