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18 results on '"Nora Ouzir"'

1. Primary liver cancer classification from routine tumour biopsy using weakly supervised deep learning

Author

Aurélie Beaufrère, Nora Ouzir, Paul Emile Zafar, Astrid Laurent-Bellue, Miguel Albuquerque, Gwladys Lubuela, Jules Grégory, Catherine Guettier, Kévin Mondet, Jean-Christophe Pesquet, and Valérie Paradis

Subjects
Primary liver cancer, biopsy, histological slides, artificial intelligence, weakly supervised learning, Diseases of the digestive system. Gastroenterology, RC799-869
Abstract

Background & Aims: The diagnosis of primary liver cancers (PLCs) can be challenging, especially on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We automatically classified PLCs on routine-stained biopsies using a weakly supervised learning method. Method: We selected 166 PLC biopsies divided into training, internal and external validation sets: 90, 29 and 47 samples, respectively. Two liver pathologists reviewed each whole-slide hematein eosin saffron (HES)-stained image (WSI). After annotating the tumour/non-tumour areas, tiles of 256x256 pixels were extracted from the WSIs and used to train a ResNet18 neural network. The tumour/non-tumour annotations served as labels during training, and the network's last convolutional layer was used to extract new tumour tile features. Without knowledge of the precise labels of the malignancies, we then applied an unsupervised clustering algorithm. Results: Pathological review classified the training and validation sets into hepatocellular carcinoma (HCC, 33/90, 11/29 and 26/47), intrahepatic cholangiocarcinoma (iCCA, 28/90, 9/29 and 15/47), and cHCC-CCA (29/90, 9/29 and 6/47). In the two-cluster model, Clusters 0 and 1 contained mainly HCC and iCCA histological features. The diagnostic agreement between the pathological diagnosis and the two-cluster model predictions (major contingent) in the internal and external validation sets was 100% (11/11) and 96% (25/26) for HCC and 78% (7/9) and 87% (13/15) for iCCA, respectively. For cHCC-CCA, we observed a highly variable proportion of tiles from each cluster (cluster 0: 5-97%; cluster 1: 2-94%). Conclusion: Our method applied to PLC HES biopsy could identify specific morphological features of HCC and iCCA. Although no specific features of cHCC-CCA were recognized, assessing the proportion of HCC and iCCA tiles within a slide could facilitate the identification of cHCC-CCA. Impact and implications: The diagnosis of primary liver cancers can be challenging, especially on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We automatically classified primary liver cancers on routine-stained biopsies using a weakly supervised learning method. Our model identified specific features of hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Despite no specific features of cHCC-CCA being recognized, the identification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma tiles within a slide could facilitate the diagnosis of primary liver cancers, and particularly cHCC-CCA.

Published
2024
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9. Multimodal image fusion via coupled feature learning

Author

Farshad G. Veshki, Nora Ouzir, Sergiy A. Vorobyov, Esa Ollila, Sergiy Vorobyov Group, Université Paris-Saclay, Dept Signal Process and Acoust, Aalto-yliopisto, Aalto University, 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), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-CentraleSupélec-Université Paris-Saclay, and School of Electrical Engineering [Aalto Univ]

Subjects
Coupled dictionary learning, Multimodal image fusion, Joint sparse representation, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Multimodal medical imaging, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Control and Systems Engineering, Signal Processing, Computer Vision and Pattern Recognition, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Electrical and Electronic Engineering, Software, Infrared images
Abstract

Publisher Copyright: © 2022 The Author(s) This paper presents a multimodal image fusion method using a novel decomposition model based on coupled dictionary learning. The proposed method is general and can be used for a variety of imaging modalities. In particular, the images to be fused are decomposed into correlated and uncorrelated components using sparse representations with identical supports and a Pearson correlation constraint, respectively. The resulting optimization problem is solved by an alternating minimization algorithm. Contrary to other learning-based fusion methods, the proposed approach does not require any training data, and the correlated features are extracted online from the data itself. By preserving the uncorrelated components in the fused images, the proposed fusion method significantly improves on current fusion approaches in terms of maintaining the texture details and modality-specific information. The maximum-absolute-value rule is used for the fusion of correlated components only. This leads to an enhanced contrast-resolution without causing intensity attenuation or loss of important information. Experimental results show that the proposed method achieves superior performance in terms of both visual and objective evaluations compared to state-of-the-art image fusion methods.

