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

TL;DR

CBCTLiTS is a synthetic paired CBCT/CT dataset designed for segmentation and style transfer research, featuring varying image qualities to facilitate advanced algorithm development in medical imaging.

Contribution

The paper introduces CBCTLiTS, a novel synthetic dataset with paired high-quality CT and CBCT images at multiple artifact levels for segmentation and style transfer tasks.

Findings

Provides a versatile dataset for multi-modal segmentation research

Establishes baseline methods for segmentation and style transfer

Enables investigation of artifact impact on image analysis

Abstract

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.