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International audience; Deep neural networks have recently surpassed other image restoration methods which rely on hand-crafted priors. However, such networks usually require large databases and need to be retrained for each new modality. In this paper, we show that we can reach nearoptimal performances by training them on a synthetic dataset made of realizations of a dead leaves model, both for image denoising and superresolution. The simplicity of this model makes it possible to create large databases with only a few parameters. We also show that training a network with a mix of natural and synthetic images does not affect results on natural images while improving the results on dead leaves images, which are classically used for evaluating the preservation of textures. We thoroughly describe the image model and its implementation, before giving experimental results.

Transfer learning from huge natural image datasets, fine-tuning of deep neural networks and the use of the corresponding pre-trained networks have become de facto the core of art analysis applications. Nevertheless, the effects of transfer learning are still poorly understood. In this paper, we first use techniques for visualizing the network internal representations in order to provide clues to the understanding of what the network has learned on artistic images. Then, we provide a quantitative analysis of the changes introduced by the learning process thanks to metrics in both the feature and parameter spaces, as well as metrics computed on the set of maximal activation images. These analyses are performed on several variations of the transfer learning procedure. In particular, we observed that the network could specialize some pre-trained filters to the new image modality and also that higher layers tend to concentrate classes. Finally, we have shown that a double fine-tuning involving a medium-size artistic dataset can improve the classification on smaller datasets, even when the task changes., Accepted at Workshop on Fine Art Pattern Extraction and Recognition (FAPER), ICPR, 2020

International audience; While pap test is the most common diagnosis methods for cervical cancer, their results are highly dependent on the ability of the cytotechnicians to detect abnormal cells on the smears using brightfield microscopy. In this paper, we propose an explainable region classifier in whole slide images that could be used by cyto-pathologists to handle efficiently these big images (100,000x100,000 pixels). We create a dataset that simulates pap smears regions and uses a loss, we call classification under regression constraint, to train an efficient region classifier (about 66.8% accuracy on severity classification, 95.2% accuracy on normal/abnormal classification and 0.870 KAPPA score). We explain how we benefit from this loss to obtain a model focused on sensitivity and, then, we show that it can be used to perform weakly supervised localization (accuracy of 80.4%) of the cell that is mostly responsible for the malignancy of regions of whole slide images. We extend our method to perform a more general detection of abnormal cells (66.1% accuracy) and ensure that at least one abnormal cell will be detected if malignancy is present. Finally, we experiment our solution on a small real clinical slide dataset, highlighting the relevance of our proposed solution, adapting it to be as easily integrated in a pathology laboratory workflow as possible, and extending it to make a slide-level prediction.

International audience; Deep learning methods are widely used for medical applications to assist medical doctors in their daily routine. While performances reach expert's level, interpretability (highlighting how and what a trained model learned and why it makes a specific decision) is the next important challenge that deep learning methods need to answer to be fully integrated in the medical field. In this paper, we address the question of interpretability in the context of whole slide images (WSI) classification with the formalization of the design of WSI classification architectures and propose a piece-wise interpretability approach, relying on gradient-based methods, feature visualization and multiple instance learning context. After training two WSI classification architectures on Camelyon-16 WSI dataset, highlighting discriminative features learned, and validating our approach with pathologists, we propose a novel manner of computing interpretability slide-level heat-maps, based on the extracted features, that improves tile-level classification performances. We measure the improvement using the tile-level AUC that we called Localization AUC, and show an improvement of more than 0.2. We also validate our results with a RemOve And Retrain (ROAR) measure. Then, after studying the impact of the number of features used for heat-map computation, we propose a corrective approach, relying on activation colocalization of selected features, that improves the performances and the stability of our proposed method.

For the development of a water quality modeling classification, parameter optimization is important. In this research, in order to enhance the strength of the used approach, we propose a hybrid approach that combines SVM classifiers with PSO and PCA selection features. This is used for classifying the status of water quality with the Radial Basis Function (RBF) SVM kernel. To enhance the classification accuracy, PSO selects the best parameter for SVM. The problem of irrelevant data in the space of functions can be solved by PCA. A binary classification based on two water quality classes (Class I: upper, Class II: lower) is considered to be the problem. Datasets were obtained for training and testing over 5 years (2014-2018) from many samples in Tilsdit dam-Algeria, and are used in this situation. A simulation of the training time and recognition rate will be carried out in order to verify the efficiency of the method. The results obtained demonstrate that the proposed method had great potential for classifying water quality.

