Neuron segmentation in epifluorescence microscopy imaging with deep learning

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

doi:10.1117/12.2621413

17 May 2022 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, Optics, Photonics and Digital Technologies for Imaging Applications VII; 1213805 (2022) https://doi.org/10.1117/12.2621413
Event: SPIE Photonics Europe, 2022, Strasbourg, France

Abstract

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.

Conference Presentation

Citation Download Citation

Fernando González, Boris Escalante-Ramírez, Jimena Olveres Montiel, José Bargas Díaz, and Miguel Serrano "Neuron segmentation in epifluorescence microscopy imaging with deep learning", Proc. SPIE 12138, Optics, Photonics and Digital Technologies for Imaging Applications VII, 1213805 (17 May 2022); https://doi.org/10.1117/12.2621413

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