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Abstract #0710

Comparison of Pure Deep Learning Approaches for Placental Extraction from Dynamic Functional MRI sequences between 19 and 37 Gestational Weeks.

Bryan Quah1, Anna Dong1, Neil Rao1, Patrick Hoang1, Michael Hirano1, Manjiri K. Dighe1, and Colin Studholme1
1University of Washington, Seattle, WA, United States

We present fully automated deep learning approaches to placental tissue segmentation on our dataset of 68 3D R2* images. Using this dataset, we employ different data schemes to get 4 new datasets consisting of full 3D images, full 2D slices, 3D patches and 2D patches. An unmodified U-Net architecture is trained and tested on these datasets to evaluate the robustness of the model when presented with different data. We find that by artificially increasing the size of the dataset, the model is able to perform better at the segmentation task.

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