  A two-step concept for 3D segmentation on 5 abdominal organs inside
volumetric CT images is presented. First each relevant organ's volume of
interest is extracted as bounding box. The extracted volume acts as input for a
second stage, wherein two compared U-Nets with different architectural
dimensions re-construct an organ segmentation as label mask. In this work, we
focus on comparing 2D U-Nets vs. 3D U-Net counterparts. Our initial results
indicate Dice improvements of about 6\% at maximum. In this study to our
surprise, liver and kidneys for instance were tackled significantly better
using the faster and GPU-memory saving 2D U-Nets. For other abdominal key
organs, there were no significant differences, but we observe highly
significant advantages for the 2D U-Net in terms of GPU computational efforts
for all organs under study.
