Following the SWITCH/TDE tissue transformation method, a robust methodology to clear and label human brain tissue, we use Two-Photon Fluorescence Microscopy to acquire 4 samples of human brain cortex originating from subjects of different ages and health condition (young, adult and elderly, both healthy and pathological). Then, a 2.5D method based on Convolutional Neural Networks is applied to automatically segment individual neurons. The raw images acquired at the microscope and the resulting vectorial data in the form of 3D meshes are made available for further analysis (e.g. to quantitatively evaluate the density and, more importantly, the mean volume of the thousands of neurons identified within the specimens).

3D reconstruction and analysis of four human brain cortex samples with two-photon fluorescence microscopy / I. Constantini; G. Mazzamuto; M. Roffilli; A. Laurino; F. Castelli; M. Neri; G. Lughi; A. Simonetto; E. Lazzeri; L. Pesce; C. Destrieux; L. Silvestri; V. Conti; R. Guerrini; F. Pavone. - ELETTRONICO. - (2020). [10.25493/snwb-yqr]

3D reconstruction and analysis of four human brain cortex samples with two-photon fluorescence microscopy

G. Mazzamuto
;
A. Laurino;F. Castelli;L. Pesce;L. Silvestri;V. Conti;R. Guerrini;F. Pavone
2020

Abstract

Following the SWITCH/TDE tissue transformation method, a robust methodology to clear and label human brain tissue, we use Two-Photon Fluorescence Microscopy to acquire 4 samples of human brain cortex originating from subjects of different ages and health condition (young, adult and elderly, both healthy and pathological). Then, a 2.5D method based on Convolutional Neural Networks is applied to automatically segment individual neurons. The raw images acquired at the microscope and the resulting vectorial data in the form of 3D meshes are made available for further analysis (e.g. to quantitatively evaluate the density and, more importantly, the mean volume of the thousands of neurons identified within the specimens).
Goal 3: Good health and well-being
Goal 9: Industry, Innovation, and Infrastructure
I. Constantini; G. Mazzamuto; M. Roffilli; A. Laurino; F. Castelli; M. Neri; G. Lughi; A. Simonetto; E. Lazzeri; L. Pesce; C. Destrieux; L. Silvestri; V. Conti; R. Guerrini; F. Pavone
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/1215801
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