Endoscopic mucosal resection with anchoring of the snare tip: multicenter retrospective evaluation of effectiveness and safety

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Visual Correspondence Hallucination

International audienceGiven a pair of partially overlapping source and target images and a keypoint in the source image, the keypoint's correspondent in the target image can be either visible, occluded or outside the field of view. Local feature matching methods are only able to identify the correspondent's location when it is visible, while humans can also hallucinate its location when it is occluded or outside the field of view through geometric reasoning. In this paper, we bridge this gap by training a network to output a peaked probability distribution over the correspondent's location, regardless of this correspondent being visible, occluded, or outside the field of view. We experimentally demonstrate that this network is indeed able to hallucinate correspondences on pairs of images captured in scenes that were not seen at training-time. We also apply this network to an absolute camera pose estimation problem and find it is significantly more robust than state-of-the-art local feature matching-based competitors

Visual Correspondence Hallucination

International audienceGiven a pair of partially overlapping source and target images and a keypoint in the source image, the keypoint's correspondent in the target image can be either visible, occluded or outside the field of view. Local feature matching methods are only able to identify the correspondent's location when it is visible, while humans can also hallucinate its location when it is occluded or outside the field of view through geometric reasoning. In this paper, we bridge this gap by training a network to output a peaked probability distribution over the correspondent's location, regardless of this correspondent being visible, occluded, or outside the field of view. We experimentally demonstrate that this network is indeed able to hallucinate correspondences on pairs of images captured in scenes that were not seen at training-time. We also apply this network to an absolute camera pose estimation problem and find it is significantly more robust than state-of-the-art local feature matching-based competitors

Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation

International audienceAbsolute camera pose estimation is usually addressed by sequentially solving two distinct subproblems: First a feature matching problem that seeks to establish putative 2D-3D correspondences, and then a Perspective-n-Point problem that minimizes, w.r.t. the camera pose, the sum of socalled Reprojection Errors (RE). We argue that generating putative 2D-3D correspondences 1) leads to an important loss of information that needs to be compensated as far as possible, within RE, through the choice of a robust loss and the tuning of its hyperparameters and 2) may lead to an RE that conveys erroneous data to the pose estimator. In this paper, we introduce the Neural Reprojection Error (NRE) as a substitute for RE. NRE allows to rethink the camera pose estimation problem by merging it with the feature learning problem, hence leveraging richer information than 2D-3D correspondences and eliminating the need for choosing a robust loss and its hyperparameters. Thus NRE can be used as training loss to learn image descriptors tailored for pose estimation. We also propose a coarse-to-fine optimization method able to very efficiently minimize a sum of NRE terms w.r.t. the camera pose. We experimentally demonstrate that NRE is a good substitute for RE as it significantly improves both the robustness and the accuracy of the camera pose estimate while being computationally and memory highly efficient. From a broader point of view, we believe this new way of merging deep learning and 3D geometry may be useful in other computer vision applications. Source code and model weights will be made available at hugogermain.com/nre

