Towards Unifying Anatomy Segmentation: Automated Generation of a
  Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines
Alexander Jaus Constantin Seibold Kelsey Hermann Alexandra Walter Kristina Giske Johannes Haubold Jens Kleesiek Rainer Stiefelhagen
In this study, we present a method for generating automated anatomy
segmentation datasets using a sequential process that involves nnU-Net-based
pseudo-labeling and anatomy-guided pseudo-label refinement. By combining
various fragmented knowledge bases, we generate a dataset of whole-body CT
scans with $142$ voxel-level labels for 533 volumes providing comprehensive
anatomical coverage which experts have approved. Our proposed procedure does
not rely on manual annotation during the label aggregation stage. We examine
its plausibility and usefulness using three complementary checks: Human expert
evaluation which approved the dataset, a Deep Learning usefulness benchmark on
the BTCV dataset in which we achieve 85% dice score without using its training
dataset, and medical validity checks. This evaluation procedure combines
scalable automated checks with labor-intensive high-quality expert checks.
Besides the dataset, we release our trained unified anatomical segmentation
model capable of predicting $142$ anatomical structures on CT data.