Abstract
Quantitative analysis of brain cytoarchitecture requires effective and efficient segmentation of the raw images. This task is highly demanding from an algorithmic point of view, because of the inherent variations of contrast and intensity in the different areas of the specimen, and of the very large size of the datasets to be processed. Here, we report a machine vision approach based on Convolutional Neural Networks (CNN) for the near real-time segmentation of neurons in three-dimensional images with high specificity and sensitivity. This instrument, together with high-throughput sample preparation and imaging, can lay the basis for a quantitative revolution in neuroanatomical studies.
G. Mazzamuto and I. Costantini—Contributed equally to this work.
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Acknowledgements
We thank Prof. Katrin Amunts from the Institute of Neuroscience and Medicine, Research Centre Jülich, Germany, for providing human brain samples used in this study. This project received funding from the European Union’s H2020 research and innovation programme under grant agreements No. 720270 (Human Brain Project) and 654148 (Laserlab-Europe), and from the EU programme H2020 EXCELLENT SCIENCE - European Research Council (ERC) under grant agreement n. 692943 (BrainBIT). The project is also supported by the Italian Ministry for Education, University, and Research in the framework of the Flagship Project NanoMAX and of Eurobioimaging Italian Nodes (ESFRI research infrastructure), and by “Ente Cassa di Risparmio di Firenze” (private foundation).
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Mazzamuto, G., Costantini, I., Neri, M., Roffilli, M., Silvestri, L., Pavone, F.S. (2018). Automatic Segmentation of Neurons in 3D Samples of Human Brain Cortex. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. https://doi.org/10.1007/978-3-319-77538-8_6
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