Statistiques de forme, de structure et de déformation à l'échelle d'une population pour l'étude de la fibrillation auriculaire

Atrial fibrillation (AF) is the most common cardiac arrhythmia, characterized by chaotic electrical activation and unsynchronized contraction of the atria. This epidemic and its life-threatening complications and fast progression call for diagnosis and effective treatment as early as possible. Catheter ablation, an invasive procedure that establishes lesions to block the trigger points of AF and creates a barrier to the propagation of the arrhythmia, is an effective treatment for patients refractory to anti-arrhythmic drugs. However, the success rate of the first-time ablation may range from 30% to 75%, such that multiple ablation procedures may be recommended, and atrial mechanical function may be adversely affected. With evolving imaging and digital technologies, the objective of the study is to understand the underlying physiology of AF better and to provide tools to assist clinical decision-making. We analyze the correlations between recurrent arrhythmia and patient characteristics before ablation, including the left atrial shape extracted from computed tomography images. Non-invasive extraction of the anatomical structures of the heart is a crucial prerequisite. We first developed semi-automatic methods to segment the left atrium and the left atrial wall from images. Next, we achieved good segmentation results with a neural network model. Then, we studied markers of shape related to both global and local remodeling, and the quantification of adipose tissue, deploying diffeomorphometry and statistical analysis tools. Finally, we extended the work to the statistical analysis of temporal deformation. We proposed a symmetric reformulation of the pole ladder, which improves the numerical consistency and stability.La fibrillation auriculaire (FA) est le type d'arythmie cardiaque la plus commun, caractérisée par une activation électrique chaotique et une contraction non synchronisée des oreillettes. Cette maladie et ses complications potentiellement mortelles ainsi que sa progression rapide exigent de diagnostiquer et de mettre en place un traitement efficace dès que possible. L'ablation par cathéter, une procédure invasive qui établit des lésions pour bloquer les points de déclenchement de la FA et la propagation de l'arythmie, est un traitement efficace pour les patients réfractaires aux médicaments. Cependant, pour 30% des patients, la FA se redéveloppe, entraînant des interventions d'ablation multiples et affectant la fonction mécanique auriculaire. Le but de cette étude est de combiner l'expertise mathématique et informatique à la médecine afin de mieux comprendre la physiologie sous-jacente à la FA et de fournir des outils d'aide à la décision aux cliniciens. Nous analysons des corrélations entre l'arythmie récurrente et les caractéristiques du patient avant l'ablation, y compris la forme de l’oreillette gauche extraite d'images tomodensitométriques. Nous développons pour ce faire des méthodes semi-automatiques pour segmenter l’oreillette gauche et sa paroi à partir d’images. Ensuite, nous avons obtenu de bons résultats de segmentation avec un modèle de réseau de neurones artificiels. En outre, nous étudions des marqueurs de forme liés au remodelage global et local, et la quantification du tissu adipeux, en combinant une approche morphométrique difféomorphe à une analyse statistique. Enfin, le travail s’étend à l’analyse statistique de la déformation temporelle. Nous proposons une reformulation symétrique de l'échelle de perroquet qui améliore la cohérence et la stabilité numérique

HVDetFusion: A Simple and Robust Camera-Radar Fusion Framework

In the field of autonomous driving, 3D object detection is a very important perception module. Although the current SOTA algorithm combines Camera and Lidar sensors, limited by the high price of Lidar, the current mainstream landing schemes are pure Camera sensors or Camera+Radar sensors. In this study, we propose a new detection algorithm called HVDetFusion, which is a multi-modal detection algorithm that not only supports pure camera data as input for detection, but also can perform fusion input of radar data and camera data. The camera stream does not depend on the input of Radar data, thus addressing the downside of previous methods. In the pure camera stream, we modify the framework of Bevdet4D for better perception and more efficient inference, and this stream has the whole 3D detection output. Further, to incorporate the benefits of Radar signals, we use the prior information of different object positions to filter the false positive information of the original radar data, according to the positioning information and radial velocity information recorded by the radar sensors to supplement and fuse the BEV features generated by the original camera data, and the effect is further improved in the process of fusion training. Finally, HVDetFusion achieves the new state-of-the-art 67.4\% NDS on the challenging nuScenes test set among all camera-radar 3D object detectors. The code is available at https://github.com/HVXLab/HVDetFusio

STACOM-SLAWT Challenge: Left Atrial Wall Segmentation and Thickness Measurement Using Region Growing and Marker-Controlled Geodesic Active Contour

International audienceAnalyzing the structure of the left atrium can provide precious insights into the pathology of atrial fibrillation, eventually resulting in optimization of treatment plans. In this paper, an interactive and patient-specific method is presented to segment the left atrial endocardium, the left atrial epicardium and measure the left atrial wall thickness from cardiac computed tomography images. A region growing algorithm was adapted to segment the left atrial endocardium, whereas the left atrial epicardium was segmented indirectly: a marker-controlled geodesic active contour model was defined on its surrounding environment. The results of the left atrial wall thickness were then mapped onto meshes generated from the endocardium segmentation. We tested our pipeline on 10 datasets as a part of the STACOM 2016 Left Atrial Wall Segmentation Challenge and we compared our method with manual segmentation. Aimed at facilitating the segmentation of the left atrial thin-wall structure, this pipeline is partially implemented in MUSIC software for clinical use. The expertise of clinicians can be added through the choice of specific parameters for each patient, although this remains optional owing to the robustness of the approach

