Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improved Segmentation and Detection Sensitivity of Diffusion-Weighted
  Brain Infarct Lesions with Synthetically Enhanced Deep Learning   Purpose: To compare the segmentation and detection performance of a deep
learning model trained on a database of human-labelled clinical
diffusion-weighted (DW) stroke lesions to a model trained on the same database
enhanced with synthetic DW stroke lesions. Methods: In this institutional
review board approved study, a stroke database of 962 cases (mean age 65+/-17
years, 255 males, 449 scans with DW positive stroke lesions) and a normal
database of 2,027 patients (mean age 38+/-24 years,1088 females) were obtained.
Brain volumes with synthetic DW stroke lesions were produced by warping the
relative signal increase of real strokes to normal brain volumes. A generic 3D
U-Net was trained on four different databases to generate four different
models: (a) 375 neuroradiologist-labeled clinical DW positive stroke
cases(CDB);(b) 2,000 synthetic cases(S2DB);(c) CDB+2,000 synthetic
cases(CS2DB); or (d) CDB+40,000 synthetic cases(CS40DB). The models were tested
on 20%(n=192) of the cases of the stroke database, which were excluded from the
training set. Segmentation accuracy was characterized using Dice score and
lesion volume of the stroke segmentation, and statistical significance was
tested using a paired, two-tailed, Student's t-test. Detection sensitivity and
specificity was compared to three neuroradiologists. Results: The performance
of the 3D U-Net model trained on the CS40DB(mean Dice 0.72) was better than
models trained on the CS2DB (0.70,P <0.001) or the CDB(0.65,P<0.001). The deep
learning model was also more sensitive (91%[89%-93%]) than each of the three
human readers(84%[81%-87%],78%[75%-81%],and 79%[76%-82%]), but less
specific(75%[72%-78%] vs for the three human readers (96%[94%-97%],92%[90%-94%]
and 89%[86%-91%]). Conclusion: Deep learning training for segmentation and
detection of DW stroke lesions was significantly improved by enhancing the
training set with synthetic lesions.

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The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys
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The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-bio
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The categories of this paper are: gr-qc
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: adap-org
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The categories of this paper are: atom-ph