225 research outputs found
Deep convolutional networks for automated detection of posterior-element fractures on spine CT
Injuries of the spine, and its posterior elements in particular, are a common
occurrence in trauma patients, with potentially devastating consequences.
Computer-aided detection (CADe) could assist in the detection and
classification of spine fractures. Furthermore, CAD could help assess the
stability and chronicity of fractures, as well as facilitate research into
optimization of treatment paradigms.
In this work, we apply deep convolutional networks (ConvNets) for the
automated detection of posterior element fractures of the spine. First, the
vertebra bodies of the spine with its posterior elements are segmented in spine
CT using multi-atlas label fusion. Then, edge maps of the posterior elements
are computed. These edge maps serve as candidate regions for predicting a set
of probabilities for fractures along the image edges using ConvNets in a 2.5D
fashion (three orthogonal patches in axial, coronal and sagittal planes). We
explore three different methods for training the ConvNet using 2.5D patches
along the edge maps of 'positive', i.e. fractured posterior-elements and
'negative', i.e. non-fractured elements.
An experienced radiologist retrospectively marked the location of 55
displaced posterior-element fractures in 18 trauma patients. We randomly split
the data into training and testing cases. In testing, we achieve an
area-under-the-curve of 0.857. This corresponds to 71% or 81% sensitivities at
5 or 10 false-positives per patient, respectively. Analysis of our set of
trauma patients demonstrates the feasibility of detecting posterior-element
fractures in spine CT images using computer vision techniques such as deep
convolutional networks.Comment: To be presented at SPIE Medical Imaging, 2016, San Dieg
Investigation on probing schemes in probe-based multicast admission control
Multicast is an efficient approach to save network bandwidth for multimedia streaming services. To provide Quality of Services (QoS) for the multimedia services while maintain the advantage of multicast in bandwidth efficiency, admission control for multicast sessions are expected. Probe-based multicast admission control (PBMAC) schemes are of a sort of scalable and simple admission control for multicast. Probing scheme is the essence of PBMAC. In this paper, after a detailed survey on three existing probing schemes, we evaluate these schemes using simulation and analysis approaches in two aspects: admission correctness and group scalability. Admission correctness of the schemes is compared by simulation investigation. Analytical models for group scalability are derived, and validated by simulation results. The evaluation results illustrate the advantages and weaknesses of each scheme, which are helpful for people to choose proper probing scheme for network
Anatomy-specific classification of medical images using deep convolutional nets
Automated classification of human anatomy is an important prerequisite for
many computer-aided diagnosis systems. The spatial complexity and variability
of anatomy throughout the human body makes classification difficult. "Deep
learning" methods such as convolutional networks (ConvNets) outperform other
state-of-the-art methods in image classification tasks. In this work, we
present a method for organ- or body-part-specific anatomical classification of
medical images acquired using computed tomography (CT) with ConvNets. We train
a ConvNet, using 4,298 separate axial 2D key-images to learn 5 anatomical
classes. Key-images were mined from a hospital PACS archive, using a set of
1,675 patients. We show that a data augmentation approach can help to enrich
the data set and improve classification performance. Using ConvNets and data
augmentation, we achieve anatomy-specific classification error of 5.9 % and
area-under-the-curve (AUC) values of an average of 0.998 in testing. We
demonstrate that deep learning can be used to train very reliable and accurate
classifiers that could initialize further computer-aided diagnosis.Comment: Presented at: 2015 IEEE International Symposium on Biomedical
Imaging, April 16-19, 2015, New York Marriott at Brooklyn Bridge, NY, US
An adaptive scheduling scheme for fair bandwidth allocation
Class-based service differentiation is provided in DiffServ networks. However, this differentiation will be disordered under dynamic traffic loads due to the fixed weighted scheduling. An adaptive weighted scheduling scheme is proposed in this paper to achieve fair bandwidth allocation among different service classes. In this scheme, the number of active flows and the subscribed bandwidth are estimated based on the measurement of local queue metrics, then the scheduling weights of each service class are adjusted for the per-flow fairness of excess bandwidth allocation. This adaptive scheme can be combined with any weighted scheduling algorithm. Simulation results show that, comparing with fixed weighted scheduling, it effectively improve the fairness of excess bandwidth allocation
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