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Non-Rigid Structure-from-Motion (NRSfM) reconstructs a deformable 3D object from the correspondences established between monocular 2D images. Current NRSfM methods lack statistical robustness, which is the ability to cope with correspondence errors.This prevents one to use automatically established correspondences, which are prone to errors, thereby strongly limiting the scope of NRSfM. We propose a three-step automatic pipeline to solve NRSfM robustly by exploiting isometry. Step 1 computes the optical flow from correspondences, step 2 reconstructs each 3D point's normal vector using multiple reference images and integrates them to form surfaces with the best reference and step 3 rejects the 3D points that break isometry in their local neighborhood. Importantly, each step is designed to discard or flag erroneous correspondences. Our contributions include the robustification of optical flow by warp estimation, new fast analytic solutions to local normal reconstruction and their robustification, and a new scale-independent measure of 3D local isometric coherence. Experimental results show that our robust NRSfM method consistently outperforms existing methods on both synthetic and real datasets., Accepted in TPAMI 2021

International audience; Purpose: We propose to learn a 3D keypoint descriptor which we use to match keypoints extracted from full-body CT scans. Our methods are inspired by 2D keypoint descriptor learning, which was shown to outperform hand-crafted descriptors. Adapting these to 3D images is challenging because of the lack of labelled training data and high memory requirements.Method: We generate semi-synthetic training data. For that, we first estimate the distribution of local affine inter-subject transformations using labelled anatomical landmarks on a small subset of the database. We then sample a large number of transformations and warp unlabelled CT scans, for which we can subsequently establish reliable keypoint correspondences using guided matching. These correspondences serve as training data for our descriptor, which we represent by a CNN and train using the triplet loss with online triplet mining.Results: We carry out experiments on a synthetic data reliability benchmark and a registration task involving 20 CT volumes with anatomical landmarks used for evaluation purposes. Our learned descriptor outperforms the 3D-SURF descriptor on both benchmarks while having a similar runtime.Conclusion: We propose a new method to generate semi-synthetic data and a new learned 3D keypoint descriptor. Experiments show improvement compared to a hand-crafted descriptor. This is promising as literature has shown that jointly learning a detector and a descriptor gives further performance boost.

Registration maps or warps form a key element in Shape-from-Template (SfT). They relate the template with the input image, which contains the projection of the deformed surface. Recently, it was shown that isometric SfT can be solved analytically if the warp and its first-order derivatives are known. In practice, the warp is recovered by interpolating a set of discrete template-to-image point correspondences. This process relies on smoothness priors but ignores the 3D geometry. This may produce errors in the warp and poor reconstructions. In contrast, we propose to create a 3D consistent warp, which technically is a very challenging task, as the 3D shape variables must be eliminated from the isometric SfT equations to find differential constraints for the warp only. Integrating these constraints in warp estimation yields the isowarp, a warp 3D consistent with isometric SfT. Experimental results show that incorporating the isowarp in the SfT pipeline allows the analytic solution to outperform non-convex 3D shape refinement methods and the recent DNN-based SfT methods. The isowarp can be properly initialized with convex methods and its hyperparameters can be automatically obtained with cross-validation. The isowarp is resistant to 3D ambiguities and less computationally expensive than existing 3D shape refinement methods. The isowarp is thus a theoretical and practical breakthrough in SfT.

International audience; Shape-from-Template (SfT) solves the registration and 3D reconstruction of a deformable 3D object, represented by the template, from a single image. Recently, methods based on deep learning have been able to solve SfT for the wide-baseline case in real-time, clearly surpassing classical methods. However, the main limitation of current methods is the need for fine tuning of the neural models to a specific geometry and appearance represented by the template texture map. We propose the first texture-generic deep learning SfT method which adapts to new texture maps at run-time, without the need for texture specific fine tuning. We achieve this by dividing the problem into a segmentation step and a registration and reconstruction step, both solved with deep learning. We include the template texture map as one of the neural inputs in both steps, training our models to adapt to different ones. We show that our method obtains comparable or better results to previous deep learning models, which are texture specific. It works in challenging imaging conditions, including complex deformations, occlusions, motion blur and poor textures. Our implementation runs in real-time, with a low-cost GPU and CPU.

3D reconstruction of deformable objects using inter-image visual motion from monocular images has been studied under Shape-from-Template (SfT) and Non-Rigid Structure-from-Motion (NRSfM). Most methods have been developed for simple deformation models, primarily isometry. They may treat a surface as a discrete set of points and draw constraints from the points only or they may use a non-parametric representation and use both points and differentials to express constraints. We propose a differential framework based on Cartan's theory of connections and moving frames. It is applicable to SfT and NRSfM, and to deformation models other than isometry. It utilises infinitesimal-level assumptions on the surface's geometry and mappings. It has the following properties. 1) It allows one to derive existing solutions in a simpler way. 2) It models SfT and NRSfM in a unified way. 3) It allows us to introduce a new skewless deformation model and solve SfT and NRSfM for it. 4) It facilitates a generic solution to SfT which does not require deformation modeling. Our framework is complete: it solves deformable 3D reconstruction for a whole class of algebraic deformation models including isometry. We compared our solutions with the state-of-the-art methods and show that ours outperform in terms of both accuracy and computation time.

International audience; Purpose. The registration of a preoperative 3D model, reconstructed for example from MRI, to intraoperative laparoscopy 2D images, is the main challenge to achieve augmented reality in laparoscopy. The current systems have a major limitation: they require that the surgeon manually marks the occluding contours during surgery. This requires the surgeon to fully comprehend the non-trivial concept of occluding contours and surgeon time, directly impacting acceptance and usability. To overcome this limitation, we propose a complete framework for object-class occluding contour detection (OC2D), with application to uterus surgery. Methods. Our first contribution is a new distance-based evaluation score complying with all the relevant performance criteria. Our second contribution is a loss function combining cross-entropy and two new penalties designed to boost 1-pixel thickness responses. This allows us to train a U-Net end-to-end, outperforming all competing methods, which tends to produce thick responses. Our third contribution is a dataset of 3818 carefully labelled laparoscopy images of the uterus, which was used to train and evaluate our detector. Results. Evaluation shows that the proposed detector has a similar false negative rate to existing methods but substantially reduces both false positive rate and response thickness. Finally, we ran a user-study to evaluate the impact of OC2D against manually marked occluding contours in augmented laparoscopy. We used 10 recorded gynecologic laparoscopies and involved 5 surgeons. Using OC2D led to a reduction of 3 minutes and 53 seconds in surgeon time without sacrificing registration accuracy. Conclusions. We provide a new set of criteria and a distance-based measure to evaluate an OC2D method. We propose an OC2D method which outperforms the state of the art methods. The results obtained from the user study indicate that fully automatic augmented laparoscopy is feasible. 2 T. François et al. Registration Preoperative 3D model with internal structures Occluding contours deformation of the 3D model Occluding contours Occlusion boundaries Uterus Connection contours Fig. 1 (left) In augmented laparoscopy, the preoperative 3D model is registered by fitting the occluding contours of the organ in laparoscopy images. The current systems require the surgeon to mark these contours manually during surgery. (right) An occluding contour arises at an organ boundary where the organ occludes another structure, as opposed to an occlusion boundary where the organ is occluded by another structure. The set of occluding contours is the silhouette. OC2D is the task of detecting the occluding contours for a specific object, here the uterus. It forms a task of semantic detection far more challenging than organ segmentation.