This paper presents a novel type-2 fuzzy model for nonlinear dynamical systems. This method can deal with the curve fitting and computational time problems of type-2 fuzzy systems. It is based on interval type-2 fuzzy systems and it is comprised of a parallel interconnection of two type-2 sub fuzzy models. The first sub fuzzy model is the primary model, which represents an ordinary model with low resolution for the nonlinear dynamical system under consideration. To overcome resolution quality problem, and obtain a model with higher resolution, we will introduce a second type-2 fuzzy sub model called error model which will represent a model for the error modelling between the primary model and the real nonlinear dynamical system. As the error model represents uncertainty in the primary model, it’s suitable to minimize this uncertainty by simple subtraction of the error model output from the primary model output, which will lead to a parallel interconnection between them, giving then a unique whole final model possessing higher resolution. To apply this approach successfully, the model’s representation and identification are considered in this investigation using type-2 fuzzy auto regressive (T2FAR) and type-2 fuzzy auto regressive moving average (T2FARMA) models. Identification is achieved by innovative metaheuristic optimization algorithms, like as firefly and biogeography-based optimization algorithms. To evaluate the effectiveness of the proposed method, it will be tested on three types of nonlinear dynamical systems. Computer investigations indicate that the proposed model may significantly improves convergence and resolution.

One of the key causes of premature disability and mortality in the world today is heart disease, which makes its prediction a vital problem in the field of healthcare systems. This work provides a contribution to the study and creation an intelligent system based on LSTM technique for heart disease prediction. A comparative study is presented between Multi Layer Perceptron (MLP) and Long Short Term Memory (LSTM) techniques in terms of accuracy and other predictive parameters for heart disease. The main aim is to develop an intelligent system based on LSTM technique for predicting heart disease in order to make an adapted decision to prevent and monitor heart disease and stroke. As it has better characteristics than those of the MLP technique, LSTM is shown to be the most effective technique for solving the aforementioned problems.

A major problem in water treatment plants is the continuous difficulty faced in online measurement by means of dedicated measuring hardware and laboratory analysis of certain variables related to the composition of water. Actually, for several reasons, such as the high cost of some sensors, their number, the dedicated time to check out the sensors, cleaning operation, calibration routines and sensor replacement, make their proper operation hard to ensure high-quality composition of water. Furthermore, in water quality monitoring, there is a huge number of heterogeneous sensors which may be time-consuming in the measurement and processing stages. Nevertheless, soft sensor approach can provide an effective and economic way to solve this problem for any cases of sensor failure. This work presents a contribution to the study and development of a soft sensor used in water quality monitoring using chlorine. A comparative study between support vector machine (SVM) and extreme learning machine (ELM) techniques in terms of learning time and other parameters for regression and classification is presented. The main objective is to set up a system architecture based on a soft sensor for water quality in order to make an adapted decision to the control and monitoring of water quality issues. ELM is shown to be the most suitable technique to address the previously mentioned problems as it has better characteristics than those of the SVM technique. An example of application is provided to focus on the interest of using a chlorine soft sensor as it is accurate, efficient and less cost-effective tool.

Weakly supervised object detection (WSOD) using only image-level annotations has attracted a growing attention over the past few years. Whereas such task is typically addressed with a domain-specific solution focused on natural images, we show that a simple multiple instance approach applied on pre-trained deep features yields excellent performances on non-photographic datasets, possibly including new classes. The approach does not include any fine-tuning or cross-domain learning and is therefore efficient and possibly applicable to arbitrary datasets and classes. We investigate several flavors of the proposed approach, some including multi-layers perceptron and polyhedral classifiers. Despite its simplicity, our method shows competitive results on a range of publicly available datasets, including paintings (People-Art, IconArt), watercolors, cliparts and comics and allows to quickly learn unseen visual categories., Comment: 26 pages, 12 figures

Deep learning methods are widely used for medical applications to assist medical doctors in their daily routines. While performances reach expert's level, interpretability (highlight how and what a trained model learned and why it makes a specific decision) is the next important challenge that deep learning methods need to answer to be fully integrated in the medical field. In this paper, we address the question of interpretability in the context of whole slide images (WSI) classification. We formalize the design of WSI classification architectures and propose a piece-wise interpretability approach, relying on gradient-based methods, feature visualization and multiple instance learning context. We aim at explaining how the decision is made based on tile level scoring, how these tile scores are decided and which features are used and relevant for the task. After training two WSI classification architectures on Camelyon-16 WSI dataset, highlighting discriminative features learned, and validating our approach with pathologists, we propose a novel manner of computing interpretability slide-level heat-maps, based on the extracted features, that improves tile-level classification performances by more than 29% for AUC., Comment: 8 pages (references excluded), 3 figures, presented in iMIMIC Workshop at MICCAI 2020

Water quality monitoring plays a vital role in the protection of water resources, environmental management, and decision-making. Artificial intelligence (AI) based on machine learning techniques has been widely used to evaluate and classify water quality for the last two decades. However, traditional machine learning techniques face many limitations, the most important of which is the inability to apply these techniques with big data generated by smart water quality monitoring stations to improve the prediction. Real-time water quality monitoring with high accuracy and efficiency for intelligent water quality monitoring stations requires new and sophisticated techniques based on machine and deep learning techniques. For this purpose, we propose a novel approach based on the integration of deep learning and feature extraction techniques to improve water quality classification. In this paper, was chosen the Tilesdit dam in Bouira (Algeria) as a case study. Moreover, we implemented the advanced deep learning method - Long Short Term Memory Recurrent Neural Networks (LSTM RNNs) to construct an intelligent model for drinking water quality classification. Furthermore, principal component analysis (PCA), linear discriminant analysis (LDA) and independent component analysis (ICA) techniques were used for features extraction and data reduction from original features. Additionally, we used three methods of cross-validation and two methods of the out-of-sample test to estimate the performance of LSTM RNNs model. From the results we found that the integration of LSTM RNNs with LDA, and LSTM RNNs with ICA yields an accuracy of 99.72%, using Random-Holdout technique.

