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Research paper thumbnail of High-resolution human brain 3D reconstruction with light-sheet fluorescence microscopy

Neural Imaging and Sensing 2023

Research paper thumbnail of 3D molecular phenotyping of the human brain Broca’s area using light-sheet fluorescence microscopy

Biomedical Spectroscopy, Microscopy, and Imaging II

Research paper thumbnail of Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data

Frontiers in Neuroscience

The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving... more The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. First developed for magnetic resonance imaging (MRI) datasets, the community-led specification evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, and quantitative MRI (qMRI). In this work, we present an extension to BIDS for microscopy imaging data, along with example datasets. Microscopy-BIDS supports common imaging methods, including 2D/3D, ex/in vivo, micro-CT, and optical and electron microscopy. Microscopy-BIDS also includes comprehensible metadata definitions for hardware, image acquisition, and sample properties. This extension will facilitate future harmonization efforts in the context of multi-modal, multi-scale imaging such as the characterization of tissue microstructure with qMRI.

Research paper thumbnail of 3D CNN Methods in Biomedical Image Segmentation

A definite trend in Biomedical Imaging is the one towards the integration of increasingly complex... more A definite trend in Biomedical Imaging is the one towards the integration of increasingly complex interpretative layers to the pure data acquisition process. One of the most researched goals in the field is the automatic segmentation of objects of interest in extensive acquisition data, target that would allow Biomedical Imaging to look beyond its main use as a diagnostic-aid tool to become a cornerstone in ambitious large-scale challenges like the extensive quantitative study of the Human Brain. In 2019 Convolutional Neural Networks represent the state of the art in Biomedical Image segmentation and scientific interests from a variety of fields, spacing from automotive to natural resource exploration, converge to their development. While most of the applications of CNNs are focused on single-image segmentation, biomedical image data-being it MRI, CT-scans, Microscopy, etc-often benefits from threedimensional volumetric expression. This work explores a reformulation of the CNN segmentation problem that is native to the 3D nature of the data, with particular interest to the applications to Fluorescence Microscopy volumetric data produced at the European Laboratories for Nonlinear Spectroscopy in the context of two different large international human brain study projects: the Human Brain Project and the White House BRAIN Initiative. in Image Segmentation Chapter 1 Deep Learning and Introductory Concepts The paradigm for intelligence was logical reasoning, and the idea of what an internal representation would look like was it would be some kind of symbolic structure. That has completely changed with these big neural nets.

Research paper thumbnail of Supplementary document for Large-scale, cell-resolution volumetric analysis allows layer-specific investigation of human brain cytoarchitecture - 5159214.pdf

Research paper thumbnail of Broca's Area Light-Sheet Microscopy

Example dataset containing light-sheet selective plane illumination microscopy (SPIM) data to ill... more Example dataset containing light-sheet selective plane illumination microscopy (SPIM) data to illustrate BIDS convention.<br> BIDS version 1.6.0 - Microscopy BEP031 version 0.0.5 (2021-09-07T14:20:00)

Research paper thumbnail of Semantic Segmentation of Neuronal Bodies in Fluorescence Microscopy Using a 2D+3D CNN Training Strategy with Sparsely Annotated Data

Machine Learning, Optimization, and Data Science, 2020

Semantic segmentation of neuronal structures in 3D highresolution fluorescence microscopy imaging... more Semantic segmentation of neuronal structures in 3D highresolution fluorescence microscopy imaging of the human brain cortex can take advantage of bidimensional CNNs, which yield good results in neuron localization but lead to inaccurate surface reconstruction. 3D CNNs on the other hand would require manually annotated volumetric data on a large scale, and hence considerable human effort. Semisupervised alternative strategies which make use only of sparse annotations suffer from longer training times and achieved models tend to have increased capacity compared to 2D CNNs, needing more ground truth data to attain similar results. To overcome these issues we propose a two-phase strategy for training native 3D CNN models on sparse 2D annotations where missing labels are inferred by a 2D CNN model and combined with manual annotations in a weighted manner during loss calculation.

Research paper thumbnail of A combined pipeline for quantitative analysis of human brain cytoarchitecture

bioRxiv, 2020

The 3D analysis of the human brain architecture at cellular resolution is still a big challenge. ... more The 3D analysis of the human brain architecture at cellular resolution is still a big challenge. In this work, we propose a pipeline that solves the problem of performing neuronal mapping in large human brain samples at micrometer resolution. First, we introduce the SWITCH/TDE protocol: a robust methodology to clear and label human brain tissue. Then, we implement the 2.5D method based on a Convolutional Neural Network, to automatically detect and segment all neurons. Our method proved to be highly versatile and was applied successfully on specimens from different areas of the cortex originating from different subjects (young, adult and elderly, both healthy and pathological). We quantitatively evaluate the density and, more importantly, the mean volume of the thousands of neurons identified within the specimens. In conclusion, our pipeline makes it possible to study the structural organization of the brain and expands the histopathological studies to the third dimension.