A better understanding of photometry in laparoscopic images can increase the reliability of computer-assisted surgery applications. Photometry requires modelling illumination, tissue reflectance and camera response. There exists a large variety of light models, but no systematic and reproducible evaluation. We present a review of light models in laparoscopic surgery, a unified calibration approach, an evaluation methodology, and a practical use of photometry. We use images of a calibration checkerboard to calibrate the light models. We then use these models in a proposed dense stereo algorithm exploiting the shading and simultaneously extracting the tissue albedo, which we call dense shading stereo. The approach works with a broad range of light models, giving us a way to test their respective merits. We show that overly complex light models are usually not needed and that the light source position must be calibrated. We also show that dense shading stereo outperforms existing methods, in terms of both geometric and photometric errors, and achieves sub-millimeter accuracy. This work demonstrates the importance of careful light modelling and calibration for computer-assisted surgical applications. It gives guidelines on choosing the best performing light model.

Single-View Geometry (SVG) studies the world-to-image mapping or warp, which is the relationship that exists between a body’s model and its image. For a rigid body observed by a projective camera, the warp is described by the usual camera matrix and its properties. However, it is clear that for a body whose deformation state changes between the body’s model and its image, the ‘simple,’ globally parameterized warp described solely by the camera matrix, breaks down. Existing work has exploited deformation to reconstruct the deformed body from its image, but did not establish the properties of the deformable warp. Studying these properties is part of deformable SVG and forms a recent research topic. Because deformations may take place anywhere in the object’s body, and because they may be uncorrelated, the warp is local in nature. Using a differential framework is thus an obvious choice. We propose a differential–algebraic projective framework based on modeling the body’s surface by a locally rational projective embedding and on the 1D projective camera. We show that this leads, via the study of univariate rational functions, to differential invariants that the warp must satisfy. It may seem surprising, given the generic hypothesis made on the observed body, hardly stronger than mere local smoothness, that constraints can still be found. Our framework generalizes the Schwarzian derivative, the first-order projective differential invariant, which holds under the assumption that the body’s shape is locally linear. Our invariants may be used to construct regularizers to be used in warp estimation. We report experimental results of two types on simulated and real data. The first type shows that the proposed invariants hold well for an independently estimated warp. The second type shows that the proposed regularizers improve warp estimation from point correspondences compared to the classical derivative-penalizing regularizers.

The visual examination of colonoscopic images fails to extract precise geometric information of the colonic surface. Reconstructing the 3D surface of the colon from colonoscopic image sequences may thus add valuable clinical information. We address this problem of extracting precise spatio-temporal 3D structure information from colonoscopic images. Using just the intrinsically calibrated monocular image stream, we develop a technique to compute the depth of certain feature points that have been tracked across images. Our method uses the prior knowledge of an approximate geometry of the colon, called the (TTP). It works by fitting a deformable cylindrical model to points reconstructed independently by non-rigid structure-from-motion (NRSfM), compromising between the data term and a novel tubular smoothing prior. Our method represents the first method ever to exploit a very weak topological prior to improve NRSfM. As such, it lies in-between standard NRSfM, which does not use a topological prior beyond the mere plane, and shape-from-template (SfT), which uses a very strong prior as a full deformable 3D object model. We validate our method on both synthetic images of tubular structures and real colonoscopic data. Our method improves the results obtained by existing NRSfM methods by 71.74% on average on synthetic data and succeeds in obtaining 3D reconstruction from a real colonoscopic sequence defeating the existing methods. Colonoscopic 3D reconstruction is a difficult problem, which is yet unresolved by the existing methods from computer vision. Our proposed dedicated NRSfM method and experiments show that the visual motion might be the right visual cue to use in colonoscopy.

Augmented Reality (AR) is a promising way to precisely locate the internal structures of an organ in laparoscopy. Several methods have been proposed to register a preoperative 3D model reconstructed from MRI or CT to the intraoperative laparoscopy 2D images. These methods assume a fixed topology of the 3D model. They thus quickly fail once the organ is cut to remove pathological internal structures. We propose to add image-based incision detection in the registration pipeline, in order to update the topology of the organ model. Whenever an incision is detected, it is transferred to the 3D model, whose topology is then updated accordingly, and registration started. We trained a UNet as incision detector from 181 labelled incision images, collected from 10 myomectomy procedures. It obtains a mean precision, recall and f1 score of 0.05, 0.36, and 0.08 from 10-fold cross-validation. Overall, topology updating improves 3D registration accuracy by 5% on average.

Deformable registration is required to achieve laparoscopic augmented reality but still is an open problem. Some of the existing methods reconstruct a preoperative model and register it using anatomical landmarks from a single image. This is not accurate due to depth ambiguities. Other methods require of non-standard devices unadapted to the clinical practice. A reasonable way to improve accuracy is to combine multiple images from a monocular laparoscope. We propose three novel registration methods exploiting information from multiple images. The first two are based on rigidly-related images (MV-B and MV-C) and the third one on non-rigidly-related images (MV-D). We evaluated registration accuracy quantitatively on synthetic and phantom data, and qualitatively on patient data, comparing our results with state of the art methods. Our methods outperforms, reducing the partial visibility and depth ambiguity issues of single-view approaches. We characterise the improvement margin, which may be slight or significant, depending on the scenario.

International audience; A major research area in Computer Assisted Intervention (CAI) is to aid laparoscopic surgery teams with Augmented Reality (AR) guidance. This involves registering data from other modalities such as MR and fusing it with the laparoscopic video in real-time, to reveal the location of hidden critical structures. We present the first system for AR guided laparoscopic surgery of the uterus. This works with pre-operative MR or CT data and monocular laparoscopes, without requiring any additional interventional hardware such as optical trackers. We present novel and robust solutions to two main sub-problems: the initial registration, which is solved using a short exploratory video, and update registration, which is solved with real-time tracking-by-detection. These problems are challenging for the uterus because it is a weakly-textured, highly mobile organ that moves independently of surrounding structures. In the broader context, our system is the first that has successfully performed markerless real-time registration and AR of a mobile human organ with monocular laparoscopes in the OR.

