  Organ segmentation of medical images is a key step in virtual imaging trials.
However, organ segmentation datasets are limited in terms of quality (because
labels cover only a few organs) and quantity (since case numbers are limited).
In this study, we explored the tradeoffs between quality and quantity. Our goal
is to create a unified approach for multi-organ segmentation of body CT, which
will facilitate the creation of large numbers of accurate virtual phantoms.
Initially, we compared two segmentation architectures, 3D-Unet and DenseVNet,
which were trained using XCAT data that is fully labeled with 22 organs, and
chose the 3D-Unet as the better performing model. We used the XCAT-trained
model to generate pseudo-labels for the CT-ORG dataset that has only 7 organs
segmented. We performed two experiments: First, we trained 3D-UNet model on the
XCAT dataset, representing quality data, and tested it on both XCAT and CT-ORG
datasets. Second, we trained 3D-UNet after including the CT-ORG dataset into
the training set to have more quantity. Performance improved for segmentation
in the organs where we have true labels in both datasets and degraded when
relying on pseudo-labels. When organs were labeled in both datasets, Exp-2
improved Average DSC in XCAT and CT-ORG by 1. This demonstrates that quality
data is the key to improving the model's performance.
