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7. A deep learning classification scheme based on augmented-enhanced features to segment organs at risk on the optic region in brain cancer patients

Author

Dolz, Jose, Reyns, Nicolas, Betrouni, Nacim, Kharroubi, Dris, Quidet, Mathilde, Massoptier, Laurent, and Vermandel, Maximilien

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Radiation therapy has emerged as one of the preferred techniques to treat brain cancer patients. During treatment, a very high dose of radiation is delivered to a very narrow area. Prescribed radiation therapy for brain cancer requires precisely defining the target treatment area, as well as delineating vital brain structures which must be spared from radiotoxicity. Nevertheless, delineation task is usually still manually performed, which is inefficient and operator-dependent. Several attempts of automatizing this process have reported. however, marginal results when analyzing organs in the optic region. In this work we present a deep learning classification scheme based on augmented-enhanced features to automatically segment organs at risk (OARs) in the optic region -optic nerves, optic chiasm, pituitary gland and pituitary stalk-. Fifteen MR images with various types of brain tumors were retrospectively collected to undergo manual and automatic segmentation. Mean Dice Similarity coefficients around 0.80 were reported. Incorporation of proposed features yielded to improvements on the segmentation. Compared with support vector machines, our method achieved better performance with less variation on the results, as well as a considerably reduction on the classification time. Performance of the proposed approach was also evaluated with respect to manual contours. In this case, results obtained from the automatic contours mostly lie on the variability of the observers, showing no significant differences with respect to them. These results suggest therefore that the proposed system is more accurate than other presented approaches, up to date, to segment these structures. The speed, reproducibility, and robustness of the process make the proposed deep learning-based classification system a valuable tool for assisting in the delineation task of small OARs in brain cancer., Comment: Submitted to the Journal of Physics in Biology and Medicine

Published
2017

15. A Transferable Belief Model Decision Support Tool over Complementary Clinical Conditions

Author

Hadj Henni, Abderraouf, Pasquier, David, Betrouni, Nacim, 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, Rojas, Ignacio, editor, and Ortuño, Francisco, editor

Published
2018
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17. Tri-axial rubidium and helium optically pumped magnetometers for on-scalp magnetoencephalography recording of interictal epileptiform discharges: a case study

Author

Feys, Odile, primary, Corvilain, Pierre, additional, Labyt, Etienne, additional, Mahmoudzadeh, Mahdi, additional, Routier, Laura, additional, Sculier, Claudine, additional, Holmes, Niall, additional, Brookes, Matthew, additional, Goldman, Serge, additional, Romain, Rudy, additional, Mitryukovskiy, Sergey, additional, Palacios-Laloy, Agustin, additional, Schwartz, Denis, additional, Betrouni, Nacim, additional, Derambure, Philippe, additional, Wallois, Fabrice, additional, Wens, Vincent, additional, and De Tiège, Xavier, additional

Published
2023
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26. Can Dopamine Responsiveness Be Predicted in Parkinson’s Disease Without an Acute Administration Test?

Author

Betrouni, Nacim, Moreau, Caroline, Rolland, Anne-Sophie, Carrière, Nicolas, Viard, Romain, Lopes, Renaud, Kuchcinski, Gregory, Eusebio, Alexandre, Thobois, Stephane, Hainque, Elodie, Hubsch, Cecile, Rascol, Olivier, Brefel, Christine, Drapier, Sophie, Giordana, Caroline, Durif, Franck, Maltête, David, Guehl, Dominique, Hopes, Lucie, Rouaud, Tiphaine, Jarraya, Bechir, Benatru, Isabelle, Tranchant, Christine, Tir, Melissa, Chupin, Marie, Bardinet, Eric, Defebvre, Luc, Corvol, Jean-Christophe, Devos, David, Lille Neurosciences & Cognition - U 1172 (LilNCog), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Institut de recherche en santé, environnement et travail (Irset), Université d'Angers (UA)-Université de Rennes (UR)-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), École des Hautes Études en Santé Publique [EHESP] (EHESP), CHU Lille, Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Plateformes Lilloises en Biologie et Santé - UAR 2014 - US 41 (PLBS), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Centre National de la Recherche Scientifique (CNRS), CHU Marseille, CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Sorbonne Université - Faculté de Médecine (SU FM), Sorbonne Université (SU), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-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)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Hôpital de la Fondation Ophtalmologique Adolphe de Rothschild [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), Centre d'Investigation Clinique [Rennes] (CIC), Université de Rennes (UR)-Hôpital Pontchaillou-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Pontchaillou [Rennes], Service de Neurologie [CHU Nice], Hôpital Pasteur [Nice] (CHU)-Centre Hospitalier Universitaire de Nice (CHU Nice), CHU Gabriel Montpied [Clermont-Ferrand], CHU Clermont-Ferrand, Différenciation et communication neuronale et neuroendocrine (DC2N), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Rouen, Normandie Université (NU), CHU Bordeaux [Bordeaux], Service de neurologie [CHRU Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Centre hospitalier universitaire de Nantes (CHU Nantes), CHU Amiens-Picardie, Laboratoire de Neurosciences Fonctionnelles et Pathologies - UR UPJV 4559 (LNFP), and Université de Picardie Jules Verne (UPJV)