The vast majority of mesh-based modelling applications iteratively transform the mesh vertices under prescribed geometric conditions. This occurs in particular in methods cycling through the constraint set such as Position-Based Dynamics (PBD). A common case is the approximate local area preservation of triangular 2D meshes under external editing constraints. At the constraint level, this yields the nonconvex optimal triangle projection under prescribed area problem, for which there does not currently exist a direct solution method. In current PBD implementations, the area preservation constraint is linearised. The solution comes out through the iterations, without a guarantee of optimality, and the process may fail for degenerate inputs where the vertices are colinear or colocated. We propose a closed-form solution method and its numerically robust algebraic implementation. Our method handles degenerate inputs through a two-case analysis of the problem’s generic ambiguities. We show in a series of experiments in area-based 2D mesh editing that using optimal projection in place of area constraint linearisation in PBD speeds up and stabilises convergence.

International audience; We address the monocular visual shape servoing problem. This pushes the challenging visual servoing problem one step further from rigid object manipulation towards deformable object manipulation. Explicitly, it implies deforming the object towards a desired shape in 3D space by robots using monocular 2D vision. We specifically concentrate on a scheme capable of controlling large isometric deformations. Two important open subproblems arise for implementing such a scheme. (P1) Since it is concerned with large deformations, perception requires tracking the deformable object's 3D shape from monocular 2D images which is a severely underconstrained problem. (P2) Since rigid robots have fewer degrees of freedom than a deformable object, the shape control becomes underactuated. We propose a template-based shape servoing scheme in which we solve these two problems. The template allows us to both infer the object's shape using an improved Shape-from-Template algorithm and steer the object's deformation by means of the robots' movements. We validate the scheme via simulations and real experiments.

We propose an original approach to absolute pose and structure-from-motion (SfM) which handles rolling shutter (RS) effects. Unlike most existing methods which either augment global shutter projection with velocity parameters or impose continuous time and motion through pose interpolation, we use local differential constraints. These are established by drawing analogies with non-rigid 3D vision techniques, namely shape-from-template and non-rigid SfM (NRSfM). The proposed idea is to interpret the images of a rigid surface acquired by a moving RS camera as those of a virtually deformed surface taken by a GS camera. These virtually deformed surfaces are first recovered by relaxing the RS constraint using SfT or NRSfM. Then we upgrade the virtually deformed surface to the actual rigid structure and compute the camera pose and ego-motion by reintroducing the RS constraint. This uses a new 3D-3D registration procedure that minimizes a cost function based on the Euclidean 3D point distance. This is more stable and physically meaningful than the reprojection error or the algebraic distance used in previous work. Experimental results obtained with synthetic and real data show that the proposed methods outperform existing ones in terms of accuracy and stability, even in the known critical configurations.

International audience; Augmented Reality (AR) in monocular liver laparoscopy requires one to register a preoperative 3D liver model to a laparoscopy image. This is a difficult problem because the preoperative shape may significantly differ from the unknown intraoperative shape and the liver is only partially visible in the laparoscopy image. Previous approaches are either manual, using a rigid model, or automatic, using visual cues and a biomechanical model. We propose a new approach called the hybrid approach combining the best of both worlds. The visual cues allow us to capture the machine perception while user interaction allows us to take advantage of the surgeon's prior knowledge and spatial understanding of the patient anatomy. The registration accuracy and repeatability were evaluated on phantom, animal ex vivo and patient data respectively. The proposed registration outperforms the state of the art methods both in terms of accuracy and repeatability. An average registration error below the 1 cm oncologic margin advised in the literature for tumour resection in laparoscopy hepatectomy was obtained.

Background and objective:Surgical tool detection, segmentation, and 3D pose estimation are crucial components in Computer-Assisted Laparoscopy (CAL). The existing frameworks have two main limitations. First, they do not integrate all three components. Integration is critical; for instance, one should not attempt computing pose if detection is negative. Second, they have highly specific requirements, such as the availability of a CAD model. We propose an integrated and generic framework whose sole requirement for the 3D pose is that the tool shaft is cylindrical. Our framework makes the most of deep learning and geometric 3D vision by combining a proposed Convolutional Neural Network (CNN) with algebraic geometry. We show two applications of our framework in CAL: tool-aware rendering in Augmented Reality (AR) and tool-based 3D measurement. Methods:We name our CNN as ART-Net (Augmented Reality Tool Network). It has a Single Input Multiple Output (SIMO) architecture with one encoder and multiple decoders to achieve detection, segmentation, and geometric primitive extraction. These primitives are the tool edge-lines, mid-line, and tip. They allow the tool’s 3D pose to be estimated by a fast algebraic procedure. The framework only proceeds if a tool is detected. The accuracy of segmentation and geometric primitive extraction is boosted by a new Full resolution feature map Generator (FrG). We extensively evaluate the proposed framework with the EndoVis and new proposed datasets. We compare the segmentation results against several variants of the Fully Convolutional Network (FCN) and U-Net. Several ablation studies are provided for detection, segmentation, and geometric primitive extraction. The proposed datasets are surgery videos of different patients. Results:In detection, ART-Net achieves 100.0 % in both average precision and accuracy. In segmentation, it achieves 81.0 % in mean Intersection over Union (mIoU) on the robotic EndoVis dataset (articulated tool), where it outperforms both FCN and U-Net, by 4.5 p p and 2.9 p p , respectively. It achieves 88.2 % in mIoU on the remaining datasets (non-articulated tool). In geometric primitive extraction, ART-Net achieves 2.45 ∘ and 2.23 ∘ in mean Arc Length (mAL) error for the edge-lines and mid-line, respectively, and 9.3 pixels in mean Euclidean distance error for the tool-tip. Finally, in terms of 3D pose evaluated on animal data, our framework achieves 1.87 mm, 0.70 mm, and 4.80 mm mean absolute errors on the X , Y , and Z coordinates, respectively, and 5 . 94 ∘ angular error on the shaft orientation. It achieves 2.59 mm and 1.99 mm in mean and median location error of the tool head evaluated on patient data. Conclusions:The proposed framework outperforms existing ones in detection and segmentation. Compared to separate networks, integrating the tasks in a single network preserves accuracy in detection and segmentation but substantially improves accuracy in geometric primitive extraction. Overall, our framework has similar or better accuracy in 3D pose estimation while largely improving robustness against the very challenging imaging conditions of laparoscopy. The source code of our framework and our annotated dataset will be made publicly available at https://github.com/kamruleee51/ART-Net .

