  Medical imaging is vital in computer assisted intervention. Particularly cone
beam computed tomography (CBCT) with defacto real time and mobility
capabilities plays an important role. However, CBCT images often suffer from
artifacts, which pose challenges for accurate interpretation, motivating
research in advanced algorithms for more effective use in clinical practice.
  In this work we present CBCTLiTS, a synthetically generated, labelled CBCT
dataset for segmentation with paired and aligned, high quality computed
tomography data. The CBCT data is provided in 5 different levels of quality,
reaching from a large number of projections with high visual quality and mild
artifacts to a small number of projections with severe artifacts. This allows
thorough investigations with the quality as a degree of freedom. We also
provide baselines for several possible research scenarios like uni- and
multimodal segmentation, multitask learning and style transfer followed by
segmentation of relatively simple, liver to complex liver tumor segmentation.
CBCTLiTS is accesssible via
https://www.kaggle.com/datasets/maximiliantschuchnig/cbct-liver-and-liver-tumor-segmentation-train-data.
