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Your search keyword '"Ferrante, Enzo"' showing total 7 results
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7 results on '"Ferrante, Enzo"'

2. ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture.

Author

Gaggion N, Ariel F, Daric V, Lambert É, Legendre S, Roulé T, Camoirano A, Milone DH, Crespi M, Blein T, and Ferrante E

Subjects
Phenotype, Plant Roots, Plants, Artificial Intelligence, Neural Networks, Computer
Abstract

Background: Deep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system that combines 3D-printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium., Results: We developed a novel deep learning-based root extraction method that leverages the latest advances in convolutional neural networks for image segmentation and incorporates temporal consistency into the root system architecture reconstruction process. Automatic extraction of phenotypic parameters from sequences of images allowed a comprehensive characterization of the root system growth dynamics. Furthermore, novel time-associated parameters emerged from the analysis of spectral features derived from temporal signals., Conclusions: Our work shows that the combination of machine intelligence methods and a 3D-printed device expands the possibilities of root high-throughput phenotyping for genetics and natural variation studies, as well as the screening of clock-related mutants, revealing novel root traits., (© The Author(s) 2021. Published by Oxford University Press GigaScience.)

Published
2021
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3. CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images.

Author

Gaggion, Nicolás, Mosquera, Candelaria, Mansilla, Lucas, Saidman, Julia Mariel, Aineseder, Martina, Milone, Diego H., and Ferrante, Enzo

Subjects
X-rays, X-ray imaging, ARTIFICIAL intelligence, QUALITY control, SCIENTIFIC community, AUTOMATIC control systems
Abstract

The development of successful artificial intelligence models for chest X-ray analysis relies on large, diverse datasets with high-quality annotations. While several databases of chest X-ray images have been released, most include disease diagnosis labels but lack detailed pixel-level anatomical segmentation labels. To address this gap, we introduce an extensive chest X-ray multi-center segmentation dataset with uniform and fine-grain anatomical annotations for images coming from five well-known publicly available databases: ChestX-ray8, CheXpert, MIMIC-CXR-JPG, Padchest, and VinDr-CXR, resulting in 657,566 segmentation masks. Our methodology utilizes the HybridGNet model to ensure consistent and high-quality segmentations across all datasets. Rigorous validation, including expert physician evaluation and automatic quality control, was conducted to validate the resulting masks. Additionally, we provide individualized quality indices per mask and an overall quality estimation per dataset. This dataset serves as a valuable resource for the broader scientific community, streamlining the development and assessment of innovative methodologies in chest X-ray analysis. [ABSTRACT FROM AUTHOR]

Published
2024
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4. Quantitative Comparison of Monte-Carlo Dropout Uncertainty Measures for Multi-class Segmentation

Author

Camarasa, Robin, Bos, Daniel, Hendrikse, Jeroen, Nederkoorn, Paul, Kooi, Eline, van der Lugt, Aad, de Bruijne, Marleen, Sudre, Carole H., Fehri, Hamid, Arbel, Tal, Baumgartner, Christian F., Dalca, Adrian, Tanno, Ryutaro, Van Leemput, Koen, Wells, William M., Sotiras, Aristeidis, Papiez, Bartlomiej, Ferrante, Enzo, Parisot, Sarah, Radiology & Nuclear Medicine, Epidemiology, Neurology, ACS - Atherosclerosis & ischemic syndromes, ANS - Neurovascular Disorders, RS: Carim - B06 Imaging, Beeldvorming, and MUMC+: DA BV Klinisch Fysicus (9)

Subjects
Artificial neural network, Computer science, business.industry, Deep learning, Bayesian probability, Monte Carlo method, Pattern recognition, Segmentation, Image segmentation, Artificial intelligence, business, Image resolution, Ensemble learning
Abstract

Over the past decade, deep learning has become the gold standard for automatic medical image segmentation. Every segmentation task has an underlying uncertainty due to image resolution, annotation protocol, etc. Therefore, a number of methods and metrics have been proposed to quantify the uncertainty of neural networks mostly based on Bayesian deep learning, ensemble learning methods or output probability calibration. The aim of our research is to assess how reliable the different uncertainty metrics found in the literature are. We propose a quantitative and statistical comparison of uncertainty measures based on the relevance of the uncertainty map to predict misclassification. Four uncertainty metrics were compared over a set of 144 models. The application studied is the segmentation of the lumen and vessel wall of carotid arteries based on multiple sequences of magnetic resonance (MR) images in multi-center data.

