Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy

Augmenting X-ray imaging with 3D roadmap to improve guidance is a common strategy. Such approaches benefit from automated analysis of the X-ray images, such as the automatic detection and tracking of instruments. In this paper, we propose a real-time method to segment the catheter and guidewire in 2D X-ray fluoroscopic sequences. The method is based on deep convolutional neural networks. The network takes as input the current image and the three previous ones, and segments the catheter and guidewire in the current image. Subsequently, a centerline model of the catheter is constructed from the segmented image. A small set of annotated data combined with data augmentation is used to train the network. We trained the method on images from 182 X-ray sequences from 23 different interventions. On a testing set with images of 55 X-ray sequences from 5 other interventions, a median centerline distance error of 0.2 mm and a median tip distance error of 0.9 mm was obtained. The segmentation of the instruments in 2D X-ray sequences is performed in a real-time fully-automatic manner.Comment: Accepted to MICCAI 201

Wissenschaftliche Auswertung der Hamburger Dioxin Kohorte: Abschlussbericht

Das Forschungsprojekt ist ein Follow-up einer Studienkohorte von rd. 1.600 ehemaligen Mitarbeitern der Firma Boehringer in Hamburg, die 1952 bis 1984 bei der Produktion von Pflanzenschutzmitteln hohen Konzentrationen von Dioxin sowie weiteren Substanzen wie Furanen und Benzol ausgesetzt waren. Ziel des Projekts war die Untersuchung von Langzeitwirkungen im Hinblick auf die Mortalität der Betroffenen. Im Ergebnis zeigten sich geschlechtsspezifisch differenziert statistisch erhöhte Risiken der Gesamtmortalität sowie für bestimmte Tumorerkrankungen. Allerdings bestehen für die Studie verschiedene Limitationen wie der nicht bekannte Raucherstatus, die z. T. geringe Fallzahl bei einzelnen Tumorerkrankungen sowie fehlende Messungen am Arbeitsplatz. Insgesamt erbrachte die Studie neue Hinweise auf Ursachenzusammenhänge zwischen Dioxin und bestimmten Erkrankungen

Proposed New Antiproton Experiments at Fermilab

Fermilab operates the world's most intense source of antiprotons. Recently various experiments have been proposed that can use those antiprotons either parasitically during Tevatron Collider running or after the Tevatron Collider finishes in about 2010. We discuss the physics goals and prospects of the proposed experiments.Comment: 6 pages, 2 figures, to appear in Proceedings of IXth International Conference on Low Energy Antiproton Physics (LEAP'08), Vienna, Austria, September 16 to 19, 200

Jet Physics in Heavy Ion Collisions with Compact Muon Solenoid detector at the LHC

The status of CMS jet simulations and physics analysis in heavy ion collisions is presented. Jet reconstruction and high transverse momentum particle tracking in the high multiplicity environment of heavy ion collisions at the LHC using the CMS calorimetry and tracking system are described. The Monte Carlo tools used to simulate jet quenching are discussed.Comment: Talk given at 5th International Conference on Physics and Astrophysics of Quark Gluon Plasma, Salt Lake City, Kolkata, India, February 8-12, 2005; 4 pages including 4 figures as EPS-files; prepared using LaTeX package for Journal of Physics

Generative adversarial network-based semi-supervised learning for pathological speech classification

A challenge in applying machine learning algorithms to pathological speech classification is the labelled data shortage problem. Labelled data acquisition often requires significant human effort and time-consuming experimental design. Further, for medical applications, privacy and ethical issues must be addressed where patient data is collected. While labelled data are expensive and scarce, unlabelled data are typically inexpensive and plentiful. In this paper, we propose a semi-supervised learning approach that employs a generative adversarial network to incorporate both labelled and unlabelled data into training. We observe a promising accuracy gain with this approach compared to a baseline convolutional neural network trained only on labelled pathological speech data