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17 results on '"Ferraris, Sebastiano"'

1. Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas

Author

Casamitjana, Adrià, Lorenzi, Marco, Ferraris, Sebastiano, Peter, Loc, Modat, Marc, Stevens, Allison, Fischl, Bruce, Vercauteren, Tom, and Iglesias, Juan Eugenio

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Joint registration of a stack of 2D histological sections to recover 3D structure (``3D histology reconstruction'') finds application in areas such as atlas building and validation of \emph{in vivo} imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as ``banana effect'' (straightening of curved structures) and ``z-shift'' (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. The framework is used for accurate 3D reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions. Moreover, we also provide the registration of the reconstructed volume to MNI space, bridging the gaps between two of the most widely used atlases in histology and MRI. The 3D reconstructed volumes and atlas registration can be downloaded from https://openneuro.org/datasets/ds003590. The code is freely available at https://github.com/acasamitjana/3dhirest., Comment: Medical Image Analysis (Accepted on 07/10/2021). 22 pages, 11 figures

Published
2021
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2. Image computing tools for the investigation of the neurological effects of preterm birth and corticosteroid administration

Author

Ferraris, Sebastiano, Vercauteren, T., Modat, M., and Deprest, J.

Subjects
618.92
Abstract

In this thesis we present a range of computational tools for medical imaging purposes within two main research projects. The first one is a methodological project oriented towards the improvement of the performance of a numerical computation utilised in diffeomorphic image registration. The second research project is a pre-clinical study aimed at the investigation of the effects of antenatal corticosteroids in a preterm rabbit animal model. In the first part we addressed the problem of integrating stationary velocity fields. This mathematical challenge had originated with early studies in fluid dynamics and had been subsequently mathematically formalised in the Lie group theory. Given a tangent velocity field defined in the tridimensional space as in input, the goal is to compute the position of the particles to which the velocity field is applied. This computation, also called numerical Lie exponential, is a fundamental component of several medical image registration algorithm based on diffeomorphisms, i.e. bijective differentiable maps with differentiable inverse. It is as well a widely utilised tool in computational anatomy to quantify the differences between two anatomical shapes measuring the parameters of the transformation that belongs to a metric vector space. The resulting new class of algorithms introduced in this thesis was created combining the known scaling and squaring algorithm with a class of numerical integrators aimed to solve systems of ordinary differential equations called exponential integrators. The introduced scaling and squaring based approximated exponential integrator algorithm have improved the computational time and accuracy respect to the state- of-the-art methods. The second part of the research is a pre-clinical trial carried forward in collab- oration with the Department of Development and Regeneration, Woman and Child Cluster at the KU Leuven University. The clinical research question is related to the understanding of the possible negative effects of administering antenatal cor- ticosteroids for preterm birth. To tackle this problem we designed and started a pre-clinical study using a New Zealand perinatal rabbit model. In this part of the research I was involved in the research team to provide the tools to automatise the data analysis and to eliminate the time consuming and non reproducible manual segmentation step. The main result of this collaboration is the creation of the first multi-modal multi-atlas for the newborn rabbit brain. This is embedded in a segmentation propagation and label fusion algorithm at the core of the proposed open-sourced automatic pipeline, having as input the native scanner format and as output the main MRI readouts, such as volume, fractional anisotropy and mean diffusivity.

Published
2019

3. Counting sub-multisets of fixed cardinality

Author

Ferraris, Sebastiano, Mendelson, Alex, Ballesio, Gerardo, and Vercauteren, Tom

Subjects
Mathematics - Combinatorics, 05A19
Abstract

This report presents an expression for the number of a multiset's sub-multisets of a given cardinality as a function of the multiplicity of its elements. This is also the number of distinct samples of a given size that may be produced by sampling without replacement from a finite population partitioned into subsets, in the case where items belonging to the same subset are considered indistinguishable. Despite the generality of this problem, we have been unable to find this result published elsewhere., Comment: 9 pages

Published
2015

4. Model-Based Refinement of Nonlinear Registrations in 3D Histology Reconstruction

Author

Iglesias, Juan Eugenio, Lorenzi, Marco, Ferraris, Sebastiano, Peter, Loïc, Modat, Marc, Stevens, Allison, Fischl, Bruce, Vercauteren, Tom, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Frangi, Alejandro F., editor, Schnabel, Julia A., editor, Davatzikos, Christos, editor, Alberola-López, Carlos, editor, and Fichtinger, Gabor, editor

Published
2018
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5. Template-Free Estimation of Intracranial Volume: A Preterm Birth Animal Model Study

Author

Iglesias, Juan Eugenio, Ferraris, Sebastiano, Modat, Marc, Gsell, Willy, Deprest, Jan, van der Merwe, Johannes L., Vercauteren, Tom, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Cardoso, M. Jorge, editor, Arbel, Tal, editor, Melbourne, Andrew, editor, Bogunovic, Hrvoje, editor, Moeskops, Pim, editor, Chen, Xinjian, editor, Schwartz, Ernst, editor, Garvin, Mona, editor, Robinson, Emma, editor, Trucco, Emanuele, editor, Ebner, Michael, editor, Xu, Yanwu, editor, Makropoulos, Antonios, editor, Desjardin, Adrien, editor, and Vercauteren, Tom, editor