This is an extended version of HAL preprint hal-01676326; International audience; Image synthesis is a core problem in modern deep learning, and many recent architectures such as autoencoders and Generative Adversarial networks produce spectacular results on highly complex data, such as images of faces or landscapes. While these results open up a wide range of new, advanced synthesis applications, there is also a severe lack of theoretical understanding of how these networks work. This results in a wide range of practical problems, such as difficulties in training, the tendency to sample images with little or no variability, and generalisation problems. In this paper, we propose to analyse the ability of the simplest generative network, the autoencoder, to encode and decode two simple geometric attributes : size and position. We believe that, in order to understand more complicated tasks, it is necessary to first understand how these networks process simple attributes. For the first property, we analyse the case of images of centred disks with variable radii. We explain how the autoencoder projects these images to and from a latent space of smallest possible dimension, a scalar. In particular, we describe a closed-form solution to the decoding training problem in a network without biases, and show that during training, the network indeed finds this solution. We then investigate the best regularisation approaches which yield networks that generalise well. For the second property, position, we look at the encoding and decoding of Dirac delta functions, also known as `one-hot' vectors. We describe a hand-crafted filter that achieves encoding perfectly, and show that the network naturally finds this filter during training. We also show experimentally that the decoding can be achieved if the dataset is sampled in an appropriate manner.

We propose a method for the weakly supervised detection of objects in paintings. At training time, only image-level annotations are needed. This, combined with the efficiency of our multiple-instance learning method, enables one to learn new classes on-the-fly from globally annotated databases, avoiding the tedious task of manually marking objects. We show on several databases that dropping the instance-level annotations only yields mild performance losses. We also introduce a new database, IconArt, on which we perform detection experiments on classes that could not be learned on photographs, such as Jesus Child or Saint Sebastian. To the best of our knowledge, these are the first experiments dealing with the automatic (and in our case weakly supervised) detection of iconographic elements in paintings. We believe that such a method is of great benefit for helping art historians to explore large digital databases., Comment: Accepted at ECCV 2018 Workshop Computer Vision for Art Analysis - VISART 2018 14 pages, 5 figures

In order to overcome the limited depth of field of usual photographic devices, a common approach is multi-focus image fusion (MFIF). From a stack of images acquired with different focus settings, these methods aim at fusing the content of the images of the stack to produce a final image that is sharp everywhere. Such methods can be very efficient, but when a global geometric alignment of images is out-of-reach, or when some objects are moving, the final image shows ghosts or other artefacts. In this paper, we propose a generic method to overcome these limitations. We first select a reference image, and then, for each image of the stack, reconstruct an image that shares the geometry of the reference and the sharpness content of the image at hand. The reconstruction is achieved thanks to a specially crafted modification of the PatchMatch algorithm, adapted to blurred images, and to a dedicated postprocessing for correcting reconstruction errors. Then, from the new image stack, MFIF is performed to produce a sharp result. We show the efficiency of the result on a database of challenging cases of hand-held shots containing moving objects.

International audience; Respiratory-induced organ motion is a technical challenge to PET imaging.This motion induces displacements and deformation of the organs tissues, which need to be taken into account when reconstructing the spatial radiation activity. Classical image-based methods that describe motion using Deformable Image Registration (DIR) algorithms cannot fully take into account the non-reproducibility of the respiratory internal organ motion nor the tissue volume variations that occur during breathing. In order to overcome these limitations, various biomechanical models of the respiratory system have been developed in the past decade as an alternative to DIRapproaches. In this paper, we describe a new method of correcting motion artefacts in PET imagereconstruction adapted to motion estimation models such as those based onthe finite element method (FEM). In contrast with the DIR-based approaches, the radiation activity was reconstructed on deforming tetrahedral meshes. For this, we have re- formulated the tomographic reconstruction problem by introducing atime-dependent system matrix based calculated using tetrahedral meshes instead of voxelized images.The MLEM algorithm was chosen as the reconstruction method. The simulationsperformed in this study show that the motion compensated reconstruction based on tetrahedral deformable meshes has the capability to correct motion artefacts. Results demonstrate that, in the case of complex deformations, when large volume variations occur, the developed tetrahedral based method is more appropriate than the classical DIR-based one. This method can be used, together with biomechanical models controlled by external surrogates, to correct motion artefacts in PET images and thus reducing the need for additional internal imaging during the acquisition.

