Convolutional Neural Networks for Classifying Laterality of Vestibular Schwannomas on Single MRI Slices-A Feasibility Study
Philipp Sager, Lukas Näf, Erwin Vu, Tim Fischer, Paul Martin Putora, Felix Ehret, Christoph Fürweger, Christina Schröder, Robert Förster, Daniel R Zwahlen, Alexander Muacevic & Paul Windisch
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abstract
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: Many proposed algorithms for tumor detection rely on 2.5/3D
convolutional neural networks (CNNs) and the input of segmentations
for training. The purpose of this study is therefore to assess the
performance of tumor detection on single MRI slices containing
vestibular schwannomas (VS) as a computationally inexpensive
alternative that does not require the creation of segmentations. : A
total of 2992 T1-weighted contrast-enhanced axial slices containing
VS from the MRIs of 633 patients were labeled according to tumor
location, of which 2538 slices from 539 patients were used for
training a CNN (ResNet-34) to classify them according to the side of
the tumor as a surrogate for detection and 454 slices from 94
patients were used for internal validation. The model was then
externally validated on contrast-enhanced and non-contrast-enhanced
slices from a different institution. Categorical accuracy was noted,
and the results of the predictions for the validation set are
provided with confusion matrices. : The model achieved an accuracy
of 0.928 (95% CI: 0.869-0.987) on contrast-enhanced slices and 0.795
(95% CI: 0.702-0.888) on non-contrast-enhanced slices from the
external validation cohorts. The implementation of Gradient-weighted
Class Activation Mapping (Grad-CAM) revealed that the focus of the
model was not limited to the contrast-enhancing tumor but to a
larger area of the cerebellum and the cerebellopontine angle. :
Single-slice predictions might constitute a computationally
inexpensive alternative to training 2.5/3D-CNNs for certain
detection tasks in medical imaging even without the use of
segmentations. Head-to-head comparisons between 2D and more
sophisticated architectures could help to determine the difference
in accuracy, especially for more difficult tasks.
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citation
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Sager P, Näf L, Vu E, Fischer T, Putora P M, Ehret F, Fürweger C,
Schröder C, Förster R, Zwahlen D R, Muacevic A, Windisch P.
Convolutional Neural Networks for Classifying Laterality of
Vestibular Schwannomas on Single MRI Slices-A Feasibility Study.
Diagnostics (Basel) 2021; 11:.
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type
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journal paper/review (English)
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date of publishing
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14-09-2021
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journal title
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Diagnostics (Basel) (11/9)
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ISSN print
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2075-4418
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PubMed
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34574017
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DOI
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10.3390/diagnostics11091676
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