Seyed-Ahmad Ahmadi

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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)
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)
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)
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)
The computer aided analysis of surgical activity and workflow 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 a(More)
In the field of computer aided medical image analysis, it is often difficult to obtain reliable ground truth for evaluating algorithms or supervising statistical learning procedures. In this paper we present a new method for training a classification forest from images labelled by variably performing experts, while simultaneously evaluating the performance(More)
We propose a novel, physics-based method for detecting multi-scale tubular features in ultrasound images. The detector is based on a Hessian-matrix eigenvalue method, but unlike previous work, our detector is guided by an optimal model of vessel-like structures with respect to the ultrasound-image formation process. Our method provides a voxel-wise(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)
Parkinson's disease (PD) is a neurodegenerative movement disorder caused by decay of dopaminergic cells in the substantia nigra (SN), which are basal ganglia residing within the midbrain area. In the past two decades, transcranial B-mode sonography (TCUS) has emerged as a viable tool in differential diagnosis of PD and recently has been shown to have(More)