6,380 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
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
Automatic organ segmentation is an important yet challenging problem for
medical image analysis. The pancreas is an abdominal organ with very high
anatomical variability. This inhibits previous segmentation methods from
achieving high accuracies, especially compared to other organs such as the
liver, heart or kidneys. In this paper, we present a probabilistic bottom-up
approach for pancreas segmentation in abdominal computed tomography (CT) scans,
using multi-level deep convolutional networks (ConvNets). We propose and
evaluate several variations of deep ConvNets in the context of hierarchical,
coarse-to-fine classification on image patches and regions, i.e. superpixels.
We first present a dense labeling of local image patches via
and nearest neighbor fusion. Then we describe a regional
ConvNet () that samples a set of bounding boxes around
each image superpixel at different scales of contexts in a "zoom-out" fashion.
Our ConvNets learn to assign class probabilities for each superpixel region of
being pancreas. Last, we study a stacked leveraging
the joint space of CT intensities and the dense
probability maps. Both 3D Gaussian smoothing and 2D conditional random fields
are exploited as structured predictions for post-processing. We evaluate on CT
images of 82 patients in 4-fold cross-validation. We achieve a Dice Similarity
Coefficient of 83.66.3% in training and 71.810.7% in testing.Comment: To be presented at MICCAI 2015 - 18th International Conference on
Medical Computing and Computer Assisted Interventions, Munich, German
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