Published
2022

10. Data-Adaptive Similarity Measures for B-mode Ultrasound Images Using Robust Noise Models

Author

Esa Ollila, Nora Ouzir, Sergiy A. Vorobyov, 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), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-CentraleSupélec-Université Paris-Saclay, School of Electrical Engineering [Aalto Univ], and Aalto University

Subjects
Noise measurement, Scattering, Computer science, business.industry, 020206 networking & telecommunications, Pattern recognition, 02 engineering and technology, Imaging phantom, Speckle pattern, symbols.namesake, Amplitude, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Robustness (computer science), Motion estimation, Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, symbols, Artificial intelligence, Electrical and Electronic Engineering, Rayleigh scattering, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, ComputingMilieux_MISCELLANEOUS
Abstract

Ultrasound imaging (UI) is characterized by the presence of multiplicative speckle noise and various acquisition artefacts. Designing ultrasound (US) similarity measures thus requires a particular attention. In the specific context of motion estimation, incorporating US characteristics does not only benefit traditional methods but also learning-based approaches, which are highly sensitive to the quality of training data. Deriving similarity measures from a maximum likelihood (ML) perspective allows us to take these specificities into account. As opposed to the classical Rayleigh modelling, the proposed similarity measures incorporate more realistic scattering conditions, such as, varying speckle densities and shadowing. Specifically, the deviations from the Rayleigh statistics are modelled using the $t$ -distribution for the complex radio-frequency (RF) signals and the Nakagami-Gamma (NG) compound model for the echo amplitudes. Furthermore, the model parameters are learnt patch-wise, which leads to data-adaptive similarity measures. The proposed criteria are investigated in the context of motion estimation using synthetic, phantom, as well as 2D and 3D in vivo images. The experimental results show an improvement in performance and robustness in comparison to the classical Rayleigh-based approach and state-of-the-art similarity measures.

Published
2020

11. ML-based Motion Estimation in Ultrasound Images Using Heavy-tailed Noise Distributions

Author

Esa Ollila, Nora Ouzir, and Sergiy A. Vorobyov

Subjects
symbols.namesake, Speckle pattern, Noise, Similarity (network science), Noise measurement, Robustness (computer science), Motion estimation, Physics::Medical Physics, symbols, Similarity measure, Rayleigh scattering, Algorithm, Mathematics
Abstract

The multiplicative Rayleigh noise model has been used for maximum likelihood (ML) motion estimation in ultrasound imaging (UI). In this work, we introduce new robust similarity measures that take into account the deviations from the Rayleigh statistics resulting, for example, from multiple scatterings or acquisition artefacts. Specifically, the $t$ -distribution is used for modelling the radio-frequency (RF) signals and the Nakagami-Gamma (NG) model is used for the echo amplitudes. Experiments using in vivo images of the carotid artery show an improvement in motion estimation accuracy in comparison with the similarity measure based on the classical Rayleigh model.

Published
2019

12. Robust cardiac motion estimation with dictionary learning and temporal regularization for ultrasound imaging

Author

Nora Ouzir, Jean-Yves Tourneret, Jose M. Bioucas-Dias, Adrian Basarab, Aalto University, Instituto de Telecomunicações [Lisboa, Portugal], Instituto Superior Técnico, Universidade Técnica de Lisboa (IST), CoMputational imagINg anD viSion (IRIT-MINDS), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Université Toulouse III - Paul Sabatier (UT3), Institut National Polytechnique (Toulouse) (Toulouse INP), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Aalto University (FINLAND), Universidade de Lisboa - ULisboa (PORTUGAL), and Instituto de Telecomunicações (Lisbonne, Portugal)

