Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark
  Study from Multi-Sequence Cardiac MR Segmentation Challenge   Accurate computing, analysis and modeling of the ventricles and myocardium
from medical images are important, especially in the diagnosis and treatment
management for patients suffering from myocardial infarction (MI). Late
gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an
important protocol to visualize MI. However, automated segmentation of LGE CMR
is still challenging, due to the indistinguishable boundaries, heterogeneous
intensity distribution and complex enhancement patterns of pathological
myocardium from LGE CMR. Furthermore, compared with the other sequences LGE CMR
images with gold standard labels are particularly limited, which represents
another obstacle for developing novel algorithms for automatic segmentation of
LGE CMR. This paper presents the selective results from the Multi-Sequence
Cardiac MR (MS-CMR) Segmentation challenge, in conjunction with MICCAI 2019.
The challenge offered a data set of paired MS-CMR images, including auxiliary
CMR sequences as well as LGE CMR, from 45 patients who underwent
cardiomyopathy. It was aimed to develop new algorithms, as well as benchmark
existing ones for LGE CMR segmentation and compare them objectively. In
addition, the paired MS-CMR images could enable algorithms to combine the
complementary information from the other sequences for the segmentation of LGE
CMR. Nine representative works were selected for evaluation and comparisons,
among which three methods are unsupervised methods and the other six are
supervised. The results showed that the average performance of the nine methods
was comparable to the inter-observer variations. The success of these methods
was mainly attributed to the inclusion of the auxiliary sequences from the
MS-CMR images, which provide important label information for the training of
deep neural networks.

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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: econ
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The categories of this paper are: chem-ph
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The categories of this paper are: math-ph
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The categories of this paper are: q-bio
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The categories of this paper are: ao-sci
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an