Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep learning analysis of the myocardium in coronary CT angiography for
  identification of patients with functionally significant coronary artery
  stenosis   In patients with coronary artery stenoses of intermediate severity, the
functional significance needs to be determined. Fractional flow reserve (FFR)
measurement, performed during invasive coronary angiography (ICA), is most
often used in clinical practice. To reduce the number of ICA procedures, we
present a method for automatic identification of patients with functionally
significant coronary artery stenoses, employing deep learning analysis of the
left ventricle (LV) myocardium in rest coronary CT angiography (CCTA). The
study includes consecutively acquired CCTA scans of 166 patients with FFR
measurements. To identify patients with a functionally significant coronary
artery stenosis, analysis is performed in several stages. First, the LV
myocardium is segmented using a multiscale convolutional neural network (CNN).
To characterize the segmented LV myocardium, it is subsequently encoded using
unsupervised convolutional autoencoder (CAE). Thereafter, patients are
classified according to the presence of functionally significant stenosis using
an SVM classifier based on the extracted and clustered encodings. Quantitative
evaluation of LV myocardium segmentation in 20 images resulted in an average
Dice coefficient of 0.91 and an average mean absolute distance between the
segmented and reference LV boundaries of 0.7 mm. Classification of patients was
evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation
experiments and resulted in an area under the receiver operating characteristic
curve of 0.74 +- 0.02. At sensitivity levels 0.60, 0.70 and 0.80, the
corresponding specificity was 0.77, 0.71 and 0.59, respectively. The results
demonstrate that automatic analysis of the LV myocardium in a single CCTA scan
acquired at rest, without assessment of the anatomy of the coronary arteries,
can be used to identify patients with functionally significant coronary artery
stenosis.

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The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-ex
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The categories of this paper are: stat
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: nlin
4
The categories of this paper are: cond-mat