International audience; Respiratory-induced organ motion is a technical challenge tonuclear imaging and to particle therapy dose calculations forlung cancer treatment in particular. Internal organ tissue dis-placements and deformations induced by breathing need to betaken into account when calculating Monte Carlo dose distri-butions or when performing tomographic reconstructions forPET imaging. This paper proposes a method to reconstructPET activities over tetrahedral meshes which are deformedbased on biomechanical patient specific model of the respi-ratory system to tackle the non reproductibility of the breath-ing. We also describe the adaptation of the popular List-ModeMaximum Likelihood Estimation (LM-MLEM) reconstruc-tion algorithm to motion estimation model using the finite el-ement method (FEM). Our simulations demonstrate the accu-racy of the proposed 4D LM-MLEM reconstruction algorithmbased on biomechanical model and its capability to correctmotion artifacts due to the breathing.

Synthetic aperture radar (SAR) images display very high dynamic ranges. Man-made structures (like buildings or power towers) produce echoes that are several orders of magnitude stronger than echoes from diffusing areas (vegetated areas) or from smooth surfaces (e.g., roads). The impulse response of the SAR imaging system is, thus, clearly visible around the strongest targets: sidelobes spread over several pixels, masking the much weaker echoes from the background. To reduce the sidelobes of the impulse response, images are generally spectrally apodized, trading resolution for a reduction of the sidelobes. This apodization procedure (global or shift-variant) introduces spatial correlations in the speckle-dominated areas that complicates the design of estimation methods. This paper describes strategies to cancel sidelobes around point-like targets while preserving the spatial resolution and the statistics of speckle-dominated areas. An irregular sampling grid is built to compensate the subpixel shifts and turn cardinal sines into discrete Diracs. A statistically grounded approach for point-like target extraction is also introduced, thereby providing a decomposition of a single look complex image into two components: a speckle-dominated image and the point-like targets. This decomposition can be exploited to produce images with improved quality (full resolution and suppressed sidelobes) suitable both for visual inspection and further processing (multitemporal analysis, despeckling, interferometry).

International audience; Purpose: This paper investigates the capabilities of a dual-rotation C-arm cone-beam computed tomography (CBCT) framework to improve non-contrast-enhanced low-contrast detection for full volume or volume-of-interest (VOI) brain imaging.Method: The idea is to associate two C-arm short-scan rotational acquisitions (spins): one over the full detector field of view (FOV) at low dose, and one collimated to deliver a higher dose to the central densest parts of the head. The angular sampling performed by each spin is allowed to vary in terms of number of views and angular positions. Collimated data is truncated and does not contain measurement of the incoming X-ray intensities in air (air calibration). When targeting full volume reconstruction, the method is intended to act as a virtual bow-tie. When targeting VOI imaging, the method is intended to provide the minimum full detector FOV data that sufficiently corrects for truncation artifacts. A single dedicated iterative algorithm is described that handles all proposed sampling configurations despite truncation and absence of air calibration.Results: Full volume reconstruction of dual-rotation simulations and phantom acquisitions are shown to have increased low-contrast detection for less dose, with respect to a single-rotation acquisition. High CNR values were obtained on 1% inserts of the Catphanmath formula 515 module in 0.94 mm thick slices. Image quality for VOI imaging was preserved from truncation artifacts even with less than 10 non-truncated views, without using the sparsity a priori common to such context.Conclusion: A flexible dual-rotation acquisition and reconstruction framework is proposed that has the potential to improve low-contrast detection in clinical C-arm brain soft-tissue imaging.

International audience; The aim of this work is to present a resampling scheme for SAR images that preserves spatial resolution and produces statistically accurate images at the same time. Indeed, SAR images are, for reasons due to their acquisition process, well sampled signals according to the Shannon sampling theory. In the presence of strong responses, that we will refer to as targets, a sinc-like function centered at the target is smeared over the entire image and is particularly visible in the range of tens of pixels surrounding the target. To mitigate this phenomenon, the usual solution is to apply an apodization window in the Fourier domain so as to change the cardinal sine impulse response into a much rapidly decaying one. This approach has two major drawbacks. It reduces the resolution of the image and introduces inaccurate statistical dependency between pixels. We propose to resample the image in an adaptive and robust way so that the target smear is canceled and the new sampled image is completely faithful to the underlying signal.

