Precision Cell Boundary Tracking on DIC Microscopy Video for Patch Clamping (bibtex)
by and
Abstract:
One method of patch clamping on brain tissue slices \emphin vitro requires a human operator to visually track a cell's boundary and delicately make contact on 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.
Reference:
Precision Cell Boundary Tracking on DIC Microscopy Video for Patch ClampingJ. Lee and C.J. Rozell. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2017.
Bibtex Entry:
@INPROCEEDINGS{lee.16,
  author = {Lee, J. and Rozell, C.J.},
  title = {Precision Cell Boundary Tracking on {DIC} Microscopy Video for Patch Clamping},
  booktitle = {{Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}},
  year = {2017},
  address = {New Orleans, LA},
  month = mar,
  abstract = {One method of patch clamping on brain tissue slices \emph{in vitro} requires a human operator to visually track a cell's boundary and delicately make contact on 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.}
  }
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