The ULS23 Challenge Public Training Dataset

The ULS23 training dataset contains 38,693 diverse lesions from chest-abdomen-pelvis CT examinations. For the challenge, we introduced two novel 3D annotated datasets targeting lesions in the pancreas and bones, which are traditionally challenging to segment. Additionally, we aggregate 10 publicly available datasets with a lesion segmentation component into a single, easily accessible data repository.

The dataset consists of fully 3D-segmented (5.633) and partially 2D-annotated lesions (33.060). Imaging data is provided to as volumes-of-interest (VOI's) of 256x, 256y, 128z voxels in the original scan spacing. The VOI's were sampled such that there always is a lesion voxel in the middle of the volume, simulating the lesion being identified by a radiologist or lesion detection model. This voxel in the center of the volume was selected randomly from within the lesion mask. Within each VOI, there is only one annotated lesion. Any masks representing nearby lesions not connected to the central lesion were removed.