Real-time Tracking of Deformable Target in 3D Ultrasound Images

International audience— In this paper, we present a novel approach for tracking a deformable anatomical target within 3D ultrasound volumes. Our method is able to estimate deformations caused by the physiological motions of the patient. The displacements of moving structures are estimated from an intensity-based approach combined with a physically-based model and has therefore the advantage to be less sensitive to the image noise. Furthermore, our method does not use any fiducial marker and has real-time capabilities. The accuracy of our method is evaluated on real data acquired from an organic phantom. The validation is performed on different types of motions comprising rigid and non-rigid motions. Thus, our approach opens novel possibilities for computer-assisted interventions where deformable organs are involved

Interactive Tracking of Soft Tissues in 2D Ultrasound Images

SURGETICA 2014, Chambery, FranceIn several medical applications such as liver or kidney biopsies, an anatomical region needs to be continuously tracked during the intervention. When using ultrasound (US) image modality, tracking soft tissues remains challenging due to the deformations caused by physiological motions or medical instruments, combined with the generally weak quality of the images. In order to overcome the previous limitation, different techniques based on physical model have been proposed in the literature. [SMSM06] proposed a registration method based on the mass-spring system in order to constrain the deformation, and Zhang et al [ZW13] introduced an other registration technique based on finite element model where the extraction of the scale invariant features is needed. However, their model are built from features which are difficult to extract in US images due to the speckle noise. Finally, Marami et al [MSFC14] presented very recently an elastic registration method applicable to multi-modality image registration where the deformation is computed from modality independent neighborhood descriptor. In this paper, we propose an approach for tracking deformable target within 2D US images based on a physical model driven by smooth displacement field obtained from dense information. This allows to take into account highly localized deformation in the US images. Section 2 presents our method based on a combination of an intensity-based approach and a physically-based model. Section 3 describes the performances of our approach and comparisons on real data. Section 4 concludes the paper

Targeting the Lactate Transporter MCT1 in Endothelial Cells Inhibits Lactate-Induced HIF-1 Activation and Tumor Angiogenesis

Switching to a glycolytic metabolism is a rapid adaptation of tumor cells to hypoxia. Although this metabolic conversion may primarily represent a rescue pathway to meet the bioenergetic and biosynthetic demands of proliferating tumor cells, it also creates a gradient of lactate that mirrors the gradient of oxygen in tumors. More than a metabolic waste, the lactate anion is known to participate to cancer aggressiveness, in part through activation of the hypoxia-inducible factor-1 (HIF-1) pathway in tumor cells. Whether lactate may also directly favor HIF-1 activation in endothelial cells (ECs) thereby offering a new druggable option to block angiogenesis is however an unanswered question. In this study, we therefore focused on the role in ECs of monocarboxylate transporter 1 (MCT1) that we previously identified to be the main facilitator of lactate uptake in cancer cells. We found that blockade of lactate influx into ECs led to inhibition of HIF-1-dependent angiogenesis. Our demonstration is based on the unprecedented characterization of lactate-induced HIF-1 activation in normoxic ECs and the consecutive increase in vascular endothelial growth factor receptor 2 (VEGFR2) and basic fibroblast growth factor (bFGF) expression. Furthermore, using a variety of functional assays including endothelial cell migration and tubulogenesis together with in vivo imaging of tumor angiogenesis through intravital microscopy and immunohistochemistry, we documented that MCT1 blockers could act as bona fide HIF-1 inhibitors leading to anti-angiogenic effects. Together with the previous demonstration of MCT1 being a key regulator of lactate exchange between tumor cells, the current study identifies MCT1 inhibition as a therapeutic modality combining antimetabolic and anti-angiogenic activities

Galaxy Training: A powerful framework for teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics, and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Exploration des potentialités du système EOS

