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Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes. In this work we propose an approach to 3D image segmentation(More)
In this paper, we contribute to the development of context-aware operating rooms by introducing a novel approach to modeling and monitoring the workflow of surgical interventions. We first propose a new representation of interventions in terms of multidimensional time-series formed by synchronized signals acquired over time. We then introduce methods based(More)
Automatic segmentation of the liver and its lesion is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This paper presents a method to automatically segment liver and lesions in CT abdomen images using cascaded fully convolutional neural networks (CFCNs) and dense 3D(More)
Freehand placement of ventricular catheters (VC) is reported to be inaccurate in 10–40 %. Endoscopy, ultrasound, or neuronavigation are used in selected cases with significant technical and time-consuming efforts. We suggest a smartphone-assisted guiding tool for the placement of VC. Measurements of relevant parameters in 3D-MRI datasets in a patient cohort(More)
Deep brain stimulation (DBS) of the internal pallidal segment (GPi: globus pallidus internus) is gold standard treatment for medically intractable dystonia, but detailed knowledge of mechanisms of action is still not available. There is evidence that stimulation of ventral and dorsal GPi produces opposite motor effects. The aim of this study was to analyse(More)
—In this work we analyse the performance of Convolu-tional Neural Networks (CNN) on medical data by benchmarking the capabilities of different network architectures to solve tasks such as segmentation and anatomy localisation, under clinically realistic constraints. We propose several CNN architectures with varying data abstraction capabilities and(More)
Workflow recovery is crucial for designing context-sensitive service systems in future operating rooms. Abstract knowledge about actions which are being performed is particularly valuable in the OR. This knowledge can be used for many applications such as optimizing the workflow, recovering average workflows for guiding and evaluating training surgeons,(More)
We present a novel approach to transcranial B-mode sonography for Parkinson's disease (PD) diagnosis by using 3-D ultrasound (3-DUS). We reconstructed bilateral 3-DUS volumes of the midbrain and substantia nigra echogenicities (SNE) and report results of a more objective abnormality detection in (PD). For classification, we analyzed volumetric measurements(More)
 Intra-operative image guidance during deep brain stimulation (DBS) surgery is usually avoided due to cost and overhead of intra-operative MRI and CT acquisitions. Recently, there has been interest in the community towards the usage of non-invasive transcranial ultrasound (TCUS) through the preauricular bone window. In this work, we investigate, for the(More)
The computer aided analysis of surgical activity and work-flow in the operating theatre has gained much interest in the past few years. Many of these works deal with or depend on detection and classification of surgical activity which is represented by multi-dimensional, continuous signal data recorded from the Operating Room (OR). In this work, we propose(More)