A longitudinal whole-body CT dataset with manually annotated tumor lesions.
Authors
Affiliations (7)
Affiliations (7)
- Department of Radiology, Stanford University, Stanford, CA, USA. [email protected].
- Department of Radiology, University Hospital Tuebingen, Tuebingen, Germany.
- Department of Neuroradiology, University Hospital of Mainz, Mainz, Germany.
- Department of Radiology, University Hospital of Mainz, Mainz, Germany.
- Fraunhofer MEVIS, Bremen, Germany.
- Radboud University Medical Center, Nijmegen, The Netherlands.
- Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tuebingen, Tuebingen, Germany.
Abstract
We introduce Longitudinal-CT, a publicly available resource of whole-body computed tomography (CT) studies with exhaustive expert manual annotations of tumor lesions across two timepoints. The dataset comprises 600 CT studies from 300 patients diagnosed with metastatic malignant melanoma, each including a baseline and a follow-up examination acquired during systemic therapy at the University Hospital Tübingen, Germany. In total, it contains 7,182 manually segmented tumor lesions - 4,079 at baseline and 3,103 at follow-up - each labeled with anatomical location, volume, and longitudinal correspondence to capture lesion evolution such as persistence, regression, merging, or new appearance. All CT data are provided in anonymized NIfTI format with corresponding segmentation masks and lesion metadata. Longitudinal-CT establishes a standardized foundation for developing and validating artificial intelligence methods for automated lesion detection, segmentation, and temporal tracking in oncology. As a reference, a baseline deep learning segmentation model trained using nnU-Net v2 demonstrates the dataset's potential for advancing research in automated oncologic whole-body CT lesion segmentation.