Published
2020

5. Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics

Author

Cardoso, Jorge, Arbel, Tal, Ferrante, Enzo, Pennec, Xavier, Adrian V., Dalca, Parisot, Sarah, Joshi, Sarang, Batmanghelich, Nematollah, Sotiras, Aristeidis, Nielsen, Mads, Sabuncu, Mert, Tom, Fletcher, Shen, Li, Durrleman, Stanley, Sommer, Stefan, University College of London [London] (UCL), McGill University = Université McGill [Montréal, Canada], Imperial College London, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), 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), Harvard Medical School [Boston] (HMS), University of Utah, University of Pennsylvania, University of Copenhagen = Københavns Universitet (UCPH), Cornell University [New York], Indiana University [Bloomington], Indiana University System, Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Jorge Cardoso, Tal Arbel, Mert Sabuncu, Fletcher Tom, Li Shen, Stanley Durrleman, Stefan Sommer, Enzo Ferrante, Xavier Pennec, Dalca Adrian V., Sarah Parisot, Sarang Joshi, Nematollah Batmanghelich, Aristeidis Sotiras, Mads Nielsen, University of Pennsylvania [Philadelphia], University of Copenhagen = Københavns Universitet (KU), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], and Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Inria de Paris

Subjects
Signal processing, Artificial intelligence, Learning systems, Clustering algorithms, Geometry, Classification, Image analysis, Medical images, Cluster analysis, Bayesian networks, Image processing, Pattern recognition, Feature selection, Image reconstruction, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Computer vision, Medical imaging, Face recognition, Data mining, Neural networks
Abstract

International audience; This book constitutes the refereed joint proceedings of the First International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2017, the 6th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2017, and the Third International Workshop on Imaging Genetics, MICGen 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017.

Published
2017

6. Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis.

Author

Larrazabal, Agostina J., Nieto, Nicolás, Peterson, Victoria, Milone, Diego H., and Ferrante, Enzo

Subjects
DIAGNOSTIC imaging, PHYSICIANS, GENDER, ARTIFICIAL intelligence, SCIENTIFIC community
Abstract

Artificial intelligence (AI) systems for computer-aided diagnosis and image-based screening are being adopted worldwide by medical institutions. In such a context, generating fair and unbiased classifiers becomes of paramount importance. The research community of medical image computing is making great efforts in developing more accurate algorithms to assist medical doctors in the difficult task of disease diagnosis. However, little attention is paid to the way databases are collected and how this may influence the performance of AI systems. Our study sheds light on the importance of gender balance in medical imaging datasets used to train AI systems for computer-assisted diagnosis. We provide empirical evidence supported by a large-scale study, based on three deep neural network architectures and two well-known publicly available X-ray image datasets used to diagnose various thoracic diseases under different gender imbalance conditions. We found a consistent decrease in performance for underrepresented genders when a minimum balance is not fulfilled. This raises the alarm for national agencies in charge of regulating and approving computer-assisted diagnosis systems, which should include explicit gender balance and diversity recommendations. We also establish an open problem for the academic medical image computing community which needs to be addressed by novel algorithms endowed with robustness to gender imbalance. [ABSTRACT FROM AUTHOR]

Published
2020
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7. Discrete symmetric image registration

Author

Aristeidis Sotiras, Nikos Paragios, Ferrante, Enzo, Mathématiques Appliquées aux Systèmes - EA 4037 (MAS), Ecole Centrale Paris, Organ Modeling through Extraction, Representation and Understanding of Medical Image Content (GALEN), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Ecole Centrale Paris, imagine [Marne-la-Vallée], Laboratoire d'Informatique Gaspard-Monge (LIGM), Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS)-Centre Scientifique et Technique du Bâtiment (CSTB), Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS), Université Paris-Est Marne-la-Vallée (UPEM), École des Ponts ParisTech (ENPC), Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec, Ecole Centrale Paris-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre Scientifique et Technique du Bâtiment (CSTB)-École des Ponts ParisTech (ENPC)-Laboratoire d'Informatique Gaspard-Monge (LIGM), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM)-Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-Université Paris-Est Marne-la-Vallée (UPEM), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM), and Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM)-Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM)-Centre Scientifique et Technique du Bâtiment (CSTB)

Subjects
Random field, Markov chain, Linear programming, business.industry, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Image registration, Markov process, 02 engineering and technology, 030218 nuclear medicine & medical imaging, Image (mathematics), 03 medical and health sciences, symbols.namesake, 0302 clinical medicine, Discrete optimization, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, 0202 electrical engineering, electronic engineering, information engineering, symbols, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, Image resolution, Algorithm, Mathematics
Abstract

International audience; Image registration is in principle a symmetric problem. Nonetheless, most intensity-based non-rigid algorithms are asymmetric. In this paper, we propose a novel symmetric deformable registration algorithm formulated in a Markov Random Fields framework where both images are let to deform towards a common domain that lies halfway between two image domains. A grid-based deformation model is employed and the latent variables correspond to the displacements of the grid-nodes towards both image domains. First-order interactions between the unknown variables model standard smoothness priors. Efficient linear programming is consider to recover the optimal solution. The discrete nature of our algorithm allows the handling of both mono- and multi-modal registration problems. Promising experimental results demonstrate the potentials of our approach.

Published
2012

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