This paper studies the 3D reconstruction of a deformable surface from a single image and a reference surface, known as the template. This problem is known as Shape-from-Template and has been recently shown to be well-posed for isometric deformations, for which the surface bends without altering geodesics. This paper studies the case of equiareal deformations. They are elastic deformations where the local area is preserved and thus include isometry as a special case. Elastic deformations have been studied before in Shape-from-Template, yet no theoretical results were given on the existence or uniqueness of solutions. The equiareal model is much more widely applicable than isometry. This paper brings Monge’s theory, widely used for studying the solutions of nonlinear first-order PDEs, to the field of 3D reconstruction. It uses this theory to establish a theoretical framework for equiareal Shape-from-Template and answers the important question of whether it is possible to reconstruct a surface exactly with a much weaker prior than isometry. We prove that equiareal Shape-from-Template has a maximum of two local solutions sufficiently near an initial curve that lies on the surface. In addition, we propose an analytical reconstruction algorithm that can recover the multiple solutions. Our algorithm uses standard numerical tools for ODEs. We use the perspective camera model and give reconstruction results with both synthetic and real examples.

In the recent past, algorithms based on Convolutional Neural Networks (CNNs) have achieved significant milestones in object recognition. With large examples of each object class, standard datasets train well for inter-class variability. However, gathering sufficient data to train for a particular instance of an object within a class is impractical. Furthermore, quantitatively assessing the imaging conditions for each image in a given dataset is not feasible. By generating sufficient images with known imaging conditions, we study to what extent CNNs can cope with hard imaging conditions for instance-level recognition in an active learning regime. Leveraging powerful rendering techniques to achieve instance-level detection, we present results of training three state-of-the-art object detection algorithms namely, Fast R-CNN, Faster R-CNN and YOLO9000, for hard imaging conditions imposed into the scene by rendering. Our extensive experiments produce a mean Average Precision score of 0.92 on synthetic images and 0.83 on real images using the best performing Faster R-CNN. We show for the first time how well detection algorithms based on deep architectures fare for each hard imaging condition studied.

International audience; "Background: Optical Coherence Tomography (OCT) is an emerging medical imaging technology. It is well suited to various medical applications requiring tissue imaging with micrometer resolution and millimeter penetration depth such as in ophthalmology and dermatology. Despite its numerous advantages, OCT has a long acquisition time for high-resolution images or volumes. This paper deals with the development of a Compressed, Sensing (CS) paradigm for faster 3-dimensional OCT image acquisition.Methods: The proposed framework includes three main steps: 1) defining a random-like and parameterizable and continuous scanning trajectories that must be compatible with a smooth mechanical scan, 2) rasterizing the scanning trajectory to make it achievable by a physical system (i.e. galvanometer mirrors), and 3) incorporating a high sparsifying data technique so-called 3D shearlet transform into the compressed sensing scheme. Actually, shearlet transform is mathematically optimal for multidimensional data decomposition and has been proven more efficient than classical ones such as those obtained by wavelet or curvelet transforms. Actually, shearlet system provides a very efficient tool for encoding anisotropic features (such as edges in images) in multivariate problem classes.Results: Numerical simulations and ex vivo experiments were carried out. The obtained results showed the ability of the proposed method to recover OCT images and volumes with high fidelity for different subsampling rates and scanning schemes, demonstrating the relevance of the proposed approach."

We show how photometric and motion-based approaches can be combined to reconstruct the 3D shape of deformable objects from monocular images. We start by motivating the problem using real-world applications. We give a comprehensive overview of the state-of-the-art approaches and discuss their limitations for practical use in these applications. We then introduce the problem of Non-Rigid Structure-from-Motion and Shading (NRSfMS), where photometric and geometric information is used for reconstruction, without prior knowledge about the shape of the deformable object. We present in detail the first technical solution to NRSfMS and close the chapter with the main remaining open problems.

Navigated panretinal photocoagulation is a standard care for proliferative diabetic retinopathy. Slit-lamp-based systems used for this treatment provide a narrow view of the retina. Retinal mosaics are used for view expansion and treatment planning. Mosaicing slit-lamp images is a hard task due to the absence of a physical model of the imaging process, large textureless regions and imaging artifacts, mostly reflections. We present a comparative study of various geometric transformation models applied to retinal image mosaicing in computer-assisted slit-lamp imaging. We propose an efficient point correspondence-based framework for transformation model evaluation in a typical closed-loop motion scenario. We compare the performance of multiple linear and nonlinear models of different complexities and assess the effect of the number of points used for parameter estimation. We use a local fitting error (LFE) metric to estimate the models’ performance in pairwise registration. Because LFE alone is not conclusive regarding the problem of accumulated drift, we propose a loop closure error (LCE) metric to quantify the effect of accumulated local registration errors. We also provide a new normalization procedure for the quadratic transformation model, widely used in retinal image registration. In total, seven transformation models were evaluated on three datasets of long image sequences. LFE decreases with increasing complexity of the model, while LCE, in contrast, shows superior performance of simple models. Varying the number of point correspondences did not reveal a common trend for the LCE metric, showing an increase in the error for simple models and an unstable behavior of the complex models. Our results show that simple models are less sensitive to drift and preferable for sequential mosaicing in slit-lamp imaging, while more complex models are the best choice for short-term registration.

The registration of preoperative 3D images to intra-operative laparoscopic 2D images is one of the main concerns for augmented reality in computer-assisted surgery. For laparoscopic liver surgery, while several algorithms have been proposed, there is neither a public dataset nor a systematic evaluation methodology to quantitatively evaluate registration accuracy. Our main contribution is to provide such a dataset with an in vivo porcine model. It is used to evaluate a state-of-the-art registration algorithm that is capable of simultaneous registration and soft-body collision reasoning. The dataset consists of 13 deformed liver states, with corresponding exploration videos and interventional CT acquisitions with 60 small artificial fiducials located on the surface of the liver and distributed within the parenchyma, where a precise registration is crucial for augmented reality. This dataset will be made public. Using this dataset, we show that collision reasoning improves performance of registration for strong deformation and independent lobe motion. This dataset addresses the lack of public datasets in this field. As an example of use, we present and evaluate a state-of-the-art energy-based approach and a novel extension that handles self-collisions.

