One of the most challenging frontiers in biological systems understanding is fluorescent label-free imaging. We present here the NeuriTES platform that revisits the standard paradigms of video analysis to detect unlabeled objects and adapt to the dynamic evolution of the phenomenon under observation. Object segmentation is reformulated using robust algorithms to assure regular cell detection and transfer entropy measures are used to study the inter-relationship among the parameters related to the evolving system. We applied the NeuriTES platform to the automatic analysis of neurites degeneration in presence of amyotrophic lateral sclerosis (ALS) and to the study of the effects of a chemotherapy drug on living prostate cancer cells (PC3) cultures. Control cells have been considered in both the two cases study. Accuracy values of 93% and of 92% are achieved, respectively. NeuriTES not only represents a tool for investigation in fluorescent label-free images but demonstrates to be adaptable to individual needs.

Mencattini, A., Spalloni, A., Casti, P., Comes, M.c., Di Giuseppe, D., Antonelli, G., et al. (2021). NeuriTES. Monitoring neurite changes through transfer entropy and semantic segmentation in bright-field time-lapse microscopy. PATTERNS, 2(6) [10.1016/j.patter.2021.100261].

NeuriTES. Monitoring neurite changes through transfer entropy and semantic segmentation in bright-field time-lapse microscopy

Mencattini A.;Casti P.;Di Natale C.;Martinelli E.
2021-01-01

Abstract

One of the most challenging frontiers in biological systems understanding is fluorescent label-free imaging. We present here the NeuriTES platform that revisits the standard paradigms of video analysis to detect unlabeled objects and adapt to the dynamic evolution of the phenomenon under observation. Object segmentation is reformulated using robust algorithms to assure regular cell detection and transfer entropy measures are used to study the inter-relationship among the parameters related to the evolving system. We applied the NeuriTES platform to the automatic analysis of neurites degeneration in presence of amyotrophic lateral sclerosis (ALS) and to the study of the effects of a chemotherapy drug on living prostate cancer cells (PC3) cultures. Control cells have been considered in both the two cases study. Accuracy values of 93% and of 92% are achieved, respectively. NeuriTES not only represents a tool for investigation in fluorescent label-free images but demonstrates to be adaptable to individual needs.
2021
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore ING-INF/07 - MISURE ELETTRICHE ED ELETTRONICHE
English
ALS disease
bright-field time-lapse microscopy
cancer cell treatment
DSML 2: Proof-of-Concept: Data science output has been formulated, implemented, and tested for one domain/problem
semantic segmentation
transfer entropy
video analysis
Mencattini, A., Spalloni, A., Casti, P., Comes, M.c., Di Giuseppe, D., Antonelli, G., et al. (2021). NeuriTES. Monitoring neurite changes through transfer entropy and semantic segmentation in bright-field time-lapse microscopy. PATTERNS, 2(6) [10.1016/j.patter.2021.100261].
Mencattini, A; Spalloni, A; Casti, P; Comes, Mc; Di Giuseppe, D; Antonelli, G; D'Orazio, M; Filippi, J; Corsi, F; Isambert, H; Di Natale, C; Longone, ...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/289499
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