Subjects
Computer science, Optical flow, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Regularization (mathematics), 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Robustness (computer science), Motion estimation, Ultrasound, 0202 electrical engineering, electronic engineering, information engineering, Traitement du signal et de l'image, Shrinkage, Multi-frame motion estimation, Augmented Lagrangian method, Temporal regularization, Son, Echocardiography, Parametric model, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 020201 artificial intelligence & image processing, Affine transformation, Algorithm
Abstract

International audience; Estimating the cardiac motion from ultrasound (US) images is an ill-posed problem that requires regularization. In a recent study, it was shown that constraining the cardiac motion fields to be patch-wise sparse in a learnt overcomplete motion dictionary is more accurate than local parametric models (affine) or global functions (B-splines, total variation). In this work, we extend this method by incorporating temporal smoothness in a multi-frame optical-flow (OF) strategy. An efficient optimization strategy using the constrained split augmented Lagrangian shrinkage algorithm (C-SALSA) is proposed. The performance is evaluated on a realistic simulated cardiac dataset with available ground-truth. A comparison with the pairwise approach shows the interest of the proposed temporal regularization and multi-frame strategy in terms of accuracy and computational time.

Published
2019

13. Robust Optical Flow Estimation in Cardiac Ultrasound Images Using a Sparse Representation

Author

Olivier Lairez, Jean-Yves Tourneret, Adrian Basarab, Nora Ouzir, CoMputational imagINg anD viSion (IRIT-MINDS), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Université Toulouse III - Paul Sabatier (UT3), Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Institut National Polytechnique (Toulouse) (Toulouse INP), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Institut National de la Santé et de la Recherche Médicale - INSERM (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), and Centre Hospitalier Universitaire de Toulouse - CHU Toulouse (FRANCE)

Subjects
Computer science, Optical flow, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Image processing, Regularization (mathematics), 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Robustness (computer science), Motion estimation, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Sparse regularization, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Computer Simulation, Electrical and Electronic Engineering, Ultrasonography, Imagerie médicale, Robust motion estimation, Radiological and Ultrasound Technology, Reproducibility of Results, Heart, Dictionary learning, Sparse approximation, Vision par ordinateur et reconnaissance de formes, Informatique, Echocardiography, Doppler, Apprentissage, Computer Science Applications, Weighting, Optical flow estimation, Motion field, Robust motion estima-tion, Echocardiography, Cardiac ultrasound, Artifacts, Algorithm, Algorithms, Software
Abstract

International audience; This paper introduces a robust 2-D cardiac motion estimation method. The problem is formulated as an energy minimization with an optical flow-based data fidelity term and two regularization terms imposing spa- tial smoothness and the sparsity of the motion field in an appropriate cardiac motion dictionary. Robustness to outliers, such as imaging artefacts and anatomical motion boundaries, is introduced using robust weighting functions for the data fidelity term as well as for the spatial and sparse regularizations. The motion fields and the weights are com- puted jointly using an iteratively re-weighted minimization strategy. The proposed robust approach is evaluated on syn- thetic data and realistic simulation sequences with available ground-truth by comparing the performance with state-of- the-art algorithms. Finally, the proposed method is validated using two sequences of in vivo images. The obtained results show the interest of the proposed approach for 2-D cardiac ultrasound imaging.

Published
2019

14. Cardiac motion estimation in ultrasound images using spatial and sparse regularizations

Author

Adrian Basarab, Jean-Yves Tourneret, Nora Ouzir, Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Centre National de la Recherche Scientifique - CNRS (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), CoMputational imagINg anD viSion (IRIT-MINDS), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Institut National Polytechnique (Toulouse) (Toulouse INP), and Université Toulouse III - Paul Sabatier (UT3)

Subjects
Smoothness (probability theory), Computer science, business.industry, Ultrasound, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 020206 networking & telecommunications, Dictionary learning, 02 engineering and technology, Motion estimation, Sparse representations, Constraint (information theory), [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Cardiac motion, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, 0202 electrical engineering, electronic engineering, information engineering, Traitement du signal et de l'image, 020201 artificial intelligence & image processing, business, Algorithm, Ultrasound imaging, Cardiac imaging, Imagerie médicale
Abstract

International audience; This paper investigates a new method for cardiac motion estimation in 2D ultrasound images. The motion estimation problem is formulated as an energy minimization with spatial and sparse regularizations. In addition to a classical spatial smoothness constraint, the proposed method exploits the sparse properties of the cardiac motion to regularize the solution via an appropriate dictionary learning step. The proposed method is evaluated in terms of motion estimation and strain accuracy and compared with state-of-the-art algorithms using a dataset of realistic simulations. These simulation results show that the proposed method provides very promising results for myocardial motion estimation.