Purpose: The estimation of the distribution patterns of energy and dose in respiratory-induced organ motion represents a technical challenge for hadron therapy treatment planning, notably in the case of lung cancer in which many difficulties arose, like tissue densities variation and the tumor position shifting during breathing. This study focuses on the comparison between deformable tetrahedral meshes and voxel-based structures used as computational phantoms in four-dimensional dose calculations. The former use a continuous representation of tissue densities by respecting mass conservation principle, while the latter is a discrete grid of density values (CT-scan). Methods: The movement used to simulate breathing is generated with deformable image registration (DIR) of CT images (Castillo, 2010) (Klein, 2010) (Shamonin, 2013). Tissue tracking for tetrahedral model is implicitly performed by the fact that the meshes maintain their topology during deformations. The dose distribution is calculated using the time-dependent tetrahedral density map issued from 4D-CT scans (Petru Manescu, 2014). Unlike image-based methods, the deposited energy is accumulated inside each deforming tetrahedron of the meshes. An implementation of this dose computation method on a deformable anatomy in the case of a passive scattering beam line is demonstrated using the Geant4 code (Agostinelli, 2003). Besides, energy values in voxel-based structures are calculated for each time step and accumulated using the transformations provided by the registration. Then, values are accumulated back onto the reference image and divided by the mass to obtain the 4D dose map. Figure 1 illustrates the process used to accumulate dose in respiratory-induced simulations. Results: The tetrahedral mesh dose distribution was compared to the conventional voxel-based structure using a thoracic 4D-CT data of a patient case. Preliminary results show that dose distributions for both representations are in a good agreement (figure 2), and dose homogeneity is about the same (table1). However, motion-induced dose accumulations are more intuitive using a tetrahedral model since they do not introduce additional uncertainties with image resampling and interpolation methods, and also for the fact that they respect mass conservation principle. Conclusion: We have developed a 4D tetrahedral model for Monte Carlo dose calculations alongside its implementation on the Geant4 platform. Results of comparison with conventional methods based on voxels have shown that dose distributions are in good agreement. This novel structure can be of a great aid for treatment planning of moving targets. An experimental validation based on 4D anthropomorphic phantom (e.g. LuCa phantom developed in paul scherrer institute) (Neihart, 2013) would draw a clear conclusion regarding the performance of the presented method in comparison with the classical methods. Nevertheless, the main advantage of this method is that, coupled with a patient-specific biomechanical model, it could be used in the future to correct motion artefacts in treatment planning.

Organ motion due to patient breathing introduces a technical challenge for dosimetry and lung tumor treatment by hadron therapy. Accurate dose distribution estimation requires patient-specific information on tumor position, size, and shape as well as information regarding the material density and stopping power of the media along the beam path. A new 4D dosimetry method was developed, which can be coupled to any motion estimation method. As an illustration, the new method was implemented and tested with a biomechanical model and clinical data. First, an anatomical model of the lung and tumor was synthesized with deformable tetrahedral grids using computed tomography (CT) images. The CT attenuation values were estimated at the grid vertices. Respiratory motion was simulated biomechanically based on nonlinear finite element analysis. Contrary to classical image-based methods where motion is described using deformable image registration algorithms, the dose distribution was accumulated over tetrahedral meshes that are deformed using biomechanical modeling based on finite element analysis. The new method preserves the mass of the objects during simulation with an error between 1.6 and 3.6 %. The new method was compared to an existing dose calculation method demonstrating significant differences between the two approaches and overall superior performance using the new method. A unified model of 4D radiotherapy respiratory effects was developed where biomechanical simulations are coupled with dose calculations. Promising results demonstrate that this approach has significant potential for the treatment for moving tumors.

In this paper, we present a dynamic patient-specific model of the respiratory system for a whole respiratory cycle, based on 4D CT scans, personalized physiological compliance (pressure-volume curves), as well as an automatic tuning algorithm to determine lung pressure and diaphragm force parameters. The amplitude of the lung pressure and diaphragm forces are specific, and differs from one patient to another and depends on geometrical and physiological characteristics of the patient. To determine these parameters at different respiratory states and for each patient, an inverse finite element (FE) analysis has been implemented to match the experimental data issued directly from 4D CT images, to the FE simulation results, by minimizing the lungs volume variations. We have evaluated the model accuracy on five selected patients, from DIR-Lab Dataset, with small and large breathing amplitudes, by comparing the FE simulation results on 75 landmarks, at end inspiration (EI), end expiration (EE) states, and at each intermediate respiratory state. We have also evaluated the tumor motion identified in 4D CT scan images and compared it with the trajectory obtained by FE simulation, during one complete breathing cycle. The results demonstrate the good quantitative results of our physic-based model and we believe that our model, despite of others takes into account the challenging problem of the respiratory variabilities.

Images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua exhibit strong detector striping. This artifact is common to most pushbroom scanners and affects both visual interpretation and radiometric integrity of remotely sensed data. A considerable effort has been made to remove stripe noise and reduce its impact on high-level products. Despite the variety of destriping algorithms proposed in the literature, complete removal of stripes without signal distortion is yet to be overcome. In this paper, we tackle the striping issue from a variational angle. Basic statistical assumptions used in previous techniques are replaced by a much realistic geometrical consideration on the striping unidirectional variations. The resulting algorithm is tested on Aqua and Terra MODIS data contaminated with severe stripes and is shown to provide optimal qualitative and quantitative results.