Published
2017
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13. Publisher Correction:Early neuropathological and neurobehavioral consequences of preterm birth in a rabbit model (Scientific Reports, (2019), 9, 1, (3506), 10.1038/s41598-019-39922-8)

Author

van der Merwe, Johannes, van der Veeken, Lennart, Ferraris, Sebastiano, Gsell, Willy, Himmelreich, Uwe, Toelen, Jaan, Ourselin, Sebastien, Melbourne, Andrew, Vercauteren, Tom, and Deprest, Jan

Abstract

In the original version of this Article, the ORCID IDs for the authors Johannes van der Merwe, Lennart van der Veeken, Sebastiano Ferraris, Jaan Toelen and Jan Deprest were omitted. This has now been corrected in the HTML and PDF versions of the Article.

Published
2019
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14. Bruker2nifti: Magnetic Resonance Images converter from Bruker

Author

Ferraris, Sebastiano, Shakir, Dzhoshkun Ismail, van der Merwe, Hannes, Gsell, Willy, Deprest, Jan, and Vercauteren, Tom

Abstract

In clinical and pre-clinical research involving medical images, the first step following a Magnetic Resonance Imaging dataset acquisition, usually entails the conversion of image data from the native scanner format to a format suitable for the intended analysis. The proprietary Bruker ParaVision software currently does not provide the tools for conversion of the data to suitable and open formats for research, such as nifti (Cox, Robert W and Ashburner, John and Breman, Hester and Fissell, Kate and Haselgrove, Christian and Holmes, Colin J and Lancaster, Jack L and Rex, David E and Smith, Stephen M and Woodward, Jeffrey B and others 2004), for which most of the available tools for medical image analysis are implemented. For this purpose we have designed and developed bruker2nifti, a pip-installable Python tool provided with a Graphical User Interface to convert from the native MRI Bruker format to the nifti format, without any intermediate step through the DICOM standard formats (Mildenberger, Eichelberg, and Martin 2002). Bruker2nifti is intended to be a tool to access the data structure and to parse all parameter files of the Bruker ParaVision format into python dictionaries, to select the relevant information to fill the Nifti header and data volume. Lastly it is meant to be a starting point where to integrate possible future variations in Bruker hardware and ParaVision software future releases. ispartof: Journal of Open Source Software vol:2 issue:16 status: accepted

Published
2017

17. A magnetic resonance multi-atlas for the neonatal rabbit brain

Author

Ferraris, Sebastiano, Van Der Merwe, Johannes, van Der Veeken, Lennart, Prados, Ferran, Iglesias, Juan Eugenio, Lorenzi, Marco, Melbourne, Andrew, Modat, Marc, Gsell, Willy, Deprest, Jan, Vercauteren, Tom, Translational imaging group [London] (TIG), Centre for Medical Image Computing (CMIC), University College of London [London] (UCL)-University College of London [London] (UCL)-Department of Medical Physics and Biomedical Engineering (UCL), University College of London [London] (UCL), Department of Development and Regeneration, Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE), Department of Imaging and Pathology [Leuven], Institute for Women's Health [London], and University College London Hospitals (UCLH)

Subjects
[STAT.AP]Statistics [stat]/Applications [stat.AP], [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Newborn rabbit brain animal model, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [SDV.MHEP.PSM]Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [SDV.MHEP.AHA]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], MRI multi-atlas, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Multi-atlas based segmentation
Abstract

International audience; Keywords: Newborn rabbit brain animal model MRI multi-atlas Multi-atlas based segmentation A B S T R A C T The rabbit model has become increasingly popular in neurodevelopmental studies as it is best suited to bridge the gap in translational research between small and large animals. In the context of preclinical studies, high-resolution magnetic resonance imaging (MRI) is often the best modality to investigate structural and functional variability of the brain, both in vivo and ex vivo. In most of the MRI-based studies, an important requirement to analyze the acquisitions is an accurate parcellation of the considered anatomical structures. Manual segmentation is time-consuming and typically poorly reproducible, while state-of-the-art automated segmentation algorithms rely on available atlases. In this work we introduce the first digital neonatal rabbit brain atlas consisting of 12 multi-modal acquisitions, parcellated into 89 areas according to a hierarchical taxonomy. Delineations were performed iteratively, alternating between segmentation propagation, label fusion and manual refinements, with the aim of controlling the quality while minimizing the bias introduced by the chosen sequence. Reliability and accuracy were assessed with cross-validation and intra-and inter-operator test-retests. Multi-atlas, versioned controlled segmentations repository and supplementary materials download links are available from the software repository documentation at https://github.com/gift-surg/SPOT-A-NeonatalRabbit.

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