CBCTLiTS: A Synthetic, Paired CBCT/CT Dataset For Segmentation And Style Transfer
This dataset addresses challenges in medical imaging for clinical practice by facilitating algorithm development, but it is incremental as it builds on existing synthetic data approaches.
The authors tackled the problem of artifacts in cone beam computed tomography (CBCT) images by creating CBCTLiTS, a synthetic, paired CBCT/CT dataset with five quality levels, enabling research in segmentation and style transfer, and they provided baselines for tasks like liver and liver tumor segmentation.
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.