804 research outputs found
Osteoporotic and Neoplastic Compression Fracture Classification on Longitudinal CT
Classification of vertebral compression fractures (VCF) having osteoporotic
or neoplastic origin is fundamental to the planning of treatment. We developed
a fracture classification system by acquiring quantitative morphologic and bone
density determinants of fracture progression through the use of automated
measurements from longitudinal studies. A total of 250 CT studies were acquired
for the task, each having previously identified VCFs with osteoporosis or
neoplasm. Thirty-six features or each identified VCF were computed and
classified using a committee of support vector machines. Ten-fold cross
validation on 695 identified fractured vertebrae showed classification
accuracies of 0.812, 0.665, and 0.820 for the measured, longitudinal, and
combined feature sets respectively.Comment: Contributed 4-Page Paper to be presented at the 2016 IEEE
International Symposium on Biomedical Imaging (ISBI), April 13-16, 2016,
Prague, Czech Republi
Spatial clustering and common regulatory elements correlate with coordinated gene expression
Many cellular responses to surrounding cues require temporally concerted
transcriptional regulation of multiple genes. In prokaryotic cells, a
single-input-module motif with one transcription factor regulating multiple
target genes can generate coordinated gene expression. In eukaryotic cells,
transcriptional activity of a gene is affected by not only transcription
factors but also the epigenetic modifications and three-dimensional chromosome
structure of the gene. To examine how local gene environment and transcription
factor regulation are coupled, we performed a combined analysis of time-course
RNA-seq data of TGF-\b{eta} treated MCF10A cells and related epigenomic and
Hi-C data. Using Dynamic Regulatory Events Miner (DREM), we clustered
differentially expressed genes based on gene expression profiles and associated
transcription factors. Genes in each class have similar temporal gene
expression patterns and share common transcription factors. Next, we defined a
set of linear and radial distribution functions, as used in statistical
physics, to measure the distributions of genes within a class both spatially
and linearly along the genomic sequence. Remarkably, genes within the same
class despite sometimes being separated by tens of million bases (Mb) along
genomic sequence show a significantly higher tendency to be spatially close
despite sometimes being separated by tens of Mb along the genomic sequence than
those belonging to different classes do. Analyses extended to the process of
mouse nervous system development arrived at similar conclusions. Future studies
will be able to test whether this spatial organization of chromosomes
contributes to concerted gene expression.Comment: 30 pages, 9 figures, accepted in PLoS Computational Biolog
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
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