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Your search keyword '"OPtimisation Imagerie et Santé (OPIS)"' showing total 3 results
Start Over Author "OPtimisation Imagerie et Santé (OPIS)" Journal diagnostic and interventional imaging
3 results on '"OPtimisation Imagerie et Santé (OPIS)"'

1. Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications

Author

Maxime Lacroix, Theodore Aouad, Jean Feydy, David Biau, Frédérique Larousserie, Laure Fournier, Antoine Feydy, Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay, OPtimisation Imagerie et Santé (OPIS), 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 de vision numérique (CVN), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-CentraleSupélec-Université Paris-Saclay

Subjects
Radiological and Ultrasound Technology, Reproducibility of Results, [SDV.CAN]Life Sciences [q-bio]/Cancer, General Medicine, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Medical Oncology, Prognosis, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [SDV.MHEP.RSOA]Life Sciences [q-bio]/Human health and pathology/Rhumatology and musculoskeletal system, Artificial Intelligence, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Radiology, Nuclear Medicine and imaging
Abstract

Artificial intelligence (AI) is increasingly being studied in musculoskeletal oncology imaging. AI has been applied to both primary and secondary bone tumors and assessed for various predictive tasks that include detection, segmentation, classification, and prognosis. Still, in the field of clinical research, further efforts are needed to improve AI reproducibility and reach an acceptable level of evidence in musculoskeletal oncology. This review describes the basic principles of the most common AI techniques, including machine learning, deep learning and radiomics. Then, recent developments and current results of AI in the field of musculoskeletal oncology are presented. Finally, limitations and future perspectives of AI in this field are discussed.

Published
2022
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2. Deep learning for lung disease segmentation on CT: Which reconstruction kernel should be used?

Author

Guillaume Chassagnon, Marie-Pierre Revel, Maria Vakalopoulou, Trieu-Nghi Hoang-Thi, Nikos Paragios, Stergios Christodoulidis, UFR Médecine [Santé] - Université Paris Cité (UFR Médecine UPCité), Université Paris Cité (UPCité), OPtimisation Imagerie et Santé (OPIS), 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 de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-CentraleSupélec-Université Paris-Saclay, Mathématiques et Informatique pour la Complexité et les Systèmes (MICS), CentraleSupélec-Université Paris-Saclay, TheraPanacea [Paris], Hôpital Cochin [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)

Subjects
Coronavirus disease 2019 (COVID-19), Wilcoxon signed-rank test, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Deep Learning, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Image Processing, Computer-Assisted, Humans, Radiology, Nuclear Medicine and imaging, Segmentation, Reconstruction kernel, Lung, ComputingMilieux_MISCELLANEOUS, Radiological and Ultrasound Technology, business.industry, SARS-CoV-2, Deep learning, Interstitial lung disease, COVID-19, General Medicine, medicine.disease, Lung disease, Kernel (statistics), Artificial intelligence, business, Nuclear medicine, Tomography, X-Ray Computed
Abstract

The purpose of this study was to determine whether a single reconstruction kernel or both high and low frequency kernels should be used for training deep learning models for the segmentation of diffuse lung disease on chest computed tomography (CT).Two annotated datasets of COVID-19 pneumonia (323,960 slices) and interstitial lung disease (ILD) (4,284 slices) were used. Annotated CT images were used to train a U-Net architecture to segment disease. All CT slices were reconstructed using both a lung kernel (LK) and a mediastinal kernel (MK). Three different trainings, resulting in three different models were compared for each disease: training on LK only, MK only or LK+MK images. Dice similarity scores (DSC) were compared using the Wilcoxon signed-rank test.Models only trained on LK images performed better on LK images than on MK images (median DSC = 0.62 [interquartile range (IQR): 0.54, 0.69] vs. 0.60 [IQR: 0.50, 0.70], P0.001 for COVID-19 and median DSC = 0.62 [IQR: 0.56, 0.69] vs. 0.50 [IQR 0.43, 0.57], P0.001 for ILD). Similarly, models only trained on MK images performed better on MK images (median DSC = 0.62 [IQR: 0.53, 0.68] vs. 0.54 [IQR: 0.47, 0.63], P0.001 for COVID-19 and 0.69 [IQR: 0.61, 0.73] vs. 0.63 [IQR: 0.53, 0.70], P0.001 for ILD). Models trained on both kernels performed better or similarly than those trained on only one kernel. For COVID-19, median DSC was 0.67 (IQR: =0.59, 0.73) when applied on LK images and 0.67 (IQR: 0.60, 0.74) when applied on MK images (P0.001 for both). For ILD, median DSC was 0.69 (IQR: 0.63, 0.73) when applied on LK images (P = 0.006) and 0.68 (IQR: 0.62, 0.72) when applied on MK images (P0.99).Reconstruction kernels impact the performance of deep learning-based models for lung disease segmentation. Training on both LK and MK images improves the performance.

