Evaluating Techniques for Neuron Identification in Complex Cultures: A Deep Learning Approach

• Puerta, Paula ^[1] ; Ozturk, Berke ^[1] ; González, Víctor M. ^[1] ; Villar, José R. ^[1] ; Serrano Pertierra, Esther ^[1] ; Antonello Novelli ^[1] ; M. Teresa Fernández-Sánchez ^[1] ; Ángel Río-Álvarez ^[1]
  1. [1] Universidad de Oviedo

     Universidad de Oviedo

     Oviedo, España

     
• Localización: CASEIB 2023. Libro de Actas del XLI Congreso Anual de la Sociedad Española de Ingeniería Biomédica: Contribuyendo a la salud basada en valor / coord. por Joaquín Roca González, Dolores Ojados González , Juan Suardíaz Muro , 2023, ISBN 978-84-17853-76-1, págs. 464-467
• Idioma: inglés
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• Resumen

  • Microscopy image analysis of neurons cultures represents a formidable challenge due to their complex structure because of the dynamic nature of neurite tissue development, the neuron movement, the morphological changes, and the pres- ence of many elements in the cultures apart from neurons such as glial cells, dead cells, vesicles, etc. A rigorous eval- uation of deep learning techniques to address this intricate problem is undertaken in this study. Several methodolo- gies, including Instance Segmentation and Object Detection models, are scrutinized within a comprehensive experimen- tal framework. The efficacy of the Instance Segmentation model is underscored by the findings, demonstrating superior quantitative results. Precise neuron quantification is facili- tated by this model through the detection of bounding boxes in images, thereby enabling the automation of tasks such as morphological and size analysis of neuronal cells and track- ing individual neurons across ...

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