Subjects
Antiparkinson Agents, Levodopa, Cellular and Molecular Neuroscience, Dopamine, [SCCO.NEUR]Cognitive science/Neuroscience, Humans, Parkinson Disease, Neurology (clinical), Magnetic Resonance Imaging
Abstract

Background: Dopamine responsiveness (dopa-sensitivity) is an important parameter in the management of patients with Parkinson’s disease (PD). For quantification of this parameter, patients undergo a challenge test with acute Levodopa administration after drug withdrawal, which may lead to patient discomfort and use of significant resources. Objective: Our objective was to develop a predictive model combining clinical scores and imaging. Methods: 350 patients, recruited by 13 specialist French centers and considered for deep brain stimulation, underwent an acute L-dopa challenge (dopa-sensitivity > 30%), full assessment, and MRI investigations, including T1w and R2* images. Data were randomly divided into a learning base from 10 centers and data from the remaining centers for testing. A machine selection approach was applied to choose the optimal variables and these were then used in regression modeling. Complexity of the modelling was incremental, while the first model considered only clinical variables, the subsequent included imaging features. The performances were evaluated by comparing the estimated values and actual values Results: Whatever the model, the variables age, sex, disease duration, and motor scores were selected as contributors. The first model used them and the coefficients of determination (R2) was 0.60 for the testing set and 0.69 in the learning set (p

Published
2022

30. Tri-axial rubidium and helium optically pumped magnetometers for on-scalp magnetoencephalography recording of interictal epileptiform discharges: a case study

Author

Feys, Odile, Corvilain, Pierre, Labyt, Etienne, Mahmoudzadeh, Mahdi, Routier, Laura, Sculier, Claudine, Holmes, Niall, Brookes, Matthew J, Goldman, Serge, Romain, Rudy, Mitryukovskiy, Sergey, Palacios-Laloy, Agustin, Schwartz, Denis, Betrouni, Nacim, Derambure, Phillipe, Wallois, Fabrice, Wens, Vincent, De Tiege, Xavier, Feys, Odile, Corvilain, Pierre, Labyt, Etienne, Mahmoudzadeh, Mahdi, Routier, Laura, Sculier, Claudine, Holmes, Niall, Brookes, Matthew J, Goldman, Serge, Romain, Rudy, Mitryukovskiy, Sergey, Palacios-Laloy, Agustin, Schwartz, Denis, Betrouni, Nacim, Derambure, Phillipe, Wallois, Fabrice, Wens, Vincent, and De Tiege, Xavier