Research paper thumbnail of A multimodal imaging and analysis pipeline for creating a cellular census of the human cerebral cortex

Cells are not uniformly distributed in the human cerebral cortex. Rather, they are arranged in a ... more Cells are not uniformly distributed in the human cerebral cortex. Rather, they are arranged in a regional and laminar fashion that span a range of scales. Here we demonstrate an innovative imaging and analysis pipeline to construct a reliable cell census across the human cerebral cortex. Magnetic resonance imaging (MRI) is used to establish a macroscopic reference coordinate system of laminar and cytoarchitectural boundaries. Cell counting is obtained with both traditional immunohistochemistry, to provide a stereological gold-standard, and with a custom-made inverted confocal light-sheet fluorescence microscope (LSFM) for 3D imaging at cellular resolution. Finally, mesoscale optical coherence tomography (OCT) enables the registration of the distorted histological cell typing obtained with LSFM to the MRI-based atlas coordinate system.

Research paper thumbnail of Large-scale, cell-resolution volumetric mapping allows layer-specific investigation of human brain cytoarchitecture

Biomedical Optics Express, 2021

Although neuronal density analysis on human brain slices is available from stereological studies,... more Although neuronal density analysis on human brain slices is available from stereological studies, data on the spatial distribution of neurons in 3D are still missing. Since the neuronal organization is very inhomogeneous in the cerebral cortex, it is critical to map all neurons in a given volume rather than relying on sparse sampling methods. To achieve this goal, we implement a new tissue transformation protocol to clear and label human brain tissues and we exploit the high-resolution optical sectioning of two-photon fluorescence microscopy to perform 3D mesoscopic reconstruction. We perform neuronal mapping of 100mm3 human brain samples and evaluate the volume and density distribution of neurons from various areas of the cortex originating from different subjects (young, adult, and elderly, both healthy and pathological). The quantitative evaluation of the density in combination with the mean volume of the thousands of neurons identified within the specimens, allow us to determine...

Research paper thumbnail of High-resolution human brain 3D reconstruction with light-sheet fluorescence microscopy

Neural Imaging and Sensing 2023

Research paper thumbnail of 3D molecular phenotyping of the human brain Broca’s area using light-sheet fluorescence microscopy

Biomedical Spectroscopy, Microscopy, and Imaging II

Research paper thumbnail of Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data

Frontiers in Neuroscience

The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving... more The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. First developed for magnetic resonance imaging (MRI) datasets, the community-led specification evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, and quantitative MRI (qMRI). In this work, we present an extension to BIDS for microscopy imaging data, along with example datasets. Microscopy-BIDS supports common imaging methods, including 2D/3D, ex/in vivo, micro-CT, and optical and electron microscopy. Microscopy-BIDS also includes comprehensible metadata definitions for hardware, image acquisition, and sample properties. This extension will facilitate future harmonization efforts in the context of multi-modal, multi-scale imaging such as the characterization of tissue microstructure with qMRI.

Research paper thumbnail of 3D CNN Methods in Biomedical Image Segmentation

A definite trend in Biomedical Imaging is the one towards the integration of increasingly complex... more A definite trend in Biomedical Imaging is the one towards the integration of increasingly complex interpretative layers to the pure data acquisition process. One of the most researched goals in the field is the automatic segmentation of objects of interest in extensive acquisition data, target that would allow Biomedical Imaging to look beyond its main use as a diagnostic-aid tool to become a cornerstone in ambitious large-scale challenges like the extensive quantitative study of the Human Brain. In 2019 Convolutional Neural Networks represent the state of the art in Biomedical Image segmentation and scientific interests from a variety of fields, spacing from automotive to natural resource exploration, converge to their development. While most of the applications of CNNs are focused on single-image segmentation, biomedical image data-being it MRI, CT-scans, Microscopy, etc-often benefits from threedimensional volumetric expression. This work explores a reformulation of the CNN segmentation problem that is native to the 3D nature of the data, with particular interest to the applications to Fluorescence Microscopy volumetric data produced at the European Laboratories for Nonlinear Spectroscopy in the context of two different large international human brain study projects: the Human Brain Project and the White House BRAIN Initiative. in Image Segmentation Chapter 1 Deep Learning and Introductory Concepts The paradigm for intelligence was logical reasoning, and the idea of what an internal representation would look like was it would be some kind of symbolic structure. That has completely changed with these big neural nets.

