Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Left Ventricle Segmentation and Volume Estimation on Cardiac MRI using
  Deep Learning   In the United States, heart disease is the leading cause of death for both
men and women, accounting for 610,000 deaths each year [1]. Physicians use
Magnetic Resonance Imaging (MRI) scans to take images of the heart in order to
non-invasively estimate its structural and functional parameters for
cardiovascular diagnosis and disease management. The end-systolic volume (ESV)
and end-diastolic volume (EDV) of the left ventricle (LV), and the ejection
fraction (EF) are indicators of heart disease. These measures can be derived
from the segmented contours of the LV; thus, consistent and accurate
segmentation of the LV from MRI images are critical to the accuracy of the ESV,
EDV, and EF, and to non-invasive cardiac disease detection.
  In this work, various image preprocessing techniques, model configurations
using the U-Net deep learning architecture, postprocessing methods, and
approaches for volume estimation are investigated. An end-to-end analytics
pipeline with multiple stages is provided for automated LV segmentation and
volume estimation. First, image data are reformatted and processed from DICOM
and NIfTI formats to raw images in array format. Secondly, raw images are
processed with multiple image preprocessing methods and cropped to include only
the Region of Interest (ROI). Thirdly, preprocessed images are segmented using
U-Net models. Lastly, post processing of segmented images to remove extra
contours along with intelligent slice and frame selection are applied, followed
by calculation of the ESV, EDV, and EF. This analytics pipeline is implemented
and runs on a distributed computing environment with a GPU cluster at the San
Diego Supercomputer Center at UCSD.

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The categories of this paper are: quant-ph
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The categories of this paper are: nlin
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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
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The categories of this paper are: q-alg
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The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: nucl-ex
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The categories of this paper are: math-ph
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The categories of this paper are: math
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The categories of this paper are: q-fin
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The categories of this paper are: adap-org
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The categories of this paper are: acc-phys