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.

Constantin Seibold

Team Lead Computer Vision

Negar Shahamiri

PhD Student

Jens Kleesiek

Professor of Translational Image-guided Oncology

Rainer Stiefelhagen

Director Computer Vision for Human-Computer Interaction Lab