TER: A Robot for Remote Ultrasonic Examination: Experimental Evaluations

This chapter: o Motivates the clinical use of robotic tele-echography o Introduces the TER system o Describes technical and clinical evaluations performed with TE

Dépistage de l'artériopathie oblitérante des membres inférieurs chez les patients porteurs d'une polyarthrite rhumatoïde

Artériopathie oblitérante des membres inférieurs est une maladie fréquente. Elle est favorisée principalement par des facteurs de risque classiques: tabagisme, diabète, dyslipidémie et hypertension artérielle mais il a été mis en évidence le rôle de l'inflammation dans la formation de la plaque athéromateuse. Il convient donc chez les patients porteurs d'une maladie chronique inflammatoire et donc chez ceux souffrant d'une polyarthrite rhumatoïde de rechercher une artériopathie oblitérante des membres inférieurs dès le début de l'évolution de la maladie par la mesure des index de pression systolique à la cheville. Dans ce travail, nous avons réalisé cette recherche dans une population de 100 patients n'ayant pas de maladie cardiovasculaire connue, suivis en consultation de rhumatologie et souffrant d'une polyarthrite rhumatoïde. Ceci nous a permis de dépister une artériopathie oblitérante des membres inférieurs chez 10 patients, chez qui l'inflammation semblait être Ie facteur déclenchant principal de l'athérosclérose. Il semble donc licite de proposer une mesure annuelle des IPSC dès Ia découverte d'une polyarthrite rhumatoïde.BREST-BU Médecine-Odontologie (290192102) / SudocSudocFranceF

Thromboses veineuses et cancers (intérêt de l'aspect échographique du thrombus)

BREST-BU Médecine-Odontologie (290192102) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Thromboses veineuses de l'enfant

BREST-BU Médecine-Odontologie (290192102) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Comparative Neural Network Based Venous Thrombosis Echogenicity and Echostructure Characterization Using Ultrasound Images

International audienceVenous thrombosis is a common pathology that creates serious public health problems. Thrombosis diagnosis, particularly the determination of their echogenicity and echostructure can be efficiently accomplished by a medical expert using ultrasound imaging. On the other hand, the predictive capability of artificial neural networks is very useful in medical applications and can support medical experts to take appropriate diagnosis decisions. Therefore, the proposed study intends to characterize by means of neural networks the thrombosis echogenicity and echostructure, using a predefined learning base that depends on the prior knowledge of physicians. We have studied six different methods to characterize the thrombosis images, along with the six corresponding neural networks. Obtained results show that the optimal feature vector size, the simplest neural network architecture, and the smallest error, are achieved by using the mean-variance approach or by the wavelet coefficients energies metho

Computer assisted venous thrombosis quantification

International audienceVenous thrombosis (VT) volume assessment, by verifying its risk of progression when anticoagulant or thrombolytic therapies are prescribed, is often necessary to screen life- threatening complications. Commonly, VT volume estimation is done by manual delineation of few contours in the ultrasound (US) image sequence, assuming that the VT has a regular shape and constant radius, thus producing significant errors. This paper presents and evaluates a comprehensive functional approach based on the combination of robust anisotropic diffusion and deformable contours to calculate VT volume in a more accurate manner when applied to freehand 2-D US image sequences. Robust anisotropic filtering reduces image speckle noise without generating incoherent edge discontinuities. Prior knowledge of the VT shape allows initializing the deformable contour, which is then guided by the noise-filtering outcome. Segmented contours are subsequently used to calculate VT volume. The proposed approach is integrated into a system prototype compatible with existing clinical US machines that additionally tracks the acquired images 3-D position and provides a dense Delaunay triangulation required for volume calculation. A predefined robust anisotropic diffusion and deformable contour parameter set enhances the system usability. Experimental results pertinence is assessed by comparison with manual and tetrahedron-based volume computations, using images acquired by two medical experts of eight plastic phantoms and eight in vitro VTs, whose independently measured volume is the reference ground truth. Results show a mean difference between 16 and 35 mm3 for volumes that vary from 655 to 2826 mm3. Two in vivo VT volumes are also calculated to illustrate how this approach could be applied in clinical conditions when the real value is unknown. Comparative results for the two experts differ from 1.2% to 10.08% of the smallest estimated value when the image acquisition cadences are similar

Fusion of images and clinical features for the prediction of Pulmonary embolism in Ultrasound imaging

International audienc

Survey on machine learning applied to medical image analysis

International audienc