Dr. Miguel Serrano Profile

Dr. Miguel Serrano

at Univ Nacional Autónoma de México

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Proceedings Article | 17 May 2022 Presentation + Paper

Neuron segmentation in epifluorescence microscopy imaging with deep learning

Fernando González, Boris Escalante-Ramírez, Jimena Olveres Montiel, José Bargas Díaz, Miguel Serrano

Proceedings Volume 12138, 1213805 (2022) https://doi.org/10.1117/12.2621413

KEYWORDS: Neurons, Video, Image segmentation, Video acceleration, Microscopy, Luminescence, Data modeling, Action potentials, Calcium, Tissues

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Epifluorescence Microscopy Imaging is a technique used by neuroscientists for observation of hundreds of neurons at the same time, with single-cell resolution and low cost from living tissue. Recording, identifying and tracking neurons and their activity in those observations is a crucial step for researching. However, manual identification of neurons is a hardworking task as well as prone to errors. For this reason, automatized applications to process the recordings to identify functional neurons are required. Several proposals have emerged; they can be classified in four kinds of approaches: 1) matrix factorization, 2) clustering, 3) dictionary learning and 4) deep learning. Unfortunately, they have resulted inadequate to solve this problem. In fact, it remains as an open problem; two major reasons are: 1) lack of datasets duly labeled and 2) existing approaches do not consider the temporal dimension or just consider a tiny fraction of it, integrating all the frames in a single image is very common but inefficient because temporal dynamics are disregarded. We propose an application for automatic segmentation of neurons with a Deep Learning approach, considering temporal dimension through recurrent neural networks and using a dataset labeled by neuroscientists. Additional aspects considered in our proposal include motion correction and validation to ensure that segmentations correspond to truly functional neurons. Furthermore, we compare this application with a previous proposal which uses sophisticated digital image processing techniques on the same dataset.

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