Parameter Estimation in FRAP using Deep Learning
| dc.contributor.author | Wåhlstrand Skärström, Victor | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för fysik | sv |
| dc.contributor.examiner | Granath, Mats | |
| dc.contributor.supervisor | Röding, Magnus | |
| dc.date.accessioned | 2020-01-23T07:07:35Z | |
| dc.date.available | 2020-01-23T07:07:35Z | |
| dc.date.issued | 2019 | sv |
| dc.date.submitted | 2019 | |
| dc.description.abstract | Fluorescence recovery after photobleaching (FRAP) is a method used in optical microscopy for determining properties of diffusion in organic and inorganic solutions, including cells, membranes and gels. FRAP may be used to determine parameters such as the diffusion coefficient and binding rates of particles in a sample, and is used in wide-ranging disciplines such as the medicine, soft materials and food science. In FRAP, fluorescently labelled particles in a sample are bleached in a region of interest using a high-intensity laser. The recovery of the mean fluorescence intensity in the region of interest is known as the recovery curve. Conventional methods for inference rely on least squares and models for the recovery curve, but recent work has come to use the entire spatio-temporal image data for estimation. In this work, we have implemented a set of deep neural network architectures for estimating parameters such as the diffusion coefficient in FRAP. This is to our knowledge a novel approach with some potential advantages over conventional methods. We have implemented a set of both spatio-temporal and purely temporal neural network models, where operating on the full image data gives the best performance in terms of error on simulated data. The downsampler neural network model is easy to implement and parallels the extraction of the recovery curve, and can be trained from numerically simulated data. We show that the downsampling neural network can be trained on limited computational resources, using the combined power of continuously generating training data and batch-mode optimization. The neural networks demonstrate a robustness against noise and computational speed unlike the conventional least squares methods. The performance of the neural networks versus the conventional methods is tested on simulated FRAP data and finally validated on experimental data, yielding good agreement with the expected values of the parameters and those obtained from the conventional methods. | sv |
| dc.identifier.coursecode | TIFX05 | sv |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/300656 | |
| dc.language.iso | eng | sv |
| dc.setspec.uppsok | PhysicsChemistryMaths | |
| dc.subject | confocal microscopy | sv |
| dc.subject | diffusion | sv |
| dc.subject | fluorescence recovery after photobleaching | sv |
| dc.subject | deep learning | sv |
| dc.subject | machine learning | sv |
| dc.subject | neural networks | sv |
| dc.title | Parameter Estimation in FRAP using Deep Learning | sv |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.uppsok | H | |
| local.programme | Complex adaptive systems (MPCAS), MSc |