This paper proposes a truly interactive virtual environment (VE) system for 2-D assembly tasks at the microscale. It is based on the application of virtual potential fields as a control aid for performing safe and reliable path planning strategies. The planner covers a whole range of problems due to microscale effects in object assignment, obstacle detection and avoidance, path trajectory finding and sequencing. We investigated various paradigms for enabling the human operator and the automatic motion planner to cooperatively solve a motion planning task through the use of virtual potential fields. Communication between the operator and the planner is made through haptic/vision/sound modalities. First, we describe algorithms based on optimization theory and Voronoi graph construction taking into account the microscale effects. As automatic motion planners fail due to the difficulty of discovering critical configurations, we propose cooperation paradigms with operator skills in order to solve motion planning strategies. Then, potential fields are being used as a tool to generate velocity commands from an automatic path planner as well as allowing the human to interact. Finally, the ideas presented here are supported by experiments for efficient pushing-based manipulation constructing 2-D microparticle patterns.

Prediction of respiratory motion has the potential to substantially improve cancer radiation therapy. Tumor motion during irradiation reduces the target coverage and increases dose to healthy tissues. In this paper, we have developed a new dynamic biomechanical model of the respiratory system, permitting the simulation of a complete cycle of respiratory motion, based on the finite element method (FEM), including the real boundary conditions of the organs (the diaphragm, the thorax, mediastinum and skin behaviors) to predict the lung tumor displacement and deformation. The model is monitored by two muscles: the diaphragm and the rib kinematics. We validate our approach with real 4D CT images. The results demonstrate that the proposed approach is able to predict the respiratory motion with an average error less than 2.0 mm in the different lobes.

International audience; Patient-specific respiratory motion modeling may help to understand pathophysiology and predict therapy planning. The respiratory motion modifies the shape and position of internal organs. This may degrade the quality of such medical acts as radiotherapy or laparoscopy. Predicting the breathing movement is complex, and it is considered as one of the most challenging areas of medical research. This paper presents a biomechanical model of the respiratory system, based on the finite element method (FEM), including the biomechanical behavior of the diaphragm as well as rib kinematics computations, on the assumption that breathing is controlled by two independent actors: the thorax and diaphragm muscles. In order to predict the type of the (geometrical or material) nonlinearities, a quantitative comparison of the clinical data was applied on 12 patients. We propose two nonlinear hyperelastic models: the Saint-Venant Kirchhoff and Mooney–Rivlin models. Our results demonstrate that the nonlinear hyperelastic Mooney–Rivlin model of the diaphragm behaves similarly to the linear elastic model with large displacement (Saint-Venant Kirchhoff). The results suggest that the approach of small strains (within the large displacement) may be globally maintained in the modeling of the diaphragm, and demonstrate that the accuracy of the proposed FEM is capable to predict the respiratory motion with an average surface error in a diaphragm/lungs region of interest contact of 2. 0 ± 2. 3 mm for the contact surface between lungs and diaphragm. The comparison study between the FEM simulations and the CT scan images demonstrates the effectiveness of our physics-based model.

International audience; Tumor motion during irradiation reduces target coverage and increases dose to healthy tissues. Prediction of respiratory motion has the potential to substantially improve cancer radiation therapy. The respiratory motion is complex and its prediction is not a simple task, especially that breathing is controlled by the independent action of the diaphragm muscles and thorax. The diaphragm is the principal muscle used in the process of respiration and its modeling is essential for assessing the respiratory motion. In this context, an accurate patient-specific finite element(FE) based biomechanical model can be used to predict diaphragm deformation. In this paper, we have developed a FE model of the respiratory system including the diaphragm behavior and the complete thorax with musculoskeletal structure. These incorporate the ribs kinematics extracted directly from the Computed Tomography (CT) scan images. In order to demonstrate the effectiveness of our biomechanical model, a qualitative and quantitative comparison between the FE simulations and the CT scan images were performed. Upon application of linear elastic models, our results show that a linear elastic model can accurately predict diaphragm deformations. These comparisons demonstrate the effectiveness of the proposed physically-based model. The developed computational model could be a valuable tool for respiratory system deformation prediction in order to be controlled and monitored by external sensors during the treatment .

International audience; To facilitate training of biological cell injection operations, we are developing an interactive virtual environment to simulate needle insertion into biological cells. This paper presents methodologies for dynamic modeling, visual/haptic display and model validation of cell injection. We first investigate the challenging issues in the modeling of the biomechanical properties of living cells. We propose two dynamic models to simulate cell deformation and puncture. The first approach is based on the assumptions that the mechanical response of living cells is mainly determined by the cytoskeleton and that the cytoskeleton is organized as a tensegrity structure including microfilaments, microtubules and intermediate filaments. Equivalent microtubules struts are represented with a linear mass-tensor finite element model and equivalent microfilaments and intermediate filaments with viscoelastic Kelvin-Voigt elements. The second modeling method assumes the overall cell as an homogeneous hyperelastic model (St-Venant-Kirchhoff). Both graphic and haptic rendering are provided in real-time to the operator through a 3D virtual environment. Simulated responses are compared to experimental data to show the effectiveness of the proposed physically-based model.

