Combines NMAR with two U-Nets to segment hip and thigh muscles in metal-contaminated CT, reducing average ASD from 1.17 to 1.10 mm on simulated data from 20 patients.
Augmentor: An Image Augmentation Library for Machine Learning
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abstract
The generation of artificial data based on existing observations, known as data augmentation, is a technique used in machine learning to improve model accuracy, generalisation, and to control overfitting. Augmentor is a software package, available in both Python and Julia versions, that provides a high level API for the expansion of image data using a stochastic, pipeline-based approach which effectively allows for images to be sampled from a distribution of augmented images at runtime. Augmentor provides methods for most standard augmentation practices as well as several advanced features such as label-preserving, randomised elastic distortions, and provides many helper functions for typical augmentation tasks used in machine learning.
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2019 1verdicts
UNVERDICTED 1representative citing papers
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Automated Segmentation of Hip and Thigh Muscles in Metal Artifact-Contaminated CT using Convolutional Neural Network-Enhanced Normalized Metal Artifact Reduction
Combines NMAR with two U-Nets to segment hip and thigh muscles in metal-contaminated CT, reducing average ASD from 1.17 to 1.10 mm on simulated data from 20 patients.