CellMincer: Self-Supervised Denoising of Functional Imaging
Name
Wang-bricelw-meng-eecs-2022-thesis.pdf
Description
Thesis PDF
Size
5.67 MB
Format
Adobe PDF
Checksum (MD5)
b8f2caf6897889313fa89d6e9aa67662
Author(s)
Wang, Brice
Advisor(s)
Babadi, Mehrtash
Uhler, Caroline
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
Abstract
All-optical electrophysiology offers accessibility and scalability in observing neuronal activity beyond what can feasibly be achieved with patch clamp techniques. However, imaging platforms like Optopatch suffer from excessive detection noise, photobleaching, and an inability to organically segment and isolate neurons of interest. These drawbacks preclude its use as a true substitute for direct electrophysiological measurement, but recent advances in deep neural network inference may enable computation to recover the difference in data quality. To date, few robust denoising algorithms have been designed and implemented for voltage imaging data, in part because the lack of ground truth imaging complicates the task of training such a model. This thesis introduces CellMincer, a self-supervised deep neural network for denoising functional imaging. By exploiting a combination of spatiotemporally local contexts and precomputed global features, CellMincer outperforms comparable algorithms at denoising several modes of optical electrophysiology on a range of metrics, including measures of biologically relevant features.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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