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.

该研究旨在探讨质量和数量之间的平衡，以创建适用于医学 CT 扫描的多器官分割统一方法，并产生大量精确的虚拟模型。通过使用具有完全标签的 XCAT 数据集，我们比较了两种分割结构，选择了表现更好的 3D-Unet 模型，并使用此模型生成了少量标签来使 CT-ORG 数据集也可用于训练。实验结果表明，应该以质量为重来提高分割模型的性能。