Towards Unifying Anatomy Segmentation: Automated Generation of a Full-Body CT Dataset
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.
Authors
- Negar Shahamiri (ORCID: https://orcid.org/0000-0001-8593-175X)
- Jens Kleesiek (ORCID: https://orcid.org/0000-0001-8686-0682)
- Johannes Haubold (ORCID: https://orcid.org/0000-0003-4843-5911)
- Alexander Jaus (ORCID: https://orcid.org/0000-0002-0669-0300)
- Alexandra Walter (ORCID: https://orcid.org/0000-0001-5044-0133)
- Kristina Giske (ORCID: https://orcid.org/0000-0001-7287-3271)
- Constantin Seibold (ORCID: https://orcid.org/0000-0001-6042-8437)
- Rainer Stiefelhagen (ORCID: https://orcid.org/0000-0001-8046-4945)
- Kelsey Hermann
Institutions
- Karlsruhe Institute of Technology (DE)
- Heidelberger Institut für Radioonkologie (DE)
- Essen University Hospital (DE)
Publication Details
- Published
- 2024-09-27
- DOI
- https://doi.org/10.1109/icip51287.2024.10647307
- Citations
- 14
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.26
Funders
- Ministry of Education
- Helmholtz Association