Machine learning based methods achieves impressive results in object classification and detection. Utilizing representative data of the visual world during the training phase is crucial to achieve good performance with such data driven approaches. However, it not always possible to access bias-free datasets thus, robustness to biased data is a desirable property for a learning system. Capsule Networks have been introduced recently and their tolerance to biased data has received little attention. This paper aims to fill this gap and proposes two experimental scenarios to assess the tolerance to imbalanced training data and to determine the generalization performance of a model with unfamiliar affine transformations of the images. This paper assesses dynamic routing and EM routing based Capsule Networks and proposes a comparison with Convolutional Neural Networks in the two tested scenarios. The presented results provide new insights into the behaviour of capsule networks.

We propose a novel learning approach, in the form of a fully-convolutional neural network (CNN), which automatically and consistently removes specular highlights from a single image by generating its diffuse component. To train the generative network, we define an adversarial loss on a discriminative network as in the GAN framework and combined it with a content loss. In contrast to existing GAN approaches, we implemented the discriminator to be a multi-class classifier instead of a binary one, to find more constraining features. This helps the network pinpoint the diffuse manifold by providing two more gradient terms. We also rendered a synthetic dataset designed to help the network generalize well. We show that our model performs well across various synthetic and real images and outperforms the state-of-the-art in consistency.

We propose a new method for the absolute camera pose problem (PnP) which handles Rolling Shutter (RS) effects. Unlike all existing methods which perform 3D-2D registration after augmenting the Global Shutter (GS) projection model with the velocity parameters under various kinematic models, we propose to use local differential constraints. These are established by drawing an analogy with Shape-from-Template (SfT). The main idea consists in considering that RS distortions due to camera ego-motion during image acquisition can be interpreted as virtual deformations of a template captured by a GS camera. Once the virtual deformations have been recovered using SfT, the camera pose and ego-motion are computed by registering the deformed scene on the original template. This 3D-3D registration involves a 3D cost function based on the Euclidean point distance, more physically meaningful than the re-projection error or the algebraic distance based cost functions used in previous work. Results on both synthetic and real data show that the proposed method outperforms existing RS pose estimation techniques in terms of accuracy and stability of performance in various configurations.

Augmented reality for monocular laparoscopy from a preoperative volume such as CT is achieved in two steps. The first step is to segment the organ in the preoperative volume and reconstruct its 3D model. The second step is to register the preoperative 3D model to an initial intraoperative laparoscopy image. To date, there does not exist an automatic initial registration method to solve the second step for the liver in the de facto operating room conditions of monocular laparoscopy. Existing methods attempt to solve for both deformation and pose simultaneously, leading to nonconvex problems with no optimal solution algorithms. We propose in contrast to break the problem down into two parts, solving for (i) deformation and (ii) pose. Part (i) simulates biomechanical deformations from the preoperative to the intraoperative state to predict the liver’s unknown intraoperative shape by modeling gravity, the abdominopelvic cavity’s pressure and boundary conditions. Part (ii) rigidly registers the simulated shape to the laparoscopy image using contour cues. Our formulation leads to a well-posed problem, contrary to existing methods. This is because it exploits strong environment priors to complement the weak laparoscopic visual cues. Quantitative results with in silico and phantom experiments and qualitative results with laparosurgery images for two patients show that our method outperforms the state-of-the-art in accuracy and registration time.

We present a novel solution to soft-body registration between a pre-operative 3D patient model and an intra-operative surface mesh of the patient lying on the operating table, acquired using an inexpensive and portable depth (RGBD) camera. The solution has several clinical applications, including skin dose mapping in interventional radiology and intra-operative image guidance. We propose to solve this with a robust non-rigid registration algorithm that handles partial surface data, significant posture modification and patient-table collisions. We investigate several unstudied and important aspects of this registration problem. These are the benefits of heterogeneous versus homogeneous biomechanical models and the benefits of modeling patient/table interaction as collision constraints. We also study how abdominal registration accuracy varies as a function of scan length in the caudal-cranial axis.

International audience; The specularity prediction task in images, given the camera pose and scene geometry, is challenging and ill-posed. A recent approach called JOint LIght-MAterial Specularity (JOLIMAS) addresses this problem using a geometric model under the assumption that specularities have an elliptical shape. We address the most recent version of the model, Dual JOLIMAS, which is limited to planar and convex surfaces where the local surface's curvature under the specularity is constant. We propose a canonical representation of the JOLIMAS model that is independent of the local surface curvature. To reconstruct our model represented by a 3D quadric, we use at least 3 ellipses fitted to specularities and transform their shape to fit a planar surface and simulate a planar mirror which does not distort the image of the reflected object. After reconstruction, we project the 3D quadric into an ellipse and transform it to fit the current local curvature of the surface on a new viewpoint. We assessed this method on both synthetic and real sequences, and compared it to the previous approach Dual JOLIMAS.

Predicting specularities in images, given the camera pose and scene geometry from SLAM, forms a challenging and open problem. It is nonetheless essential in several applications such as retexturing. A recent geometric model called JOLIMAS partially answers this problem, under the assumptions that the specularities are elliptical and the scene is planar. JOLIMAS models a moving specularity as the image of a fixed 3D quadric. We propose dual JOLIMAS, a new model which raises the planarity assumption. It uses the fact that specularities remain elliptical on convex surfaces and that every surface can be divided in convex parts. The geometry of dual JOLIMAS then uses a 3D quadric per convex surface part and light source, and predicts the specularities by a means of virtual cameras, allowing it to cope with surface's unflatness. We assessed the efficiency and precision of dual JOLIMAS on multiple synthetic and real videos with various objects and lighting conditions. We give results of a retexturing application. Further results are presented as supplementary video material.

We present a global and convex formulation for the template-less 3D reconstruction of a deforming object with the perspective camera. We show for the first time how to construct a Second-Order Cone Programming (SOCP) problem for Non-Rigid Structure-from-Motion (NRSfM) using the Maximum-Depth Heuristic (MDH). In this regard, we deviate strongly from the general trend of using affine cameras and factorization-based methods to solve NRSfM, which do not perform well with complex nonlinear deformations. In MDH, the points' depths are maximized so that the distance between neighbouring points in camera space are upper bounded by the geodesic distance. In NRSfM both geodesic and camera space distances are unknown. We show that, nonetheless, given point correspondences and the camera's intrinsics the whole problem can be solved with SOCP. This is the first convex formulation for NRSfM with physical constraints. We further present how robustness and temporal continuity can be included in the formulation to handle outliers and decrease the problem size, respectively. We show with extensive experiments that our methods accurately reconstruct quasi-isometric objects from partial views under articulated and strong deformations. Compared to the previous methods, our approach gives better or similar accuracy. It naturally handles missing correspondences, non-smooth objects and is very simple to implement compared to previous methods, with only one free parameter (the neighbourhood size).

