CellMincer: Self-Supervised Denoising of Functional Imaging
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...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/145032 |
| _version_ | 1826195063244324864 |
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| author | Wang, Brice |
| author2 | Babadi, Mehrtash |
| author_facet | Babadi, Mehrtash Wang, Brice |
| author_sort | Wang, Brice |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T10:06:19Z |
| format | Thesis |
| id | mit-1721.1/145032 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T10:06:19Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1450322022-08-30T03:51:06Z CellMincer: Self-Supervised Denoising of Functional Imaging Wang, Brice Babadi, Mehrtash Uhler, Caroline Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 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. M.Eng. 2022-08-29T16:28:30Z 2022-08-29T16:28:30Z 2022-05 2022-05-27T16:18:19.603Z Thesis https://hdl.handle.net/1721.1/145032 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Wang, Brice CellMincer: Self-Supervised Denoising of Functional Imaging |
| title | CellMincer: Self-Supervised Denoising of Functional Imaging |
| title_full | CellMincer: Self-Supervised Denoising of Functional Imaging |
| title_fullStr | CellMincer: Self-Supervised Denoising of Functional Imaging |
| title_full_unstemmed | CellMincer: Self-Supervised Denoising of Functional Imaging |
| title_short | CellMincer: Self-Supervised Denoising of Functional Imaging |
| title_sort | cellmincer self supervised denoising of functional imaging |
| url | https://hdl.handle.net/1721.1/145032 |
| work_keys_str_mv | AT wangbrice cellmincerselfsuperviseddenoisingoffunctionalimaging |