Research paper thumbnail of Supplementary document for Large-scale, cell-resolution volumetric analysis allows layer-specific investigation of human brain cytoarchitecture - 5159214.pdf

Research paper thumbnail of Broca's Area Light-Sheet Microscopy

Example dataset containing light-sheet selective plane illumination microscopy (SPIM) data to ill... more Example dataset containing light-sheet selective plane illumination microscopy (SPIM) data to illustrate BIDS convention.<br> BIDS version 1.6.0 - Microscopy BEP031 version 0.0.5 (2021-09-07T14:20:00)

Research paper thumbnail of Semantic Segmentation of Neuronal Bodies in Fluorescence Microscopy Using a 2D+3D CNN Training Strategy with Sparsely Annotated Data

Machine Learning, Optimization, and Data Science, 2020

Semantic segmentation of neuronal structures in 3D highresolution fluorescence microscopy imaging... more Semantic segmentation of neuronal structures in 3D highresolution fluorescence microscopy imaging of the human brain cortex can take advantage of bidimensional CNNs, which yield good results in neuron localization but lead to inaccurate surface reconstruction. 3D CNNs on the other hand would require manually annotated volumetric data on a large scale, and hence considerable human effort. Semisupervised alternative strategies which make use only of sparse annotations suffer from longer training times and achieved models tend to have increased capacity compared to 2D CNNs, needing more ground truth data to attain similar results. To overcome these issues we propose a two-phase strategy for training native 3D CNN models on sparse 2D annotations where missing labels are inferred by a 2D CNN model and combined with manual annotations in a weighted manner during loss calculation.

Research paper thumbnail of A combined pipeline for quantitative analysis of human brain cytoarchitecture

bioRxiv, 2020

The 3D analysis of the human brain architecture at cellular resolution is still a big challenge. ... more The 3D analysis of the human brain architecture at cellular resolution is still a big challenge. In this work, we propose a pipeline that solves the problem of performing neuronal mapping in large human brain samples at micrometer resolution. First, we introduce the SWITCH/TDE protocol: a robust methodology to clear and label human brain tissue. Then, we implement the 2.5D method based on a Convolutional Neural Network, to automatically detect and segment all neurons. Our method proved to be highly versatile and was applied successfully on specimens from different areas of the cortex originating from different subjects (young, adult and elderly, both healthy and pathological). We quantitatively evaluate the density and, more importantly, the mean volume of the thousands of neurons identified within the specimens. In conclusion, our pipeline makes it possible to study the structural organization of the brain and expands the histopathological studies to the third dimension.

Research paper thumbnail of A multimodal imaging and analysis pipeline for creating a cellular census of the human cerebral cortex

Cells are not uniformly distributed in the human cerebral cortex. Rather, they are arranged in a ... more Cells are not uniformly distributed in the human cerebral cortex. Rather, they are arranged in a regional and laminar fashion that span a range of scales. Here we demonstrate an innovative imaging and analysis pipeline to construct a reliable cell census across the human cerebral cortex. Magnetic resonance imaging (MRI) is used to establish a macroscopic reference coordinate system of laminar and cytoarchitectural boundaries. Cell counting is obtained with both traditional immunohistochemistry, to provide a stereological gold-standard, and with a custom-made inverted confocal light-sheet fluorescence microscope (LSFM) for 3D imaging at cellular resolution. Finally, mesoscale optical coherence tomography (OCT) enables the registration of the distorted histological cell typing obtained with LSFM to the MRI-based atlas coordinate system.

Research paper thumbnail of Large-scale, cell-resolution volumetric mapping allows layer-specific investigation of human brain cytoarchitecture

Biomedical Optics Express, 2021

Although neuronal density analysis on human brain slices is available from stereological studies,... more Although neuronal density analysis on human brain slices is available from stereological studies, data on the spatial distribution of neurons in 3D are still missing. Since the neuronal organization is very inhomogeneous in the cerebral cortex, it is critical to map all neurons in a given volume rather than relying on sparse sampling methods. To achieve this goal, we implement a new tissue transformation protocol to clear and label human brain tissues and we exploit the high-resolution optical sectioning of two-photon fluorescence microscopy to perform 3D mesoscopic reconstruction. We perform neuronal mapping of 100mm3 human brain samples and evaluate the volume and density distribution of neurons from various areas of the cortex originating from different subjects (young, adult, and elderly, both healthy and pathological). The quantitative evaluation of the density in combination with the mean volume of the thousands of neurons identified within the specimens, allow us to determine...