One of the important goals of medical augmented reality is to reveal the hidden anatomy, such as a tumor in an organ. However, conveying a hidden tumor's depth to the user effortlessly and precisely is still an unsolved problem. This is especially difficult in monocular laparoscopy. First, the number of available depth cues is in practice limited to only two: occlusion and relative size. Second, exploiting these cues is not an easy task either. We propose a specific visualization consisting of auxiliary orthographic tumor silhouettes on the front and back surfaces of the organ and a semi-transparent tumor in between. This creates two depth planes forming a perceivable ratio-scaled metric space for the tumor. We conducted a user study to evaluate the proposed visualization. The results show that subsurface tumor depth perception is improved dramatically compared to the conventional transparent overlay.

Significant progress has been recently made in Non-Rigid Structure-from-Motion (NRSfM). However, existing methods do not handle poorly-textured surfaces that deform non-smoothly. These are nonetheless common occurrence in real-world applications. An important unanswered question is whether shading can be used to robustly handle these cases. Shading is complementary to motion because it constrains reconstruction densely at textureless regions, and has been used in several other reconstruction problems. The challenge we face is to simultaneously and densely estimate non-smooth, non-rigid shape from each image together with non-smooth, spatially-varying surface albedo (which is required to use shading). We tackle this using an energy-based formulation that combines a physical, discontinuity-preserving deformation prior with motion, shading and contour information. This is a large-scale, highly non-convex optimization problem, and we propose a cascaded optimization that converges well without an initial estimate. Our approach works on both unorganized and organized small-sized image sets, and has been empirically validated on four real-world datasets for which all state-of-the-art approaches fail.

The aim of Diminished Reality (DR) is to remove a target object in a live video stream seamlessly. In our approach, the area of the target object is replaced with new texture that blends with the rest of the image. The result is then propagated to the next frames of the video. One of the important stages of this technique is to update the target region with respect to the illumination change. This is a complex and recurrent problem when the viewpoint changes. We show that the state-of-the-art in DR fails in solving this problem, even under simple scenarios. We then use local illumination models to address this problem. According to these models, the variation in illumination only affects the specular component of the image. In the context of DR, the problem is therefore solved by propagating the specularities in the target area. We list a set of structural properties of specularities which we incorporate in two new models for specularity propagation. Our first model includes the same property as the previous approaches, which is the smoothness of illumination variation, but has a different estimation method based on the Thin-Plate Spline. Our second model incorporates more properties of the specularity's shape on planar surfaces. Experimental results on synthetic and real data show that our strategy substantially improves the rendering quality compared to the state-of-the-art in DR.

Specularities are often problematic in computer vision since they impact the dynamic range of the image intensity. A natural approach would be to predict and discard them using computer graphics models. However, these models depend on parameters which are difficult to estimate (light sources, objects’ material properties and camera). We present a geometric model called JOLIMAS: JOint LIght-MAterial Specularity, which predicts the shape of specularities. JOLIMAS is reconstructed from images of specularities observed on a planar surface. It implicitly includes light and material properties, which are intrinsic to specularities. This model was motivated by the observation that specularities have a conic shape on planar surfaces. The conic shape is obtained by projecting a fixed quadric on the planar surface. JOLIMAS thus predicts the specularity using a simple geometric approach with static parameters (object material and light source shape). It is adapted to indoor light sources such as light bulbs and fluorescent lamps. The prediction has been tested on synthetic and real sequences. It works in a multi-light context by reconstructing a quadric for each light source with special cases such as lights being switched on or off. We also used specularity prediction for dynamic retexturing and obtained convincing rendering results. Further results are presented as supplementary video material, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TVCG.2017.2677445 .

We propose a new monocular 3-D reconstruction method adapted for reconstructing organs in the abdominal cavity. It combines both motion and shading cues. The former uses a conformal deformation prior and the latter the Cook-Torrance reflectance model. Our method runs in two phases: first, a 3-D geometric and photometric template of the organ at rest is reconstructed in vivo. The geometric shape is reconstructed using rigid shape-from-motion while the surgeon is exploring-but not deforming-structures in the abdominal cavity. This geometric template is then used to retrieve the photometric properties. A nonparametric model of the light's direction of the laparoscope and the Cook-Torrance reflectance model of the organ's tissue are estimated. Second, the surgeon manipulates and deforms the environment. Here, the 3-D template is conformally deformed to globally match a set of few correspondences between the 2-D image data provided by the monocular laparoscope and the 3-D template. Then, the coarse 3-D shape is refined using shading cues to obtain a final 3-D deformed shape. This second phase only relies on a single image. Therefore, it copes with both sequential processing and self-recovery from tracking failure. The proposed approach has been validated using 1) ex vivo and in vivo data with ground-truth, and 2) in vivo laparoscopic videos of a patient's uterus. Our experimental results illustrate the ability of our method to reconstruct natural 3-D deformations typical in real surgical procedures.

The slit lamp is an essential instrument for eye care. It is used in navigated laser treatment with retina mosaicing to assist diagnosis. Specifics of the imaging setup introduce bothersome illumination artifacts. They not only degrade the quality of the mosaic but may also affect the diagnosis. Existing solutions in SLIM manage to deal with strong glares which corrupt the retinal content entirely while leaving aside the correction of semitransparent specular highlights and lens flare. This introduces ghosting and information loss. We propose an effective technique to detect and correct light reflections of different degrees in SLIM. We rely on the specular-free image concept to obtain glare-free image and use it coupled with a contextually driven probability map to segment the visible part of the retina in every frame before image mosaicing. We then perform the image blending on a subset of all spatially aligned frames. We detect the lens flare and label each pixel as ‘flare’ or ‘non flare’ using a probability map. We then invoke an adequate blending method. We also introduce a new quantitative measure for global photometric quality. We tested on a set of video sequences obtained from slit lamp examination sessions of 11 different patients presenting healthy and unhealthy retinas. The segmentation of glare and visible retina was evaluated and compared to state-of-the-art methods. The correction of lens flare and semitransparent highlight with content-aware blending was applied and its performance was evaluated qualitatively and quantitatively. The experiments demonstrated that integrating the proposed method to the mosaicing framework significantly improves the global photometric quality of the mosaics and outperforms existing works in SLIM.

The deformable registration of a preoperative organ volume to an intraoperative laparoscopy image is required to achieve augmented reality in laparoscopy. This is an extremely challenging objective for the liver. This is because the preoperative volume is textureless, and the liver is deformed and only partially visible in the laparoscopy image. We solve this problem by modeling the preoperative volume as a Neo-Hookean elastic model, which we evolve under shading and contour cues. The contour cues combine the organ’s silhouette and a few curvilinear anatomical landmarks. The problem is difficult because the shading cue is highly nonconvex and the contour cues give curve-level (and not point-level) correspondences. We propose a convergent alternating projections algorithm, which achieves a \(4\%\) registration error.