Le vieillissement de la population des pays industrialisés s'accentuant, des pathologies liées à l'âge telles que l'ostéoporose, posent des problèmes majeurs de santé publique. Les praticiens et les autorités de santé publique sont à la recherche d'outils de diagnostic et de dépistage du risque fracturaire. La mesure de la densité minérale osseuse (DMO) par DXA fait actuellement référence dans l'évaluation du risque fracturaire de l'extrémité supérieure du fémur, mais cette technique a toutefois des limites: il existe un chevauchement des valeurs de DMO entre les sujets fracturés et les sujets non fracturés. Par ailleurs, plusieurs études cliniques ont montré que des paramètres macro-architecturaux étaient des facteurs déterminants de la solidité osseuse, mais ces mesures étant bidimensionnelles, elles sont dépendantes des biais de projection 2D. L'objectif de notre étude est de mettre au point un outil permettant d'associer mesures DMO et géométrie 3D afin de prédire d'une meilleure manière le risque fracturaire. Pour ce faire, nous nous sommes basés sur un système de stéréoradiographie basse dose (le système EO

Tracking of Non-rigid Targets in 3D US Images: Results on CLUST 2015

International audienc

Tracking of Deformable Target in 2D Ultrasound Images

International audienceIn several medical applications such as liver or kidney biopsies, an anatomical region needs to be continuously tracked during the intervention. In this paper, we propose a novel approach for automatically tracking deformable target within 2D ultrasound images. Our approach uses only dense information combined with a physically-based model and has therefore the advantage of not using any fiducial marker nor a priori knowledge on the anatomical environment. The physical model is represented by a mass-spring damper system driven by different types of forces where the external forces are obtained by maximizing image similarity metric between a reference target and a deformed target across the time. This deformation is represented by a parametric warping model where the optimal parameters are estimated from the intensity variation. This warping function is well-suited to represent localized deformations in the ultrasound images because it directly links the forces applied on each mass with the motion of all the pixels in its vicinity. The internal forces constrain the deformation to physically plausible motions, and reduce the sensitivity to the speckle noise. The approach was validated on simulated and real data, both for rigid and free-form motions of soft tissues. The results are very promising since the deformable target could be tracked with a good accuracy for both types of motion. Our approach opens novel possibilities for computer-assisted interventions where deformable organs are involved and could be used as a new tool for interactive tracking of soft tissues in ultrasound images

Automatic update of reference data in Galaxy using BioMAJ.

International audienceMany bioinformatic tools require the use of reference data like genome assemblies or sequence databanks.Galaxy offers multiple ways to give access to this data in its web interface: data libraries, *.loc files and more recently the introcution of data managers.However, until now, the process of adding a new reference data was essentially manual and time consuming, even more when this data need to be indexed in avariety of formats (blast, bowtie, bwa, 2bit, ...).The recent release of data managers is a first step for the automation of data download and indexing, but it still requires some manual intervention to launchthe download and subsequent automatic indexing. Furthermore, it was designed with a galaxy-centric view, not taking into account that reference data are oftenused outside Galaxy, for example using command line or concurrent systems like Mobyle.BioMAJ is a widely used and stable software designed to automate the download and transformation of data from various sources. This data can be used directlyfrom the command line, or in more complex systems like Mobyle, or using a REST API. We have developed BioMAJ post-processes to automatically populate the Galaxydata libraries or data managers, avoiding data and transformation duplications.In this talk we will make a brief overview of the difference way to manage reference data in Galaxy. We will then present the solution that was developed tofill the gap between BioMAJ and Galaxy. We will then present some considerations in regard to security aspects when a reference data needs to be available onlyto a group of users. Finally on-going developments and ideas will be evoked

Real-time Target Tracking of Soft Tissues in 3D Ultrasound Images Based on Robust Visual Information and Mechanical Simulation

International audienceIn this paper, we present a real-time approach that allows tracking deformable structures in 3D ultrasound sequences. Our method consists in obtaining the target displacements by combining robust dense motion estimation and mechanical model simulation. We perform evaluation of our method through simulated data, phantom data, and real-data. Results demonstrate that this novel approach has the advantage of providing correct motion estimation regarding different ultrasound shortcomings including speckle noise, large shadows and ultrasound gain variation. Furthermore, we show the good performance of our method with respect to state-of-the-art techniques by testing on the 3D databases provided by MICCAI CLUST'14 and CLUST'15 challenges