Published
2018

15. Cardiac Motion Estimation with Dictionary Learning and Robust Sparse Coding in Ultrasound Imaging

Author

Adrian Basarab, Jean-Yves Tourneret, Nora Ouzir, Patricia Chiril, Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), and Laboratoire de recherche en télécommunications spatiales et aéronautiques - TéSA (FRANCE)

Subjects
Computer science, business.industry, Attenuation, Robust sparse coding, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, Dictionary learning, Anomaly detection, 030204 cardiovascular system & hematology, Cardiac motion estimation, Regularization (mathematics), 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Cardiac motion, Motion estimation, Outlier, Clutter, Traitement du signal et de l'image, Artificial intelligence, business, Neural coding
Abstract

Cardiac motion estimation from ultrasound images is an ill-posed problem that needs regularization to stabilize the solution. In this work, regularization is achieved by exploiting the sparseness of cardiac motion fields when decomposed in an appropriate dictionary, as well as their smoothness through a classical total variation term. The main contribution of this work is to robustify the sparse coding step in order to handle anomalies, i.e., motion patterns that significantly deviate from the expected model. The proposed approach uses an ADMM-based optimization algorithm in order to simultaneously recover the sparse representations and the outlier components. It is evaluated using two realistic simulated datasets with available ground-truth, containing native outliers and corrupted by synthetic attenuation and clutter artefacts.

Published
2018

16. Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning

Author

Brahim Harbaoui, Nora Ouzir, Jean-Yves Tourneret, Herve Liebgott, Adrian Basarab, Institut de recherche en informatique de Toulouse ( IRIT ), Institut National Polytechnique [Toulouse] ( INP ) -Université Toulouse 1 Capitole ( UT1 ) -Université Toulouse - Jean Jaurès ( UT2J ) -Université Paul Sabatier - Toulouse 3 ( UPS ) -Centre National de la Recherche Scientifique ( CNRS ), 3 - Imagerie Ultrasonore, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé ( CREATIS ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon ( INSA Lyon ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Hospices Civils de Lyon ( HCL ) -Université Jean Monnet [Saint-Étienne] ( UJM ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Hospices Civils de Lyon ( HCL ) -Université Jean Monnet [Saint-Étienne] ( UJM ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Institut National des Sciences Appliquées de Lyon - INSA (FRANCE), Institut National de la Santé et de la Recherche Médicale - INSERM (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Université Claude Bernard-Lyon I - UCBL (FRANCE), Université Jean Monnet - St Etienne (FRANCE), Traitement et Compréhension d’Images (IRIT-TCI), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Université Toulouse III - Paul Sabatier (UT3), Imagerie Ultrasonore, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), CoMputational imagINg anD viSion (IRIT-MINDS), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Toulouse Capitole (UT Capitole), Université Fédérale Toulouse Midi-Pyrénées-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse Capitole (UT Capitole), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), (OATAO), Open Archive Toulouse Archive Ouverte, Institut National Polytechnique de Toulouse - INPT (FRANCE), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL)

Subjects
Computer science, Physics::Medical Physics, Cardiologie et système cardiovasculaire, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, 030204 cardiovascular system & hematology, Motion (physics), 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Motion estimation, Traitement du signal et de l'image, Computer vision, H- INFORMATIQUE, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Motion estimation Sparse representations, business.industry, Dictionary learning, Sparse approximation, [ SDV.MHEP.CSC ] Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Computer Graphics and Computer-Aided Design, [SDV.MHEP.CSC] Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Quarter-pixel motion, Constraint (information theory), Data set, Cardiac ultrasound, Noise (video), Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithm, Software
Abstract