How Cu I and Na I Interact with Faujasite Zeolite? A Theoretical Investigation

International audienceZeolite materials have complex structures that can be determined by X-ray diffraction (XRD), but characterizing the nonperiodic defects, the distribution of the aluminum atoms, and the position of the exchanged cations remain a challenge. It was shown that quantum chemistry methods (QMs) are well suited to predict the structure, even with low symmetry. Here, QMs were used to determine the location and coordination of NaI and CuI cations in Si-rich faujasites of Y-type (with moderate Si/Al ratio) and Al-rich faujasites of X-type (Si/Al = 1). Focusing on the first shell of the metal site, we used QM analysis tools to study the various distortions induced by the presence of Al in the rings of faujasites. Such microscopic data were not accessible using experimental XRD methods. In contrast, using the present theoretical approach, it was possible to predict the absence of symmetry at the atomic level and that sites I were not occupied by NaI nor by CuI cations, even for Al-rich faujasites of X-type. The infrared CO probe was used to analyze the interaction of both NaI and CuI with the zeolite framework. Single CO adsorption on NaI and CuI via the carbon atom showed that the calculated νCO stretching frequency bands are mainly upshifted in comparison with isolated CO. The νCO stretching frequency range was predicted to be larger for CuI than that for NaI, and the bandwidth would be affected by different Al distributions in the six-membered rings (6MR): the more the Al atoms in the 6MR, the larger the bandwidth. To gain insights into the metal bonding picture with its neighbors, we performed natural bond orbital (NBO) analysis combined with the quantum theory of atoms in molecules and electron localization function topological analyses (QTAIM and ELF methods, respectively). While it is generally reported that Na cations provide electrostatic interactions with zeolite materials, Cu cations are often assumed to favor covalent interactions. The upshifting of the calculated νCO stretching frequency and our topological analyses rather indicated that the interactions of NaI and CuI with the oxygen atoms of the hosted zeolite were mainly ionic with a weak covalent character in the case of CuI. The adsorption of CO on NaI proceeds via an ionic Na···C interaction, while for CuI, the Cu···CO bond was calculated to be dative with a strong polar character. Whatever the Lewis metal cation, CuI or NaI, the present topological analyses predict that their interactions with the O atoms of the zeolite were ionic

A nasobiliary drain connected to a waterjet system to facilitate endoscopic ultrasound-guided gastrojejunostomy

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Risk factors for stent dysfunction during long-term follow-up after EUS-guided biliary drainage using lumen-apposing metal stents: A prospective study

Background: EUS-guided choledoco-duodenostomy using electrocautery-enhanced lumen-apposing metal stents (ECE-LAMS) is becoming the gold standard in case of endoscopic retrograde cholangio-pancreatography failure for distal malignant obstruction. Long-term data in larger samples are lacking.Methods: This was a prospective monocentric study including all patients who underwent EUS-guided choledochoduodenostomy (CDS) between September 2016 and December 2021. The primary endpoint was the rate of biliary obstruction during follow-up. Secondary endpoints were technical and clinical success rates, adverse event rates, and identification of risk factors for biliary obstruction.Results: One hundred and twenty-three EUS-guided CDS using ECE-LAMS were performed at Limoges University Hospital were performed during the study period and included in the study. The main cause of obstruction was pancreatic adenocarcinoma in 91 (74.5%) cases. The technical and clinical success rates were 97.5% and 91%, respectively. Twenty patients (16.3%) suffered from biliary obstructions during a mean follow-up of 242 days. The clinical success rate for endoscopic desobstruction was 80% (16/20). In uni- and multivariate analyses, only the presence of a duodenal stent (odds ratio [OR]: 3.6, 95% confidence interval [CI] 95%: 1.2-10.2; P = 0.018) and a bile duct thinner than 15 mm (OR: 3.9, CI 95%: 1.3-11.7; P = 0.015) were the significant risk factors for biliary obstruction during the follow-up.Conclusion: Obstruction of LAMS occurred in 16.3% of cases during follow-up and endoscopic desobstruction is efficacious in 80% of cases. The presence of duodenal stent and a bile duct thinner than 15 mm are the risk factors of obstruction. Except in these situation, EUS-CDS with ECE-LAMS could be proposed in the first intent in case of distal malignant obstruction

Back to the Feature: Learning Robust Camera Localization from Pixels to Pose

Camera pose estimation in known scenes is a 3D geometry task recently tackled by multiple learning algorithms. Many regress precise geometric quantities, like poses or 3D points, from an input image. This either fails to generalize to new viewpoints or ties the model parameters to a specific scene. In this paper, we go Back to the Feature: we argue that deep networks should focus on learning robust and invariant visual features, while the geometric estimation should be left to principled algorithms. We introduce PixLoc, a scene-agnostic neural network that estimates an accurate 6-DoF pose from an image and a 3D model. Our approach is based on the direct alignment of multiscale deep features, casting camera localization as metric learning. PixLoc learns strong data priors by end-to-end training from pixels to pose and exhibits exceptional generalization to new scenes by separating model parameters and scene geometry. The system can localize in large environments given coarse pose priors but also improve the accuracy of sparse feature matching by jointly refining keypoints and poses with little overhead. The code will be publicly available at github.com/cvg/pixloc