International audience; Specularities, which are often visible in images, may be problematic in computer vision since they depend on parameters which are difficult to estimate in practice. We present an empirical model called JOLIMAS: JOint LIght-MAterial Specularity, which allows specularity prediction. JOLIMAS is reconstructed from images of specular reflections observed on a planar surface and implicitly includes light and material properties which are intrinsic to specularities. This work was motivated by the observation that specularities have a conic shape on planar surfaces. A theoretical study on the well known illumination models of Phong and Blinn-Phong was conducted to support the accuracy of this hypothesis. A conic shape is obtained by projecting a quadric on a planar surface. We showed empirically the existence of a fixed quadric whose perspective projection fits the conic shaped specularity in the associated image. JOLIMAS predicts the complex phenomenon of specularity using a simple geometric approach with static parameters on the object material and on the light source shape. It is adapted to indoor light sources such as light bulbs or fluorescent lamps. The performance of the prediction was convincing on synthetic and real sequences. Additionally, we used the specularity prediction for dynamic retexturing and obtained convincing rendering results. Further results are presented as supplementary material.

Shape-from-Template (SfT) uses an object's shape template and a deformation law to achieve single-image reconstruction. SfT is a fundamental tool to retexture or augment a deformable object in a monocular video. It has matured for isometric deformations, but the non-isometric case is yet largely open. This is because modeling is generally more complicated and the constraints certainly weaker. Existing algorithms use, for instance, linear elasticity, require one to provide boundary conditions represented by known deformed shape parts and need nonconvex optimization. We use a very simple and generic model to show that non-isometric SfT has a unique solution up to scale under strong perspective imaging and mild deformation curvature. Our model uses a novel type of homography interpretation that we call Perspective-Projection-Affine-Embedding. It may use boundary conditions if available and can be estimated with Linear Least Squares optimization. We provide experimental results on synthetic and real data.

The construction of seamless and accurate mosaics from long slit-lamp retinal video sequences is an important and challenging task in navigated Pan-Retinal Photocoagulation. The main difficulty is accumulated registration drift due to the small number of features away from the optic nerve and the distortion induced by the geometry of the eye and the contact lens. We present a new approach to reduce the drift. Our main idea is to create long-term high precision point correspondences by associating a simple global model with local correction and perform key-frame based Bundle Adjustment. We evaluate the method's performance compared to state-of-the-art. The results obtained with our method show significantly lower accumulated error.

Developable surfaces have been extensively studied in computer graphics because they are involved in a large body of applications. This type of surfaces has also been used in computer vision and document processing in the context of three-dimensional (3D) reconstruction for book digitization and augmented reality. Indeed, the shape of a smoothly deformed piece of paper can be very well modeled by a developable surface. Most of the existing developable surface parameterizations do not handle boundaries or are driven by overly large parameter sets. These two characteristics become issues in the context of developable surface reconstruction from real observations. Our main contribution is a generative model of bounded developable surfaces that solves these two issues. Our model is governed by intuitive parameters whose number depends on the actual deformation and including the “flat shape boundary”. A vast majority of the existing image-based paper 3D reconstruction methods either require a tightly controlled environment or restricts the set of possible deformations. We propose an algorithm for reconstructing our model's parameters from a general smooth 3D surface interpolating a sparse cloud of 3D points. The latter is assumed to be reconstructed from images of a static piece of paper or any other developable surface. Our 3D reconstruction method is well adapted to the use of keypoint matches over multiple images. In this context, the initial 3D point cloud is reconstructed by structure-from-motion for which mature and reliable algorithms now exist and the thin-plate spline is used as a general smooth surface model. After initialization, our model's parameters are refined with model-based bundle adjustment. We experimentally validated our model and 3D reconstruction algorithm for shape capture and augmented reality on seven real datasets. The first six datasets consist of multiple images or videos and a sparse set of 3D points obtained by structure-from-motion. The last dataset is a dense 3D point cloud acquired by structured light. Our implementation has been made publicly available on the authors' web home pages. Copyright © 2012 John Wiley & Sons, Ltd.

Generalized procrustes analysis computes the best set of transformations that relate matched shape data. In shape analysis the transformations are usually chosen as similarities, while in general statistical data analysis other types of transformation groups such as the affine group may be used. Generalized procrustes analysis has a nonlinear and nonconvex formulation. The classical approach alternates the computation of a so-called reference shape and the computation of transformations relating this reference shape to each shape datum in turn. We propose the stratified approach to generalized procrustes analysis. It first uses the affine transformation group to analyze the data and then upgrades the solution to the sought after group, whether Euclidean or similarity. We derive a convex formulation for each of these two steps, and efficient practical algorithms that gracefully handle missing data (incomplete shapes). Extensive experimental results show that our approaches perform well on simulated and real data. In particular our closed-form solution gives very accurate results for generalized procrustes analysis of Euclidean data.

In abdominal surgery, augmented reality has been attempted by registering preoperative 3D data onto the intraoperative laparoscopic view. The registration may be aided by an interventional 3D imaging system such as a rotational C-arm. It has been shown that one can determine the transformation between an intraoperative 3D volume and the laparoscopic view by letting the laparoscope tip enter the C-arm acquisition field. However, the transformation estimation was upi¾źto a 1D rotation and a 2D translation. We propose to complete this registration by using local shading constraints with a piecewise constant albedo hypothesis on the surface of the surgical scene. Thus, the registration becomes fully automatic with no extra apparatus required. Results from experiments on in vivo data show a millimetric registration accuracy.

Shape-from-Template (SfT) aims to reconstruct a deformable object from a single image using a texture-mapped 3D model of the object in a reference position. Most existing SfT methods require well-textured surfaces that deform smoothly, which is a significant limitation. Due to the sparsity of correspondence constraint and strong regularizations, they usually fail to reconstruct strong changes of surface curvature such as surface creases. We investigate new ways to solve SfT for creased surfaces. Our main idea is to implicitly model creases with a dense mesh-based surface representation with an associated robust bending energy term, which deactivates curvature smoothing automatically where needed. Crucially, the crease locations are not required a priori since they emerge as the lowest-energy state during optimization. We show with real data that by combining this model with correspondence and surface boundary constraints we can successfully reconstruct creases while also preserving smooth regions.