Published
2021
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3. Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning

Author

Thomas Caramella, Imad Bousaid, Jean-Yves Airaud, Zoubir M Bensalah, Nicolas Gogin, Caroline Dam Hieu, Mario Viti, Yann Diascorn, Mickaël Ohana, Nathalie Lassau, Bassam Abdallah, Umit Gencer, Elie Mousseaux, Marc-Samir Guillot, Luc Nicodème, Hugues Talbot, Magloire Mekukosokeng, General Electric Medical Systems [Buc] (GE Healthcare), General Electric Medical Systems, OPtimisation Imagerie et Santé (OPIS), 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 de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-CentraleSupélec-Université Paris-Saclay, Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), CentraleSupélec, Paris-Centre de Recherche Cardiovasculaire (PARCC (UMR_S 970/ U970)), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Centre Hospitalier [Douai, Nord], Centre Hospitalier Universitaire de Nice (CHU Nice), Institut Arnault Tzanck, Centre Hospitalier Saint Jean de Perpignan, Département d'imagerie médicale [Gustave Roussy], Institut Gustave Roussy (IGR), LaBoratoire d'Imagerie biOmédicale MultimodAle Paris-Saclay (BIOMAPS), Service Hospitalier Frédéric Joliot (SHFJ), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace (FMNGE), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPC), Unité BioMaps (BIOMAPS), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Imaging Department Gustave Roussy, Université Paris Saclay, 94805 Villejuif, France, Direction de Recherche Fondamentale (CEA) (DRF (CEA)), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Cardiac Volume, X-ray computed, Coronary Artery Disease, 030204 cardiovascular system & hematology, Convolutional neural network, 030218 nuclear medicine & medical imaging, Multidetector computed tomography, Coronary artery disease, 03 medical and health sciences, Electrocardiography, 0302 clinical medicine, Deep Learning, Artificial Intelligence, Mitral valve, medicine, Humans, [INFO]Computer Science [cs], Radiology, Nuclear Medicine and imaging, Segmentation, Convolutional neural networks (CNN), cardiovascular diseases, Tomography, Radiological and Ultrasound Technology, medicine.diagnostic_test, business.industry, Deep learning, General Medicine, medicine.disease, Coronary Vessels, medicine.anatomical_structure, Calcium, Artificial intelligence, business, Agatston score, Nuclear medicine, Tomography, X-Ray Computed
Abstract

International audience; Rationale and Objectives: The purpose of this study was to develop and validate an algorithm that can automatically estimate the amount of coronary artery calcium (CAC) from non-enhanced ECG-gated computed tomography (CT) cardiac volume acquisitions by using multiple convolutional neural networks (CNN).Materials and Methods: Our method used an ensemble of 5 CNN with 3D U-Net architecture trained on a database of 783 CT examinations to detect and segment coronary artery calcifications in a 3D volume. The Agatston score, the conventional CAC scoring, was then computed slice by slice from the resulting segmentation mask and compared to the ground truth manually estimated by radiologists.The quality of the estimation was assessed with the Concordance Index (C-index) on a separate testing set of 98 independent CT examinations.Results: The final ensemble model yields a C-index of 0.9509 951 on the testing set. The remaining errors of the method were mainly observed on small-size and/or low-density calcifications, or calcifications located near the mitral valve or ring. Our model was also compared to other methods from the literature showing state-of-the-art performance for CAC quantification.Conclusion: The deep learning-based method we proposed to compute automatically the CAC score from a non-enhanced ECG-gated cardiac CT is fast, robust and could improve workflow efficiency, eliminating the time spent on manually selecting coronary calcifications to compute the Agatston score.

Published
2021
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