International audience; Organ motion, especially respiratory motion, is a technical challenge to hadrontherapy planning and dosimetry. This motion induces the displacement and the deformation of the organs tissues along the radiation beam path which need to be taken into account when computating dose distribution during the treatment. In this paper we present an original approach of virtual patient modeling for 4D radiation therapy simulations. As opposed to classical image-based models, where the necessary information is distributed over a rigid structured grid of voxels, we represent the human anatomy with a deformable grid of tetrahedra where the mass density is mapped to the vertices of the grid. In this way, we can simulate within the same structure organ motion, mass density variations and dose distribution without having to perform voxel tissue tracking.

International audience; Super-resolution combines several low resolution images having different sampling into a high resolution image. L1-norm data fit minimization has been proposed to solve this problem in a robust way. The outlier rejection capability of this methods has been shown experimentally for super-resolution. However, existing approaches add a regularization term to perform the minimization while it may not be necessary. In this paper, we recall the link between robustness to outliers and the sparse recovery framework. We use a slightly weaker Null Space Property to characterize this capability. Then, we apply these results to super resolution and show both theoretically and experimentally that we can quantify the robustness to outliers with respect to the number of images.

International audience; Predicting respiratory motion is challenging, due to the complexity and irregularity of the underlying motion pattern. In order to predict respiratory motion, we have developed a biomechanical modeling of the respiratory system which utilizes the 4D scan data (geometrical and mechanical properties) to accurately predict the tumor position due to the deformations and displacement caused by the respiratory movement, to increase the precision of the treatment. The diaphragm is the principal muscle used in the process of respiration. In this case, we introduce a method that enables the simulation of the contractile force generated by the diaphragm muscles. Physiologically, respiratory motion modeling involves the use pressurevolume relationship to apply pressure loading on the surface of the diaphragm. Additionally, the real diaphragm boundary conditions are included to the model and simulated responses are compared to clinical data. Finally, these comparisons show the effectiveness of the proposed physically-based model.

International audience; Training simulators that provide realistic visual and haptic feedback during cell indentation tasks are currently inves tigated. Complex cell geometry inherent to biological cells and intricate mechanical properties drive the need for precise mechan ical and numerical modeling to assure accurate cell deformation and force calculations. Advances in alternative finite-element for mulation, such as the mass-tensor approach, have reached a state, where they are applicable to model soft-cell deformation in real time. The geometrical characteristics and the mechanical proper ties of different cells are determined with atomic force microscopy (AFM) indentation. A real-time, haptics-enabled simulator for cell centered indentation has been developed, which utilizes the AFM data (mechanical and geometrical properties of embryonic stem cells) to accurately replicate the indentation task and predict the cell deformation during indentation in real time. This tool can be used as a mechanical marker to characterize the biological state of the cell. The operator is able to feel the change in the stiff ness during cell deformation between fixed and live cells in real time. A comparative study with finite-element simulations using a commercial software and the experimental data demonstrate the effectiveness of the proposed physically based model.

International audience; Comprehensive and training simulators that provide realistic visual and haptic feedback during cell indentation tasks are currently investigated. Complex cell geometry inherent to biological cells and intricate mechanical properties drive the need for precise mechanical and numerical modeling to assure accurate cell deformation and force calculations. Advances in alternative finite element formulation, such as mass-tensor approach, have reached the state where they are applicable to model soft cell deformation in real time. The geometrical characteristics and the mechanical properties for different cells are determined with AFM indentation. A real-time, haptics-enabled simulator for cell centered indentation has been developed which utilizes the atomic force microscopy data (mechanical and geometrical properties of embryonic stem cells (mESC)) to accurately replicate the indentation task and predict the cell deformation during indentation in real-time. This tool can be used as a mechanical marker to characterize the biological state of the cell. The operator is able to feel in real-time the change in the stiffness during cell deformation between fixed and live cells. A comparison study with finite element simulations using a commercial software and the experimental data demonstrate the effectiveness of the proposed physically-based model.

The Moderate Resolution Imaging Spectrometer (MODIS) monitors the earth in 36 spectral bands using a cross-track double-sided continuously rotating scan mirror. The imperfect calibration of the linear arrays of detectors and additional random noise in the internal calibration system induce detector-to-detector stripes, mirror side stripes and noisy stripes visible in most emissive bands. This artefact affects seriously the visual quality and radiometric integrity of measured data. Several approaches including fourier filtering [1,2], wavelet analysis [3,4] and statistical techniques such as moment matching or histogram matching have been used to reduce striping on MODIS Data [5,6,7]. Despite an extensive and diverse destriping litterature, most techniques display residual stripes if not strong distortion from the original image. In this paper, we introduce a robust destriping methodology based on a variational approach.