Prediction of Post-Ablation Outcome in Atrial Fibrillation Using Shape Parameterization and Partial Least Squares Regression

International audienceTo analyze left atrial remodeling may reveal shape features related to post-ablation outcome in atrial fibrillation, which helps in identifying suitable candidates before ablation. In this article, we propose an application of diffeomorphometry and partial least squares regression to address this problem. We computed a template of left atrial shape in control group and then encoded the shapes in atrial fibrillation with a large set of parameters representing their diffeomorphic deformation. We applied a two-step partial least squares regression. The first step eliminates the influence of atrial volume in shape parameters. The second step links deformations directly to post-ablation recurrence and derives a few principle modes of deformation, which are unrelated to volume change but are involved in post-ablation recurrence. These modes contain information on ablation success due to shape differences, resulting from remodeling or influencing ablation procedure. Some details are consistent with the most complex area of ablation in clinical practice. Finally, we compared our method against the left atrial volume index by quantifying the risk of post-ablation recurrence within six months. Our results show that we get better prediction capabilities (area under receiver operating characteristic curves (AUC = 0.73) than left atrial dilation (AUC = 0.47), which outperforms the current state of the art

Algorithms for left atrial wall segmentation and thickness – Evaluation on an open-source CT and MRI image database

© 2018 The Authors Structural changes to the wall of the left atrium are known to occur with conditions that predispose to Atrial fibrillation. Imaging studies have demonstrated that these changes may be detected non-invasively. An important indicator of this structural change is the wall\u27s thickness. Present studies have commonly measured the wall thickness at few discrete locations. Dense measurements with computer algorithms may be possible on cardiac scans of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). The task is challenging as the atrial wall is a thin tissue and the imaging resolution is a limiting factor. It is unclear how accurate algorithms may get and how they compare in this new emerging area. We approached this problem of comparability with the Segmentation of Left Atrial Wall for Thickness (SLAWT) challenge organised in conjunction with MICCAI 2016 conference. This manuscript presents the algorithms that had participated and evaluation strategies for comparing them on the challenge image database that is now open-source. The image database consisted of cardiac CT (n=10) and MRI (n=10) of healthy and diseased subjects. A total of 6 algorithms were evaluated with different metrics, with 3 algorithms in each modality. Segmentation of the wall with algorithms was found to be feasible in both modalities. There was generally a lack of accuracy in the algorithms and inter-rater differences showed that algorithms could do better. Benchmarks were determined and algorithms were ranked to allow future algorithms to be ranked alongside the state-of-the-art techniques presented in this work. A mean atlas was also constructed from both modalities to illustrate the variation in thickness within this small cohort

mRNA Cap Methylation in Pluripotency and Differentiation

YesThe mRNA cap stabilizes transcripts and recruits processing and translation factors. Grasso et al. report that the mRNA cap methyltransferase RNMTRAM is highly expressed in embryonic stem cells and is important for pluripotency-associated gene expression. Repression of RAM occurs during neural differentiation and is important for expression of neuralassociated genes.Wellcome Trus

Upregulation of Cyclin B1 by miRNA and its implications in cancer

It is largely recognized that microRNAs (miRNAs) function to silence gene expression by targeting 3′UTR regions. However, miRNAs have also been implicated to positively-regulate gene expression by targeting promoter elements, a phenomenon known as RNA activation (RNAa). In the present study, we show that expression of mouse Cyclin B1 (Ccnb1) is dependent on key factors involved in miRNA biogenesis and function (i.e. Dicer, Drosha, Ago1 and Ago2). In silico analysis identifies highly-complementary sites for 21 miRNAs in the Ccnb1 promoter. Experimental validation identified three miRNAs (miR-744, miR-1186 and miR-466d-3p) that induce Ccnb1 expression in mouse cell lines. Conversely, knockdown of endogenous miR-744 led to decreased Ccnb1 levels. Chromatin immunoprecipitation (ChIP) analysis revealed that Ago1 was selectively associated with the Ccnb1 promoter and miR-744 increased enrichment of RNA polymerase II (RNAP II) and trimethylation of histone 3 at lysine 4 (H3K4me3) at the Ccnb1 transcription start site. Functionally, short-term overexpression of miR-744 and miR-1186 resulted in enhanced cell proliferation, while prolonged expression caused chromosomal instability and in vivo tumor suppression. Such phenotypes were recapitulated by overexpression of Ccnb1. Our findings reveal an endogenous system by which miRNA functions to activate Ccnb1 expression in mouse cells and manipulate in vivo tumor development/growth

1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.Comment: MaCVi 2023 was part of WACV 2023. This report (38 pages) discusses the competition as part of MaCV