Quantitative investigation of placental volumes can be used for characterization of Zika virus (ZIKV) infection, which causes several complications for developing fetuses. To provide more rapidly available image segmentation for analysis, efforts are being made to produce Convolutional Neural Networks (CNN) for autonomous segmentation of placental volume images. We investigated a number of data augmentation techniques for training machine learning models to determine which methods may be most suited for further development of ZIKV-quantifying placental segmentation models. We found rotational and reflective data augmentation to produce the greatest improvement in machine-segmentated Dice Coefficient comparisons.
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