Engineered nanomaterials play an even more relevant role in nanotechnology advances. However, care must be taken due to their suspected detrimental effects on human cells. Such alterations can be monitored through Atomic Force Microscopy (AFM) equipment and image digitalization. With the purpose to depict a metrological compliant scenario, a novel vision-based evaluation system is proposed with an evaluation unit based on a deep learning architecture. Inspired by the recent trends in trying to extend the standard concept of quantities to nominal properties and measurement to evaluation, we proposed here a platform for the evaluation of morphological alterations in AFM images of human cells exposed to different concentrations of carbon nanotubes. Results reveal the feasibility to automatically investigate such alterations with the aim to improve occupational medicine protocols and cells cataloguing procedures.
Mencattini, A., Casti, P., Di Giuseppe, D., Callari, G., Salmeri, M., Bertazzoni, S., et al. (2018). A Deep Learning Strategy for Vision-Based Evaluation on the Effect of Nanoparticles Exposure. In MeMeA 2018 - 2018 IEEE International Symposium on Medical Measurements and Applications, Proceedings (pp.1-5). Institute of Electrical and Electronics Engineers Inc. [10.1109/MeMeA.2018.8438633].
A Deep Learning Strategy for Vision-Based Evaluation on the Effect of Nanoparticles Exposure
Mencattini A.;Casti P.;Callari G.;Salmeri M.;Bertazzoni S.;Martinelli E.;Sammarco I.;Pietroiusti A.;Magrini A.;
2018-01-01
Abstract
Engineered nanomaterials play an even more relevant role in nanotechnology advances. However, care must be taken due to their suspected detrimental effects on human cells. Such alterations can be monitored through Atomic Force Microscopy (AFM) equipment and image digitalization. With the purpose to depict a metrological compliant scenario, a novel vision-based evaluation system is proposed with an evaluation unit based on a deep learning architecture. Inspired by the recent trends in trying to extend the standard concept of quantities to nominal properties and measurement to evaluation, we proposed here a platform for the evaluation of morphological alterations in AFM images of human cells exposed to different concentrations of carbon nanotubes. Results reveal the feasibility to automatically investigate such alterations with the aim to improve occupational medicine protocols and cells cataloguing procedures.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
