# CBCTLiTS: A Synthetic, Paired CBCT/CT Dataset For Segmentation And Style Transfer

arXiv: https://arxiv.org/abs/2407.14853 · HTML: https://mlsanity.com/paper/2407.14853/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 20 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction](https://mlsanity.com/paper/2312.12990/) (2023)

## Built on by (most cited first, 3 of 3)

- [Enhancing Synthetic CT from CBCT via Multimodal Fusion: A Study on the Impact of CBCT Quality and Alignment](https://mlsanity.com/paper/2506.08716/) (2025)
- [Initial Study On Improving Segmentation By Combining Preoperative CT And Intraoperative CBCT Using Synthetic Data](https://mlsanity.com/paper/2412.02294/) (2024)
- [Enhancing Synthetic CT from CBCT via Multimodal Fusion and End-To-End Registration](https://mlsanity.com/paper/2507.06067/) (2025)

## Abstract (arXiv)

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.