International audience; This paper introduces a new method for cardiac motion estimation in 2-D ultrasound images. The motion esti- mation problem is formulated as an energy minimization, whose data fidelity term is built using the assumption that the images are corrupted by multiplicative Rayleigh noise. In addition to a classical spatial smoothness constraint, the proposed method exploits the sparse properties of the cardiac motion to regularize the solution via an appropriate dictionary learning step. The proposed method is evaluated on one data set with available ground-truth, including four sequences of highly realistic sim- ulations. The approach is also validated on both healthy and pathological sequences of in vivo data. We evaluate the method in terms of motion estimation accuracy and strain errors and compare the performance with state-of-the-art algorithms. The results show that the proposed method gives competitive results for the considered data. Furthermore, the in vivo strain analysis demonstrates that meaningful clinical interpretation can be obtained from the estimated motion vectors.

Published
2017

17. Tissue motion estimation using dictionary learning: application to cardiac amyloidosis

Author

Nora Ouzir, Jean-Yves Tourneret, Olivier Lairez, Adrian Basarab, Traitement et Compréhension d’Images (IRIT-TCI), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM), CoMputational imagINg anD viSion (IRIT-MINDS), Institut National Polytechnique (Toulouse) (Toulouse INP), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Institut National de la Santé et de la Recherche Médicale - INSERM (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), and Centre Hospitalier Universitaire de Toulouse - CHU Toulouse (FRANCE)

Subjects
medicine.medical_specialty, Optical flow, 02 engineering and technology, 030204 cardiovascular system & hematology, Cardiac amyloidosis, 03 medical and health sciences, Speckle pattern, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Internal medicine, Motion estimation, 0202 electrical engineering, electronic engineering, information engineering, medicine, Computer vision, business.industry, Ultrasound, Dictionary learning, Cardiac motion estimation, Apprentissage, Parametric model, Cardiology, cardiovascular system, 020201 artificial intelligence & image processing, Affine transformation, Artificial intelligence, business
Abstract

International audience; Cardiac strain estimation from ultrasound images is an efficient tool for the diagnosis of cardiac diseases. This study focuses on cardiac amyloidosis, a pathology characterized by non-specific early symptoms such as the increased wall thickness. Recent clinical studies have demonstrated that patients with cardiac amyloidosis present an apex-to-base gradient longitudinal strain pattern, i.e., a normal strain in apex and abnormally lower values for base segments. Existing cardiac motion estimation methods belong to three categories based on optical flow, speckle tracking and elastic registration. To overcome the ill-posedness of motion estimation, they use local parametric models (e.g., affine) or global regularizations (e.g., B-splines). The objective of this study is to evaluate a recently proposed cardiac motion estimation method based on dictionary learning on patients subjected to cardiac amyloidosis.

Published
2017

18. Optical Flow Estimation in Ultrasound Images Using a Sparse Representation

Author

Adrian Basarab, Jean-Yves Tourneret, Nora Ouzir, Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), and Laboratoire de recherche en télécommunications spatiales et aéronautiques - TéSA (FRANCE)

Subjects
Computer science, Physics::Medical Physics, Optical flow, 02 engineering and technology, Motion estimation, Regularization (mathematics), Sparse representations, Cardiac Ultrasound, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Cardiac motion, 0202 electrical engineering, electronic engineering, information engineering, Traitement du signal et de l'image, Computer vision, business.industry, Ultrasound, Dictionary learning, Sparse approximation, Vision par ordinateur et reconnaissance de formes, Optical flow estimation, Cardiac ultrasound, 020201 artificial intelligence & image processing, Artificial intelligence, business
Abstract

This paper introduces a 2D optical flow estimation method for cardiac ultrasound imaging based on a sparse representation. The optical flow problem is regularized using a classical gradient-based smoothness term combined with a sparsity inducing regularization that uses a learned cardiac flow dictionary. A particular emphasis is put on the influence of the spatial and sparse regularizations on the optical flow estimation problem. A comparison with state-of-the-art methods using realistic simulations shows the competitiveness of the proposed method for cardiac motion estimation in ultrasound images.

Published
2017

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