Towards Unifying Anatomy Segmentation: Automated Generation of a Full-Body CT Dataset

Abstract

In this paper, 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. We validate its usefulness via Human expert evaluation and medical validity. This dataset enables the analysis of whole-body anatomy segmentation for cancer patients. Besides the DAP Atlas dataset, we release our trained anatomy segmentation models capable of predicting 142 anatomical structures on CT data.