International audience; Mapping an endometrial tissue seen in Transvaginal Ultrasound (TVUS) to a Magnetic Resonance (MR) image is an important approach to improve the accuracy of diagnosis and preoperative surgery planning. To date, no technical methods solve this problem. In a clinical setting where only one 2D TVUS image is available, methods in the state of the art require the selection of a 2D MR slice and use 2D/2D deformable image registration. This is a limitation since the standard TVUS and MR imaging techniques used for diagnosing endometriosis are 2D and 3D, respectively. In this work, we introduce a novel deformable slice-to-volume registration process to overcome this limitation. To this end, we register a 2D TVUS image to a 3D MR data in order to transform the TVUS planar image to a curved 2D surface in MR volume. The proposed method facilitates the transfer of two types of information from a 2D TVUS image to the MR volume: 1) the location and shape of small implants and 2) the implants' depth of infiltration in the host tissue. Our TVUS-MR registration method uses contour to surface correspondences through a novel variational one-step deformable Iterative Closest Point (ICP) method. We demonstrate the accuracy of the proposed method by quantitative and qualitative tests on semi-synthetic and clinical data sets. Experimental error analysis shows high registration accuracy, illustrating the robustness of the method.

We present a method for the automatic detection of the uterus and the Fallopian tube/Uterus junctions (FU-junctions) in a monocular laparoscopic image. The main application is to perform automatic registration and fusion between preoperative radiological images of the uterus and laparoscopic images for image-guided surgery. In the broader context of computer assisted intervention, our method is the first that detects an organ and registration landmarks from laparoscopic images without manual input. Our detection problem is challenging because of the large inter-patient anatomical variability and pathologies such as uterine fibroids. We solve the problem using learned contextual geometric constraints that statistically model the positions and orientations of the FU-junctions relative to the uterus’ body. We train the uterus detector using a modern part-based approach and the FU-junction detector using junction-specific context-sensitive features. We have trained and tested on a database of 95 uterus images with cross validation, and successfully detected the uterus with Recall = 0.95 and average Number of False Positives per Image (NFPI) = 0.21, and FU-junctions with Recall = 0.80 and NFPI = 0.50. Our experimental results show that the contextual constraints are fundamental to achieve high quality detection.

International audience; Two singing synthesis instruments are presented : Digitalis Cantor and Digitartic. Both instruments use bimanual writing or drawing gestures on graphic tablets. The voice signal is computed with the help of a parametric synthesizer, including a voice source model, consonantal noise models and series/parallel formant filters. Cantor Digitalis is a vowel and semi-vowel singing instrument. Digitartic allows for singing syllables, including plosives, fricative, liquid and nasal consonants. The issue of consonant gestures and musical beat synchronization is discussed. These instruments allow for expressive musical performances. They are regularly used for concerts.EXTENDED ABSTRACTGestural control of speech or singing synthesis is difficult, because of the very fast articulators motions encountered in speech and singing. For singing, another difficult question is accurate rhythmic coordination and precision, because syllables must coincide with musical beats, at a given tempo. Then, the precise location of beats for different syllables must be controlled.Two singing synthesis instruments are presented : Digitalis Cantor and Digitartic. Both instruments use bimanual writing or drawing gestures on graphic tablets. The voice signal is computed with the help of a parametric synthesizer, including a voice source model, consonantal noise models and series/parallel formant filters. Cantor Digitalis is a vowel and semi-vowel singing instrument. Digitartic is an extension of Cantor Digitalis and allows for singing syllables, including plosives, fricative, liquid and nasal consonants. Any in-between canonical place of articulation is possible by linear interpolation of the consonant parameters, for each mode of articulation.In this paper, the focus is given on Digitartic through the issue of consonant gestures and musical beat synchronization. Three modes of Vowel-Consonant-Vowel (VCV) articulation are discussed according to three levels of rhythmic precision and musical context. A VCV articulation is composed of the onset phase (articulators approaching the position of maximum constriction), the medial phase (maximum of constriction) and the offset phase (constriction release). Offset phase of plosives is very short compared to other consonants.The first control mode consists of triggering the syllable at the beginning of the onset phase. However, when the syllable starts on the musical beat, it is perceived with a delay depending on the duration of articulation phases. Anticipating precisely this delay is very difficult. The second control mode of control allows for triggering the VCV dissyllable in two steps. In the first step, the onset phase is triggered, and in the second step, the offset phase is triggered. In this way, plosives can be synchronized with musical beats without any delay. The third control mode is a continuous control of the phases of articulation, without any triggering. This requires a fast synthesis engine, a high interface sampling rate, as well as an expert control gesture, fast and precise enough to reproduce speech articulation phases.The continuous control mode of articulation is performed by a back-and-forth gesture with the pen of the non-preferred hand, along the vertical dimension of the graphic tablet. Place of articulation is continuously controlled along the horizontal dimension, and the mode of articulation is assigned to different areas on the tablet. This back-and-forth gesture is analog to the somewhat symmetric articulation of the VCV dissyllable. The gesture amplitude allows for different degrees of articulation (hypoarticulation to hyperarticulation). Controlling durations of each phase of articulation is another mean to increase expressiveness. The preferred hand is controlling pitch, vocal effort and vowel quality on another graphic tablet. Then it is possible to modify pitch and vocal effort during each phase of articulation. Cantor Digitalis and Digitartic allow for expressive musical performances. They are regularly used for concerts.; Deux instruments de synthèse vocale sont présentés : le Cantor Digitalis et le Digitartic. Ces deux instruments utilisent des gestes bimanuels dérivés de l’écriture ou du dessin sur une tablette graphique. Le signal est calculé par un synthétiseur vocal paramétrique, comprenant des modèles de source voisée et de bruits consonantiques et une structure à formants série/parallèle, pour le conduit vocal. Le Cantor Digitalis permet de chanter des voyelles et des semi-voyelles. Le Digitartic permet de chanter des syllabes, avec des consonnes plosives, fricatives, liquides et nasales. La question de la synchronisation des gestes consonantiques et des appuis rythmiques demandés par la musique est discutée. Ces instruments permettent un jeu musical expressif et sont régulièrement utilisés en concert.

Automatically segmenting organs in monocular laparoscopic images is an important and challenging research objective in computer-assisted intervention. For the uterus this is difficult because of high inter-patient variability in tissue appearance and low-contrast boundaries with the surrounding peritoneum. We present a framework to segment the uterus which is completely automatic, requires only a single monocular image, and does not require a 3D model. Our idea is to use a patient-independent uterus detector to roughly localize the organ, which is then used as a supervisor to train a patient-specific organ segmenter. The segmenter uses a physically-motivated organ boundary model designed specifically for illumination in laparoscopy, which is fast to compute and gives strong segmentation constraints. Our segmenter uses a lightweight CRF that is solved quickly and globally with a single graphcut. On a dataset of 220 images our method obtains a mean DICE score of 92.9%.