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Item type:Publication, Access status: without files , Towards Deep Learning-Based EEG Electrode Detection Using Automatically Generated LabelsElectroencephalography (EEG) allows for source measurement of electrical brain activity. Particularly for inverse localization, the electrode positions on the scalp need to be known. Often, systems such as optical digitizing scanners are used for accurate localization with a stylus. However, the approach is time-consuming as each electrode needs to be scanned manually and the scanning systems are expensive. We propose using an RGBD camera to directly track electrodes in the images using deep learning methods. Studying and evaluating deep learning methods requires large amounts of labeled data. To overcome the time-consuming data annotation, we generate a large number of ground-truth labels using a robotic setup. We demonstrate that deep learning-based electrode detection is feasible with a mean absolute error of 5.69 +- 6.1mm and that our annotation scheme provides a useful environment for studying deep learning methods for electrode detection.Publicationtype: Conference Paper81 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , An online model checking approach to soft-tissue detection for rupture(Hochschule Reutlingen, Fakultät Informatik, 2019-09); ; ; Robotic needle insertion based on haptic feedback can be imprecise and error\-prone, especially for sudden force changes in case of ruptures. To predict rupture events early during tissue deformation, knowledge is required about the type and characteristics of the tissues involved. Several approaches to this exist and increase system complexity by including additional sensors or imaging modalities. We introduce a new approach based on formal model checking, which allows us to identify tissue by a directed search through the state space of a needle insertion model. Using force data measured at the needle shaft during cutting motion, our method identifies the most probable tissue iteratively at run\-time, based on a priori information of possible tissues. In a case study of needle insertions into gelatin phantoms with varying gelatin\-water ratios, our approach allowed 90.7% correct identifications and may thus be considered to identify tissue during robotic needle insertionPublicationtype: Conference Paper150 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A New Setup for Markerless Motion Compensation in TMS by Relative Head Tracking with a Small-Scale TOF Camera(2019-09); ; Electroencephalography (EEG) allows for source measurement of electrical brain activity. Particularly for inverse localization, the electrode positions on the scalp need to be known. Often, systems such as optical digitizing scanners are used for accurate localization with a stylus. However, the approach is time-consuming as each electrode needs to be scanned manually and the scanning systems are expensive. We propose using an RGBD camera to directly track electrodes in the images using deep learning methods. Studying and evaluating deep learning methods requires large amounts of labeled data. To overcome the time-consuming data annotation, we generate a large number of ground-truth labels using a robotic setup. We demonstrate that deep learning-based electrode detection is feasible with a mean absolute error of 5.69 \± 6.10 mm and that our annotation scheme provides a useful environment for studying deep learning methods for electrode detection.Publicationtype: Conference Paper171 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Machbarkeit CNN-basierter Erzeugung von Kandidatenstrahlen für Radiochirurgie der Prostata(Hochschule Reutlingen, Fakultät Informatik, 2019-09); ; ; Bei der Radiochirurgie wird ein Roboterarm verwendet, um Dosisabgabe aus nahezu beliebig vielen Richtungen zu ermöglichen. Allerdings ist wegen dieser Flexibilität die Behandlungsplanung eine anspruchsvolle Aufgabe. Üblicherweise wird eine Heuristik auf Grundlage randomisierter Kandidatenstrahlen verwendet, um die Anzahl der tatsächlich betrachteten Einstrahlrichtungen zu begrenzen. Im Gegensatz dazu schlagen wir die Verwendung eines Convolutional Neural Networks vor, um Kandidatenstrahlen auf Basis von Behandlungsplänen anderer Patienten zu generieren. Unsere Ergebnisse zeigen, dass dieser Ansatz nur halb so viele Kandidatenstrahlen für eine vergleichbare Planqualität benötigt.Publicationtype: Conference Paper136 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , IVUS-Simulation for Improving Segmentation Performance of Neural Networks via Data Augmentation(2019-09); ; Convolutional neural networks (CNNs) produce promising results when applied to a wide range of medical imaging tasks including the segmentation of tissue structures like the artery lumen and wall layers in intravascular ultrasound (IVUS) image data. However, large annotated datasets are needed for training to achieve sufficient performances. To increase the dataset size, data augmentation techniques like random image transformations are commonly used. In this work, we propose a new systematic approach to generate artificial IVUS image data with the ultrasound simulation software Field II in order to perform data augmentation. A simulation model was systematically tuned to a clinical data set based on the Frechet Inception Distance (FID). We show that the segmentation performance of a state\-of\-the\-art CNN with U\-Net architecture improves when pre\-trained with our synthetic IVUS dataPublicationtype: Conference Paper122