Abstract

Cryogenic magnetoencephalography (MEG) enhances the presurgical assessment of refractory focal epilepsy (RFE). Optically pumped magnetometers (OPMs) are cryogen-free sensors that enable on-scalp MEG recordings. Here, we investigate the application of tri-axial OPMs [ 87 Rb (Rb-OPM) and 4 He gas (He-OPM)] for the detection of interictal epileptiform discharges (IEDs). IEDs were recorded simultaneously with 4 tri-axial Rb- and 4 tri-axial He-OPMs in a child with RFE. IEDs were identified visually, isolated from magnetic background noise using independent component analysis (ICA) and were studied following their optimal magnetic field orientation thanks to virtual sensors. Most IEDs (>1,000) were detectable by both He- and Rb-OPM recordings. IEDs were isolated by ICA and the resulting magnetic field oriented mostly tangential to the scalp in Rb-OPMs and radial in He-OPMs. Likely due to differences in sensor locations, the IED amplitude was higher with Rb-OPMs. This case study shows comparable ability of Rb-OPMs and He-OPMs to detect IEDs and the substantial benefits of triaxial OPMs to detect IEDs from different sensor locations. Tri-axial OPMs allow to maximize spatial brain sampling for IEDs detection with a limited number of sensors., SCOPUS: ar.j, info:eu-repo/semantics/published

Published
2023

33. Computer-assisted diagnosis of prostate cancer using DCE-MRI data: design, implementation and preliminary results

Author

Puech, Philippe, Betrouni, Nacim, Makni, Nasr, Dewalle, Anne-Sophie, Villers, Arnauld, and Lemaitre, Laurent

Abstract

Abstract: Objects: We present computer-assisted diagnosis (CAD) software designed to improve prostate cancer detection using perfusion MRI data. Methods: In addition to standard visualization features, this software allows for the 2D and multislice 2D contouring of suspicious areas based on a seeded region growing algorithm, and area labeling based on zonal anatomy. Tumor volume assessment and the semiquantitative analysis of DCE-MRI sequences can both be performed. We retrospectively analyzed DCE-MRI examinations of 100 patients and found 121 lesions showing a suspiciously high intensity with early enhancement in 84 of them. Seventy-one patients turned out to be malignant, whereas 50 were benign. Based on an analysis of the median wash-in and wash-out values of these foci, we designed a standardized 5-level cancer suspicion score (ranging from “probably benign” to “highly suspicious”). This comprehensive score provides a scaled likelihood of malignancy in the region of interest taking account of its location in relation to prostate zonal anatomy. We compared its accuracy with that of visual assessments of time–intensity curves performed by specialist and non-specialist radiologists. Results: Parameters of the scoring algorithm were designed to provide the greatest possible sensitivity in our sample population. A re-substitution evaluation provided an Se/Sp of 100/45% for peripheral zone cancer, and 100/40% for transition zone cancer characterization. When identifying malignant areas using time–intensity curves data, this simple algorithm performed significantly better (AUC = 0.77) than a non-specialist (AUC = 0.57, P < 0.0001) radiologist, and better than a trained (AUC = 0.70) radiologist, although this difference was not significant. Conclusion: Our new prostate MRI CAD software provides a standardized cancer suspicion score for suspicious foci detected in DCE-MRI T1-w images. Our results suggest that it may improve radiologists’ performances in prostate cancer identification, especially when they are not specialized in prostate imaging.

Published
2024
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34. Correction of images in an open-configuration MR imaging system for radiation therapy planning and Interventional MRI

Author

Viard, Romain, Mordon, Serge, Betrouni, Nacim, Vermandel, Maximilien, Vanhoutte, Matthieu, and Rousseau, Jean

Abstract

Abstract: Purpose: In this paper, we present a method to remove system induced geometric distortions in MR images. Methods: A large 3D phantom with spherical balls was used to characterize geometric distortion on an AIRIS Mate 0.2 T MR Scanner (Hitachi). MR images of the phantom were acquired in axial, sagittal planes. Using 2D Fast Spin Echo (FSE) sequence, distortions were measured at each control point. Two piecewise interpolation methods were then applied to correct distortions and greyvalues. Results: The distortion was reduced from 16 to 1.2 mm at 180 mm from the magnet center. Conclusion: Distortions were characterized and corrected in any axial, sagittal or coronal slice within an effective FOV of 330(LR) × 180(AP) × 210(HF) mm3. A fast and accurate method for correction of geometric distortion was performed within large distances from the magnet isocenter.