Real time simulation of deformable object is increasingly important to simulate the behavior of the biological cell and a key challenge of deformable simulation is to satisfy the conflicting requirements of real-time interactivity and physical realism. This paper presents methodologies for dynamic modeling, visual/haptic display and model validation of cell injection. We first investigate the challenging issues in the modeling of the bio-mechanical properties of living cells. We propose a dynamic model to simulate cell deformation mainly determined by the cytoplasm and the cytoskeleton. The cytoskeleton is organized as a tensegrity structure including microfilaments, microtubules and intermediate filaments whereas the cytoplasm is modeled as an homogeneous volume. Both structures are modeled by using FEM with viscoelastic Kelvin-Voigt elements. The virtual environment has been implemented with both graphic and haptic interfaces in order to facilitate training of biological cell injection operations.

International audience; We study the problem of finding the characteristic scale of a given satellite image. This feature is defined so that it does not depend on the spatial resolution of the image. This is a different problem than achieving scale invariance, as often studied in the literature. Our approach is based on the use of a linear scale-space and the total variation. The critical scale is defined as the one at which the normalized total variation reaches its maximum. It is shown experimentally, both on synthetic and real data, that the computed characteristic scale is resolution independent.

The field of monitoring drinking water acquires a particular importance in the last few years. The control of risks in the factories that produce and distribute water ensures the quality of this vital resource. Several methods and techniques were implemented in order to reduce these risks. We present here by a new technique called: support vector machines (SVMs). This method is developed from the statistical learning theory, which displays optimal training performances and generalization in several fields, among others the field of pattern recognition. The exposed technique ensures within a monitoring system, a direct and quasi permanent quality control of water. For a validation of the performances of this technique used as classification tool, a study in simulation of the training time, the recognition rate and the noise sensitivity, is carried out. With an aim of showing its functionality, an application test is presented.

This paper presents a new three-dimensional biomicromanipulation system for biological objects such as embryos, cells or oocytes. As the cell is very small, kept in the liquid, and observed through a microscope, the two-dimensional visual feedback makes difficult accurate manipulation in the 3-D world. To improve the manipulation work, we proposed an augmented human-machine interface. A 3-D visual information is provided to the operator through a 3-D reconstruction method using vision-based tracking deformations of the cell embryo. In order to stable injection tasks, the operator needs force feedback and haptic assistance during penetrating the cell envelop, the chorion. The proposed human-machine user's interface allows real-time realistic visual and haptic control strategies for constrained motion in image coordinates. Virtual haptic rendering allows to constrain the path of insertion and removal in the 3-D scene or to avoid cell destruction by controlling adequately position, velocity and force parameters

We study the problem of finding the characteristic scale of a given satellite image. We want to define this feature so that it does not depend on the spatial resolution of the image. Our approach is based on the use of a linear scale space and the total variation. The critical scale is defined as the one at which the normalized total variation is maximum.

Purpose: Respiratory-induced organ motion is a technical challenge to nuclear imaging and to particle therapy dose calculations for lung cancer treatment in particular. Internal organ tissue displacements and deformations induced by breathing need to be taken into account when calculating Monte Carlo dose distributions or when performing tomographic reconstructions for PET imaging. Current techniques based on Deformable Image Registration (DIR) cannot fully take into account complex internal deformations of the lungs nor the fact that respiratory motion is not reproducible. This study proposes a method to calculate dose distribution and to reconstruct PET activities over adaptive tetrahedral meshes which deform with time. As an illustration, biomechanical modeling based on Finite Element Analysis (FEA) was used for motion field computation.

Water quality is one of the major concerns of countries around the world. Monitoring of water quality is becoming more and more interesting because of its effects on human life. The control of risks in the factories that produce and distribute water ensures the quality of this vital resource. Many techniques were developed in order to improve this process attending to rigorous follow-ups of the water quality. In this paper, we present a comparative study of the performance of three techniques resulting from the field of the artificial intelligence namely: Artificial Neural Networks (ANN), RBF Neural Networks (RBF-NN), and Support Vector Machines (SVM). Developed from the statistical learning theory, these methods display optimal training performances and generalization in many fields of application, among others the field of pattern recognition. In order to evaluate their performances regarding the recognition rate, training time, and robustness, a simulation using generated and real data is carried out. To validate their functionalities, an application performed on real data is presented. Applied as a classification tool, the technique selected should ensure, within a multisensor monitoring system, a direct and quasi permanent control of water quality.

International audience; Speckle reduction is a necessary step for many applications. Very effective methods have been developed in the recent years for single-image speckle reduction and multi-temporal speckle filtering. However , to reduce the presence of sidelobes around bright targets, SAR images are spectrally weighted and this processing impacts the speckle statistics by introducing spatial correlations. These correlations severely impact speckle reduction methods that require uncor-related speckle as input. Thus, spatial down-sampling is typically applied to reduce the speckle spatial correlations prior to speckle filtering. To better preserve the spatial resolution, we describe how to correctly resample SAR images and extract bright targets in order to process full-resolution images with speckle-reduction methods.