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Abstract #0532

Evaluation of Data Augmentation Methods for Autonomous Segmentation of Placental Volume for Detecting Viral Complications

Thomas Lilieholm1, Ruiming Chen1, Ruvini Navaratna1, Daniel Seiter1, Walter F Block1,2,3, and Oliver Wieben1,2,3
1Medical Physics, University of Wisconsin at Madison, Madison, WI, United States, 2Biomedical Engineering, University of Wisconsin at Madison, Madison, WI, United States, 3Radiology, University of Wisconsin at Madison, Madison, WI, United States

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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