Published
2024
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40. Fast Unsupervised Texture Segmentation Using Active Contours Model Driven by Bhattacharyya Gradient Flow

Author

Derraz, Foued, Taleb-Ahmed, Abdelmalik, Pinti, Antonio, Peyrodie, Laurent, Betrouni, Nacim, Chikh, Azzeddine, Bereksi-Reguig, Fethi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Bayro-Corrochano, Eduardo, editor, and Eklundh, Jan-Olof, editor

Published
2009
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41. Automatic MRI Brain Segmentation with Combined Atlas-Based Classification and Level-Set Approach

Author

Bourouis, Sami, Hamrouni, Kamel, Betrouni, Nacim, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Campilho, Aurélio, editor, and Kamel, Mohamed, editor

Published
2008
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44. Preliminary study for intonation classification of imagined speech for brain-computer interface applications

Author

Casso, Isabel, Rouillard, José, Si-Mohammed, Hakim, Betrouni, Nacim, Cabestaing, François, Basirat, Anahita, Laboratoire Sciences Cognitives et Sciences Affectives - UMR 9193 (SCALab), Université de Lille-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Lille Neurosciences & Cognition - U 1172 (LilNCog), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), and Rouillard, José

Subjects
brain-computer interfaces, Intonation, prosody, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, imagined speech, EEG, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], BCI brain computer interface
Abstract

International audience; In the current study, we focused on decoding speech prosody from EEG. Prosody (i.e., melody and rhythm of speech) is important during communication as it allows to convey emotion and meaning. However, it has received little attention in the field of brain-computer interfaces. To address this issue, we contrasted the production of two syllables, "ba" and "da", produced mentally as an affirmation (e.g., "ba.") or a question (e.g., "ba?") using two different intonations. We focused on spectral features. After classification in the time-frequency domain, we found above chance-level accuracies in specific frequency ranges of the alpha band (7-12 Hz) early on during the production phase. We also obtained above chance-level results on a range of the low-beta band (16-20 Hz) during a late time window. Based on the visual inspection of topographies and the literature, we suggest that the results during the early time window, but not that during the late time window, reflect a genuine difference between imagined affirmation and question production. Future studies should provide more information about neural markers and underlying neuro-cognitive processes to improve the understanding of the imagined intonation production. This would pave the way for the development of speech-based BCI capable of differentiating intonation and prosody in general.

Published
2022

45. Texture features of magnetic resonance images: A marker of slight cognitive deficits in Parkinson's disease

Author

Betrouni, Nacim, Betrouni, Nacim, Lopes, Renaud, Defebvre, Luc, Leentjens, Albert F. G., Dujardin, Kathy, Betrouni, Nacim, Betrouni, Nacim, Lopes, Renaud, Defebvre, Luc, Leentjens, Albert F. G., and Dujardin, Kathy

Abstract

Background Cognitive impairment is a frequent nonmotor symptom of Parkinson's disease. Depending on severity, patients are considered to have mild cognitive impairment or dementia. However, among the cognitively intact patients, some may have deficits in a less severe range. The early detection of such subtle symptoms may be important for the initiation of care strategies. Objective To identify imaging markers of early cognitive symptoms, potentially before usual signs, such as atrophy, become manifest. Methods A total of 102 patients with Parkinson's disease and 17 age-matched cognitively intact healthy controls underwent extensive neuropsychological assessment and T1-weighted magnetic resonance imaging. Parkinson's disease patients were separated into 3 groups according to their cognitive status: intact, with slight slowing, and with mild deficits in executive functions. Texture features as measured by first-order and second-order statistics were computed in the following 6 brain regions: the hippocampus, thalamus, amygdala, putamen, caudate nucleus, and pallidum. They were tested between the groups, and their correlation with cognition was examined. Volumetric measurements were made for comparison. Results Texture analysis showed significant between-group differences for 2 features-skewness and entropy in the hippocampus, the thalamus, and the amygdala-and the volume analysis revealed no between-group difference. These features were significantly correlated with cognitive performance. Conclusion These results support the assumption that signal alterations associated with Parkinson's disease-related cognitive decline can be captured very early by texture analysis. As these changes appear to reflect clinical phenomena, texture analysis may be a promising marker for helping cognitive phenotyping in Parkinson's disease. (c) 2019 International Parkinson and Movement Disorder Society

Published
2020

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